Optimization of Biomass Delivery Through Artificial Intelligence Techniques

Marta Wesolowska (), Dorota Żelazna-Jochim, Krystian Wisniewski, Jaroslaw Krzywanski (), Marcin Sosnowski and Wojciech Nowak
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Marta Wesolowska: Department of Regional Studies, University of Lodz Branch in Tomaszow Mazowiecki, Konstytucji 3 Maja 65/67, 97-200 Tomaszow Mazowiecki, Poland
Dorota Żelazna-Jochim: Department of Regional Studies, University of Lodz Branch in Tomaszow Mazowiecki, Konstytucji 3 Maja 65/67, 97-200 Tomaszow Mazowiecki, Poland
Krystian Wisniewski: Department of Regional Studies, University of Lodz Branch in Tomaszow Mazowiecki, Konstytucji 3 Maja 65/67, 97-200 Tomaszow Mazowiecki, Poland
Jaroslaw Krzywanski: Faculty of Science and Technology, Jan Dlugosz University in Czestochowa, Armii Krajowej 13/15, 42-200 Czestochowa, Poland
Marcin Sosnowski: Faculty of Science and Technology, Jan Dlugosz University in Czestochowa, Armii Krajowej 13/15, 42-200 Czestochowa, Poland
Wojciech Nowak: Faculty of Energy and Fuels, AGH University, A. Mickiewicza 30, 30-059 Krakow, Poland

Energies, 2025, vol. 18, issue 18, 1-17

Abstract: Efficient and cost-effective biomass logistics remain a significant challenge due to the dynamic and nonlinear nature of supply chains, as well as the scarcity of comprehensive data on this topic. As biomass plays an increasingly important role in sustainable energy systems, managing its complex supply chains efficiently is crucial. Traditional logistics methods often struggle with the dynamic, nonlinear, and data-scarce nature of biomass supply, especially when integrating local and international sources. To address these challenges, this study aims to develop an innovative modular artificial neural network (ANN)-based Biomass Delivery Management (BDM) model to optimize biomass procurement and supply for a fluidized bed combined heat and power (CHP) plant. The comprehensive model integrates technical, economic, and geographic parameters to enable supplier selection, optimize transport routes, and inform fuel blending strategies, representing a novel approach in biomass logistics. A case study based on operational data confirmed the model’s ability to identify cost-effective and quality-compliant biomass sources. Evaluated using empirical operational data from a Polish CHP plant, the ANN-based model demonstrated high predictive accuracy (MAE = 0.16, MSE = 0.02, R 2 = 0.99) within the studied scope. The model effectively handled incomplete datasets typical of biomass markets, aiding in supplier selection decisions and representing a proof-of-concept for optimizing Central European biomass logistics. The model was capable of generalizing supplier recommendations based on input variables, including biomass type, unit price, and annual demand. The proposed framework supports both strategic and real-time logistics decisions, providing a robust tool for enhancing supply chain transparency, cost efficiency, and resilience in the renewable energy sector. Future research will focus on extending the dataset and developing hybrid models to strengthen supply chain stability and adaptability under varying market and regulatory conditions.

Keywords: biomass logistics; CHP; fuel supply optimization; energy systems; artificial intelligence; neural networks; sustainability; energy efficiency (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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