Version 1
: Received: 27 October 2023 / Approved: 27 October 2023 / Online: 27 October 2023 (10:02:08 CEST)
How to cite:
Colantoni, A.; Ortenzi, L.; Lops, A.; Lombardi, P. Predictive Analysis of Electricity Consumption of a Mechanical Biological Waste Treatment Plant Using Machine Learning Techniques. Preprints2023, 2023101778. https://doi.org/10.20944/preprints202310.1778.v1
Colantoni, A.; Ortenzi, L.; Lops, A.; Lombardi, P. Predictive Analysis of Electricity Consumption of a Mechanical Biological Waste Treatment Plant Using Machine Learning Techniques. Preprints 2023, 2023101778. https://doi.org/10.20944/preprints202310.1778.v1
Colantoni, A.; Ortenzi, L.; Lops, A.; Lombardi, P. Predictive Analysis of Electricity Consumption of a Mechanical Biological Waste Treatment Plant Using Machine Learning Techniques. Preprints2023, 2023101778. https://doi.org/10.20944/preprints202310.1778.v1
APA Style
Colantoni, A., Ortenzi, L., Lops, A., & Lombardi, P. (2023). Predictive Analysis of Electricity Consumption of a Mechanical Biological Waste Treatment Plant Using Machine Learning Techniques. Preprints. https://doi.org/10.20944/preprints202310.1778.v1
Chicago/Turabian Style
Colantoni, A., Alessandro Lops and Pierpaolo Lombardi. 2023 "Predictive Analysis of Electricity Consumption of a Mechanical Biological Waste Treatment Plant Using Machine Learning Techniques" Preprints. https://doi.org/10.20944/preprints202310.1778.v1
Abstract
The aims of this paper can be traced back to goals number seven, twelve and thirteen introduced by Agenda 2030, namely "affordable and clean energy", “responsible consumption and production” and "climate action”, respectively.
This is due to the fact that the present work supports decarbonization processes acting in a direct way on the electricity consumption achieved by plants pertaining to the waste industry; in fact, this study aims at the realization of a machine learning model for predicting the energy consumption achieved by a mechanical biological treatment (MBT) waste plant located in central Italy, given the distribution of the entering waste. This model is implemented in MATLAB. The model can be used to tune the distribution of the entering waste in order to adapt the plant energy consumption to the capability of the energy sources and can serve as a play-ground model for other energy transformation plants.
The results of the study, which feed the literature on the application of artificial intelligence to real industrial plants, could be used to determine energy efficiency actions that could be incorporated into Property’s strategic planning.
Keywords
Machine Learning; artificial neural network; predictive analysis; MBT; energy efficiency
Subject
Engineering, Control and Systems Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
