Founded in 1959 SIMS is the Scandinavian Simulation Society dedicated to the advancement of modeling and simulation science.
Renewable Energy Resource Risk Quantification and Mitigation Assessment for Mining Micro-Grid
As one of the most energy-intensive industries, mining accounts for over one-third of industrial final energy consumption. With the growing mineral demand, combined with declining ore grades, it is expected that the energy demand in mining will only grow, potentially increasing its already large greenhouse gas footprint. With rising energy costs, renewable energy presents a viable option not only to improve the environmental footprint but also to reduce overall costs with optimized operation of mines. While renewable energy generators i.e., solar photovoltaics and wind turbines offer numerous benefits like modularity, environmentally friendliness, and natural availability; the major drawbacks are their temporal intermittency and seasonal and long-term variability. Hence, these generators pose a resource risk that the actual quantity of wind and solar irradiation can be less than expected. The resource risk imposes uncertainty in short-, medium- & long-term energy generation and consumption. Hence such risk needs to be actively considered and mitigated during the evaluation and operational phase of renewable or hybrid energy system projects. This paper provides a comprehensive review of renewable resource risk quantification techniques. Subsequently, a list of renewable energy resource risk quantification methods is discussed i.e., renewable reliability (i.e., the percentage of demand met by renewables), energy deficit and energy oversupply index, probability of exceedance (PoE) for annual energy production (AEP), probability of generating at least k MW of renewable power, capacity factor. Finally, some selected matrices are used to assess the effect of different risk mitigation options, e. g. the optimal size of energy storage.
Copyright (c) 2025 Moksadur Rahman, Stefan Thorburn https://creativecommons.org/licenses/by/4.0
Evaluation of environmental and economic impact of wind turbine blade manufacture at life-cycle level
Life cycle analysis is considered as a valuable decision-making tool to oversee the environmental impact of a product through its various stages. Starting from the raw material sourcing up to the end-of-life processes of the product. Life cycle costing is added to the life cycle analysis to augment the economic aspects. One of the main drawbacks of the life cycle analysis is the focus on single path for the life stages as it evaluates single option for each life stage and adds the impact to the following stages. This study presents a tool to evaluate the environmental and economic impact of different options in life cycle stages, determine the possible combination of different life cycle choices, and calculate the emissions, energy intensity and cost of each combination scenario. The study takes wind turbine blade as a case study, where glass fiber reinforced polymers and carbon fibers reinforced polymers are considered as a row material alternative with two supply options Europe or China markets, four manufacturing site options (onsite, Denmark, Germany, and China) and four end of life processing options (reuse, pyrolysis, landfill, and mechanical grinding). The results range the different combinations scenarios emissions in the range of (74 – 17) tons of CO2 eq, the energy intensity between 261 GJ and 863 GJ, and the cost vary from 89000€ to 22,000€. This work presented a logical method for mapping, analyzing, and evaluating the environmental and economic sustainability of a wind turbine blade through different life cycle pathways.
Copyright (c) 2025 Mohammed Taha, Stavros Vouros, Konstantinos Kyprianidis https://creativecommons.org/licenses/by/4.0
Dynamic simulation models in the planning of experiments for control development
This paper focuses on the utilization of dynamic simulation models in the planning of experiments for control development. The simulation system is a set of models based on the first principles for system level simulation of the complete TCP-100 research facility at Plataforma Solar de Almería (CIEMAT). This new research facility replaced the 32-year-old ACUREX facility with which so many advances in Automatic Control were reached by the research community. The dynamic models are developed to speedup this research for the new field. The part for control development is the solar field whose parabolic trough collectors (PTCs) are modelled in module level and combined into PTCs and loops. The models will be validated with experimental data and the loops are controlled. The sequential loops have different operating conditions. This research uses the parameters based on the parameter selection from providers’ data sheets and the engineering design project of the TCP-100. The system level model has been implemented in the Modelica language. All state variables are temperatures according to the modelling hypothesis applied, solar radiation, ambient temperature, setpoints for both circuits pumps, setpoints for two loops control valves and setpoint for air cooling power. The simulation experiments are first focused on the modules, PTCs and loops of the solar field and the full model need to be extended with dynamic LE models before going to the full simulation tests. In the test campaigns with the new facility, these models will be used for planning the test cases.
Copyright (c) 2025 Esko Juuso, Luis J. Yebra https://creativecommons.org/licenses/by/4.0
Experimental and Numerical Testing of a Multi-Modular Floating Structure with Varying Connection Stiffness
This work is a step towards conceptualizing a smart multi-modular structure, whose main application is solar energy harvest, with the innovative idea of connectors that can be controlled to mitigate motions and loads in a changing environment. The paper presents selected preliminary results from experimental tests of an array of floating column-based modules exposed to regular waves of different periods. Each pair of neighboring modules was connected by two spring connectors with both tension and compression stiffness. The paper presents an investigation of motion responses versus load frequencies corresponding to four tested spring stiffnesses.The model test results serve as a basis for validating a numerical model that is implemented for control design and simulation purposes. Wave-, mooring- and connector forces are considered in the simulations. The proposed method will act as a tool for further evaluation of the effect of changing the connection stiffness according to the incoming waves and the investigation of whether it is beneficial to apply a smart connector that can adapt to varying sea states.
Copyright (c) 2025 Trine Aas-Hansen, Vegard Njøten Fagerbakke, Trygve Kristiansen, Svein Sævik, Dong Trong Nguyen https://creativecommons.org/licenses/by/4.0
Life Cycle Assessment of Floating Offshore Wind Farms: The Case of Hywind Tampen in Norway
To address climate change and energy security issues from fossil fuels, wind power is a promising renewable energy source, projected to grow significantly by 2050. Offshore wind energy, especially floating offshore wind farms shows great potential due to higher and more consistent wind speeds at sea. However, these turbines have negative environmental burdens throughout their life cycle. This The present study focuses on a comprehensive cradle-to-grave life cycle assessment of the Hywind Tampen floating offshore wind farm in Norway. The assessment covers all stages from manufacturing, transportation, installation, operation, and maintenance to decommissioning, utilizing openLCA® software and ecoinvent 3.9 database with the ReCiPe 2016 impact assessment method. Key findings indicate that manufacturing is the primary contributor to total emissions, followed by operation and maintenance. The study emphasizes the necessity of developing more sustainable manufacturing methods, designing turbines that are more efficient and versatile, and better maintenance forecasting and planning in order to minimize the environmental impact of these turbines.
Copyright (c) 2025 Omid Lotfizadeh, Zahir Barahmand, Hadi Amlashi https://creativecommons.org/licenses/by/4.0
Computationally Efficient Optimization of Long Term Energy Storage Using Machine Learning
Energy storage can be charged when energy is cheap and discharged when it is expensive to make an energy system more profitable or used to make the plant operation more efficient to reduce CO2 emissions. To optimize long term energy storage with conventional methods a long time horizon must be used. When the long term energy storage is combined with a complex energy system the computational cost becomes large when using conventional methods. To reduce the time horizon, an algorithm will be used to decide the state of charge of the long term energy storage at the end of the day. This algorithm is trained using machine learning with data of the optimal state of charge obtained by running computationally heavy long time mixed integer linear programming ahead of time. Then a one-day or week mixed integer linear programming optimization will be done for the production planning. The seasonal patterns of the long term energy storage can then be captured while giving the plant operator a simple one-day or week production plan. A case study will be done with a combined heat and power plant system with 4 boilers, a long-term thermal storage, and a hydrogen storage system. Using this method the complexities of a multi energy system with long term energy storage can be captured while doing day ahead production planning
Copyright (c) 2025 Simon Karlsson, Stavros Vouros, Kristian Sandström, Konstantinos Kyprianidis https://creativecommons.org/licenses/by/4.0
A Battery model for transportation and stationary applications
Batteries are used in electric vehicles as well as for stationary applications. In the first case we usually want a high energy density as kWh/kg, while stationary applications are less sensitive to the energy density. Principally it may be a good idea to first use batteries for transportation applications and then when capacity has reached a certain level start using them for other applications in a “second life”. Both for optimizing the performance of operations in 1st and 2nd life as well as for making fair commercial agreements when selling used batteries for 2nd life applications, we need to make prediction of remaining useful life (RUL) as well as SOH (State of Health). For this purpose battery models are needed. In the paper we show a methodology for building useful battery models built on own experiments as well as literature data. Single cells of NMC (Li-NiMnCo-batteries) as well as LFP (Li-ionphosphate batteries) have been cycled as well as cells in series. EIS, Electrochemical Impedance spectra as well as dQ/dV has been measured for each cycle. These data then have been used for development of SOH and RUL models using different regression methods. The models are described, discussed and results shown in the paper.
Copyright (c) 2025 Erik Dahlquist, Maher Azaza, Masoume Shabani, Josefin Rojas Vasquez, Meysam Majidi Nezhad , Anas Fattouh, Amare Desalegn Fentaye https://creativecommons.org/licenses/by/4.0
Numerical simulation of thermal runaway kinetic mechanisms and battery thermal model for safety assessment of different lithium-ion battery chemistries
The importance of EVs and li-ion batteries are pinpointed in the automotive industry during the last decade by increased growth of electrified powertrain. Li-ion batteries offer significant improvements in terms of energy and power density; however, safety challenges continue to exist. Different thermal, mechanical, or electrical abuse conditions in li-ion batteries can trigger a series of exothermic chain reactions in the battery cells and finally result in thermal runaway (TR) and combustion of battery cells and EVs. Furthermore, different battery technologies exploit various cell chemistries, leading to the distinct thermal behavior of battery cells during normal and abuse conditions. This work aims at investigating the TR kinetic mechanisms to evaluate thermal behavior of the battery cells under thermal abuse conditions. Furthermore, this work investigates the different li-ion battery cathode, anode and electrolyte materials to assess the safety of battery systems in EV application. The results revealed that unlike batteries with LiCoO2 cathodes with temperature threshold of 150 ℃, Li1.1(Ni1/3Co1/3Mn1/3)0.9O2 batteries do not undergo TR process at temperatures below 170 ℃. Moreover, the temperature peak is more hazardous in LiCoO2 batteries with LiPF6/PC: DMC electrolyte compared to the same battery with standard electrolyte. In addition, batteries with Lithiated Li4Ti5O12 anode showed safer TR process compared to all the previous battery types.
Copyright (c) 2025 Sadegh Mehranfar, Amin Mahmoudzadeh Andwari, Juho Könnö, Antonio Garcia Martinez, Carlos Mico Reche https://creativecommons.org/licenses/by/4.0
Assessing the impact of rising wind power with energy storage on grid resilience in Sweden to mitigate volatility and enhance grid flexibility
This paper assesses the impact of increasing wind power production and energy storage systems on grid resilience in Sweden. Wind power currently makes up 17% of Sweden’s electricity mix, and this share is expected to rise significantly in the coming decades as Sweden aims for 100% renewable energy generation by 2040. However, the variable and intermitted output can negatively impact grid stability. A microgrid model is developed, incorporating a wind turbine, battery storage, power grid, and a representative demand profile. Wind speed data is analyzed to select profiles representing high and low variability, with variance used as a metric for resilience. Planned production is constructed in 12-hour intervals based on wind speed forecasts. The model compares grid dependency and electricity delivery with and without battery storage of varying capacities. The results show that battery storage reduces grid interactions and grid dependency. Furthermore, it aligns actual wind power production with the planned production profile. Optimization analyses find that minimizing operational costs and battery usage increases grid reliance while minimizing costs and grid supplies provides a more stable supply but overuses batteries. Sensitivity analysis demonstrates higher grid dependency in high-variance wind conditions. The paper contributes to understanding how to enhance wind power resilience through improved production planning and battery integration. It proposes using variance analysis in wind profile selection and identifies trade-offs between system stability, costs and battery lifespan under different optimization strategies.
Copyright (c) 2025 Jordy Jorritsma, Stavros Vouros, Konstantinos Kyprianidis, Klaus Hubacek https://creativecommons.org/licenses/by/4.0
CFD validation of optimized compact heat exchanger designs
In offshore oil and gas production gas turbines are used for both power production and to provide process heat. CO2 emissions from the gas turbines accounts for about 25 % of the total Norwegian emissions and installing a bottoming cycle to produce power by recovering heat from the gas turbine exhaust is one way to reduce these missions. When installing a steam bottoming cycle offshore, the total weight and size will be important, and there is a need for a compact heat recovery steam generator (HRSG). A compact HRSG will often need to be designed with smaller tube diameters than conventional on-shore steam generators. To increase confidence in the compact design, the heat transfer and pressure loss models need to be accurate for the relevant geometry ranges. In this work, a compact Once Through Steam Generator (OTSG) is designed using optimisation procedures where the total weight of the steam generator has been minimised for a desired duty with restrictions for pressure losses. A range of correlations from the literature were used for the calculation of the performance. The results from the optimisation show that the ’heaviest’ results were about three times the minimum weight than the ’lightest’. To increase confidence in the results, and to provide arecommendation for design models, a validated CFD model was used to perform a numerical analysis of the optimised geometry and compare this with the correlations.
Copyright (c) 2025 Johan Espelund, Geir Skaugen https://creativecommons.org/licenses/by/4.0
Simulation of ammonia cracker process with Aspen HYSYS
This paper presents simulations of an ammonia cracker process using Aspen HYSYS. Ammonia is identified as both a promising low-emission maritime fuel and an energy carrier. This study focuses on converting ammonia to hydrogen through an ammonia cracker process. In the literature, there are found simulations of similar processes, but not much about optimization of the ammonia cracker process. A centralized ammonia cracking process was designed using the Peng-Robinson fluid package and Gibbs reactor in Aspen HYSYS. Gibbs reactors were employed to simulate both the cracker and the furnace (ammonia combustion reaction). Simplified assumptions included using a 100 % efficient splitter instead of a pressure swing adsorber. The ammonia feed had a molar flow rate of 500 kmole/h. The simulations included a base case scenario and an improved case for energy optimization. The base case scenario resulted in a total production of 0.13 kg of hydrogen per kg of ammonia feed. The improved case resulted in a production of 0.14 kg hydrogen. This was due to using the energy content present in the hydrogen and nitrogen product streams for warming up the ammonia before entering the cracker. This work demonstrates that Aspen HYSYS is a useful tool for optimizing the energy efficiency of an ammonia cracker process.
Copyright (c) 2025 Per Morten Hansen, Chinthaka Attanayake, Vahid Farrokhi, Mohsen Gholizadefalah, Ladan Samaei, Zahra Sanidanesh, Lars Erik Øi https://creativecommons.org/licenses/by/4.0
Steady State and Transient Modelling of A Three-Core Once-Through Steam Generator
To reduce emissions and save fuel in offshore power production using gas turbines, one can use the gas turbine exhaust as a heat source for a bottoming cycle for heat and power production. This can replace about one in four gas turbines. In offshore applications weight and size become more important and thus a once-through steam generator (OTSG) is a way to achieve low weight for the bottoming cycle. To reduce the size and weight of the OTSG further, one can reduce the tube diameter in the tube bundles. In this work a three-core OTSG, representing the economizer, evaporator, and superheater, was modelled and the design optimized to achieve minimum weight, while producing a certain amount of power and keeping within constraints of flue gas and steam pressure losses. This was done for varying tube diameters in each of the cores, in steady state. Afterwards transient simulations were performed for each optimized design to find their response times to a step change in the gas turbine load. The evaporator has the biggest impact on both the weight and the response time, while the superheater and economizer had similar and smaller impacts on both the weight and response time.
Copyright (c) 2025 Håvard Falch, Geir Skaugen https://creativecommons.org/licenses/by/4.0
A comparative study of conventional lime kilns and plasma calcination: Techno-economic assessment and decarbonization potential
Lime production is essential in the chemical recovery cycle of chemical pulping mills, typically relying on fuel combustion and thus contributing to greenhouse gas emissions. While Nordic pulp mills mainly use carbon-neutral biofuels, future biomass scarcity underscores the need for sustainable biomass management and alternative lime calcination methods. Electrification presents a promising solution, as CO₂ emissions depend on the carbon intensity of the electricity grid, which increasingly relies on renewable sources. Electrified solutions offer chemical pulp mills the opportunity to function as biorefineries and potentially produce higher-value biofuels in a constrained market. Plasma calcination provides benefits over conventional lime kilns, such as faster reaction times, reduced reactor volume, and lower shell losses. This work develops mathematical models for conventional kilns and plasma calcination to evaluate their techno-economic feasibility and decarbonization potential. A sensitivity analysis identifies influential parameters, and energetic requirements for both technologies under different fuel scenarios are assessed along with CO₂ emissions and economic factors. Results indicate that while plasma calcination’s current decarbonization potential depends on the electricity grid’s carbon intensity, future projections show its competitiveness over conventional kilns, with significantly lower CO₂ emissions across regions. The economic viability of plasma calcination is further influenced by projected carbon prices and process parameters, which impact its specific electricity consumption.
Copyright (c) 2025 Maximilian Dylong, Moksadur Rahman https://creativecommons.org/licenses/by/4.0
Assessment of Data-Driven Techniques for Flow Rate Estimation in sub sea oil production
Accurate measurement of flow rate of the multiphase flow of oil, gas and water from the oil wells, is an important part of the oil and gas industry. This enables the safe operation and proper optimization of the production. With the increasing availability of process data, machine learning algorithms are used to create models for various applications. The application of these algorithms for flow rate estimation provides a more accurate representation of the oil and gas production process. In this paper, two oil wells and ten machine learning algorithms are evaluated. Long short-term memory (LSTM) provides the best results with Mean absolute percentage error of 1.96% for Well 1 and 1.56% for Well 2. In addition, the effects of noise on the models are explored. Median filter with window size of three provides good noise reduction. The uncertainty of the predictions are quantified using 95% confidence intervals in XGBoost model.
Copyright (c) 2025 Neville Aloysius D'Souza, Carlos Pfeiffer, Gaurav Mirlekar https://creativecommons.org/licenses/by/4.0
Simulation and Cost Estimation of CO2 Capture with alternatives for doubled capacity
This study presents a techno-economic assessment of an amine-based carbon capture technology. The aim is to compare different methods to evaluate the cost effect of doubling the capacity. A base case was established in Aspen HYSYS with 15 m absorber packing height, 6 m desorber packing height, removal efficiency of 85 % and a heat exchanger minimum temperature approach (ΔTmin) of 10 °C. Then dimensioning and cost estimation was carried out using Aspen HYSYS spreadsheets to automatically calculate CAPEX, OPEX and carbon capture cost per ton CO2 captured. To estimate the Bare Erected Cost (BEC), the Enhanced Detailed Factor (EDF) and the Aspen Process Economic Analyzer (APEA) were employed. The EDF method determines the installation cost of each piece of equipment, while the Nazir-Amini method only offers the Total Plant Cost (TPC). Applying the EDF method, the TPC for the base case, the doubled feed gas case and the two-absorber case were calculated to 76, 141 and 150 MEuro respectively. The estimated annual OPEX for the base case was 42.5 MEuro, while for the two alternatives the OPEX was very close to the double of the base case. The estimated carbon capture cost for the base case, two-absorber case, and double feed gas scenario were 52.4 €/ton, 51.8 €/ton, and 50.5 €/ton, respectively. The study demonstrates that a combination of Aspen HYSYS simulation, APEA and the EDF method is an effective method to evaluate different alternatives for increasing the capacity.
Copyright (c) 2025 Lars Erik Øi, Masoumeh Dehghanizadeh, Nils Eldrup https://creativecommons.org/licenses/by/4.0
Simulation of Biogenic Carbon Capture and Utilization Process Chain
Carbon capture and utilization (CCU) is a growing field in chemical engineering with high expectations to replace fossil carbon. This paper focuses on modeling and simulation of a CCU process chain utilizing biogenic CO2. A scenario with a pulp mill recovery boiler effluent is assumed. CO2 capture is performed with a membrane-based system. This is followed by methanol synthesis, and the majority of produced methanol is directed to dimethyl carbonate (DMC) synthesis.The process chain with fixed process design was simulated for different scenarios of the flue gas properties. The key process indicators were observed. Further, the flexibility of the processes was evaluated to mitigate the changes in process indicators due to fluctuating flue gas properties. Finally, model parameter uncertainties and modeling assumptions were discussed. The results indicate the level of uncertainties of CCU models and their key process indicators that should be considered when moving on to the system level simulations and techno-economic or life cycle analyses.
Copyright (c) 2025 Kristian Tiiro, Markku Ohenoja, Outi Ruusunen, Riitta Keiski, Mika Ruusunen https://creativecommons.org/licenses/by/4.0
Simulation model for an amine-based CO2 capture rig
The amine-based CO2 capture rig at USN in Porsgrunn has been operating since 2007. In this study, the main aim was to develop predictive models in Aspen HYSYS and Aspen Plus for the CO2 test rig. The models accuracy were verified by comparing different test scenarios with results from the models. Aspen HYSYS and Aspen Plus have simulated eleven scenarios (test series) with varying process parameters. In Aspen HYSYS, Murphree efficiencies (stage efficiences) were fitted, and in Aspen Plus two approaches were used, fitting the interfacial area or the holdup factor to minimize the deviation between the model and experimental data. The Aspen HYSYS model with the fitted Murphree efficiencies (from top to bottom 0.11, 0.1, 0.09 and 0.07) predicted seven scenarios with an average deviation of 12-24 % from experimental data. In the Aspen Plus rate-based model with interfacial area fitted, most of the scenarios were predicted by a model with correlation Brf-85 (mass transfer), Brf-85 (heat transfer) and an interfacial área factor of 0.5. Minimum and maximum deviations for different scenarios were 2.1 and 9 %. In the approach with fitting of the holdup factor, the Brf-92 holdup method with a holdup factor of 0.5 gave the best fit, resulting in an average deviation of 1.4-9 % from the test results across all scenarios.
Copyright (c) 2025 Soudeh Shamsiri, Neda Razi, Lars Erik Øi https://creativecommons.org/licenses/by/4.0
Design of electrified fluidized bed calciner for direct capture of CO2 from cement raw meal
Using green electricity to calcine the raw materials and combining this with storage of the pure CO2 generated in the calcination process can significantly reduce CO2 emissions in the cement industry, which generates around 7 % of the global CO2 emissions.In this study, a lab-scale electrically heated fluidized bed calciner, operating with a mixture of fine meal particles and coarse inert particles, is simulated using CPFD software. The electrification of the reactor is done using several horizontal cylinders, which are electrically heated to provide energy both for heating the raw meal (with 77% CaCO3) up to the calcination temperature and for calcination (CaCO3  CaO + CO2). The reactor design is done based on a specified electrical energy input, the gas velocity required for fluidization of coarse inert particles and the velocity required for entrainment of the fine calcined particles. A fluidization velocity of 0.3 m/s appears to be optimal for the reactor, whereas 0.8 m/s resulted in complete entrainment of the bed. The maximum calcination degree achieved was 90% when operating with preheated meal. The average meal residence time was found to be 24-26 s.
Copyright (c) 2025 Ladan Samaei, Lars-Andre Tokheim, Christoffer Moen https://creativecommons.org/licenses/by/4.0
Performance Analysis of Advanced Wells in Reservoirs Using CO2 Enhanced Oil Recovery
Oil and gas will remain an important source of energy for years and it is crucial to improve oil recovery with less carbon footprint. Carbon capture utilization and storage offers a potential solution to mitigate the effects of anthropogenic CO2. The captured CO2 can be utilized to enhanced oil recovery (EOR) and is injected into the oil fields for storage and/or EOR. However, the injected CO2 can be reproduced without contributing to EOR. This is due to the breakthrough of CO2 into the well. Also, the corrosive mixture of CO2 and water can be produced from the production well. This may cause damages to the pipeline and process equipment on the platform. Autonomous inflow control valves (AICVs) can mitigate these problems. They may reduce or stop the reproduction of CO2 from the zones with CO2 breakthrough and reduce the production of mixture of CO2 and water. The main objective of this study is modelling and simulation of oil production in a heterogenous reservoir using CO2-EOR in combination with AICVs. The outcome of numerical simulations is analyzed to study the effect of various parameters on oil recovery. In addition, the impact of AICVs on EOR is assessed against perforated casing completion (without AICV). The results demonstrate that oil recovery factor, water cut, and cumulative gas production are better in the wells completed with AICVs than perforated casing completion. This will result into both increased oil production and a better CO2 storage potential.
Copyright (c) 2025 Prakash Bhattarai, Soheila Taghavi Hosnaroundi, Britt M.E. Moldestad https://creativecommons.org/licenses/by/4.0
CO2 Enhanced Oil Recovery in Reservoirs with Advanced Wells: Simulations and Sensitivity Analysis
Injection of CO2 for enhanced oil recovery (CO2-EOR) is used in fields with high amount of residual oil. CO2-EOR refers to a technology where supercritical CO2 is injected into an oil reservoir to increase the oil production. Utilizing autonomous inflow control valves (AICVs) in CO2-EOR projects contributes to a better distribution of CO2 in the reservoir, reduction in production of water and CO2 mixture, and thereby increased storage capacity of CO2. The main objective of this study is modelling and simulation of oil production from an oil reservoir using CO2 water alternating gas (CO2 WAG) injection in combination with advanced wells that are completed with AICVs. The results from the simulations indicate that well completion with AICV can maintain good oil production while the production of water is decreased from 3e+06 m3 to 9.8e+04 m3 which corresponds to 97% reduction in water production. The sensitivity analysis of the simulation results affirms that permeability, well placement, and well spacing have impact on oil recovery and water production. The results indicate that permeability increase has a slight increment effect on oil recovery. The well spacing analysis shows that increasing the distance between the wells will increase the oil recovery and delay the water breakthrough. Lastly the well placement analysis shows that vertical injection of miscible CO2 produces more oil than horizontal injection of miscible CO2. AICVs restrict the production of mixture of CO2 and water, and thereby cause a better distribution of CO2 in the reservoir.
Copyright (c) 2025 Isu Uchechukwu Agha, Nora C.I. Furuvik, Soheila Taghavi https://creativecommons.org/licenses/by/4.0
CO2 Storage and Evaluation of Important Parameters Affecting the CO2 Plume Distribution: Simulation and Sensitivity Analysis
Carbon capture utilization and storage (CCUS) offers a potential solution to reduce the direct CO2 emissions from stationary sources into the atmosphere. The captured CO2 is injected into deep saline-water saturated formations or in depleted oil and gas fields, or into the oil fields for storage and/or enhanced oil recovery (EOR). The primary objective of this study is to identify and analyze the critical parameters affecting CO2 plume development in the reservoir. Understanding the subsurface dynamics of carbon sequestration will facilitate to plan the subsurface process better. The plume dynamics over 30 years of injection and 170 years of post-injection period is investigated. The simulation results show that CO2 plume propagates at an increased rate during the injection period and continues to disperse at a comparatively reduced rate after the injection ends. The horizontal spread of plume is significantly greater than the vertical propagation when the horizontal permeability is larger than the vertical. Additionally, the plume volume shows a linear relationship with the injected CO2 amount. In terms of storage efficiency, the most prevalent CO2 is free phase super critical CO2 that contributes around 80% of the stored CO2 whereas the rest are structurally or residually trapped and dissolved CO2. From the sensitivity analysis in a homogenous reservoir, it can be concluded that the horizontal permeability is impacting the most (42%) for structural and residual trapping of CO2 whereas porosity impacts the most (38%) for dissolution of CO2 contributing to solubility trapping mechanism.
Copyright (c) 2025 Mohammad Rakibul Hasan Chowdhury, Soheila Taghavi https://creativecommons.org/licenses/by/4.0
Equilibrium analysis for methanation focusing on CO2 derived substitute natural gas
In this study the methanation of synthesis gas (syngas) is investigated with a focus on achieving maximum methane and minimum CO by full methanation of CO2. For this study, we have considered a comprehensive thermodynamics analysis of CO2 hydrogenation. This will help us to understand the thermodynamic behaviour of the reactions involved in the methanation process. We have discussed the behavior of the species, CO2, H2, CH4, and H2O at the equilibrium with temperature, pressure, and fuel ratio variation in order to get the desired output. The preliminary study will focus on selecting the optimum conditions (temperature, pressure, and H2/CO2 ratio) for performing the experiments and for catalyst development.
Copyright (c) 2025 Rakhi, Fabian Mauss https://creativecommons.org/licenses/by/4.0
Modelling and simulation of CO2 capture through aqueous indirect mineralization using CaO-containing by-products
Mineralization of CaO-rich industrial wastes by CO2 is a way to simultaneously obtain CO2 sequestration and tackle waste pollution problems. In such a process, CO2 reacts with the CaO in the waste and the produced CaCO3 can be utilized.In this study, four different mineralization processes applying different chemicals, all with a relatively high performance documented from laboratory experiments, are scaled up to industrial size and outlined with the required process equipment. Based on published lab results, mass and energy balances of the up-scaled processes are performed, and performance parameters of the processes are calculated using an in-house made process simulation tool. Furthermore, an economic analysis is done for all processes, and the results are compared. Factors impacting the techno-economic feasibility of each process are evaluated through a sensitivity study.The results indicate that the potential of capturing CO2 and producing CaCO3 can be as high as 530 kg and 1200 kg per ton of the waste while the yearly energy consumption can be as low as 0.7 kWh per kilogram of captured CO2. The aqueous indirect mineralization of CO2 can be profitable and the emitted CO2 by the process can be so low as 6% of the captured amount.
Copyright (c) 2025 Amirhossein Ghazi, Lars-Andre Tokheim https://creativecommons.org/licenses/by/4.0
Phase Transformations in Steelmaking Slags: A Thermodynamic Approach
In addition to solidification, steelmaking slags may undergo phase transformations in solid state during their cooling process. The mineralogy of these oxide slags is significantly influenced by the chemical composition and cooling rate. For the phases forming, two distinct solidification modes can be assumed, depending on the cooling rate: equilibrium cooling and Scheil–Gulliver cooling. Characterization methods, such as scanning electron microscopy (SEM) and electron probe microanalyzer (EPMA) allow analyzing the elemental composition of individual phases. Here, computational thermodynamics were applied in phase identification of crystallized electric arc furnace (EAF) slags. FactSage 8.3 thermodynamic calculation software was used to estimate the composition of stable phases as a function of temperature. Solid solutions with varying compositions were considered in this study. The calculation results from two solidification modes, i.e., equilibrium cooling and Scheil-Gulliver cooling, were saved in Excel spreadsheets. A MATLAB script was developed to go through the results and find the phase with a composition closest to the input values. For both solidification modes, the composition and temperature best fitting the input analysis was determined. The input is the elemental composition of the phase of interest, acquired using EPMA. After the data processing, the results are visualized in graphs, illustrating the analyzed and estimated compositions of the identified solid solution phase and its occurrence temperature.
Copyright (c) 2025 Tuomas Alatarvas, Rita Kallio, Eetu-Pekka Heikkinen, Qifeng Shu https://creativecommons.org/licenses/by/4.0
Utilizing computational thermodynamics in characterization and classification of non-metallic inclusions in Ti-deoxidized steels
Non-metallic inclusions (NMIs) are micrometer-sized particles observed in all steel materials, often considered detrimental. In this study, NMIs in titanium-deoxidized steels were investigated, complemented with thermodynamic assessment for more accurate phase characterization. The NMIs were analyzed with a Jeol JSM-7900F FESEM-EDS (Field Emission Scanning Electron Microscope equipped with Energy Dispersive X-ray Spectroscope). For automated particle analyses on FESEM, Aztec Feature runs were carried out on polished steel samples, providing the elemental composition, in addition to morphological data, for each observed NMI. Utilizing the obtained EDS analyses, the fractions of oxides (Al2O3, MnO, TiOx), manganese sulfide (MnS), and titanium nitride (TiN) in each NMI are estimated with a MATLAB script. Based on the estimated phase contents, a composition-based classification method for the NMIs is presented. To visualize the phase contents of the observed NMIs, the calculated compositions are plotted on MnO–TiO2–Ti2O3 ternary diagrams. Computational thermodynamics software FactSage 8.3 was firstly utilized to estimate the fully liquid NMI composition region at steelmaking temperatures in the considered ternary oxide system of MnO–TiO2–Ti2O3. Secondly, the thermodynamic stability of NMI phases in the steel was assessed with decreasing temperature during the solidification of steel. The current study demonstrates how computational thermodynamics can be utilized in characterization and classification of non-metallic inclusions and giving insight on their formation during solidification of steel.
Copyright (c) 2025 Tuomas Alatarvas, Henri Tervo, Antti Kaijalainen, Qifeng Shu https://creativecommons.org/licenses/by/4.0
Cellular automata model for austenite formation and grain growth during heating and holding above austenization temperature
Understanding the steel microstructure formation during thermal treatments is crucial for controlling the mechanical properties of a steel product. One of the important factors affecting the subsequent microstructure development is the austenite grain size. To gain understanding of the effect of temperature dependent nucleation and growth rates, as well as providing the tools for quantitatively control the austenite grain size distribution, we have implemented a cellular automata (CA) model for describing austenite nucleation and growth during heating, as well as austenite grain growth during holding in temperatures above the austenitization temperature. The model implementation is based on previous study of Sieradzki and Madej for grain growth during recrystallization now augmetned with the relevant equations for describing the austenite nucleation and growth. The model parameters and their effect on austenite grain size distributions are tested with numerical experiments. The developed computational tool will serve as a basis that can be parameterized with experimental data in the future, which will then enable quantitative predictions for austenite phase transformation and grain size development.
Copyright (c) 2025 Aarne Pohjonen, Oskari Seppälä, Olli Väinölä, Jari Larkiola https://creativecommons.org/licenses/by/4.0
Non-interacting lattice random walks for calculating diffusion controlled growth in solid state for dilute concentrations
To connect the molecular length scale phenomena to the macroscopic length scale in diffusion controlled growth in solid state, there is need to consider the movement of individual atoms in the crystal lattice and examine the length scale effect where the average density of the atoms approaches to the continuum macro scale. For this purpose a lattice random walk model has been constructed to represent the diffusion of atoms to form a precipitate. Once the atom is in contact with the precipitate surface, the precipitate grows and the atom is not anymore contributing to the random walk. Through the model, it is possible to evaluate the concentration fluctuations at different length scales in diffusion controlled growth and connect the continuum description of diffusion to the atomic level description. We connect the different length scales in theoretical description from atomistic scale through random atom movements to macroscale. In the current study, two-dimensional lattice random walks and growth are considered. The study contributes to the modelling efforts of understanding diffusion controlled precipitate growth in steels.
Copyright (c) 2025 Aarne Pohjonen, Touko Puro, Assa Aravindh Sasikala Devi https://creativecommons.org/licenses/by/4.0
On the Growth Kinetics of Lamellar and Blocky Austenite During Intercritical Annealing of Hot-Rolled Medium Manganese Steel: Thermodynamic and Diffusion-Controlled Transformation Simulations
Metastable austenite significantly impacts the mechanical properties of Advanced High-Strength Steels (AHSS), especially Medium Mn Steel (MMnS), where its formation rate during intercritical annealing depends strongly on the initial microstructure. This study employs thermodynamic and diffusion-controlled simulations to investigate the formation of two distinct morphologies of retained austenite–lamellar and blocky known also as globular– commonly observed in an intercritically annealed hot-rolled MMnS. Utilizing Thermo-Calc software, coupled with its DIffusion-Controlled TRAnsformation module (DICTRA), phase equilibria are computed, and one-dimensional diffusion equations are solved. Characterization of the microstructure of a medium manganese steel (MMnS) with a nominal composition of Fe–0.4C–6Mn–2Al–1Si–0.05Nb (in wt. %), hot rolled and intercritically annealed for 1 hour at 680°C, was performed using Energy Dispersive Spectroscopy coupled with Transmission Electron Microscopy (EDS-TEM) and Transmission Kikuchi Diffraction (TKD). These techniques were used for experimental validation and verification of the simulations.Simulations explore the competition between cementite and austenite growth. Specifically, the growth of austenite starting on various interphase boundaries was examined using spherical and planar geometries. This approach resulted in the formation of blocky and lamellar austenite morphologies, respectively. The findings indicate that austenite first nucleates at the BCC/BCC interface and transforms 40% of the BCC phase within 1 second at 680°C. Cementite then starts to form, limiting further austenite transformation. Finally, cementite particles continue to grow to a size of about 100 nm. These simulation results align well with experimental findings.
Copyright (c) 2025 Roohallah Surki Aliabad, Saeed Sadeghpour, Pentti Karjalainen, Jukka Kömi, Vahid Javaheri https://creativecommons.org/licenses/by/4.0
Effect of Slag Particle Diameter on the Re-melting of Ferrochrome Slag by means of Steelmaking Liquid Slag
Stainless steelmaking slags are, currently, one of the most common non-utilized slags in steelmaking. Hence, in an integrated stainless steelmaking process with a ferrochrome submerged arc furnace, this means not only losing iron to the slag but also valuable chrome. Hence, recovery of iron and chrome have a business incentive and an important function for green industry initiative by reducing the requirement of virgin material.However, one of the challenges of slag recycling can be the energy-intensive nature of such a practice. Therefore, an energy efficient approach in material recovery could enhance the incentive of recycling of slag instead of the current practice of land field storage; one such approach is mixing the solid ferrochrome slag into liquid slag from the steelmaking production line.To that end, a static model of a suspended slag particle inside a melt has been developed to investigate the effect of particle size on evolution of temperature within the solid particles. The simulation showed that changes in the diameter of particle can have a significant effect on energy diffusion from the melt into the slag particle. As an example, the simulation suggests that the temperature magnitude at the centre of a 2mm-in-diameter particle reaches 1200 °C after 1s simulation time while, with 5mm particles the temperature magnitude is less than 200 °C. This behaviour is amplified further when the diameter of particle increases further showing a delaying behaviour of particle’s diameter on energy diffusion and, consequently, remelting of solid particles.
Copyright (c) 2025 Reza Safavi Nick, Elisa Olson, Matti Aula, Severi Anttila, Esa Puukko https://creativecommons.org/licenses/by/4.0
Optimizing Energy Consumption in Hydrogen Reduction of Iron Ore Pellet: Insights from HSC Chemistry Analysis
Iron ore pellet reduction in shaft furnaces represents a critical process in the steelmaking industry, with energy consumption being a key factor influencing both economic viability and environmental sustainability. This study employs HSC Chemistry software to model and simulate the energy consumption of hydrogen reduction of iron ore pellets under varying water vapor content within the shaft furnace. Thermodynamic modeling was carried out as the first step to analyze the effect of water vapor on the thermodynamic equilibrium, determining the possible range of water vapor content. Subsequently, energy consumption of the process was modeled based on heat and mass balance. Through comprehensive analysis, we investigate the impact of water vapor on the overall energy efficiency of the process based on the two scenarios of supplying the required heat by preheating the feed materials or injection of oxygen to the furnace. Our findings reveal significant insights into optimizing energy consumption and operational parameters to enhance the sustainability and cost-effectiveness of iron ore pellet reduction. This research contributes to the ongoing efforts towards achieving greater efficiency and reduced environmental footprint in the steelmaking industry.
Copyright (c) 2025 Aidin Heidari, Timo Fabritius https://creativecommons.org/licenses/by/4.0
Using an advanced simulation tool for successful conversion of reheating furnace to full oxyfuel operation
Oxyfuel combustion complements decarbonization efforts by reducing the energy needs in high-temperature industries. Steel reheating furnaces are good candidates for full oxyfuel operation since this can lead to up to 30% energy savings. Linde uses an in-house tool to simulate reheating furnaces for airfuel to oxyfuel conversion. This paper follows a real customer case, starting with an airfuel simulation setup used to analyze the furnace, followed by oxyfuel simulations for burner design and energy savings estimations. These simulations lead to a successful installation of oxyfuel burners for the reheating furnace located at Ovako Imatra site. After the commissioning is completed, performance evaluation is done by comparing a reference airfuel operation period with an oxyfuel combustion period. Full oxyfuel conversion results in 27% energy savings for hot charge and high production rate periods thanks to significantly lower flue gas losses. Removing nitrogen from the oxidizer decreases the flue gas volume, reducing the total heat capacity of the off-gas stream. The savings are around 30% for cold charge and average production rate periods.
Copyright (c) 2025 Esin Iplik, Tomas Ekman, Kristofer Bölke, Otto Kankaanpää https://creativecommons.org/licenses/by/4.0
Computational Designing Approach for Medium Manganese Steels with Potential Better Hydrogen Embrittlement Resistance
Medium manganese steels (MMnS) are known as third-generation high-strength steels, providing an excellent balance of high strength and ductility at a lower cost than second-generation steels. However, the increasing demand for steels with improved hydrogen embrittlement resistance highlights the need for the effective development of new alloys. This study explores the computational design of MMnS with a better combination of strength, ductility, and hydrogen embrittlement resistance. Mechanical properties vary due to changes in chemical composition and processing routes. Computational approaches enable precise optimization of these parameters, avoiding the inefficiencies of traditional trial-and-error. Therefore, CALPHAD-based thermodynamic calculations were employed to design a novel MMnS chemistry, increasing the fraction and stability of the retained austenite and providing efficient traps for hydrogen. As a result, the optimised chemical compositions were determined to be (in wt.%): 0.35C-9Mn-1Si-1Mo-1&3Al-0.1Nb, and 0.35C-9Mn-1Si-1Mo-3Al-0.05Nb and 0.3V. Thermo-Calc precipitation simulations identified 0.1% Nb as optimal since higher Nb contents reduce carbon in austenite, lowering its stability, and increase the size of the carbides. This Nb content results in NbC formation with an size distribution around 1 nm, 36 nm, and a size distribution of 1.2×1030, and 5.4×1027respectively. 3% Al promotes the delta ferrite formation and avoids the formation of kappa carbides, and 1% Mo compromises the volume fraction of NbC, strengthening the alloy and serving as an effective hydrogen deep trapping site. 0.3% V was chosen, compromising its effects on the size distribution of VC and available C for the austenitic phase, improving its mechanical stability.
Copyright (c) 2025 Mahmoud Elaraby, Mohammed Ali, Mamdouh Eissa, Jukka Kömi, Pentti Karjalainen, Vahid Javaheri https://creativecommons.org/licenses/by/4.0
Physical simulation of heat-affected zones in a weld metal used with 500 MPa offshore steel
Offshore steels are designed for high strength and toughness to endure extreme offshore and marine conditions. Welding thick steel sections often requires multiple passes, affecting the microstructures of previous passes due to thermal cycles. These heat-affected zones (HAZ) in the weld metal are less studied than those in the base metal. Real welding HAZs are narrow and challenging to study. Physical simulation can create various HAZs on a larger scale for microstructural and mechanical characterization, allowing easy study of different welding methods and parameters.This study aimed to produce coarse-grained (CGHAZ-W), intercritical (ICHAZ-W), and intercritically reheated HAZs (ICCGHAZ-W) in weld metal using physical simulation. The submerged arc welding (SAW) method was used to create the original weld. HAZs with two cooling times (t8/5 = 5 and 30 s) were simulated using the Gleeble 3500 thermomechanical simulator. Microstructures were analyzed with a Zeiss Sigma field emission scanning electron microscope. Results showed the original weld metal had acicular ferrite nucleated on oxide inclusions, and thermal cycles caused distinct microstructural changes in each simulation variant. Physical simulation microstructures were supported by numerical simulation results from JMatPro software.
Copyright (c) 2025 Henri Tervo, Marcell Gáspár, Judit Kovács, Antti Kaijalainen, Vahid Javaheri, Johannes Sainio, Jukka Kömi https://creativecommons.org/licenses/by/4.0
Comparison of ML and ASM models for effluent nutrient estimation in the Hias Process
The aim of this article is to develop and compare machine learning (ML) methods with activated sludge models (ASM) for estimation of effluent nutrients in the Hias Process. The Hias Process is a novel moving bed bioreactor with enhanced biological phosphorus removal and simultaneous nitrification and denitrification (MBBR-EBPR-SND). As the main energy cost of the nutrient removal process is aeration, it is necessary to design of energy-efficient control strategies that ensure compliance with legal requirements for nutrient removal in real-time while optimizing the aeration rates. The first step in control strategy design is development of models that represent the main process dynamics.The case study data set of four months was collected from a 192 000 PE municipal MBBR process at Hias water resource recovery facility in Norway. The online measurements include used water flowrate, aeration rates, dissolved oxygen, suspended solids, and soluble nutrients PO4, COD, NO2 and NO3. Reduced ASM model, support vector regression (SVR) and long short-term memory neural network (LSTM), with and without dynamic time-delay, were developed to predict the effluent PO4 in the Hias process. The model prediction accuracies were compared using correlation coefficients and trend figures. The SVR model with fine gaussian kernel gave best results with strong R index of 0.9. The LSTM model reached a sufficient R index of 0.6 and the reduced ASM2d model a weak R index of 0.2. Including the dynamic time-delay improved the model accuracy. Models with dynamic time-delay will be developed further for energy-efficient control strategy development.
Copyright (c) 2025 Tiina M. Komulainen, Malik Baqeri, Katrine Marsteng Jansen, Arvind Keprate https://creativecommons.org/licenses/by/4.0
Anaerobic digestion of biosolid pyrolysis liquid and hydrolyzed sludge - simulation with extended ADM1 model
Pyrolysis of biosolids aims to reduce solid volumes and improve energy recovery; however, the pyrolysis liquid (PL) is a by-product that has no good direct application. One idea is to link pyrolysis and anaerobic digestion (AD), in which PL can be valorized for methane production. PL contains various compounds that potentially threaten the stability of AD. This study, therefore, aims to extend the current Anaerobic Digestion Model No.1 (ADM1) and evaluate the influence of phenol, furfural, 5-hydroxymethylfurfural (5-HMF), styrene, and ammonia from PL on AD. Two lab-scale AD reactors were simulated and compared with experimental data: one fed with hydrolyzed sludge and the other fed with an additional stream of PL. The simulation accurately predicts hydrolyzed sludge as substrate, while the simulation of the reactor co-digesting hydrolyzed sludge and PL overestimates methane production. Ammonia, phenol, and styrene were identified as the most significant inhibitors. However, based on the overestimation of methane production, it is clear that the PL has more inhibitors present than those implemented in the model. Simulations further showed that an additional stream of PL increased methane production by 4.3%, even with significant inhibition by the compounds.
Copyright (c) 2025 Thea Indrebø, Gudny Øyre Flatabø, Wenche Hennie Bergland, Gamunu L. Samarakoon Arachchige https://creativecommons.org/licenses/by/4.0
Green infrastructure for resilient urban design: the mapping and management of green roofs in Oslo
Achieving “Climate-Neutral and Smart Cities” is now high on the agenda and the city of Oslo has set an even more ambitious goal of becoming a zero-emission city. However, the promotion of more compact development may lead to some negative effects such as the entrapment of polluted air, wind tunnel effects or urban heat islands. Green infrastructure (GI) can be used as a mitigation measure, bringing many benefits such as improving air quality, regulating thermal environment, reducing energy consumption, managing storm water, or promoting urban biodiversity. In this work, we aim to map the existing green roof infrastructure in Oslo and develop an evidence-base strategy for its further development. Interviews with stakeholders revealed the practical challenges such as structural limitations, high installation and maintenance costs, and regulatory compliance issues. However, they also recognized the significant environmental advantages that highlight the importance of green roofs in urban sustainability strategies. Geographical information system (GIS) tools are used to identify the potential areas for further green roof implementation, considering the spatial, morphological and environmental conditions. 91 Priority green roof areas (PRIOGRAs) and 13 Potential green roof areas (PGRAs) are identified as the most suitable after applying filters like roof surface area, and dominating roof area and slope criteria, exclusion of cultural heritage buildings and existing green roofs, tree density per person deficit, and building age. 2044 roofs can be considered suitable without the building age criteria. These findings will potentially help providing actionable insights for policymakers, urban planners, and the research community.
Copyright (c) 2025 Albert Likang Hu, Joanna Maria Badach, Arnab Chaudhuri https://creativecommons.org/licenses/by/4.0
Process simulation for biogas upgrading and biomethane recovery using biofilm-based reactors
Microbial biofilm matrices offer numerous benefits in bioprocessing and are crucial in various industrial and remediation processes. They facilitate electron exchange from solid surfaces when they interact with the environment. Emerging technologies such as biofilm-containing trickle bed reactors (TBR) and bioelectrochemical systems (BESs) for carbon dioxide (CO2) utilization, mostly rely on microbial biofilm matrices. Metabolic modeling of biofilm-based reactors enables detailed analysis of CO2 reduction within microorganisms, enhancing reactor efficiency. This study employed simulation models to analyze biomethane synthesis within TBR and BES systems. AQUASIM simulation tool was used for conducting the simulation. Parameters such as non-stoichiometric and stoichiometric ratios of substrates, hydraulic retention time (HRT), biofilm surface area, and applied voltage in BES were varied to evaluate methane (CH4) production and microbial biomass growth in TBR and BES. Results demonstrated that 1 day HRT resulted in methanation process failure due to biomass development problem in both TBR and BES. The substrate ratio 1:4 of CO2 to H2 increased CH4 production in the investigated reactors. In BES, in-situ CO2 and proton (H+) generation from oxidation reactions can increase CH4 production. Whereas in TBR, external H2 (hydrogen) should be supplied to consume higher amount of CO2. The lag phase in TBR was shorter than that in BES because of the greater surface area in TBR. In BES, higher voltage increased the current generation because of development of more biomass on the cathode. The simulation underlines the influence of different variables on biofilm-based reactors, offering critical insights for experimental process design.
Copyright (c) 2025 Vafa Ahmadi, Aryan Bhusal, Gamunu L Samarakoon Arachchige, Nabin Aryal https://creativecommons.org/licenses/by/4.0
Modelling and simulation of full-scale sequential batch reactor biological process operation using GPS-X
Sequencing Batch Reactors (SBR) have become a popular wastewater treatment technology and are now used widely for the removal of nitrogen and carbon compounds from reject water (i.e. water from sludge dewatering process) and high-strength industrial wastewater. SBR is a fill-and-draw wastewater treatment system where the biological activities are identical to the conventional activated sludge process. The two different biological processes in SBR are the nitrification and denitrification process which takes place within one reactor. The reject water at the municipal wastewater treatment plant in Porsgrunn, Norway, contains 2500 mg/L of total chemical oxygen demand (COD) and 600 mg/L ammonium nitrogen (NH4-N) concentration which cause disturbances in the treatment process when it is mixed with the receiving wastewater. Hence, the wastewater treatment plant recently upgraded with two similar SBRs operating in 24-hour cycles with a working volume of 115 m3 and 100.4 m3, respectively. Modelling and simulation of the simultaneous nitrification/denitrification (SND) process using key control parameters, such as dissolved oxygen (DO), alkalinity and pH, helps to understand more about the processes as well as to monitor and regulate plant operations efficiently. The objective of this research was to model and simulate the nitrification-denitrification process in these SBRs with simple and advanced cycle settings using GPS-X tool, a modelling and simulation software designed for planning and optimizing both new and upgraded municipal and industrial wastewater treatment plants. The complete treatment process in the plant was modelled integrating the in-built SBR model in GPS-X. The built model was simulated with key inputs parameters such as inflow rate, total carbon, total nitrogen, and aeration. In conclusion, the GPS-X model helps to understand the biological process in the SBR reactor which enables to improve the process efficiency by adjusting the different operating parameters.
Copyright (c) 2025 Ida K.B. Stenberg, Robel S. Bekele, Gamunu L.S. Arachchige, Eshetu Janka https://creativecommons.org/licenses/by/4.0
Kinetic modelling and simulation of bioanode and biocathode in a bioelectrochemical cell for carbon dioxide reduction
Bioelectrochemical systems (BESs) have garnered extensive research attention for their biosynthesis and environmental remediation applications. One of the challenges to upscaling BES for carbon dioxide (CO2) methanation is energy-efficient process development. Investigations are ongoing to determine the relationship between the yield of electroactive microorganisms, the key candidates for electrochemical reactions with external electricity input. Consequently, simulating processes, particularly with biocathode for biosynthesis and bioanode for remediation, gives crucial insights for designing efficient BESs. The framework for establishing Nernst-Monod equations for modeling BES, starts from bioanode, where anode respiring bacteria (ARB) oxidize organic carbon compounds to CO2, and generate the proton (H+). In this work, kinetic modeling was applied to calculate the biomass yield of ARBs corresponding to the applied anodic voltage. The generated CO2 and H+ from the anode determined the biomass yield of electroactive methanogens and acetogens on the cathode. Two biofilm models were established for anodic and cathodic biofilm growth in the Aquasim simulation tool. Results showed that the concentration of organic carbon compound (acetate) available for ARB, had a significant impact on the biofilm thickness and biomass concentration on the biofilm, especially at +0.3 V. The optimum anode voltage which released the highest CO2 and H+, was +0.3 V. The anodic and cathodic biofilm thickness reached 3 mm and 55 µm, respectively, at +0.3 V and 10 g.L-1 acetate input to the anode chamber. Moreover, methanogens surpassed acetogens on the biocathode for CO2 reduction to methane rather than acetate. In addition, acetate consumption rate by ARB at anode was remarkably faster than acetate production at cathode.
Copyright (c) 2025 Vafa Ahmadi, Nabin Aryal https://creativecommons.org/licenses/by/4.0
Alternative fuels for the maritime industry and its impact on flue gas composition
The maritime industry contributes to 80-90% of global trade and is on an increasing trend. However, it is also responsible for substantial amounts of greenhouse gas (GHG) emissions such as carbon dioxide (CO2), nitrogen oxides (NOx), sulfur oxides (SOx), carbon monoxide (CO), and hydrocarbons (HC). Therefore, industries are searching for alternative solutions to reduce GHG emissions by using alternative fuels. This study presents a novel investigation exploring the performance of various alternative marine fuels such as liquefied natural gas (LNG), methanol (MeOH), ammonia (NH3), and hydrogen(H2) in terms of combustion and emissions. Such comprehensive evaluation is limited in literature, making this study uniquely valuable in contributing to the field. The study assesses the impact of different equivalence ratios on emissions for the studied fuel profiles using Cantera and Aspen HYSYS simulations. Results show that CO2 peaks at the stoichiometric ratio, with CO rising from 0.8 to 1.1. Non-carbon fuels like NH3 and H2 emit fewer GHGs than carbonaceous fuels such as LNG and MeOH. H2 has the highest energy release at 87.21 MJ per kg, while NH3 shows lower emission levels, suggesting its potential as a sustainable maritime fuel. This research emphasizes the significance of choosing the right fuel to mitigate maritime emissions, highlighting NH3 and H2 as promising alternatives.
Copyright (c) 2025 Mostafa Abedini, Mohammad Rakibul Hasan Chowdhury, Saman Pershen, Joachim Rød Knarrum, Ishmael Nii Nyarko Solomon, Pramod Ghimire, Nabin Aryal https://creativecommons.org/licenses/by/4.0
Performance of direct air capture process in honeycomb channel configuration: A CFD study
This study presents a kinetic reaction modeling method for direct air capture (DAC) process of CO2 adsorption using computational fluid dynamics (CFD). Here, CO2 is adsorbed by amine coated air surface contact area. The Langmuir model is employed to represent the kinetics of CO2 adsorption. Despite neglecting the diffusive phase of the adsorption, which is dominant only in the later stages of adsorption, the surface reaction model gives a satisfactory representation of the adsorption for a major part of the process. Honeycomb reactors with coated adsorbent may yield a better control of reaction rate and pressure drop compared to commonly used packed bed adsorption columns. Their enhanced performance in distributing the flow homogeneously between and within channels creates unique features for the reactor. In this study, we have analyzed mechanical and electrical energy demand for adsorbing CO2 per unit mass of adsorbed CO2 as a function of air flow rate. Adsorption performance of honeycomb structure is anticipated to significantly improve in comparison to the packed beds.
Copyright (c) 2025 Majid Nejadseifi, Shervin Karimkashi, Tero Tynjälä, Payman Jalali https://creativecommons.org/licenses/by/4.0
Computational analysis of conjugate heat transfer in a 2D rectangular channel with mounted obstacles using lattice Boltzmann method
The objective of this paper is to investigate the fluid flow and conjugate heat transfer in a 2D channel using lattice Boltzmann method (LBM). In this work, fluid flow and heat transfer are studied for the Reynolds numbers varying between 250 and 1000. The working fluid in the simulations is air with the Prandtl number of 0.72. At the Reynolds number of 600, the effect of different conductivity ratio (1, 10, 100, 400) between solid and fluid are investigated. Furthermore, at this Reynolds number, the distance between obstacles for the conductivity ratio of 10 is evaluated. The results show that any increase in Reynolds number leads to a heat transfer improvement. Moreover, increase in the conductivity ratio leads to an isothermal surface and enhanced heat transfer. The more the distance between the obstacles, the better the heat transfer rate. The results obtained from LBM are in good agreement with experimental and conventional computational fluid dynamics methods.
Experimental and computational studies to investigate flow dynamics of Geldart A and Gledart B particles in a Circulating Fluidized Bed (CFB)
Circulating fluidized beds is one of the emerging technologies to convert waste to energy and an attractive method on a large scale. Key components such as the loop seal, gas distributor and cyclone separator play pivotal roles in facilitating solid recirculation and heat transfer within the system. This study focuses on the design and optimization of a CFB reactor using data derived from Barracuda Virtual Reactor software (CPFD). Initially, data from a small scale CFB reactor with main dimensions of 84 mm diameter and a loop seal diameter of 34 mm was utilized for simulation validation. By comparing simulation results with experimental data, the accuracy and reliability of the computational model were ensured. Subsequently, different reactor models were constructed and analyzed to explore various configurations and operating conditions. The results obtained from simulation based design and optimization provided valuable insights into achieving the optimal performance of the CFB system. By refining geometry, efficiency was increased by 32%. Overall, this study contributes to advancing the understanding, application and design modification of CFB technology in waste to energy conversion and large-scale industrial processes.
Copyright (c) 2025 Subham Kandel, Niroj Koirala, Nirajan Raut, Rajan Jaiswal, Sunil Prasad Lohani, Britt Margrethe Emilie Moldestad https://creativecommons.org/licenses/by/4.0
Impact of grid sensitivity and drag model along with the height of recirculating pipe on a cold flow circulating fluidized bed
Fluidized bed technology known for its efficient heat and mass transfer and controlled material handling, is widely used across industries. However, CFD simulation of fluidized beds presents challenges that require extensive validation. This study leverages the Multiphase Particle-In-Cell (MP-PIC) method, a recent Lagrangian modeling technique to improve computational efficiency and accuracy. The CAD model was developed using SolidWorks 2020 and simulation was carried out in the commercial CFD package Barracuda VR 21.1.0. The sensitivity of grid size, drag models and the impact of recirculating pipe height after loop seal was examined. Sand particles 63-200 μm and air were used as bed material and fluidization gas respectively achieving full flow circulation at 650 SL/min and 12 SL/min aeration in the riser and loop seal. A total of 19 different simulations were conducted, varying grid size and drag models each for a duration of 45 seconds with a time step of 0.0005 seconds. Pressure transducers along the CFB walls provided validation data. The Wen-Yu Ergun drag model showed a minimal error margin of 0.60%, followed by the Wen-Yu 80000 model at 0.62%, demonstrating high predictive accuracy.
Copyright (c) 2025 Subham Kandel, Nirajan Raut, Niroj Koirala, Sunil Prasad Lohani, Britt Margrethe Emilie Moldestad https://creativecommons.org/licenses/by/4.0
The Application and Advantages of a Generic Component-Based SI/CI Engine Model with VVA Compatibility
Most engine models are developed for control purposes and, in some cases, hard coded with a single engine type usage in mind. This is a problem since a new model is also needed when new engines are developed, as it usually takes less time than changing or modifying the old one. To facilitate a more rapid development process, there is a desire to have control-oriented models that can be adapted to new types of hardware with ease while at the same time providing fundamental insights into the physics of the engine that limit the control performance. These objectives are fulfilled by creating an open-source mean value MATLAB/Simulink model, a generic engine model with parametrization and compatibility with both VVT/VVA and SI/CI combustion.The main idea is to build on a component-based structure where the components are designed to be reused for similar processes. The engine model models the air filter, intercooler, and exhaust system components as incompressible flow restrictions. Bypass, throttle, intake/exhaust valves, and wastegate are modeled as compressible flow restrictions. Adiabatic control volumes are placed between each component to keep track of masses, pressures, and temperatures. The few remaining components are modeled separately, with unique functions for each model. As a concept demonstration of the generality of the approach, two engines, a 6-cylinder 12.7-liter Scania diesel engine and a 4-cylinder 2.0-liter Volvo petrol engine, are used as case studies where the generic simulation platform is parameterized and validated against experimental data for both engines.
Copyright (c) 2025 Oskar Lind Jonsson, Lars Eriksson https://creativecommons.org/licenses/by/4.0
Modeling of a tire mounted energy harvester using an inertial and analytical tire deformation model
In this work, an analytical tire deformation model is created, which can be parameterized using simple measurements. The model consists of three equations which are solved to provide a shape function for the tire.This model can be used to provide excitation input for energy harvesters embedded inside the tire for example in FEM simulations. Additionally the model can be used in differential equation based simulations for quick parameterized simulations. With this model it is possible to study the effect of tyre inflation state to the energy harvesting performance of the system.Two different simulation cases are presented in this work. First is a vibration energy harvester simulation using the model with an inertial energy harvester. The second case illustrates an energy harvester using the deformation of the tire as the excitation for the energy harvester as opposed to inertial type harvester.
Copyright (c) 2025 Mikko Leinonen, Jaakko Palosaari, Jari Juuti https://creativecommons.org/licenses/by/4.0
Interoperability Challenges and Opportunities in Vehicle-in-the-loop Testings: Insights from NUVE Lab's Hybrid Setup
Research and innovation in Vehicle-in-the-loop (VIL) testing is garnering more attention than ever. Integrating cyber-physical systems into the VIL setups further enhances their functionality and hybridizes the testing. Setting up any VIL infrastructure involves substantial investments and critical analysis of the resources. This study focuses on such a VIL testing infrastructure development at NUVE-Lab, aiming to provide state-of-the-art facilities for hybrid automotive testing. The facility includes components such as a heavy tractor, dynamometers, an Actuators power need generation system (APGS) system, and battery emulators (BE), complemented by digital twins (DTs) of each physical machine, process and environment to automate the testing facilities. This research examines various interoperability challenges within the current VIL framework. Three distinct testing scenarios are created to assess the overall functionalities of the hybrid setup: dynamometer-in-the-loop, APGS-in-the-loop, and BE-in-the-loop. Analyzing individual cases highlighted the need for different modeling and simulation (M/S) tools to develop digital twins. Among the tools, SIMULINK is used to build and refine the models of DTs, whereas MATLAB is used to develop control algorithms. The study also explores the adoption of Functional Mock-up Interface (FMI) standards to facilitate seamless interoperability among modeling and simulation tools. Additionally, the potential integration of the Eclipse Arrowhead framework, an IoT-edge-based automation tool, is discussed to enhance efficient data management, service interoperability, and the integration of various cyber-physical system components. In conclusion, this paper outlines the interconnection of the digital and physical platforms to evolve a hybrid VIL test laboratory, envisioning the future trajectory of the NUVE-Lab.
Copyright (c) 2025 Sarthak Acharya, Aparajita Tripathy, Juho Alatalo, Pekka Seppänen, Aki Lamponen, Jukka Säkkinen, Tero Päivärinta https://creativecommons.org/licenses/by/4.0
New Chemical Kinetics Mechanism for Simulation of Natural Gas/Hydrogen/Diesel multi-fuel combustion in Engines
Reactivity Controlled Compression Ignition (RCCI) stands out as a promising combustion method for the next wave of internal combustion engines, offering cleaner and more efficient operation, particularly in heavy-duty engines. A key approach within this strategy involves pairing diesel as the high reactivity fuel with natural gas (NG) as the low reactivity counterpart. Further optimization can be achieved by introducing hydrogen to replace portions of NG, thereby enhancing combustion quality while reducing greenhouse gas emissions. For accurate numerical simulation of engines employing this strategy, specialized chemical kinetics reaction mechanism tailored for internal combustion engines becomes essential. To facilitate computationally efficient 3-D Computational Fluid Dynamics (CFD) simulations, the mechanism has been reduced to include 60 species and 372 reactions, with N-heptane acting as a diesel fuel surrogate. This compact mechanism is optimized to align with experimental ignition delay time (IDT) data for N-heptane. The accuracy of the mechanism's predictions for IDT and laminar burning velocity (LBV) is validated using available experimental data. Furthermore, 3-D CFD and quasi-dimensional multi-zone engine simulations are performed with the new mechanism to validate engine operating parameters against experimental data.
Copyright (c) 2025 Mohammad Mahdi Salahi, Amin Mahmoudzadeh Andwari, Alireza Kakoee, Kian Golbaghi, Jari Hyvönen, Ayat Gharehghani, Maciej Mikulsky, Eric Lendormy https://creativecommons.org/licenses/by/4.0
Driving force model for a real-time control concept of a hybrid heavy duty vehicle
The electrification of heavy vehicles and work machinery is developing rapidly. The main motivators are green transition and requirements from the customers. In Finland, there are many hightech market-leading companies in this segment. Mass-produced equipment and machines are suitable for general applications and thus tailoring design for specific conditions and/or needs results in better productivity and efficiency. In heavy electric vehicle applications, the challenge is to make new products economically viable and configure them to meet customer needs. In these applications, the number of solutions is an order of magnitude higher than in traditional mechanical solutions. However, electronic solutions enable new features and energy efficiency improvements to have measurable benefits in the application. The research investigates the effects of electric axle solutions for hybrid heavy duty vehicles. Modelling and simulations consider both the effects of engine and usage of battery charge and surroundings of vehicle, for example road profile, traffic, outdoor temperature, and friction. A system level model of a vehicle has been utilized to simulate its longitudinal dynamics interacting with estimated surroundings followed by model-based control. The planned route can be made further favorable by utilizing real-time model predictive control (MPC) receiving online data from changing conditions. MPC gives new suggestions for optimal battery usage based on deviations from the best matching model from a database. Control strategy is important when considering economic benefits for a hybrid heavy duty vehicle with a high degree of freedom in system design.
Copyright (c) 2025 Jari Ruuska, Aki Sorsa, Isa Banagar, Perttu Niskanen, Amin Mahmoudzadeh Andwari, Mika Ruusunen, Emil Kurvinen, Juho Könnö https://creativecommons.org/licenses/by/4.0
Model Predictive Control for Integrated Photovoltaic (PV) and Electrolysers System
The European Union (EU) has set an ambitious target to reach carbon neutrality by 2050, prompting industries to develop roadmaps to achieve this goal. In this context, hydrogen and hydrogen-based fuels play a crucial role in achieving net-zero emissions. Instead of relying on hydrogen production from steam reforming natural gas systems, electrolysers offer a sustainable alternative to address climate and energy challenges. The integration of solar energy systems with electrolysers can further diminish carbon emissions and enhance sustainability. Typically, these processes are simulated using process simulation software platforms that employ first-principle models based on the mass and energy balances within the system. The adoption of Model Predictive Control (MPC) algorithms not only benefit from improves advance control methods and optimization but also facilitates the automation and efficient operation of these processes. This study aims to mathematically model and simulate an integrated photovoltaic (PV) and Proton Exchange Membrane (PEM) water electrolyser system for hydrogen production. Additionally, it assesses the impact of MPC algorithms on the system's efficiency. The study undertakes modeling of PV systems incorporating a maximum power point tracking algorithm to capitalize on optimal power generation and ensure a consistent direct current supply to the electrolysers. Mathematical modeling of PEM water electrolysers is performed to establish the current-voltage curve in steady-state mode and to assess water and gas permeability through the membrane in dynamic mode. Finally, the study identifies input variables, such as electrolyser temperature, and evaluates their effects on key indicators like system efficiency through performance analysis.
Copyright (c) 2025 Ali Reza Pirouzfar, Sambeet Mishra, Gaurav Mirlekar, Koteswara Rao Putta https://creativecommons.org/licenses/by/4.0
Dynamic Reactor Modelling and Operability Analysis of Xylose Dehydration to Furfural Using an Extractive-reaction Process in an Agitated Cell Reactor
Valorisation hemicellulose into furans chemicals is of great interest to create sustainable furan alternatives to fossil-derived products. A route of particular interest is acid-catalysed dehydration of the hemicellulose pentoses in aqueous medium, with simultaneous extraction of furfural using organic solvent. Agitated Cell Reactor (ACR) could be effectively used to intensify this process and decouple mixing from the long reaction time. This study presents a mathematical model for dehydration of C5 sugars to produce furfural in an ACR. The model can be used to study the effect of feed concentration to the product properties, the concentration profiles along the reactor length, and the dynamic behaviour of the system under feed disturbances or flow rate adjustments. The model was successfully fitted to the experimental data of a laboratory scale ACR for the target product. A simulation study was conducted to analyse the controllability of the process. Operability analysis with the nominal input space and the design space was used for mapping the most feasible region for the process design to meet the flexibility or controllability already at the design phase of the reactor system.
Copyright (c) 2025 Markku Ohenoja, Pekka Uusitalo, Fernando Russo Abegão, Abdullahi Adamu, Kamelia Boodhoo, Mika Ruusunen https://creativecommons.org/licenses/by/4.0
CARLA-based digital twin via ROS for hybrid mobile robot testing
Autonomously driving vehicles and robots that drive in public environments need to be safe and reliable under all weather conditions, including arctic winter conditions. Digital twins provide an opportunity to test autonomous vehicles in a safer, faster, and less expensive environment than carrying out tests in real-life conditions. We developed the data connection via ROS (Robot Operating System) between a mobile robot and its digital twin. This allows for almost real-time exchange of commands, information, and sensor data between the twins.The digital twins of the robo t and the testing ground are constructed in a CARLA-based autonomous driving simulator, which simulates realistic arctic winter weather conditions.The digital twin design was informed by the intended future use cases: Testing, optimizing, controlling, and monitoring autonomous driving and snow cleaning functions first with the digital twin, then in hybrid approaches.In our test setup we tested the hybrid case, where both robot twins were moving in the simulation and the real-world test area at the same time. We verified our digital twin, assessed delays, and the applicability in the intended use cases. Our results show that the digital testing ground would profit from inbuilt reference points to examine the alignment with its real-world counterpart. The communication via ROS was occurring in almost real-time , therefore, the digital twin setup was found to be applicable in hybrid digital twin testing. In the future, we will introduce an autonomous car into this digital twin setup and equip the testing ground with a 5G network.
Copyright (c) 2025 Charlotte Stubenvoll, Tauno Tepsa, Tommi Kokko, Petri Hannula, Heli Väätäjä https://creativecommons.org/licenses/by/4.0
Identifiability and Kalman Filter Parameter Estimation Applied to Biomolecular Controller Motifs
In this paper we apply Augmented Extended Kalman filters (AEKFs) to performparameter estimation in two different biological controller motifs under both noise-free andnoisy conditions. Based on measurements of the two states of the controller motifs, we showthat under both noise conditions it is possible to estimate all 5 and 6 parameters, respectively,which is in accordance with previously published results that investigated the theoretical conceptof structural identifiability. We further investigate how the level of process/measurement noiseand the initial estimates of both the parameters and states in the AEKFs affect the estimationperformance, and the results indicate that the degree of non-linearity affects filter performance.
Copyright (c) 2025 Eivind S. Haus, Malin Harr Overland, Damiano Rotondo, Kristian Thorsen, Tormod Drengstig https://creativecommons.org/licenses/by/4.0
A Novel Approach to Simulating the Performance of Autonomous Inflow Control Devices
Improving the efficiency of oil recovery is a crucial necessity in the current energy landscape. The widespread adoption of advanced wells, equipped with Autonomous Inflow Control Devices (AICDs), represents a leading strategy for this purpose. However, the absence of a predefined and straightforward option for modeling advanced wells in dynamic multiphase flow simulators like OLGA® poses a significant challenge. To address the issue, this paper proposes a novel approach based on developing a mathematical model derived from experimental data characterizing the AICD behavior. The Algebraic Controller option in OLGA is then leveraged to integrate the AICD effects into the simulation seamlessly. The proposed methodology undergoes rigorous testing on the PUNQ-S3 reservoir model as a benchmark case study with Water Alternating Gas (WAG) injection. Results demonstrate that AICD has a better water reduction rate of 36.3% and 3.7% compared to OPENHOLE and ICD. This result also indicates the accurate modeling and simulation of AICD performance in the software, showcasing the effectiveness of the developed mathematical model. Comparative analyses of advanced wells with different Flow Control Devices (FCDs) underscore the conclusion that AICDs significantly enhance oil recovery efficiency, thereby maximizing profit and minimizing the carbon footprint.
Copyright (c) 2025 Ismail Hossain Rafi, Ali Moradi, Soheila Taghavi, Britt M. E. Moldestad https://creativecommons.org/licenses/by/4.0
Integration of Dynamic Multiphase Flow and Reservoir Models for Improved Oil Recovery Simulation
The utilization of advanced multilateral wells to enhance well-reservoir contact, coupled with water injection, stands out as a common approach to boost oil extraction efficiency. It is imperative to develop precise, fully integrated, dynamic, well-reservoir models tailored for this type of oil recovery to enhance the design of advanced multilateral well completions. This study addresses the challenge by constructing a well model using OLGA®, which is, a dynamic multiphase flow simulator, and a reservoir model using EclipseTM, a reservoir simulator. Subsequently, these models are seamlessly integrated to perform comprehensive simulations. The proposed approach is tested on a case study involving oil recovery through an advanced multilateral well completed with various Flow Control Devices (FCDs) supported by water injection. Results from the simulations demonstrate the success of the integration approach, offering a reliable method for accurately modelling oil recovery from advanced multilateral wells to improve oil recovery. Notably, according to this study, wells completed with Autonomous Inflow Control Valves (AICVs) exhibit superior performance, optimizing oil recovery with a reduced carbon footprint.
Copyright (c) 2025 Charith Rajapaksha, Ismail Hossain Rafi, Nirajan Raut, Ali Moradi, Soheila Taghavi, Britt M. E. Moldestad https://creativecommons.org/licenses/by/4.0
Integration of Optimization Methods into Simulation Technology for Manufacturing via Warehouse Optimization
The manufacturing industry is transitioning towards digital, intelligent, and sustainable practices. However, small and medium-sized enterprises (SMEs) often lack the resources and expertise to leverage digital tools in their research and development (R&D) activities. This paper demonstrates warehouse optimization using a Genetic algorithm to optimize pallet transfers within a flexible manufacturing system (FMS) cell. The simulation model, created with Visual Components software and an external Python application, includes a warehouse with nine Euro pallets and a stacker crane. The optimization reduced the total duration of pallet transfers by approximately 20 seconds (8.1%). Integrating simulation and optimization tools offers significant benefits, including enhanced production efficiency, reduced operational costs, and improved decision-making capabilities. This demonstration highlights the practical applications and advantages of these digital tools for SMEs, showcasing their potential to streamline processes and foster sustainable manufacturing practices. The results have sparked longer-term collaborations with local industry, emphasizing the practical applications and advantages of these digital tools in enhancing manufacturing efficiency.
Copyright (c) 2025 Hannu Hakalahti, Alisa Ala-Huikku, Toni Luomanmäki, Juha Hirvonen https://creativecommons.org/licenses/by/4.0
Evaluating Modelling Performance: Sensitivity Analysis of Data Volume in Industrial Batch Processes
The iron and steel industry, a cornerstone of global industrial development, is accountable for a significant environmental footprint, contributing to 7.2% of global greenhouse gas emissions. This significant portion underscores the sector's substantial impact on climate change. The projected increase in steel production by an estimated 30% by the year 2050, further accentuates the urgent need for innovation and sustainable practices within this industry. Given these considerations, prioritising the development of more efficient and environmentally friendly production methods becomes not only a matter of environmental responsibility but also a crucial aspect of ensuring the industry's long-term viability. This work presents an investigation that evaluate the impact of product series and modelling complexity regarding the prediction of products downstream properties for industrial batch processes. The system under observation is the production of thermocouple wire-rod materials, starting from the smelt-shop and concluding after the hot-rolling mill. The first perspective considered is how to model processes with more than one product of the same product series, in this case different alloy products that are of the same product series, namely thermocouples. In addition, models of escalating complexity are being implemented. This involves examining whether the successful generalisation of simpler models necessitates the adoption of a more sophisticated approach.
Copyright (c) 2025 Simon Mählkvist, Thomas Helander, Konstantinos Kyprianidis https://creativecommons.org/licenses/by/4.0
Machine Learning -based Optimization of Biomass Drying Process: Application of Utilizing Data Center Excess Heat
The utilization of biomass as a renewable energy source holds significant promise for climate mitigation efforts. Excess heat from Nordic data centers offers opportunities for sustainable energy utilization. This research explores the feasibility of using data center excess heat for biomass drying to enhance the biomass energy value. In this study, the challenge of predicting biomass moisture under demanding measurement conditions is addressed by developing a predictive model for exhaust air humidity from the dryer. This model indirectly describes biomass moisture and employs machine learning methods such as linear regression model (LM), gradient boosting machines (GBM), eXtreme gradient boosting (XGBoost), random forest (RF), and multilayer perceptron (MLP), while enhancing transparency through explainable artificial intelligence (XAI) techniques for analyzing and visualizing humidity fluctuations. Based on this study, it can be demonstrated that tree-based ensemble methods GBM, RF, and XGBoost can accurately predict the humidity of air exiting the dryer with coefficient of determination from 0.88 to 0.89. Weather conditions, supply air humidity, and dryer fan speed emerged as key factors affecting drying efficiency, providing actionable insights for process optimization. Specific thresholds for these features can be defined to facilitate process settings. Moreover, improving system air tightness enhances drying efficiency and mitigates weather effects. The model shows promising predictive capabilities for exhaust air humidity, enabling future dynamic modeling to indirectly predict biomass end moisture, enabling adaptive control of drying processes, optimizing production capacities, and advancing sustainable energy through AI-driven solutions.
Copyright (c) 2025 Henna Tiensuu, Virpi Leinonen, Jani Isokääntä, Jaakko Suutala https://creativecommons.org/licenses/by/4.0
Enhanced Anomaly Detection in Aero-Engines using Convolutional Transformers
Gas turbines are vital in power generation and propulsion systems. However, these engines are exposed to complex and variable operating conditions, which makes early and accurate fault detection essential for predictive maintenance and minimizing unplanned downtime. This paper proposes a novel approach that combines convolutional neural networks (CNNs) with transformer architectures to address these challenges. The proposed Convolutional transformer model aims to enhance the accuracy and robustness of turbofan fault classification by integrating the feature extraction capabilities of CNNs with the contextual learning strengths of transformers. Through rigorous experiments, we seek to demonstrate our approach's performance in classification accuracy and generalization across different operating conditions. We utilize a comprehensive synthetic dataset, C- MAPSS, derived from multiple aircraft engine units as the benchmark for this study. The results for the proposed model show an accuracy of 99.6% on the test dataset. The outcome has the potential to be extended and fine-tuned for different types of gas turbines for diverse applications.
Copyright (c) 2025 Mohammad Reza Babaei, Amare Desalegn Fentaye, Konstantinos Kyprianidis https://creativecommons.org/licenses/by/4.0
A Deep-Unfolding Approach to RIS Phase Shift Optimization Via Transformer-Based Channel Prediction
Reconfigurable intelligent surfaces (RISs) have emerged as a promising solution that can provide dynamic control over the propagation of electromagnetic waves. The RIS technology is envisioned as a key enabler of sixth-generation networks by offering the ability to adaptively manipulate signal propagation through the smart configuration of its phase shift coefficients, thereby optimizing signal strength, coverage, and capacity. However, the realization of this technology's full potential hinges on the accurate acquisition of channel state information (CSI). In this paper, we propose an efficient CSI prediction framework for a RIS-assisted communication system based on the machine learning (ML) transformer architecture. Architectural modifications are introduced to the vanilla transformer for multivariate time series forecasting to achieve high prediction accuracy. The predicted channel coefficients are then used to optimize the RIS phase shifts. Simulation results present a comprehensive analysis of key performance metrics, including data rate and outage probability. Our results confirm the effectiveness of the proposed ML approach and demonstrate its superiority over other baseline ML-based CSI prediction schemes such as conventional deep neural networks and long short-term memory architectures, albeit at the cost of slightly increased complexity.
Copyright (c) 2025 Ishan Rangajith Koralege, Arthur Sousa de Sena, Nurul Huda Mahmood, Farjam Karim, Dimuthu Lesthuruge, Samitha Gunarathne https://creativecommons.org/licenses/by/4.0
Data Center Resource Usage Forecasting with Convolutional Recurrent Neural Networks
Energy efficiency, scalability, and reliability are increasingly important for sustainable data centers. In this paper, we focus on forecasting real-world resource usage using neural network time series models, specifically utilizing convolutional recurrent long short-term Memory (LSTM) and gated recurrent unit (GRU) architectures. In our analysis, we compare LSTM and GRU in terms of forecasting accuracy and computational complexity during model training. We demonstrate that recurrent neural networks are more accurate and robust compared to the traditional autoregressive integrated moving average (ARIMA) time series model in this complex forecasting problem. GRU achieved a 9% reduction and LSTM a 5% reduction in forecasting mean squared error (MSE) compared to ARIMA. Furthermore, the GRU architecture with a 1D convolution layer outperforms LSTM architecture in both forecast accuracy and training time. The proposed model can be effectively applied to load forecasting as part of a data center computing cluster. In this application, the proposed GRU architecture has 25% fewer trainable parameters in the recurrent layer than the commonly used LSTM.
Copyright (c) 2025 Miika Malin, Jaakko Suutala https://creativecommons.org/licenses/by/4.0
Evaluation of model uncertainty propagation in mineral process flowsheet designs
Increasing demand for critical raw materials and energy transition metals sets new targets for the mineral processing, also resulting as higher requirements for the simulation tools during process design and optimization. This study presents a framework for global uncertainty evaluation of modelled plant-wide processes, where the propagation of uncertainty sources is addressed. The uncertainties exist, for example in operational and design parameters and in material properties. The approach was demonstrated with a typical mineral processing flowsheet simulated with commercial software. First, domain knowledge was adopted to screen the parameter space and then Monte Carlo simulation was performed. After this, the generated data set was used to identify surrogate models between the uncertain inputs and process performance indicators. Finally, a global sensitivity analysis was conducted to identify the effects of uncertainties to the decision-making in process design. The results were particularly used to locate the process points where accurate information is needed for the robust process design, or where on-line measurements would be preferred to establish on-line optimization.
Copyright (c) 2025 Henri Välikangas, Markku Ohenoja, Stéphane Brochot, Manuel González Fernández, Jari Ruuska, Mika Ruusunen https://creativecommons.org/licenses/by/4.0
Optimizing Annual-Coupled Industrial Energy Systems with Sequential Time Dependencies in a Two-Stage Algorithm
The use of mathematical methods in simulation and optimization models is widely spread to solve the current and future problems of an efficient and sustainable energy supply. Especially MILP is commonly used for industrial and municipal energy systems, where hourly resolved demand profiles are addressed in the time frame of one year in a quasi-stationary optimization. Certain technical or regulatory circumstances make necessitate considering all time steps in one coupled optimization problem. This results in a level of model complexity where today's solvers often struggle to find a solution within a reasonable timeframe. Application examples are annual maximum runtime restrictions or finding the optimum loading strategy of a seasonal storage. Regulatory examples in Germany include the full-load hour restricted CHP-surcharge, the high-efficiency-criterion or the maximum emission of a CO2-Budget which lead to an annual integral limitation. In this work, we present a two-stage approach with a simplified year-round-coupled first stage and a fully resolved second stage with a rolling horizon. To compress the input data in the simplified first stage while maintaining the order of the time sequence, we use different resolutions of downsampling and LP-relaxation. For the second stage, we derive corresponding additional boundary conditions and evaluate these in this study.Various use-cases involving both MILP and MIQCP models are evaluated using different compression parameters. The aim is to achieve high accuracy while saving computation time and furthermore enabling the solution of problems that would otherwise be computationally unsolvable without this method.
Copyright (c) 2025 Marion Powilleit, Stefan Kirschbaum, Joram Wasserfall, Clemens Felsmann https://creativecommons.org/licenses/by/4.0
Numerical Methods for the Flow Fields; A Comparative Review
This paper provides a comparative overview of four numerical methods widely employed in computational fluid dynamics and related fields: Finite Volume (FV), Lattice Boltzmann Method (LBM), Smoothed Particle Hydrodynamics (SPH), and Spectral Methods. FV discretizes the domain into control volumes, emphasizing conservation laws and flux integrals across cell faces. It's renowned for its robustness, particularly in complex geometries. LBM is a mesoscopic approach simulating fluid dynamics through particle interactions on a lattice grid. Its intrinsic parallelism and ability to handle complex boundary conditions make it suitable for multiphase flows and porous media simulations. SPH represents fluids as a set of particles, where properties are smoothed over neighboring particles using a kernel function. SPH excels in free surface flows, astrophysical simulations, and fluid-structure interaction due to its Lagrangian nature and adaptive resolution. Spectral Methods discretize functions using orthogonal basis functions, such as Fourier or Chebyshev polynomials, enabling high-order accuracy and spectral convergence. They are preferred for problems with smooth solutions and periodic boundary conditions, like turbulence simulations and wave propagation.
Copyright (c) 2025 Jamshid Moradi, Amin Mahmoudzadeh Andwari, Juho Könnö https://creativecommons.org/licenses/by/4.0
Nonlinearity analysis of variables for modelling and control
Nonlinearities become essential in various systems when the operating area widens. The linear models are special cases for narrow areas. The behaviour is often asymmetric and can become gradually steeper or flatter depending on the case. These nonlinear effects can be analysed from data distributions for chosen operating areas. Further extensions require recursive analysis. The widely used Gaussian distribution is seldom valid for a wide area. The variable specific scaling can be presented with two second order polynomial defined by five parameters interpreted as the operating point and four corner points of the feasible range. These parameters define the shape factors which may require adjusting to fill the only requirement that the functions need to be monotonously increasing. Alternative constraints provide good solutions for combining expert knowledge with the data-based analysis. If the nonlinear behaviour is analysed correctly, only linear interactions are needed in the models. As the analysis is based on the same methodology, different applications can be combined by using appropriate process data. The smooth operation and high quality of products is the main goal of all these applications, and this can be achieved by combining these indicators with process control in the same way as it has been one for smaller indicators used in lime kiln control and water treatment. Different parts of the methodology have been tested in versatile applications. The main benefit is that the same structures can be used in various applications since the scaling functions take care of linking to the real world.
Copyright (c) 2025 Esko Juuso https://creativecommons.org/licenses/by/4.0
GPU acceleration of average gradient method for solving partial differential equations
Previously presented method of calculating local average gradients for solvingpartial differential equations (PDEs) is enhanced by accelerating it with graphics processingunits (GPUs) and combining a previous technique of interpolating between grid points in thecalculation of the gradients instead of using interpolation to create a denser grid.For accelerating the calculation with GPUs, we have ported the original naive Matlabimplementation to C++ and CUDA, and after optimizing the code we observe a speedupfactors more than two thousand, which is largely due to the original code not being optimized.
Copyright (c) 2025 Touko Puro, Aarne Pohjonen https://creativecommons.org/licenses/by/4.0