Feasibility Analysis in Batch Process: A Machine Learning Approach

Scheduling is a major issue in process operations, and it is essential for maximizing production output. Short-term scheduling is used in batch operations to allocate a set of restricted resources across time to manufacture one or more items according to a batch recipe. For such processes, a feasibl...

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Autores principales:	Piyush Kumar Kumawat, Nitin Dutt Chaturvedi
Formato:	article
Lenguaje:	EN
Publicado:	AIDIC Servizi S.r.l. 2021
Materias:	Chemical engineering TP155-156 Computer engineering. Computer hardware TK7885-7895
Acceso en línea:	https://doaj.org/article/9ec937486b37442a8c2999ddaace2a2c
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spelling	oai:doaj.org-article:9ec937486b37442a8c2999ddaace2a2c2021-11-15T21:48:21ZFeasibility Analysis in Batch Process: A Machine Learning Approach10.3303/CET21880752283-9216https://doaj.org/article/9ec937486b37442a8c2999ddaace2a2c2021-11-01T00:00:00Zhttps://www.cetjournal.it/index.php/cet/article/view/11868https://doaj.org/toc/2283-9216Scheduling is a major issue in process operations, and it is essential for maximizing production output. Short-term scheduling is used in batch operations to allocate a set of restricted resources across time to manufacture one or more items according to a batch recipe. For such processes, a feasible design space can be described by the decision-maker as per the process constraints and objectives. A machine learning approach using support vector machines is implemented for the classification of the feasible region from a data set. An algebraic equation is obtained using support vectors (data points participating in creating classification boundary), which could be incorporated into the mathematical model. The equation will lead to attaining several objectives in the finite obtained region. In this paper, kernel-based support vector classification is tailored to a continuous-time formulation for the short-term scheduling of batch processes. First, using a classification SVC over a data set, it was observed that the mathematical model is infeasible for a set of production combination of two different products. Using the proposed methodology, a design space is presented graphically for feasible production targets. In the second case, the study is applied to perform optimization operations to target feasible region within a specified limit as a choice of decision-maker.Piyush Kumar KumawatNitin Dutt ChaturvediAIDIC Servizi S.r.l.articleChemical engineeringTP155-156Computer engineering. Computer hardwareTK7885-7895ENChemical Engineering Transactions, Vol 88 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Chemical engineering TP155-156 Computer engineering. Computer hardware TK7885-7895
spellingShingle	Chemical engineering TP155-156 Computer engineering. Computer hardware TK7885-7895 Piyush Kumar Kumawat Nitin Dutt Chaturvedi Feasibility Analysis in Batch Process: A Machine Learning Approach
description	Scheduling is a major issue in process operations, and it is essential for maximizing production output. Short-term scheduling is used in batch operations to allocate a set of restricted resources across time to manufacture one or more items according to a batch recipe. For such processes, a feasible design space can be described by the decision-maker as per the process constraints and objectives. A machine learning approach using support vector machines is implemented for the classification of the feasible region from a data set. An algebraic equation is obtained using support vectors (data points participating in creating classification boundary), which could be incorporated into the mathematical model. The equation will lead to attaining several objectives in the finite obtained region. In this paper, kernel-based support vector classification is tailored to a continuous-time formulation for the short-term scheduling of batch processes. First, using a classification SVC over a data set, it was observed that the mathematical model is infeasible for a set of production combination of two different products. Using the proposed methodology, a design space is presented graphically for feasible production targets. In the second case, the study is applied to perform optimization operations to target feasible region within a specified limit as a choice of decision-maker.
format	article
author	Piyush Kumar Kumawat Nitin Dutt Chaturvedi
author_facet	Piyush Kumar Kumawat Nitin Dutt Chaturvedi
author_sort	Piyush Kumar Kumawat
title	Feasibility Analysis in Batch Process: A Machine Learning Approach
title_short	Feasibility Analysis in Batch Process: A Machine Learning Approach
title_full	Feasibility Analysis in Batch Process: A Machine Learning Approach
title_fullStr	Feasibility Analysis in Batch Process: A Machine Learning Approach
title_full_unstemmed	Feasibility Analysis in Batch Process: A Machine Learning Approach
title_sort	feasibility analysis in batch process: a machine learning approach
publisher	AIDIC Servizi S.r.l.
publishDate	2021
url	https://doaj.org/article/9ec937486b37442a8c2999ddaace2a2c
work_keys_str_mv	AT piyushkumarkumawat feasibilityanalysisinbatchprocessamachinelearningapproach AT nitinduttchaturvedi feasibilityanalysisinbatchprocessamachinelearningapproach
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Feasibility Analysis in Batch Process: A Machine Learning Approach

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