Machine Learning -based Optimization of Biomass Drying Process: Application of Utilizing Data Center Excess Heat

Abstract

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.