Machine Learning Application in Energy Consumption Calculation and Assessment in Food Processing Industry

Milczarski, Piotr; Zieliński, Bartosz; Stawska, Zofia; Hłobaż, Artur; Maślanka, P.; Kosiński, Piotr

doi:10.1007/978-3-030-61534-5_33

Cited by 9 publications

(8 citation statements)

References 12 publications

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“…The results showed BPNN is more accurate than multiple regression analysis in the prediction of the results of LCA (Park and Seo 2003). Milczarski et al applied ANN to validate the production process's quality and parameters in the food processing industry (Milczarski et al 2020).…”

Section: Neural Networkmentioning

confidence: 99%

Advances in application of machine learning to life cycle assessment: a literature review

Ghoroghi

Rezgui

Petri

et al. 2022

Int J Life Cycle Assess

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Purpose Life Cycle Assessment (LCA) is the process of systematically assessing impacts when there is an interaction between the environment and human activity. Machine learning (ML) with LCA methods can help contribute greatly to reducing impacts. The sheer number of input parameters and their uncertainties that contribute to the full life cycle make a broader application of ML complex and difficult to achieve. Hence a systems engineering approach should be taken to apply ML in isolation to aspects of the LCA. This study addresses the challenge of leveraging ML methods to deliver LCA solutions. The overarching hypothesis is that: LCA underpinned by ML methods and informed by dynamic data paves the way to more accurate LCA while supporting life cycle decision making. Methods In this study, previous research on ML for LCA were considered, and a literature review was undertaken. Results The results showed that ML can be a useful tool in certain aspects of the LCA. ML methods were shown to be applied efficiently in optimization scenarios in LCA. Finally, ML methods were integrated as part of existing inventory databases to streamline the LCA across many use cases. Conclusions The conclusions of this article summarise the characteristics of existing literature and provide suggestions for future work in limitations and gaps which were found in the literature.

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Section: Neural Networkmentioning

confidence: 99%

Advances in application of machine learning to life cycle assessment: a literature review

Ghoroghi

Rezgui

Petri

et al. 2022

Int J Life Cycle Assess

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“…Predictive models for machine energy efficiency and optimization tools to minimize energy consumption were developed for the application in the semiconductor industry (Kuo-Hao Chang et al, 2021) (Chang et al, 2022). Classification machine learning models like K-Nearest Neighbors, Neural Networks, Random Forest, and Support Vector Machines are used to validate the quality of production processes and their parameters in the food processing industry (Milczarski et al, 2020). In plastic-processing SME's, machine learning is applied to optimize energy consumption and reduce incorrectly produced plastic parts (Willenbacher et al, 2021).…”

Section: Literature Reviewmentioning

confidence: 99%

Modeling Energy Consumption Using Machine Learning

Sarswatula

Pugh²,

Prabhu

2022

Front. Manuf. Technol.

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Electrical, metal, plastic, and food manufacturing are among the major energy-consuming industries in the U.S. Since 1981, the U.S. Department of Energy Industrial Assessments Centers (IACs) have conducted audits to track and analyze energy data across several industries and provided recommendations for improving energy efficiency. In this article, we used statistical and machine learning techniques to draw insights from this IAC dataset with over 15,000 samples collected from 1981 to 2013. We developed predictive models for energy consumption using machine learning techniques such as Multiple Linear Regression, Random Forest Regressor, Decision Tree Regressor, and Extreme Gradient Boost Regressor. We also developed classifier models using Support Vector Machines, Random Forest, K-Nearest Neighbor (KNN), and deep learning. Results using this data set indicate that Random Forest Regressor is the best prediction technique with an R2 of 0.869, and the Random Forest classifier is the best technique with precision, recall, F1 score, and accuracy of 0.818, 0.884, 0.844, and 0.883, respectively. Deep learning also performed competitively with an accuracy of about 0.88 in training and testing after 10 epochs. The machine learning models could be useful in benchmarking the energy consumption of factories and identifying opportunities to improve energy efficiency.

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“…The application of ML methods in frozen vegetable production is shown in [21][22][23], where the authors use expert knowledge to assess the production process with classification methods, e.g., support vector machine, random forest, multilayer perceptron, etc. Sharma et al [21] analyze machine learning methods in their discussion.…”

Section: Related Workmentioning

confidence: 99%

“…The stages S1 and S4 had the most significant impact on energy utilization because they are connected with freezing processes. These are described in [22,23].…”

Section: Carbon Footprint Assessment In the Frozen Vegetable Industrymentioning

confidence: 99%

“…During each supervision of the production line in the company, we gathered values (not only average values) of, e.g., the input mass of raw materials and output production assets, the temperatures of the materials, etc. In [22,23], the stage S1 energy consumption was very chaotic, but S3 and S4 had smooth power values except for during the technological breaks described above.…”

Section: Carbon Footprint Assessment In the Frozen Vegetable Industrymentioning

confidence: 99%

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

Scherer

Milczarski

2021

Energies

Self Cite

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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.

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Machine Learning Application in Energy Consumption Calculation and Assessment in Food Processing Industry

Cited by 9 publications

References 12 publications

Advances in application of machine learning to life cycle assessment: a literature review

Advances in application of machine learning to life cycle assessment: a literature review

Modeling Energy Consumption Using Machine Learning

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

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