https://ecp.ep.liu.se/index.php/sims/issue/feed 2023-10-19T13:22:18+02:00 Open Journal Systems <p>Founded in 1959 SIMS is the <strong>Scandinavian Simulation Society</strong> dedicated to the advancement of modeling and simulation science.</p> https://ecp.ep.liu.se/index.php/sims/article/view/742 2023-10-19T13:21:38+02:00 Karin Israelsson Vartan Ahrens Kayayan Fatemeh Johari Mattias Gustafsson Magnus Åberg

Due to desirable emission reductions and population growth, increasing energy demand is identified as a dire issue for energy systems. The introduction of low-energy building districts enables increased energy system efficiency. This study’s aim is twofold. Firstly, an extensive urban building energy model is used to simulate the hourly use and geographic distribution of the heat demand for residential and commercial buildings that are to be supplied by a low-temperature district heating system. The simulated buildings are a part of a planned city district, located in Gävle, Sweden. Two building energy performance cases are studied; one where all buildings are assumed to be of Passive House standard, and one where the building energy performance is in line with conventional new-building regulations in Sweden. Secondly, one specific building is modeled in detail and simulated in the building energy simulation software IDA ICE to investigate what building heating system is best suited for low-temperature heat supply. The temperature demands of floor heating and ventilation with heat recovery are investigated and compared to conventional water-based radiators. The building’s temperature demand results can be used when designing a lowtempered district heating system which will provide the supply temperature to identify a compatible heating system technique. Varying supply temperature demand will enable optimization for choosing building heating systems and consequently, possible cost reductions. The results could be used as an example for future city district planning as well as presenting relevant heating systems for low-temperature district heating.

2023-10-19T00:00:00+02:00 Copyright (c) 2023 Karin Israelsson, Vartan Ahrens Kayayan, Fatemeh Johari, Mattias Gustafsson, Magnus Åberg https://ecp.ep.liu.se/index.php/sims/article/view/743 2023-10-19T13:21:39+02:00 Alaa Krayem Mohammed Guezgouz Fredrik Wallin

As cities grow and develop, urban planners face an increasing challenge to create more sustainable and environment friendly communities. The Kopparlunden district in Västerås, Sweden, is no exception, with plans underway to transition the area to a more sustainable neighborhood. To assist this effort, this paper presents a simple grey box modeling approach to predict the heat demand of eight buildings in the area. As the city transforms from a historical industrial district to a mixed district with residential buildings, shops, and offices, the model will allow urban planners to predict their new heat demand. The model is calibrated using a genetic algorithm, then validated using real historical data. The results show a good accuracy of the model and highlight the importance of increasing the insulation efficiency of the walls in the modelled buildings. The model can be used to predict the heat demand variations, with minimum error of 2.49 kW and up to 16.6 kW for large buildings. The model highlights the importance of energy modeling for urban development projects and shows its significance as a tool to aid in decision-making towards sustainable and more efficient urban areas.

2023-10-19T00:00:00+02:00 Copyright (c) 2023 Alaa Krayem, Mohammed Guezgouz, Fredrik Wallin https://ecp.ep.liu.se/index.php/sims/article/view/744 2023-10-19T13:21:39+02:00 Federico Mereu Jayangi D. Wagaarachchige Zulkifli Idris Klaus-Joachim Jens Maths Halstensen

The increasing CO2 concentration in the atmosphere is the most urgent global challenge. The most mature CO2 abatement option is post-combustion CO2 capture employing Monoethanolamine (MEA) solvent. One challenge of using MEA is its in-service degradation to 2-oxazolidinone (OZD), a heterocyclic five-membered organic ring compound. Furthermore, OZD degrades more MEA leading to CO2 capture solvent loss and hence increased operational cost. It is therefore of interest to investigate methods to convert OZD back to MEA. This work reports the conversion of 2-oxazolidinone to MEA by heat treatment at an alkaline condition. Raman spectroscopy and Ion-Exchange chromatography were applied to qualify and quantify the reaction. The optimal reaction parameters were identified by an experimental design model using the Response Surface Methodology (RSM). A second-order model with three variables and five levels of focus was employed, with the OZD conversion percentage as the response. This methodology was chosen because such a model could estimate the main effects, interactions and quadratic terms by relying on a relatively small number of experiments. 17 experimental runs were designed by the software using this method. At a reaction time of 35 minutes, reaction temperature of 100°C, and 2.5 mole of hydroxide per mole of OZD resulted in a complete conversion of OZD to MEA.

2023-10-19T00:00:00+02:00 Copyright (c) 2023 Federico Mereu, Jayangi D. Wagaarachchige, Zulkifli Idris, Klaus-Joachim Jens, Maths Halstensen https://ecp.ep.liu.se/index.php/sims/article/view/745 2023-10-19T13:21:40+02:00 Alessandra Ghilardi Guido Francesco Frate Antonio Piazzi Mauro Tucci Konstantinos Kyprianidis Lorenzo Ferrari

Grid-scale energy storage systems are essential to support renewables integration and ensure grid flexibility simultaneously. As an alternative to electrochemical batteries, Pumped Thermal Energy Storage is a new storage technology suitable for grid-scale applications. This device stores electric energy as thermal exergy, which can be discharged directly for thermal uses or converted back into power depending on the necessities of the grid. The capability of the proposed energy storage to act as electric and thermal storage fits with the sector coupling necessities of multi-energy systems in which electrical and thermal energy carriers are involved. This paper investigates the effects on optimal grid management of integrating a Brayton Pumped Thermal Energy Storage into a multi-energy system. The case study includes renewable generation from photovoltaic modules and residential and industrial users' electrical and thermal load profiles. The system day-ahead optimization, performed through a Mixed Integer Linear Programming approach, aims to minimize the operational cost computed over a 24-hour horizon. The simulation highlights how the proposed storage technology interacts with the users' requirements during different seasons. The final results highlight that using multi-energy storage (i.e., providing power, heating, and cooling) brings a 5% reduction in operating costs during the year compared to a traditional electric-to-electric storage operation.

2023-10-19T00:00:00+02:00 Copyright (c) 2023 Alessandra Ghilardi, Guido Francesco Frate, Antonio Piazzi, Mauro Tucci, Konstantinos Kyprianidis, Lorenzo Ferrari https://ecp.ep.liu.se/index.php/sims/article/view/746 2023-10-19T13:21:41+02:00 Pontus Netzell Hussain Kazmi Konstantinos Kyprianidis

With the growing demand, electrification, and renewable proliferation, the necessity of being able to forecast future demand in combination with flexible energy usage is tangible. Distribution network operators often have a power capacity limit agreed with the regional grid, and economic penalties await if crossed. This paper investigates how cities could deal with these issues using data-driven approaches. Hierarchical electric load data is analyzed and modeled using Multiple Linear Regression. Key calendar variables holidays, industry vacation, ”Hour of day” and ”Day of week” are identified alongside the meteorological heating-, and cooling degree hours, global irradiance, and wind speed. This inexpensive algorithm outperforms the benchmark ”weekly Naïve” with a relative Root Mean Squared Error of 35% for the year-long rolling origin evaluation. Learnings from the data exploration and modeling are then used to evaluate the AI-based model Light Gradient Boosting Machine. Using similar explanatory variables for this expensive algorithm results in a relative error of 45%, although it outperforms the previous one during the summer. The models have varying strengths and weaknesses and could advantageously be combined into an ensemble model for improving accuracy. Incorporating detailed knowledge of local renewable electricity production in combination with hierarchical forecasting could further increase accuracy. With domain knowledge and statistical analysis, it is possible to create robust load forecasts with acceptable accuracy using easily available machine-learning libraries. Both models have good potential to be used as input to economic optimization and load shifting.

2023-10-19T00:00:00+02:00 Copyright (c) 2023 Pontus Netzell, Hussain Kazmi, Konstantinos Kyprianidis https://ecp.ep.liu.se/index.php/sims/article/view/747 2023-10-19T13:21:42+02:00 Vahid Zangeneh Lars Erik Øi

The rapid growth of technology and digitalization lead to an increase in the number of data centers around the world. Data centers produce a considerable amount of heat because of their servers and a large number of electric components. The heat generated by the data centers can be used as a potential source of heating, but the quality (temperature level) of the heat is normally low. In this work, the temperature of the excess (cooling) water from a data center is 45 °C. Generally, there is a possibility to use heat pumps to improve the quality of the heat. To obtain a district heating temperature of 60 °C, 70 °C and 80 °C, the coefficient of performance (COP) was calculated to 5.5, 4.3 and 3.5, respectively. This work is about utilization of the excess heat from a data center with three alternative heat pump solutions with a payback period and economic potential for 10 and 20 years. The simulation process was done by Aspen HYSYS. It was observed that the payback period as expected increases with decreasing COP. The payback period was calculated to values between 2.6 and 5.5 years, depending on the market situation and the delivery temperature. In this work, it is shown that Aspen HYSYS is a reasonable tool to calculate alternatives for heat recovery from data centers based on heat pumps.

2023-10-19T00:00:00+02:00 Copyright (c) 2023 Vahid Zangeneh, Lars Erik Øi https://ecp.ep.liu.se/index.php/sims/article/view/748 2023-10-19T13:21:43+02:00 Antonia Antoniadou Konstantinos Kyprianidis Ioanna Aslanidou Anestis Kalfas Dimitrios Siafakas

The need to improve reliability and support decision-making in manufacturing has drawn attention to the application of diagnostic and decision-support tools. Particularly in the investment casting industry, data-driven methods can be the enabler for process diagnostics and decision support. Images from the microscopic examination in the investment casting process are used as data input, to detect defects in produced pieces. The microscopic examination usually relies solely upon the ability of the operator to determine whether an image from the microscope contains a defect. Therefore, an effective strategy for this decision-making process is crucial to improve the reliability of the examination. The use of the machine learning classifier Random Forest is introduced to derive predictions on the existence of a defect in the input image. This work focuses on employing machine learning tools for image recognition and the developed approach constitutes a decision support model to assist the operator and improve the reliability of their assessment.

2023-10-19T00:00:00+02:00 Copyright (c) 2023 Antonia Antoniadou, Konstantinos Kyprianidis, Ioanna Aslanidou, Anestis Kalfas, Dimitrios Siafakas https://ecp.ep.liu.se/index.php/sims/article/view/749 2023-10-19T13:21:43+02:00 Lars Erik Øi Shirvan Shirdel Sumudu Karunarathne Solomon Aromada

A standard process for CO2 capture has been simulated with an equilibrium-based model in Aspen HYSYS. The simulation has been combined with equipment dimensioning and cost calculation in an integrated spreadsheet facility. New in this work is that Murphree efficiencies are varied to obtain automatic optimization of absorber height and inlet temperature. The optimum process was found as the process with minimum calculated sum of capital and operational cost over 25 years. The cost optimum process parameters for the standard process were calculated to 15 m absorber packing height, 13 K minimum approach temperature and 34 °C in inlet gas temperature. This study demonstrates that it is possible to calculate the optimum packing height and inlet temperature automatically by varying the Murphree efficiency in a case study function.

2023-10-19T00:00:00+02:00 Copyright (c) 2023 Lars Erik Øi, Shirvan Shirdel, Sumudu Karunarathne, Solomon Aromada https://ecp.ep.liu.se/index.php/sims/article/view/750 2023-10-19T13:21:44+02:00 Amirhossein Ghazi Zahir Barahmand Lars Erik Øi

Norway has a wide range of climatic conditions throughout the country. The climate varies from coastal to inland areas. Geographic latitude and longitude, as well as the gulf stream oceanic flow, account for this phenomenon. Different climate types can certainly affect residential building heating energy demands and make overheating more likely. On the other hand, a building's orientation has an impact on its heating energy requirements. A building's orientation affects how much solar gain it receives and how much wind it receives over the course of the year. Employing DesignBuilder® software, This study examines how different orientations affect the energy performance of a pre-designed house with and without solar photovoltaic panels in typical Norwegian climates. The results confirm that in different locations, the optimal situation is South-East and the lowest energy consumption without and with photovoltaic panels belongs to Bergen with 83305 Wh/m2 and Oslo with 29442 Wh/m2 respectively. This comparative study will be helpful to stakeholders in the building ecosystem (municipalities, engineers, and designers, building companies, suppliers, and residents) in making more informed decisions.

2023-10-19T00:00:00+02:00 Copyright (c) 2023 Amirhossein Ghazi, Zahir Barahmand, Lars Erik Øi https://ecp.ep.liu.se/index.php/sims/article/view/751 2023-10-19T13:21:45+02:00 Matias Waller Leonardo Espinosa Leal

Supported by an identification experiment using random-phase multisines, uncertain parameters in a grey-box model for a multiple-input multiple-output laboratory-scale heat exchanger are fitted to experimental data. By defining desired trajectories for the controlled system concerning setpoint changes, simulations and a cost function taking control signal activity into account, we determine both a linear and a nonlinear PI-controller. The resulting control systems are evaluated through practical experiments and analysis with encouraging results. The approach to modelling and controller design raises questions about what is needed from an educational point of view, e.g., what skills are needed for simulation-based control design and analysis?

2023-10-19T00:00:00+02:00 Copyright (c) 2023 Matias Waller, Leonardo Espinosa Leal https://ecp.ep.liu.se/index.php/sims/article/view/752 2023-10-19T13:21:45+02:00 Noorain Syed Kazmi Ru Yan Håkon Viumdal Saba Mylvaganam

Multiphase flow metering is a challenging task because of the complexity of multiphase flow. In this paper, nonintrusive multiphase flow metering techniques, including machine learning (ML) / artificial intelligence models for the identification of flow regimes and estimation of flow parameters of a two-phase flow in a horizontal pipe are proposed that use data from Electrical Capacitance Tomography (ECT) and conventional measurements such as differential pressure in the pipe. The flow regimes are classified into five types, namely plug, slug, annular, wavy and stratified. Two-phase air/water flow experimental data from ECT are collected by running extensive experiments using the horizontal section of the multiphase flow rig at the University of South-Eastern Norway (USN). Exploratory data analysis (EDA) is performed on these data to extract features for use in classification and regression algorithms. Time series of normalized capacitance data from ECT sensors are used to classify flow regimes and identify flow parameters. ML techniques of Artificial Neural Network, Support Vector Machine (SVM), K-Nearest Neighbors (KNN) and Decision Tree (DT) are used to classify flow regimes by using features extracted from ECT data. The cross-correlation technique is used to estimate flow velocity using data from a twinplane ECT module. ML regression techniques are used to estimate phase fractions. Fusing data from differential pressure sensors enhances the flow regime classification. An overall system performance is given with suggestions for designing dedicated control algorithms for actuators used in multiphase flow control.

2023-10-19T00:00:00+02:00 Copyright (c) 2023 Noorain Syed Kazmi, Ru Yan, Håkon Viumdal, Saba Mylvaganam https://ecp.ep.liu.se/index.php/sims/article/view/753 2023-10-19T13:21:46+02:00 Madhawee Anuththara Ali Moradi Amaranath S. Kumara Britt M. E. Moldestad

Oil recovery can be enhanced by maximizing the well-reservoir contact using long horizontal wells. One of the main challenges of using such wells is the early breakthrough of unwanted fluids due to the heel-toe effect and heterogeneity along the well. To tackle this problem, advanced wells are widely applied today. The successful design of such wells requires an accurate integrated dynamic model of the well and reservoir. This paper aims at developing appropriate integrated well-reservoir models for achieving optimal long-term oil recovery from advanced well models.In this study, OLGA® which is a dynamic multiphase flow simulator is implicitly coupled to ECLIPSETM which is a dynamic reservoir simulator for developing accurate models to simulate oil production from advanced wells under various production/injection strategies. A realistic heterogeneous light oil reservoir with an advanced horizontal well is used as a case study. Flow Control Devices (FCDs) are the key component of advanced wells and the functionality of the main types of FCDs in improving the oil production, minimizing the cost and carbon footprint is investigated.According to the obtained results, by implementation of FCDs the water breakthrough time is delayed by 180 days and the cumulative water production with ICD, AICD, and AICV completions is reduced by 26.8%, 33.1%, and 49.1%, respectively, compared to the open-hole case. Besides, the results show that linking OLGA and ECLIPSE is a numerically stable and accurate approach for modeling the interaction between the dynamic reservoir and dynamic well behavior for simulation oil recovery from advanced wells.

2023-10-19T00:00:00+02:00 Copyright (c) 2023 Madhawee Anuththara, Ali Moradi, Amaranath S. Kumara, Britt M. E. Moldestad https://ecp.ep.liu.se/index.php/sims/article/view/754 2023-10-19T13:21:47+02:00 Akshay Goyal

This paper explores the use of 3D simulation software for visualizing industrial production processes and its potential to enhance decision-making for improved production efficiency, quality, and profitability. Industrial production processes are complex and involve many variables and factors that can interact in unpredictable ways. Visualization helps to simplify these complex interactions, identify patterns and relationships, and enable more informed decision-making.The research question that guides this paper is: How can the use of 3D simulation software for visualization of industrial production processes improve decision-making and optimize production efficiency, quality, and profitability? This paper will investigate the benefits and challenges of using 3D simulation software for visualizing industrial production processes, including the ability to identify bottlenecks, and optimize the production process. Further, the paper examines the role of visualization in enabling more informed decision-making, including the ability to analyze production data and make data-driven decisions. To illustrate this, an industrial automation case study consisting of a manufacturing industry modelled in a 3D simulation software has been presented.The results of this 3D-simulation model provide insights into the advantages and disadvantages of utilizing 3D simulation software to visualize industrial manufacturing processes. The article further presents the significance of these findings for production managers, engineers, and decision-makers. Thus, the purpose of this study is to help readers understand how using 3D simulation software for visualization of industrial production processes can improve decision-making and optimize production efficiency, quality, and profitability.

2023-10-19T00:00:00+02:00 Copyright (c) 2023 Akshay Goyal https://ecp.ep.liu.se/index.php/sims/article/view/755 2023-10-19T13:21:48+02:00 Mathilda Cederbladh August Dahlberg Stavros Vouros Konstantinos Kyprianidis Costanza Saletti Mirko Morini

Buildings are currently non optimally controlled, using a weather compensation controller that depends only on external temperature. A rich amount of real-time data is available and can be used for better control. This work is focused on developing a general and dynamic model for utilizing the building as an energy storage for a peak-shaving control strategy.A dynamic grey-box model is developed using industry standard operators’ data from a multifamily building, Building A, located in Västerås, Sweden. The training period is set to 408 hours, and the prediction horizon to 48 hours. The model is verified in 4 steps: prediction ability on the historic data, parametric verification on the time constant, simulation of heat supply separated from the historic data and model generality by implementing the model on a second multifamily building, Building B. The modelling errors over a two-month simulated period are 8% for Building A and 9% for Building B. To demonstrate the utilization possibilities, an optimizer is constructed to evaluate a peak shaving control strategy. Different flexibilities for the indoor temperature have been examined yielding heat load peak shaving between 30 to 45%. Flexibility paves the way for improvement in pricing models for the heating sector. This work demonstrates the potential for utilizing building heat storage capacity to reduce peak consumption and costs.

2023-10-19T00:00:00+02:00 Copyright (c) 2023 Mathilda Cederbladh, August Dahlberg, Stavros Vouros, Konstantinos Kyprianidis, Costanza Saletti, Mirko Morini https://ecp.ep.liu.se/index.php/sims/article/view/756 2023-10-19T13:21:49+02:00 Jordi Béjar Rabascall Gaurav Mirlekar

In recent years, green hydrogen has emerged as an important energy carrier for future sustainable development. Due to the possibility of not emitting CO2 during its generation and use, hydrogen is considered a perfect substitute for current fossil fuels. However, a major drawback of hydrogen production by water electrolysis, supplied by renewable electricity, is its limited economic competitiveness compared to conventional energy sources. Therefore, this work focuses on analyzing the sustainability of a green hydrogen production plant, not only considering its environmental parameters, as well as its economic, energy and efficiency parameters. The polymer electrolyte membrane (PEM) is selected as the most promising method of green hydrogen production in the medium and long term. Subsequently, a small-scale production plant is simulated using chemical process simulation software to obtain key data for computing a set of sustainability indicators. The selected indicators are based on the Gauging Reaction Effectiveness for the Environmental Sustainability of Chemistries with a Multi-Objective Process Evaluator (GREENSCOPE) methodology and are used to compare the sustainability of the simulated PEM plant with alkaline water electrolysis (AWE) plant. Finally, the process is scaled-up to analyze the feasibility of the simulated PEM system and validated against data to determine the operation of the electrolyser at a large production scale.

2023-10-19T00:00:00+02:00 Copyright (c) 2023 Jordi Béjar Rabascall, Gaurav Mirlekar https://ecp.ep.liu.se/index.php/sims/article/view/757 2023-10-19T13:21:49+02:00 Lars Erik Øi Madhawee Anuththara Shahin Haji Kermani Mostafa Mirzapour Soudeh Shamsiri Sumudu Karunarathne

A CO2 capture process from a natural gas based power plant has been simulated and cost estimated using an equilibrium-based model in Aspen HYSYS using the amine acid gas package. The aim has been to calculate cost optimum process parameters for the standard process and also for a vapor recompression process. After process simulation using Aspen HYSYS, the process equipment was dimensioned and cost estimated using Aspen In-plant. The Enhanced Detailed Factor (EDF) method was used to select factors to calculate the total investment. Operating cost for heat and electricity was calculated from the simulation with estimated cost on consumed heat and electricity. The cost was calculated to 21.2 EURO per ton CO2 removed and a vapor recompression process was calculated to 21.6 EURO per ton. A recompression case with 1.2 bar flash pressure was calculated to 21.3 EURO/ton CO2. The ΔTMIN in the amine/amine heat exchanger was varied, and the optimum at 15°C was 20.9 EURO per ton CO2. The vapor recompression alternative was in this work slightly more expensive than the traditional case. In earlier works, the vapor recompression process has been claimed to be more economical than the standard process. The difference in this work is mainly due to different cost estimates of the compressor investment. This work shows that Aspen HYSYS is well suited for optimizing process parameters in a CO2 capture process with and without vapor recompression.

2023-10-19T00:00:00+02:00 Copyright (c) 2023 Lars Erik Øi, Madhawee Anuththara, Shahin Haji Kermani, Mostafa Mirzapour, Soudeh Shamsiri, Sumudu Karunarathne https://ecp.ep.liu.se/index.php/sims/article/view/758 2023-10-19T13:21:51+02:00 Nima Monghasemi Stavros Vouros Konstantinos Kyprianidis Amir Vadiee

In recent years the demand for cooling in buildings has grown steadily due to factors such as climate change and increased use of technology in Sweden. The increase of cooling demand occurs mainly during peak demand periods, where there is limited cooling capacity combined with limited distribution capacity in the district cooling network. Sweden has experienced considerable growth in the solar energy market in recent years, though its utilization has been mostly limited to power generation. To fulfill the cooling demand increase, solar driven cooling is a viable solution alternative to traditional cooling methods. The use of solar cooling is still in its early stages in Sweden. The aim of this work is to design a simulation model of a solar absorption cooling system for a full-service restaurant prototype building. The system layout consists of photovoltaic/thermal collector, storage tank, single-effect absorption chiller, auxiliary heater, and cooling tower. The results revealed the system ability to meet the cooling load while delivering sufficient hot water for the establishment. Higher solar fraction confirmed that using photovoltaic/thermal collector is more competitive than solar thermal collectors based on restaurant operational activities. A levelized cost of cooling of 0.164 €kWh⁄ indicated the system cost-effectiveness in comparison to similar setups in other favorable European locations for solar energy utilization.

2023-10-19T00:00:00+02:00 Copyright (c) 2023 Nima Monghasemi, Stavros Vouros, Konstantinos Kyprianidis, Amir Vadiee https://ecp.ep.liu.se/index.php/sims/article/view/759 2023-10-19T13:21:51+02:00 Dimitra Eirini Diamantidou Valentina Zaccaria Anestis Kalfas

This work presents a comprehensive analysis of hybrid electric propulsion systems in commuter aircraft, aimed at enhancing energy efficiency. The study utilizes an aircraft conceptual design library, OpenConcept, to perform evaluations of various aircraft components and their interrelationships. The methodology integrates aerodynamics, propulsion, and mission analysis within a common framework to optimize the aircraft design. The analysis focuses on a 19-passenger commuter aircraft, employing a series/parallel hybrid-electric architecture. The gradient-based Sequential Least Squares Programming optimizer is utilized to optimize design variables such as battery weight, engine power, and the selected power ratios, while adhering to operational constraints. Through a rigorous Design of Experiments study, the paper highlights that even when considering the current battery technology, hybrid-electric propulsion yields substantial energy savings for short-haul missions. The fuel and energy consumption reductions are evident, particularly for shorter ranges. However, for extended missions, the critical role of advanced battery energy density is emphasized to achieve significant energy efficiency improvements.

2023-10-19T00:00:00+02:00 Copyright (c) 2023 Dimitra Eirini Diamantidou, Valentina Zaccaria, Anestis Kalfas https://ecp.ep.liu.se/index.php/sims/article/view/760 2023-10-19T13:21:52+02:00 Andreas Lund Rasmussen Kjetil Fjalestad Ru Yan Håkon Viumdal Saba Mylvaganam Tonni Franke Johansen

There is a strong interest in quantifying the amount of gas and its flow rate to facilitate better control of the processes involved in many industries. There are usually many sensors monitoring these processes, both intrusive and invasive, as well as non-invasive sensors which are usually clamped on to the process pipelines in which the multiphase flow occurs. In the multiphase flow rigs at Equinor and the University of South-Eastern Norway, experiments have been performed with different combinations and velocities of the phases and multiple sensors have been logged. The data from these sensors have been used to estimate volume fractions of the phases as well as their flow rates. This paper presents the estimated results of volume fractions and velocities of selected phases, obtained by fusing data from multiple sensors that monitor density, differential pressure, temperature, and acoustic emission using machine learning (ML) algorithms. These ML algorithms use neural networks with the non-linear input-output type with Levenberg-Marquardt training and provide estimates of volume fractions and phase velocities with RMSE values in the range of 4.6 to 16 m3/h, with the lowest RMSE for gas and the highest for multiphase flow. The total flow rate for the multiphase flow was in the range 30 to 120 m3/h. Results are compared with ML models using data from non-invasive sensors.

2023-10-19T00:00:00+02:00 Copyright (c) 2023 Andreas Lund Rasmussen, Kjetil Fjalestad, Ru Yan, Håkon Viumdal, Saba Mylvaganam, Tonni Franke Johansen https://ecp.ep.liu.se/index.php/sims/article/view/761 2023-10-19T13:21:53+02:00 Lars Erik Øi Esmaeil Aboukazempour Amiri

CO2 capture from gas turbine exhaust gas using heat from the power generation cycle is a possibility for CO2 emission reduction from natural gas-based power plants. A simplified power plant was simulated in Aspen HYSYS with a compressor, a combustion chamber, a turbine, a steam circuit with a steam heater, a high-pressure steam turbine, a low-pressure steam turbine, a steam condenser, and a circulating pump. CO2 capture was simulated with an absorption column, a rich amine pump, a lean/rich amine heat exchanger, a desorber with a reboiler and condenser, a lean pump and an amine cooler. The equipment cost was obtained from Aspen In-plant Cost Estimator, and an enhanced detailed factor method was used to estimate the total investment. A base case with combustion at 30 bar, ΔTMIN of 10 °C, and 10 stages (meters of absorber packing) was simulated, dimensioned, and cost estimated. In earlier works, optimum parameters have been found by minimizing the cost of CO2 capture. In this work, optimum was defined as the maximum profit for a combined process with 85 % capture efficiency. Optimized parameters were calculated to 25 bar for the combustion pressure, 13 °C for the minimum temperature approach in the lean/rich amine heat exchanger, and 10-meter packing height in the absorption column. These values are comparable to values in literature.

2023-10-19T00:00:00+02:00 Copyright (c) 2023 Lars Erik Øi, Esmaeil Aboukazempour Amiri https://ecp.ep.liu.se/index.php/sims/article/view/762 2023-10-19T13:21:54+02:00 Bernt Lie

Optimal operation of petroleum production is important in a transition from energy systems based on fossil fuel to sustainable systems. One sub-process in petroleum production deals with transport from the (subsea) well-bore to a topside separator. Here, a simple model in Sharma & Glemmestand (2014) has been streamlined into a dynamic model suitable for illustration of the dynamics of oil transport, as well for control studies. The advantages of using dimensionless equipment models are emphasized. The model is then used to compare two popular modeling languages: Modelica, and ModelingToolkit for Julia. Key advantages and disadvantages of these two languages are emphasized.

2023-10-19T00:00:00+02:00 Copyright (c) 2023 Bernt Lie https://ecp.ep.liu.se/index.php/sims/article/view/763 2023-10-19T13:21:54+02:00 Rebecca Allen Eirik Svortevik Henrik Bergersen

Heat pumps are an attractive heating system in residential buildings. They operate based on the vapor compression cycle used in refrigeration systems. Design questions surrounding heat pumps can be investigated and answered using modelling tools that incorporate the necessary thermodynamics, fluid mechanics, and machinery component efficiency. Several modelling tools are available, however there is a need for more open-source, script-based programs that are competitive to those already available. This work presents a Python-based code for modeling the thermodynamics of the vapor compression cycle (VCC) in typical heat pumps. The main contribution of this work is an openly available online code, complete with a few examples to show its functionality, that provides the basic thermodynamic model of a heat pump for researchers or development engineers to use, modify, and extend. Its current features include choice of refrigerant, heat exchanger size and characteristics, compressor, and other design parameters such as heating load, and fluid temperatures in and out of the heat exchangers. Simulation outputs include the P-h and T-s diagrams and coefficient of performance (COP). The code is flexible and suggestions for future code development are given.

2023-10-19T00:00:00+02:00 Copyright (c) 2023 Rebecca Allen, Eirik Svortevik, Henrik Bergersen https://ecp.ep.liu.se/index.php/sims/article/view/764 2023-10-19T13:21:55+02:00 Nils Dressler Lars Eriksson

The basic mechanics of a bolted joint are well-known and have been studied for a long time. The dominating principle is to represent the parts in a joint as a series connection of linear compression and tension springs. However, traditional models often neglect the tightening dynamics and their interrelation with, for instance the friction or embedment. To study these phenomena further and determine their impact on the tightening process and dynamics, and for developing new tightening control strategies, it is necessary to model a threaded fastener and implement it in a suitable simulation environment.Existing models and experimental data have been studied to find equations that fit the observed behavior. Novel models were combined with standard Modelica components to form a threaded fastener model. The simulation results were compared with tightening data from experiments. This work proposes new models for the first three tightening phases, embedment, and threaded fastener friction. These models are implemented in the modeling language Modelica. The results show that it is possible to resemble a typical threaded fastener tightening with power tools. The friction and tightening phases show the expected behavior, while the embedment model needs further experimental verification. During modeling, the model is susceptible to the chosen parameters. Parameters for the joint stiffness, obtained via the VDI guidelines, needed to be reduced by 30% to resemble the joint in a dynamic simulation.

2023-10-19T00:00:00+02:00 Copyright (c) 2023 Nils Dressler, Lars Eriksson https://ecp.ep.liu.se/index.php/sims/article/view/765 2023-10-19T13:21:56+02:00 Heng Hu Narmin Hushmandi Magnus Genrup

With a significant impact on turbomachinery blade performance, surface curvature distribution becomes one of the essential factors in the design of high-efficiency blades. This study focuses on applying computational fluid dynamics (CFD) to evaluate turbine rotor blade performance. The main aim is to analyze the influence of incidence and geometry shape on the performance of a gas-turbine blade in two dimensions. To achieve this, an investigation was conducted to identify a suitable turbulence model for this case, with two turbulence models combined with two different solvers explored in ANSYS Fluent: Realizable k-ε model in pressure and density based solver; k-ω shear stress transport (SST) model in pressure and density based solver. The blade total pressure loss across different blade exit Mach numbers is the comparison factor, with validation against experimental data. Subsequently, the chosen pressure-based k-ω SST model mode is used to study the performance of various air inflow incidence angles and compare two different blade geometries. In this paper, two geometries, Geometry 1 and Geometry 2, were designed by setting two different exit blade angles, β2=79.5° and β2=70° respectively, while the inlet blade angles have the same value, β1=48.8°. Furthermore, the effect of varying air inflow incidence angles between -48.8° and 10° on the blade performance distribution is also investigated. Within the studied range, the inflow incidence angle of 10° is found to have the best performance in terms of turbine work output. On the other hand, the blade performance of Geometry 2 appears superior to Geometry 1.

2023-10-19T00:00:00+02:00 Copyright (c) 2023 Heng Hu, Narmin Hushmandi, Magnus Genrup https://ecp.ep.liu.se/index.php/sims/article/view/766 2023-10-19T13:21:56+02:00 Rakhi Rakhi Binod R. Giri Vivien Günther Fabian Mauss

The reforming of light hydrocarbons to produce synthesis gas, H2 and CO, is an important intermediate for manufacturing valuable basic chemicals and synthesis fuels. In order to understand these reforming processes better, elementary step reaction mechanisms are developed. In the available literature, the surface reaction mechanisms are usually achieved with the help of reaction kinetic parameters without using the thermochemistry of the species referred to kinetic models due to the unavailability of the thermochemistry of the intermediate species involved in the multi-step reaction mechanism. In this work, investigations are made to obtain the thermochemistry of the intermediate species to establish thermodynamic equilibrium in order to develop thermodynamic model for steam reforming of methane over nickel. The thermochemistry of the surface bound species is taken from different sources available in the literature and after that a detailed sensitivity analysis is performed to match the results with experiments. The simulation set up is adapted from the literature experiments given in [1]. The results produced with the one-dimensional tool using the thermodynamic model developed in the present investigation consisting of 21 reversible reactions are compared with the kinetic scheme with 42 irreversible reactions from reference simulation along with their experimental results. Both the models show some major differences in the reaction pathways which provides a useful insight into the key rate determining steps and needs further investigations.

2023-10-19T00:00:00+02:00 Copyright (c) 2023 Rakhi Rakhi, Binod R. Giri, Vivien Günther, Fabian Mauss https://ecp.ep.liu.se/index.php/sims/article/view/767 2023-10-19T13:21:57+02:00 Hamed Mohseni Pahlavan Natasa Nord

Building energy models are developed to describe energy performance. The energy performance of buildings is influenced by physical and human influenced factors. Therefore, to improve energy efficiency and renewable energy implementation in buildings on large scale, there is a need to analyze buildings on a large scale. In this study, URBANopt, a multi-building energy evaluation tool, was used to develop an accurate Multi building scale energy model for a university campus. This model will be useful in the future work to evaluate various available and emerging building-level and district-level technologies and retrofitting options to improve energy performance. URBANopt is a unique tool that leverages high-fidelity simulations of buildings, community-scale systems, distributed energy resources, and the associated interactions with local distribution electric infrastructure. A university campus in Norway was chosen as a case study. Results obtained from URBANopt were compared with a typical building energy simulation model in IDA-ICE for a representative building. This representative building was developed based on building characteristics, functionality, and geographic location, including indoor and outdoor climate conditions. Both models were validated by using measurement data. The results showed better simulation accuracy of the multi-building method of URBANopt with the measurement data, mainly due to the averaging of the characteristics of all buildings in the development of the representative building. Furthermore, the URBANopt allowed assigning a different scenario of technologies and retrofit options to each building in the evaluation process, which is impossible in the typical model due to its nature. However, it should be pointed out that the computational time of the model developed in URBANopt was higher and will increase more with the increased number of buildings.

2023-10-19T00:00:00+02:00 Copyright (c) 2023 Hamed Mohseni Pahlavan, Natasa Nord https://ecp.ep.liu.se/index.php/sims/article/view/768 2023-10-19T13:21:58+02:00 Tiina Komulainen Bilal Mukhtar Truls Ødegaard Hilde Johansen Kristine Haualand Kjell Rune Jonassen Simen Antonsen

Wastewater treatment sector uses about 1 percent of total energy consumption in European Union, hence development of energy-efficient digital technologies is an urgent challenge. The aim of this article is to develop energy-efficient control strategies for biogas production from sewage sludge at water resource recovery facilities (WRRF). The case study is developed in collaboration Veas WRRF, Norway. The Veas biogas plant is operated semi-continuously in mesophilic conditions. The process includes inlet sludge pumps, four anaerobic digesters, heat exchangers for sludge heating, pumps for sludge recirculation and a compressor for gas recirculation. The process has two controlled variables, biogas flowrate and digester temperature, the main disturbance is the inlet substrate composition. The manipulated variables are flowrates of the inlet sludge, heating medium, and sludge recirculation. The real semicontinuous operation approximated as continuous operation with two hour moving averaging. Transfer functions were identified from the pre-processed data. The accuracy of the models was sufficient 14−60%. The transfer functions were used to design control strategies with PID-controllers and model predictive controller (MPC). The results show that both control strategies can increase biogas production and decrease variability in controlled and manipulated variables compared to the plant operation. MPC gave the best results, increasing biogas production up to 10 % and decreasing variability in controlled variables by 50−80% and by 92−99% in manipulated variables. These results indicate that implementation of advanced control technologies can improve the energy efficiency of biogas production.

2023-10-19T00:00:00+02:00 Copyright (c) 2023 Tiina Komulainen, Bilal Mukhtar, Truls Ødegaard, Hilde Johansen, Kristine Haualand, Kjell Rune Jonassen, Simen Antonsen https://ecp.ep.liu.se/index.php/sims/article/view/769 2023-10-19T13:21:59+02:00 Daniel Eckhoff Magne Bratland Mads Mowinckel

Using finite element analysis for rapid dynamic loads without validation of the results can lead to major miscalculation, thus making it necessary to examine the accuracy of the software. The structural response from a hydrogen-air mixture explosion in a confined channel is investigated with experiments and numerical methods. The channel measures 1000 mm in length, with an inside diameter of 65 mm, and 15 mm thick transparent polycarbonate sidewalls. Hydrogen and air were released into the channel and ignited. Four Kistler transducers record the internal pressures. A biaxial HBM rosette strain gauge was bonded to the polycarbonate sidewall, used for recording strains during the explosion experiments, where von Mises stresses were calculated from these recordings. The channel was then idealized as a computer-aided design model in the engineering software SOLIDWORKS. By utilizing the pressure data from the experiments and creating a three-pointed loading curve, finite element analysis was applied for obtaining numerical von Mises stress results. Comparing the experimental and numerical results of von Mises stress show a variation of 4.9%.

2023-10-19T00:00:00+02:00 Copyright (c) 2023 Daniel Eckhoff, Magne Bratland, Mads Mowinckel https://ecp.ep.liu.se/index.php/sims/article/view/770 2023-10-19T13:22:00+02:00 Muhammad Baqir Hashmi Mohammad Mansouri Amare Desalegn Fentaye Shazaib Ahsan

Hydrogen fueled gas turbines are susceptible to rigorous health degradation in form of corrosion and erosion in the turbine section of a retrofitted gas turbine due to drastically different thermophysical properties of flue gas stemming from hydrogen combustion. In this context fault diagnosis of hydrogen fueled gas turbines becomes indispensable. To authors knowledge, there is a scarcity of fault diagnosis studies for retrofitted gas turbines considering hydrogen as a potential fuel. The present study, however, develops an artificial neural network (ANN) based fault diagnosis model using MATLAB environment. Prior to fault detection, isolation and identification modules, physics-based performance data of 100 kW micro gas turbine (MGT) was synthesized using GasTurb tool. ANN based classification algorithm showed a 99.4% classification accuracy of fault detection and isolation. Moreover, the feedforward neural network-based regression algorithm showed quite good training, testing and validation accuracies in terms of root mean square error (RMSE). The study revealed that presence of hydrogen induced corrosion fault (both as single corrosion fault or as simultaneous fouling and corrosion) led to false alarms thereby prompting other wrong faults during fault detection and isolation modules. Additionally, performance of fault identification module for hydrogen fuel scenario was found to be marginally lower than that of natural gas case due to assuming small magnitudes of faults arising from hydrogen induced corrosion.

2023-10-19T00:00:00+02:00 Copyright (c) 2023 Muhammad Baqir Hashmi, Mohammad Mansouri, Amare Desalegn Fentaye, Shazaib Ahsan https://ecp.ep.liu.se/index.php/sims/article/view/771 2023-10-19T13:22:01+02:00 Sumudu Karunarathne Kristoffer Eikeseth Lars Erik

Desorption of CO2 from the rich amine solvent is one of the main operations in the amine-based CO2 capture process. Proper vapour and liquid flow through the packing materials would enhance the heat transfer that is needed for stripping CO2 from solvent. This is achieved by increasing the surface area of the flowing solvent by using the packing material. In this study, the created CFD (Computational Fluid dynamics) model in OpenFOAMTM was able to simulate the factors influencing TCM (Technology Centre Mongstad) desorption performance, including liquid distribution, wettability and film thickness within the packing material. Three scenarios were considered including a base case for a better understanding of the hydrodynamics in the desorption column. Two of these are to compare the influence of mass flow rates, while one is used to investigating potential improvement. Simulation revealed that introducing a deflector plate and CO2 bypass tube has a positive hydrodynamic effect in the desorption column.

2023-10-19T00:00:00+02:00 Copyright (c) 2023 Sumudu Karunarathne, Kristoffer Eikeseth, Lars Erik https://ecp.ep.liu.se/index.php/sims/article/view/772 2023-10-19T13:22:01+02:00 Eirik Illing Nils-Olav Skeie Ole Magnus Brastein

In the process of system upgrades or migrations, the utilization of existing layouts and object structures for designing new Human Machine Interfaces (HMI) can significantly save time and effort. Operator interface images, commonly referred to as HMI´s, contain valuable information crucial to industrial operations, but access to source code or design files can be limited. Modern frameworks for object detection and text recognition offer a solution by extracting information directly from images. However, these methods require time-consuming data acquisition and manual effort to initiate. This paper proposes a novel approach utilizing traditional Computer Vision (CV) and Machine Learning (ML) techniques to extract objects from images. The extracted objects are used as training data to transfer learn a ResNet model for multi-label image classification. The combination of this model with techniques such as sliding window, pyramid scaling, and non-maximum suppression forms the basis for a semi-automated annotation tool. This tool generates training data for more optimized object detection methods, specifically the YOLO (You Only Look Once) one-stage object detector. The semi-automated annotation tool allows engineers to manually refine the training data and export state-of-the-art training images for YOLO. The YOLO model achieves an impressive mean Average Precision at IoU 50% (mAP50) score of 95.5% when transfer learned on the annotated data. Additionally, an Optical Character Recognition (OCR) engine is utilized to extract text information from preprocessed images, followed by postprocessing to filter tag data. An algorithm is then employed to link objects and tags together. The final solution is implemented in software designed to optimize user interaction, resulting in an analysis document in Excel format, which can be easily exported for end-user access. With the novel use of this software to automate image analysis, the time required to analyze HMI images prior to migration or rebuild can be reduced by an estimate of 90%.

2023-10-19T00:00:00+02:00 Copyright (c) 2023 Eirik Illing, Nils-Olav Skeie, Ole Magnus Brastein https://ecp.ep.liu.se/index.php/sims/article/view/773 2023-10-19T13:22:02+02:00 Mathias Dyvik Didrik Efjestad Fjereide Damiano Rotondo

This paper deals with the modeling and identification of the Quanser Aero. The Quanser Aero is an aerospace laboratory setup designed for teaching aerospace concepts. Two propellers generate thrust and allow the user to control its dynamic response. The ability to lock axes individually makes it capable of abstracting a variety of aerospace systems, such as half-quadrotor, 1-Degree of freedom (DOF), vertical take-off and landing (VTOL), and 2-DOF helicopter. This paper focuses on the latter of these modes. In this configuration, the Quanser Aero can produce different pitch and yaw angles based on the angular velocity of the propellers, which produces an interesting identification and control problem, due to the presence of nonlinearities and significant cross-couplings between different variables. In this paper, a nonlinear model derived from Newton’s law and Euler’s rotational dynamics is obtained, and the unknown model parameters are identified through an experimental approach, with the model validated through real-time testing. In particular, it is shown that by means of a more detailed description of the friction, which includes the Karnopp’s model that keeps the sum of the magnitude of all forces equal to zero until the applied forces are strong enough to overcome the friction force, and of the centripetal forces acting on the Aero, significant improvements are obtained when compared to state-of-the-art models. These improvements may hold the potential to enhance the performance of advanced nonlinear model-based control algorithms for this device.

2023-10-19T00:00:00+02:00 Copyright (c) 2023 Mathias Dyvik, Didrik Efjestad Fjereide, Damiano Rotondo https://ecp.ep.liu.se/index.php/sims/article/view/774 2023-10-19T13:22:03+02:00 Matteo Pettinari Guido Francesco Frate Konstantinos Kyprianidis Lorenzo Ferrari

Among the environmental-friendly technologies recently proposed in the literature, high-temperature heat pumps represent a promising solution to foster the complete penetration of renewables within the power grid. Such systems may be based on closed Brayton cycles and leverage many existing components. As they are meant to provide high-temperature heat while using renewable electricity, their potential field of application ranges from industrial heating to energy storage. Several variants are currently under development to assess the feasibility of such systems in providing flexibility to the electricity grid. To do so, they need to operate in part-load conditions and quickly react when the load must be adjusted. In this regard, this study investigates the transient capabilities of Brayton heat pump technology. To this extent, a detailed transient model of a novel prototype proposed in the literature is presented, accounting for controls, thermal inertia and volume dynamics related to heat exchangers and piping. Furthermore, the model is used to assess the transient performance of the system in response to sudden load variations, which is achieved by adapting the turbomachinery operating velocities. Results show that the system can safely operate in part-load conditions with regulation times compatible with industrial needs.

2023-10-19T00:00:00+02:00 Copyright (c) 2023 Matteo Pettinari, Guido Francesco Frate, Konstantinos Kyprianidis, Lorenzo Ferrari https://ecp.ep.liu.se/index.php/sims/article/view/775 2023-10-19T13:22:04+02:00 Justin Warners Stavros Vouros Konstantinos Kyprianidis R. Benders P. Nienhuis

This research addresses the potential for increasing wind power in Sweden’s bidding area SE3. Sweden currently faces an energy imbalance, with larger production in the north and high demand in the south. Four bidding areas were introduced to incentivize energy production in the south. SE3, the largest bidding area, represents 60% of total demand. Using Seasonal Auto-Regressive Integrated Moving Average (SARIMA), historic data analysis from 2007 to 2022 is forecasted to a medium long-term future of 2035. Forecasting the observed trends reveals a potential supply deficit even under minimum demand growth scenarios made in literature. Closure of nuclear plants contributes to the shortfall, and the increasing trend in solar and wind power falls short. To study the impact wind power can have, the monthly wind patterns are analyzed, and used to calculate the power potential of different turbine capacities. Offshore areas show the highest potential for increasing wind power capacity in SE3. Economic factors, like payback time, are considered. The research concludes that there is technically and economically viable potential for wind power capacity to address the demand-supply gap by 2035. However, it depends on permitted areas, excluding built areas, UNESCO sites, and fishing routes. Future research should further explore these restrictions and address the seasonal variability in wind power to improve the understanding of the potential for wind power in the SE3 bidding area.

2023-10-19T00:00:00+02:00 Copyright (c) 2023 Justin Warners, Stavros Vouros, Konstantinos Kyprianidis, R. Benders, P. Nienhuis https://ecp.ep.liu.se/index.php/sims/article/view/776 2023-10-19T13:22:04+02:00 Chidapha Deeraksa Britt Margrethe Emilie Moldestad Nora Cecilie Ivarsdatter S. Furuvik

Hydrogen is an efficient energy carrier and an important contribution to sustainable energy development. Hydrogen can be produced based on different methods and on different raw materials. Blue hydrogen is hydrogen produced from natural gas via a steam-methane reformer with subsequent carbon capture and storage. The CO2 from the process can be stored in matured oil and gas fields or in an aquifer.This paper studies the potential of producing blue hydrogen from methane from the Troll gas field on the Norwegian continental shelf. The production rate of methane from the Troll field is predicted and based on the calculated methane production the steam-methane reformation process is modelled and simulated. The model includes the required steps to convert natural gas into hydrogen and CO2 and further to catch the CO2. The volume of captured CO2 per m3 of produced hydrogen is calculated. Production of blue hydrogen also includes storage of CO2, and the required storage capacity is calculated.The purpose of this paper was to investigate whether blue hydrogen produced by natural gas from the Troll field is an alternative to reducing CO2 emissions to reach the climate target. The simulation was performed with Aspen HYSYS 12 and the calculation on how much CO2 must be stored and the storage capacity needed were performed manually. The mass of CO2 resulting from the conversion of about 2400 tons natural gas/h to blue hydrogen and CO2 at the Troll field is 5600 tons CO2/hour or 49 megatons CO2/year. The produced hydrogen had a purity of 95%. The predicted storage capacity for CO2 at the Troll field is found to be 136 megatons. A profitability analysis is performed and the results are promissing.

2023-10-19T00:00:00+02:00 Copyright (c) 2023 Chidapha Deeraksa, Britt Margrethe Emilie Moldestad, Nora Cecilie Ivarsdatter S. Furuvik https://ecp.ep.liu.se/index.php/sims/article/view/777 2023-10-19T13:22:05+02:00 Farhan Hasin Alam Amin Tahami Nora C.I. Furuvik Britt M.E. Moldestad Soheila Taghavi

The demand for non-conventional oil has increased globally. Non-conventional oil is categorized as extra heavy oil and bitumen. In reservoirs with extra heavy oil and bitumen, thermal methods are used to reduce the oil viscosity. Steam assisted gravity drainage (SAGD) is a thermal recovery method to enhance the bitumen recovery. In this method, steam is injected to bitumen and heavy oil to reduce the viscosity and make the oil mobile and extractable. To obtain an efficient SAGD process, the residence time for steam in the reservoir must be long enough for the steam to condense and release the latent energy to be transferred to the cold bitumen. Early breakthrough of steam in some parts of the well will eventually limit the oil production and must be avoided. Autonomous inflow control valve (AICV) can prevent the steam breakthrough and restrict the excessive production of steam. The objective of this paper is to investigate the performance of AICV and its impacts on increased oil production in a SAGD production well. This is achieved by focusing on the implementation, and performance evaluation of inflow control devices (ICDs) and AICVs compared with standard well perforations. CMG STARS, a multi-phase, multi-component thermal reservoir simulator, is used to perform numerical simulation studies. The simulation results demonstrate the significant benefit of AICV in steam reduction compared to ICD and well perforations. The simulation results demonstrate that utilizing AICV in a SAGD reservoir will lead to higher oil production, less steam production, and a more uniform temperature distribution, and steam chamber conformance. Reduction in steam production, will improve the overall SAGD operation performance. This will also result in more cost-effective oil production, as less steam is needed to be generated for production of each barrel of oil.

2023-10-19T00:00:00+02:00 Copyright (c) 2023 Farhan Hasin Alam, Amin Tahami, Nora C.I. Furuvik, Britt M.E. Moldestad, Soheila Taghavi https://ecp.ep.liu.se/index.php/sims/article/view/778 2023-10-19T13:22:06+02:00 Tiina Komulainen Abdul Malik Baqeri Einar Nermo Arvind Keprate Torgeir Saltnes Katrine Marsten Jansen Olga Korostynska

The recently updated European Union’s Urban Waste Water Treatment Directive proposal, European Green Deal, Biodiversity Strategy for 2030, and EU’s Energy System Integration highlight a pressing need for innovative biological nutrient removal processes and energy-efficient control methods to reduce pollution and minimize the carbon footprint at water resource recovery facilities. The aim of the PACBAL research project is to develop estimation methods for nutrient profile in a novel industrial Moving Bed Biofilm Reactor (MBBR) process. This study devises and assesses a range of data-driven methods to estimate effluent phosphorus concentration by utilizing a combination of real sensors with software models. The resulting virtual sensor could facilitate the design of energy-efficient control strategies. The case study data are collected from the MBBR process at Hias water resource recovery facility in Norway. Data sets from December 2022 to March 2023 include varying weather conditions, such as rain, dry, and snow. The Hias Process consists of three anaerobic and seven aerobic zones, where biomass carriers removes over 90 percent of the phosphorus from the wastewater in simultaneous biological processes. The industrial online measurements include wastewater flowrate, aeration rates, dissolved oxygen and nutrients COD and NO2/ NO3 at inlet and total suspended solids at outlet. Dynamic data-driven models indluding transfer functions, state-space models and ARX models, were developed and compared to estimate the outlet phosphorus concentration. Model fitness to validation data was around 7% with ARX models, and up to 18% with tranfer function models and state-space models. The first and second order models gave similar results. The state-space models will be developed further and implemented to into virtual sensors that will enable energy-efficient control strategy development.

2023-10-19T00:00:00+02:00 Copyright (c) 2023 Tiina Komulainen, Abdul Malik Baqeri, Einar Nermo, Arvind Keprate, Torgeir Saltnes, Katrine Marsten Jansen, Olga Korostynska https://ecp.ep.liu.se/index.php/sims/article/view/779 2023-10-19T13:22:07+02:00 Heidi Lynn Ivan Jean-Paul André Ivan

The harsh operating environment in a wastewater treatment process (WWTP) makes sensor faults commonplace. Detecting these faults can be challenging due to the complex process dynamics, unknown inputs, and general noise in the process and measurements. Comparing sensor readings against predictions from a physics-based or data-driven model of the WWTP is a common strategy for detecting such faults. In this work sensor measurements are directly modelled using Gaussian process (GP) regression, a data-driven multivariate approach. These GP sensor models are, with a generalised product of experts, combined into a dedicated fault isolation scheme resembling traditional observer bank methods. The residuals are monitored with a multivariate exponentially weighted moving average chart which is used for fault detection and isolation. The method is evaluated using simulated data generated with the Benchmark Simulation Model No. 1 WWTP. Fault detection performance is reported using several standard metrics such as false alarms, missed detections, time to detection, and successful fault isolations, with emphasis on reporting across a wide range of sensors and faults to provide a point of comparison for future studies. The proposed approach performs well across these metrics. Given sufficient data representative of normal operation, this approach can easily be adapted across a wide variety of plant configurations and can be used to create operatorfriendly diagnostics resembling classical control charts.

2023-10-19T00:00:00+02:00 Copyright (c) 2023 Heidi Lynn Ivan, Jean-Paul André Ivan https://ecp.ep.liu.se/index.php/sims/article/view/780 2023-10-19T13:22:08+02:00 John Tinnerholm Mariano Zapatero Adrian Pop Peter Fritzson Rodrigo Castro

System Dynamics is a modelling paradigm that has been applied to a wide range of systems, from economic to physical and from managerial to ecological. The main strength of the paradigm is its ease of use. A System Dynamics modeller does not need to focus on equations; instead, models are expressed in terms of stocks and flows. Modelica, on the other hand, is an equation-based modelling language capable of multi-domain modelling using equations. It gives the user more freedom but requires more mathematical focus and skills. Therefore, a unification of equation-based modelling and the System Dynamics paradigm is seen as highly beneficial. Advantages include the ability for System Dynamics modellers to use the tools available in the Modelica ecosystem. Furthermore, it allows the integration of System Dynamics models into Modelica models. To achieve this goal, we developed an XMILE-to-Modelica translator that maps System Dynamics models represented in the XMILE standard exchange format to Modelica models. We also applied a Modelica-to-Julia translator to demonstrate the generality of the approach.We translated several models to test the correctness of the translator. In particular, the Earth System Climate Interpretable Model (ESCIMO) was translated from its original version in the Vensim toolkit into the OpenModelica toolkit, and a correct validation was obtained by comparing simulation results between simulators. Our work improves tool interoperability and further demonstrates the feasibility of using Modelica as a unified, standard language to integrate models created using System Dynamics, including large and complex socio-biophysical systems.

2023-10-19T00:00:00+02:00 Copyright (c) 2023 John Tinnerholm, Mariano Zapatero, Adrian Pop, Peter Fritzson, Rodrigo Castro https://ecp.ep.liu.se/index.php/sims/article/view/781 2023-10-19T13:22:09+02:00 Anas Fattouh Smrutirekha Sahoob

Monitoring the temperature of induction traction motors is crucial for the safe and efficient operation of railway propulsion systems. Several thermal models were developed to capture the thermal behaviour of the induction motors. With proper calibrating of the thermal model parameters, they can be used to predict the motor’s temperature. Moreover, calibrated thermal models can be used in simulation to evaluate the motor’s performance under different operating conditions and find the optimal control strategies.Parameterization of the thermal model is usually performed in dedicated labs where the induction motor is operated under predefined operating conditions and calibrating algorithms are then used to find the model’s parameters. With the development of digital tools, including smart sensors, Internet of Things (IoT) devices, software applications, and various data collection platforms, operational data can be collected and used later to calibrate the parameters of the thermal model. Nevertheless, calibrating the model’s parameters from operational data collected from different driving cycles is challenging as the model has to capture the thermal behaviour from all driving cycles’ data.In this paper, a data-driven reinforcement learning-based parametrization method is proposed to calibrate a thermal model in induction traction motors. First, the thermal behaviour of the induction motor is modelled as a thermal equivalent network. Second, a reinforcement learning (RL) agent is designed and trained to calibrate the model parameters using the data collected from multiple driving cycles. The proposed method is validated by numerical simulation results. The results showed that the trained RL agent came up with a policy that adeptly handles diverse driving cycles with different performance characteristics.

2023-10-19T00:00:00+02:00 Copyright (c) 2023 Anas Fattouh, Smrutirekha Sahoob https://ecp.ep.liu.se/index.php/sims/article/view/782 2023-10-19T13:22:10+02:00 Oskar Olsson Mattias Gustafsson Magnus Åberg

In Europe, the prices of natural gas and electricity reached an all-time high in 2022. A way to mitigate high electricity costs is to expand district heating systems in urban areas, this will reduce electric load as well as increase the power generation possibilities in combined heat and power plants. District heating has been the dominant heat supply technology in urban areas in Sweden since the 1980s. However, as the energy efficiency in buildings increase, district heating distribution losses must be reduced to ensure a cost-efficient heat supply. This has led to the idea of the 4th generation district heating which is characterized by low distribution temperatures. In this study, low-temperature district heating distribution in a planned future city district is simulated using a Python-based tool. Two different low-temperature distribution systems are investigated: 1) 2-pipe low-temperature system, and 2) a cascading 3-pipe low-temperature system. The focus is on simulating the distribution losses, temperature drop, and mass flow in the pipe network. The scope of the analysis also includes an investigation of the effect of lower return temperatures on the central district heating network. The results indicate that the low-temperature distribution system with the 2-pipe system performs better than the cascading system when considering distribution losses and temperature drop. The mass flow depends on the temperature demand in the heating systems in the buildings and is considerably high for both low-temperature distribution systems investigated.

2023-10-19T00:00:00+02:00 Copyright (c) 2023 Oskar Olsson, Mattias Gustafsson, Magnus Åberg https://ecp.ep.liu.se/index.php/sims/article/view/783 2023-10-19T13:22:11+02:00 Tajana Nepal Diwakar Bista Thomas Øyvang Roshan Sharma

Hydro Power plant (HPP), being one of the most convenient options for power generation, has been modelled considering very wide aspects of their application. A model is simply a mathematical representation of a system and it may serve different purposes like dynamic simulation of hydro power, energy systems modelling involving policy making, condition monitoring, etc. The purpose of modelling HPPs may lead to various kind of models for a single Hydropower. This paper aims at reviewing hydropower models developed using different methods along with the purpose for modelling them. This will provide brief insights about state of the art on hydropower modelling and its emerging techniques. Furthermore, this paper presents in more detail about tracking the advancements in dynamic models for classical and variable speed hydropower plants highlighting the need for the development of more accurate models. The work mainly involves narrative review of published works on hydro power modelling techniques. Also, it includes systematic reviews about dynamic representation of hydropower plants. As this paper aims at presentation of hydro power models in a classified manner based on purpose of modelling, the areas of improvement in each type of model have been discussed. Models for control can be made to be more accurate by including more realistic featured like penstock dynamics, uncertainties, etc which further help in design of advanced control systems. There are several potential benefits of HPP modelling, such as optimizing plant performance, improving control, reducing maintenance costs, and enhancing overall system efficiency and reliability.

2023-10-19T00:00:00+02:00 Copyright (c) 2023 Tajana Nepal, Diwakar Bista, Thomas Øyvang, Roshan Sharma https://ecp.ep.liu.se/index.php/sims/article/view/784 2023-10-19T13:22:12+02:00 Jorge Lovaco Ingo Staack Petter Krus

The System of Systems (SoS) framework plays a pivotal role in delimiting aircraft design spaces by examining interactions among its Constituent Systems (CS). Each CS has a distinct collection of capabilities, some of which may be shared with other CS. The framework explores emergent behaviours that arise from communication between the CS within the SoS. These emergent behaviours are characterized by their unattainability by any individual CS and result from their collaborative nature. The identification of these emergent behaviours enables System of Systems Engineering (SoSE) to pinpoint the most valuable configurations of the SoS, thereby maximizing the collective value. Furthermore, these emergent behaviours aid in stipulating design requirements for new systems based on the capabilities outlined in the SoS study. To map the relationship between needs, capabilities, requirements, and behaviours, maintaining traceability throughout the study is paramount.This research employs workflows created using the Remote Component Environment (RCE), a specialized tool for structured and automated task development. The objective is to showcase RCE's integration capabilities- specifically for software tools and Python scripts- with task scheduling. This integration enables swift extraction of results, making them available at every step, thus augmenting analysis efficiency. The study focuses on the perspective of an aircraft designer during the early concept generation phase, specifically applied to the development of an electric Unmanned Aerial Vehicle (UAV) concept for wildfire detection.

2023-10-19T00:00:00+02:00 Copyright (c) 2023 Jorge Lovaco, Ingo Staack, Petter Krus https://ecp.ep.liu.se/index.php/sims/article/view/785 2023-10-19T13:22:12+02:00 Chen Yang Li Sun

As modern societies face increasing energy demands and a complex smart grid with multiple inputs of traditional and intermittent renewable energy power generation systems, the need for energy storage systems has become a general trend. Among these systems, compressed air energy storage (CAES) has received extensive attention due to its low cost and high efficiency. This study proposes a novel design framework for a hybrid energy system comprised of CAES system, gas turbine, and high-temperature solid oxide fuel cells, aiming for power generation and energy storage solutions. The overall model of the hybrid power generation system was constructed in Aspen Plus, and the mass balance, energy balance, and thermodynamic properties of the thermal system were simulated and analyzed. The results demonstrate that the hybrid system utilizes the functional complementarity of CAES and solid oxide fuel cells (SOFC), resulting in the cascade utilization of energy, flexible operation mode and increased efficiency. The overall round trip efficiency of the system is 63%, and the overall exergy efficiency is 67%, with a design net power output of 12.5 MW. Additionally, thermodynamic analysis shows that it is advisable to operate the system under higher compressor and turbine isentropic efficiencies, and optimal SOFC/MGT (Micro Gas Turbine) split air flow rates. The results of this article provide guidance for designing innovative hybrid systems and system optimization.

2023-10-19T00:00:00+02:00 Copyright (c) 2023 Chen Yang, Li Sun https://ecp.ep.liu.se/index.php/sims/article/view/786 2023-10-19T13:22:13+02:00 Marianne Eikeland Sebastian Larsen Oliver Numme Eivind Johan Trasti Terje Bråthen

In the transition to a fossil-free transport sector, the use of Dimethyl ether (DME) can be an environmentally friendly alternative. DME is a synthetically produced alternative to fuels like diesel or liquified petroleum gas (LPG), and has lower emissions of CO2, NOx and particles compared with diesel. To be a green renewable alternative, DME needs to be produced from carbon neutral resources such as biomass. DME can be produced from synthesis gas produced by gasification of biomass. The synthesis gas can be used to produce either DME directly in a single stage process with a bi-functional catalyst, or in a twostep process in which methanol is produced in the first step and converted to DME via dehydration in the second step. In this study process simulations of the DME synthesis from methanol is assessed. The paper involves assessment of process parameters and energy improvement of the DME synthesis. The study evaluates the effects of different thermodynamic models like PRSV, NRTL, WILSON and UNIQUAC in Aspen Hysys. Conversion reactor and Gibbs reactor configurations, and sensitive analysis of process parameters is studied. Heat integration is evaluated for energy resource management and cost estimation. The Gibbs reactor with the UNIQUAC model and internal heat integration resulted in an increase in DME production of 0.5% and a reduction in energy demand of 46%.

2023-10-19T00:00:00+02:00 Copyright (c) 2023 Marianne Eikeland, Sebastian Larsen, Oliver Numme, Eivind Johan Trasti, Terje Bråthen https://ecp.ep.liu.se/index.php/sims/article/view/787 2023-10-19T13:22:14+02:00 Shahrzad Marashian Amir Vadiee Omid Abouali Sasan Sadrizadeh

Computational Fluid Dynamics (CFD) simulations are extensively used to model indoor environments, including airflow patterns, temperature distribution, and contaminant dispersion. These simulations provide valuable insights for improving indoor air quality, enhancing thermal comfort, optimizing energy efficiency, and informing design decisions. The recent global pandemic has emphasized the importance of understanding airflow patterns and particle dispersion in indoor spaces, highlighting the potential of CFD simulations to guide strategies for improving indoor air quality and public health. Consequently, there has been a significant increase in research focused on studying the transport and dispersion of pollutants in indoor environments using CFD techniques. These simulations are vital in advancing engineers' understanding of indoor environments; however, achieving accurate results requires careful method selection and proper implementation of each step. This paper aims to review the state-of-the-art CFD simulations of indoor environments, specifically focusing on strategies employed for three main simulation components: geometry and grid generation, ventilation strategies, and turbulence model selection. Researchers can select suitable techniques for their specific applications by comparing different indoor airflow simulation strategies.

2023-10-19T00:00:00+02:00 Copyright (c) 2023 Shahrzad Marashian, Amir Vadiee, Omid Abouali, Sasan Sadrizadeh https://ecp.ep.liu.se/index.php/sims/article/view/788 2023-10-19T13:22:15+02:00 Abdulkarim Habbab Anas Fattouh Bobbie Frank Koteshwar Chirumalla Markus Bohlin

The Construction and mining Industry comprises complex operations and interactions between various actors at different levels. Simulation has emerged as a valuable tool in this domain to better understand the site's behavior and optimize its operation. However, developing a simulation platform that can handle all the operations on the site is challenging due to the computational cost of the digital representation of reality along with the required accuracy level.This paper aims at extracting and mapping the optimization requirements of construction sites at three main levels: site level, operational level and dynamics level. More precisely, this work seeks to define and map the most important requirements between these levels that ensure simulation credibility and reliability.Based on interviews with experts in the domain, both from academia and industry, several key insights and recommendations emerged: at the site level, the layout and the key performance indicators, such as productivity, time, cost, number of machines and workers, need to be modeled and simulated. At the operational level, the simulation platform must include the main activities, such as loading, excavating, transporting and dumping. Moreover, the dynamics level should involve machine models and their interactions with the site's environment, such as earthmoving, drilling, excavating and blasting.

2023-10-19T00:00:00+02:00 Copyright (c) 2023 Abdulkarim Habbab, Anas Fattouh, Bobbie Frank, Koteshwar Chirumalla, Markus Bohlin https://ecp.ep.liu.se/index.php/sims/article/view/789 2023-10-19T13:22:16+02:00 Joakim Örneskans Konstantinos Kyprianidis Stavros Vouros Gunnar Bengtsson

In this paper we present a standard platform XC05 for an Edge Controller based on an Industrial Control System, where functions made in Modelica and Python can be run as an integrated part of an automation system. We demonstrate how the platform is used to run a complex Model Predictive Control (MPC) strategy to optimize indoor heating in a residential building. MPC strategies have been increasingly popular due to their ability to handle nonlinear dynamics with constraints and multi-objective optimization. Since industrial control systems are real-time based, consideration must also be taken to running security and the real-time characteristics and timing of the overall system solution. We also show that heavy calculation, protected by the industrial control system operative, can run safely together within fast automation using standard electronics. The controlled variable in the MPC strategy is the supply water temperature (Space heating), and the objective is to keep the indoor temperature at a predefined setpoint despite variations in outdoor weather conditions by using local measurements and weather forecasts from the Swedish weather service SMHI. The model used in the MPC is trained automatically with real-time data during running. We describe the controller architecture and briefly the model predictive control algorithm, analyze the overall system performance regarding safety and real-time characteristics. The proposed model predictive control application showed stable operation and expected real-time characteristics during operation. Furthermore, a reduction in indoor temperature deviations was achieved.

2023-10-19T00:00:00+02:00 Copyright (c) 2023 Joakim Örneskans, Konstantinos Kyprianidis, Stavros Vouros, Gunnar Bengtsson https://ecp.ep.liu.se/index.php/sims/article/view/790 2023-10-19T13:22:17+02:00 Rebecca Allen Berthe Dongmo-Engeland Saja Al-Batat

Porous pavement is a well-documented, low-impact stormwater management technique. When it comes to design of the top layer, the amount of void space (porosity) is often of interest as it influences both infiltration and strength of the pavement. Laboratory equipment can be used to measure the porosity of core samples, but when more detail is required, other equipment or methods must be used. One such method is to scan the entire sample using a computer tomography (CT) machine and then perform some image processing techniques on the scanned data to reconstruct the sample digitally. While the workflow of scanning and processing to produce the 3D digital twin of porous pavement is not new and can be in fact done by open-source or commercial software, there are still some parts of the process that deserve a deeper investigation, for example binarization and segmentation algorithms applied to the solid-and-void space and void space, respectively. This is difficult to do with commercial software which operates like a black-box, and there needs to be more open-source codes that are user-friendly, extendable, and competitive to what commercial software can do. This work presents a MATLAB-based code that allows for a deeper investigation of how one can accurately and efficiently quantify the effective (or connected) void space of a porous pavement sample from a 3D digital model. We demonstrate the effect of dataset coarsening, which can be used to reduce the computational intensity of the algorithm while preserving accuracy. The code is publicly available online to allow for reproducible research and the possibility of extensions for increased functionality and complexity.

2023-10-19T00:00:00+02:00 Copyright (c) 2023 Rebecca Allen, Berthe Dongmo-Engeland, Saja Al-Batat https://ecp.ep.liu.se/index.php/sims/article/view/791 2023-10-19T13:22:17+02:00 Avinash Renuke Stavros Vouros Konstantinos Kyprianidis

District heating system often consists of a long, complex network of piping carrying heat from a power plant to the consumers. The supply temperature from the plant is either controlled by the operator from experience or a predefined curve based on the outdoor temperature. An optimized supply temperature which would be lower than the one obtained traditionally would lead to lower heat loss and reduced peak load on the power plant. In this paper, we investigate the machine learning models for heat load forecasting which is a crucial parameter in the optimizing process. Models are generated using supervised machine learning algorithms: Linear models (Linear Regression, Ridge and Gaussian Process Regressor), Random Forest Regressor, Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) recurrent neural network (RNN). Data-driven models are used extensively in the literature to predict heat load prediction based on the weather and the time effect on a fixed training set, however, in this study, we model the heat load in the network in real-time scenarios i.e., adaptive training and forecasting. The model is adaptively updated as well as the training of the machine learning model in real time. It provides a “plug-and-play” solution for real-time prediction without significant pre-tuning requirements. The results of all the models are compared with various time horizons i.e., 6 hrs, 10 hrs, 24 hrs and 1 week, using the district heating data obtained for the city of Vasteras in Sweden. The performance of the prediction algorithms is evaluated using Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE). An algorithm with the best accuracy is selected based on the performance comparison. Also, models suitable for short-term and long-term forecasting are discussed towards the end of the article

2023-10-19T00:00:00+02:00 Copyright (c) 2023 Avinash Renuke, Stavros Vouros, Konstantinos Kyprianidis https://ecp.ep.liu.se/index.php/sims/article/view/792 2023-10-19T13:22:18+02:00 Hao Chen Daheem Mehmood Erik Dahlquist Konstantinos Kyprianidis

Thermochemical conversion processes of biomass, such as gasification and pyrolysis, can convert a wide range of feedstocks into liquid fuels, including forest residue, agricultural, food, and municipal solid waste. These more widely available and theoretically lower cost feedstocks make biofuel production through thermochemical pathway more cost-competitive. Furthermore, the thermochemical conversion pathway for biomass conversion could be relatively easy to integrate with the existing biomass combined heat and power plant, making it an attractive technology for the future commercialization of biofuel production through biomass. A detailed analysis was undertaken of a retrofitted biomass combined heat and power plant for biofuel production in this work. The biofuel production plant is designed to explore the polygeneration of hydrogen, biomethane, and biooil via the integration of gasification, pyrolysis, and renewable-powered electrolysis. The G-valve in the biomass circulating fluid bed plant, which is generally used for sand and char recycling, is retrofitted in the proposed system to fit the pyrolysis reaction for bio-oil production. Centering around the biomass circulating fluid bed gasifier, the system is also outfitted with a condensation and distillation process for bio-oil production, and a membrane reactor system for biomethane production. A mathematical model of the proposed biofuel production plant is established in Aspen Plus, followed by a performance investigation of the biofuel production plant under various design conditions. The limitations and opportunities of this retrofitted biomass combined heat and power plant for biofuel production are explored in this study.

2023-10-19T00:00:00+02:00 Copyright (c) 2023 Hao Chen, Daheem Mehmood, Erik Dahlquist, Konstantinos Kyprianidis