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Performance Improvement in Hot Rolling Process with Novel Neural Architectural Search

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Machine Learning in Industry

Part of the book series: Management and Industrial Engineering ((MINEN))

Abstract

State-of-the-art infrastructure, excellent computational facilities and ubiquitous connectivity across the industries have led to the generation of large amounts of heterogeneous process data. At the same time, the applicability of machine learning and artificial intelligence is witnessing a significant rise in academics and engineering, leading to the development of a large number of resources and tools. However, the number of research works and applications aimed at implementing data sciences to problems in process industries is far less. The proposed work aims to fill the niche by proposing Artificial Neural Network (ANN)-based surrogate construction using extremely nonlinear, static, high dimensional (32 features) noisy data sampled irregularly from inlet and outlet streams of hot rolling process in iron and steel making industry. Though ANNs are used extensively for modelling nonlinear data, literature survey has shown that their modelling is governed by heuristics thus making them inefficient for use in process industries. This aspect is of high relevance in contemporary times as hyper-parameter optimization, automated machine learning and neural architecture search (NAS) constitute a major share of current research in data sciences. We propose a novel multi-objective evolutionary NAS algorithm to optimally design multi-layered feed-forward ANNs by balancing the aspects of parsimony and accuracy. The integer nonlinear programming problem of ANN design is solved using binary coded Non-Dominated Sorting Genetic Algorithm (NSGA-II). ANNs designed for the hot rolling process are found to demonstrate an accuracy of 0.98 (averaged on three outputs) measured in terms of correlation coefficient R2 on the test set. The successful construction of accurate and optimal ANNs provides a first-of-its-kind model for the hot rolling process in the iron and steel making industry. The proposed method can minimize the chances of over-fitting in ANNs and provides a generic method applicable to any kind of data/model from process industries.

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Author information

Authors and Affiliations

  1. Global Optimization and Knowledge Unearthing Laboratory, Department of Chemical Engineering, Indian Institute of Technology Hyderabad, Sangareddy, 502285, Telangana, India

    Srinivas Soumitri Miriyala & Kishalay Mitra

  2. Research & Development, Tata Steel Limited, Jamshedpur, 831001, Jharkhand, India

    Itishree Mohanty

Authors
  1. Srinivas Soumitri Miriyala
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  2. Itishree Mohanty
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  3. Kishalay Mitra
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Correspondence to Kishalay Mitra .

Editor information

Editors and Affiliations

  1. Department of Mechanical Engineering, SRM Institute of Science and Technology, Chennai, Tamil Nadu, India

    Shubhabrata Datta

  2. Department of Mechanical Engineering, University of Aveiro, Aveiro, Portugal

    J. Paulo Davim

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Miriyala, S.S., Mohanty, I., Mitra, K. (2022). Performance Improvement in Hot Rolling Process with Novel Neural Architectural Search. In: Datta, S., Davim, J.P. (eds) Machine Learning in Industry. Management and Industrial Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-75847-9_9

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  • DOI: https://doi.org/10.1007/978-3-030-75847-9_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-75846-2

  • Online ISBN: 978-3-030-75847-9

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