Abstract
In the paper, the application of the machine learning methods in the food processing industry is presented to validate the quality of the production process and its parameters. These parameters e.g. raw products’ carbon footprint, energy resources and their carbon footprint usually may vary from day-to-day production because of meters’ instrumental errors or human random errors. One of the human factor is false accounting of the production in the system that sometimes happen. One of the instrumental errors can be the malfunction of the meters. In the authors’ project, the main goal is to optimize the production process so as to limit the carbon footprint. The problem that aroused is the trustworthiness of the data read from meters or provided by people operating the production line. That is why we applied the set of machine learning methods to validate the processes in order to choose the trustworthy ones. In the paper, we compare the results of processes classification k-Nearest Neighbors, Neural Network, C4.5, Random Forest and Support Vector Machines.
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Acknowledgments
The paper is written as a part of the project CFOOD that is supported by The National Centre for Research and Development, Poland, grant number BIOSTRATEG3/343817/17/NCBR/2018.
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Milczarski, P., Zieliński, B., Stawska, Z., Hłobaż, A., Maślanka, P., Kosiński, P. (2020). Machine Learning Application in Energy Consumption Calculation and Assessment in Food Processing Industry. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2020. Lecture Notes in Computer Science(), vol 12416. Springer, Cham. https://doi.org/10.1007/978-3-030-61534-5_33
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