Machine-Learning-Based Carbon Footprint Management in the Frozen Vegetable Processing Industry

In the paper, we present a method of automatic evaluation and optimization of production processes towards low-carbon-emissions products. The method supports the management of production lines and is based on unsupervised machine learning methods, i.e., canopy, k-means, and expectation-maximization...

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Autores principales:	Magdalena Scherer, Piotr Milczarski
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	management carbon footprint machine learning Technology T
Acceso en línea:	https://doaj.org/article/b5158055439a47b195741fc03868d8ab
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spelling	oai:doaj.org-article:b5158055439a47b195741fc03868d8ab2021-11-25T17:28:40ZMachine-Learning-Based Carbon Footprint Management in the Frozen Vegetable Processing Industry10.3390/en142277781996-1073https://doaj.org/article/b5158055439a47b195741fc03868d8ab2021-11-01T00:00:00Zhttps://www.mdpi.com/1996-1073/14/22/7778https://doaj.org/toc/1996-1073In the paper, we present a method of automatic evaluation and optimization of production processes towards low-carbon-emissions products. The method supports the management of production lines and is based on unsupervised machine learning methods, i.e., canopy, k-means, and expectation-maximization clusterization algorithms. For different production processes, a different clustering method may be optimal. Hence, they are validated by classification methods (k-nearest neighbors (kNN), multilayer perceptron (MLP), binary tree C4.5, random forest (RF), and support vector machine (SVM)) that identify the optimal clusterization method. Using the proposed method with real-time production parameters for a given process, we can classify the process as optimal or non-optimal on an ongoing basis. The production manager can react appropriately to sub-optimal production processes. If the process is not optimal, then during the process the manager or production technologist may change the production parameters, e.g., speed up or slow down certain batches, so that the process returns to the optimal path. This path is determined by a model trained via the proposed method based on the selected clustering method. The method is verified on an onion production line with more than a hundred processes and then applied to production lines with a smaller number of cases. We use data from real-world measurements from a frozen food production plant. Our research demonstrates that proper process management using machine learning can result in a lower carbon footprint per ton of the final product.Magdalena SchererPiotr MilczarskiMDPI AGarticlemanagementcarbon footprintmachine learningTechnologyTENEnergies, Vol 14, Iss 7778, p 7778 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	management carbon footprint machine learning Technology T
spellingShingle	management carbon footprint machine learning Technology T Magdalena Scherer Piotr Milczarski Machine-Learning-Based Carbon Footprint Management in the Frozen Vegetable Processing Industry
description	In the paper, we present a method of automatic evaluation and optimization of production processes towards low-carbon-emissions products. The method supports the management of production lines and is based on unsupervised machine learning methods, i.e., canopy, k-means, and expectation-maximization clusterization algorithms. For different production processes, a different clustering method may be optimal. Hence, they are validated by classification methods (k-nearest neighbors (kNN), multilayer perceptron (MLP), binary tree C4.5, random forest (RF), and support vector machine (SVM)) that identify the optimal clusterization method. Using the proposed method with real-time production parameters for a given process, we can classify the process as optimal or non-optimal on an ongoing basis. The production manager can react appropriately to sub-optimal production processes. If the process is not optimal, then during the process the manager or production technologist may change the production parameters, e.g., speed up or slow down certain batches, so that the process returns to the optimal path. This path is determined by a model trained via the proposed method based on the selected clustering method. The method is verified on an onion production line with more than a hundred processes and then applied to production lines with a smaller number of cases. We use data from real-world measurements from a frozen food production plant. Our research demonstrates that proper process management using machine learning can result in a lower carbon footprint per ton of the final product.
format	article
author	Magdalena Scherer Piotr Milczarski
author_facet	Magdalena Scherer Piotr Milczarski
author_sort	Magdalena Scherer
title	Machine-Learning-Based Carbon Footprint Management in the Frozen Vegetable Processing Industry
title_short	Machine-Learning-Based Carbon Footprint Management in the Frozen Vegetable Processing Industry
title_full	Machine-Learning-Based Carbon Footprint Management in the Frozen Vegetable Processing Industry
title_fullStr	Machine-Learning-Based Carbon Footprint Management in the Frozen Vegetable Processing Industry
title_full_unstemmed	Machine-Learning-Based Carbon Footprint Management in the Frozen Vegetable Processing Industry
title_sort	machine-learning-based carbon footprint management in the frozen vegetable processing industry
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/b5158055439a47b195741fc03868d8ab
work_keys_str_mv	AT magdalenascherer machinelearningbasedcarbonfootprintmanagementinthefrozenvegetableprocessingindustry AT piotrmilczarski machinelearningbasedcarbonfootprintmanagementinthefrozenvegetableprocessingindustry
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Machine-Learning-Based Carbon Footprint Management in the Frozen Vegetable Processing Industry

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