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A three-step machine learning framework for energy profiling, activity state prediction and production estimation in smart process manufacturing

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  • Tan, Daniel
  • Suvarna, Manu
  • Shee Tan, Yee
  • Li, Jie
  • Wang, Xiaonan

Abstract

The dynamic nature of chemical processes and manufacturing environments, along with numerous machines, their unique activity states, and mutual interactions, render challenges to energy monitoring at a machine level. In this study, we introduce MIGRATE (Machine learnInGfoRsmArTEnergy), a novel three-step framework to predict the machine-specific load profiles via energy disaggregation, which are in turn used to predict the machine’s activity state and the respective production capacities. Various supervised tree-based and recurrent neural network algorithms were evaluated on their capacities to predict load profiles and production capacities of four machines investigated in this study. Light gradient boosting machines and ensemble bi-directional long-term short memory were identified as the respective best performing algorithms with a mean absolute error and root mean squared error of 0.035 and 0.105 (units in Watts) for the disaggregation studies and 1.639 and 11.401 (units in quantities of samples processed) for production estimation. Four unsupervised machine learning algorithms were evaluated to cluster the machine’s activity state from their disaggregated load profiles, where the gaussian mixture model had a superior performance with the V score and Fowlkes Mallows index of 0.852 and 0.983, respectively. The MIGRATE framework is purely data-driven, cross-deployable and serves as promising catalyst to foster smart energy management practices and sustainable productions in the chemical and industrial manufacturing processes.

Suggested Citation

  • Tan, Daniel & Suvarna, Manu & Shee Tan, Yee & Li, Jie & Wang, Xiaonan, 2021. "A three-step machine learning framework for energy profiling, activity state prediction and production estimation in smart process manufacturing," Applied Energy, Elsevier, vol. 291(C).
    • Handle: RePEc:eee:appene:v:291:y:2021:i:c:s030626192100310x
    DOI: 10.1016/j.apenergy.2021.116808
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