Artificial Intelligence in Assessing Electricity and Water Demand in Oilseed Processing

Jędrzej Trajer (), Bogdan Dróżdż, Robert Sałat and Janusz Wojdalski
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Jędrzej Trajer: Institute of Mechanical Engineering, Warsaw University of Life Sciences, 166 Nowoursynowska St., 02-787 Warsaw, Poland
Bogdan Dróżdż: Institute of Mechanical Engineering, Warsaw University of Life Sciences, 166 Nowoursynowska St., 02-787 Warsaw, Poland
Robert Sałat: Faculty of Electrical and Computer Engineering, Cracow University of Technology, 24 Warszawska St., 31-155 Krakow, Poland
Janusz Wojdalski: Institute of Mechanical Engineering, Warsaw University of Life Sciences, 166 Nowoursynowska St., 02-787 Warsaw, Poland

Energies, 2025, vol. 18, issue 16, 1-11

Abstract: The aim of this study was to explore the use of neural networks as a decision-support tool for sustainable oilseed processing. The investigation focused on how different production profiles (crude vegetable oil, refined oil, hydrogenated oil and margarine) affect electricity and water use in selected Polish processing plants. The collected data were first grouped with cluster analysis to identify similar operational cases. The clusters were then visualized with a Self-Organizing Map (SOM), producing a two-dimensional topological feature map. This analysis indicated a subset of data for which it was appropriate to build predictive models of electricity and water consumption. Multi-layer perceptron (MLP) neural networks yielded highly accurate predictions of electricity (R 2 = 0.967 on the test set) and water (R 2 = 0.967 on the test set) use in oilseed processing. The resulting models can assist in selecting the most energy- and water-efficient processing configuration.

Keywords: artificial intelligence; oilseed processing; neural models of energy and water consumption; sustainable production (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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