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Energy efficiency characteristics analysis for process diagnosis under anomaly using self-adaptive-based SHAP guided optimization

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  • Bardeeniz, Santi
  • Panjapornpon, Chanin
  • Fongsamut, Chalermpan
  • Ngaotrakanwiwat, Pailin
  • Hussain, Mohamed Azlan

Abstract

Understanding energy efficiency patterns is crucial for developing more effective energy management strategies. However, disruptions from physical characteristics, such as particle accumulation, inhibit the construction of energy efficiency models, pose diagnostic challenges, and require additional fault detection models to isolate this uncertainty. Therefore, this study introduces a self-adaptive, long short-term memory-based energy efficiency model with adaptive moment estimation fine-tuning enhanced by Shapley additive explanation guided optimization. The model adapts its learnable parameters in real-time according to changes in process behavior, which helps in revealing energy inefficiency and particle accumulation through Shapley benchmarking under current operations and energy efficiency characteristics. Validated using a benchmark dataset and applied in a large-scale detergent industry, the model outperforms conventional methods, achieving testing r-squared values of 0.9895 and 0.9859, respectively. Moreover, the proposed model avoided formulating the relationship with faulty variables and demonstrated robust fault detection through energy efficiency patterns without needing fault labels, offering a novel approach to monitoring and optimizing energy efficiency. The adaptive weight analysis emphasized how energy efficiency is influenced by various input variables, leading to an hourly energy saving of 0.0271 GJ/t, equivalent to cost savings of USD 34,408 and a reduction of 115.44 t of carbon emissions.

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  • Bardeeniz, Santi & Panjapornpon, Chanin & Fongsamut, Chalermpan & Ngaotrakanwiwat, Pailin & Hussain, Mohamed Azlan, 2024. "Energy efficiency characteristics analysis for process diagnosis under anomaly using self-adaptive-based SHAP guided optimization," Energy, Elsevier, vol. 309(C).
    • Handle: RePEc:eee:energy:v:309:y:2024:i:c:s0360544224028494
    DOI: 10.1016/j.energy.2024.133074
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    1. Mariia Sobulska & Pawel Wawrzyniak & Meng Wai Woo, 2022. "Superheated Steam Spray Drying as an Energy-Saving Drying Technique: A Review," Energies, MDPI, vol. 15(22), pages 1-22, November.
    2. Yang, Junwen & Chen, Bin, 2021. "Energy efficiency evaluation of wastewater treatment plants (WWTPs) based on data envelopment analysis," Applied Energy, Elsevier, vol. 289(C).
    3. Tostado-Véliz, Marcos & Rezaee Jordehi, Ahmad & Amir Mansouri, Seyed & Jurado, Francisco, 2022. "Day-ahead scheduling of 100% isolated communities under uncertainties through a novel stochastic-robust model," Applied Energy, Elsevier, vol. 328(C).
    4. Walmsley, Timothy G. & Walmsley, Michael R.W. & Atkins, Martin J. & Neale, James R. & Tarighaleslami, Amir H., 2015. "Thermo-economic optimisation of industrial milk spray dryer exhaust to inlet air heat recovery," Energy, Elsevier, vol. 90(P1), pages 95-104.
    5. Qi, Chu & Zeng, Xianglong & Wang, Yongjian & Li, Hongguang, 2022. "Adaptive time window convolutional neural networks concerning multiple operation modes with applications in energy efficiency predictions," Energy, Elsevier, vol. 240(C).
    6. Guoyang Yan & Jiangyuan Mei & Shen Yin & Hamid Reza Karimi, 2014. "Metric Learning Method Aided Data-Driven Design of Fault Detection Systems," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-9, March.
    7. Tostado-Véliz, Marcos & Jordehi, Ahmad Rezaee & Mansouri, Seyed Amir & Jurado, Francisco, 2023. "A two-stage IGDT-stochastic model for optimal scheduling of energy communities with intelligent parking lots," Energy, Elsevier, vol. 263(PD).
    8. Yin, Sihua & Yang, Haidong & Xu, Kangkang & Zhu, Chengjiu & Zhang, Shaqing & Liu, Guosheng, 2022. "Dynamic real–time abnormal energy consumption detection and energy efficiency optimization analysis considering uncertainty," Applied Energy, Elsevier, vol. 307(C).
    9. Mansouri, Seyed Amir & Rezaee Jordehi, Ahmad & Marzband, Mousa & Tostado-Véliz, Marcos & Jurado, Francisco & Aguado, José A., 2023. "An IoT-enabled hierarchical decentralized framework for multi-energy microgrids market management in the presence of smart prosumers using a deep learning-based forecaster," Applied Energy, Elsevier, vol. 333(C).
    10. Liu, Jintao & Chen, Liangchao & Xu, Wei & Feng, Mingfei & Han, Yongming & Xia, Tao & Geng, Zhiqiang, 2023. "Novel production prediction model of gasoline production processes for energy saving and economic increasing based on AM-GRU integrating the UMAP algorithm," Energy, Elsevier, vol. 262(PB).
    11. Mansouri, Seyed Amir & Nematbakhsh, Emad & Jordehi, Ahmad Rezaee & Marzband, Mousa & Tostado-Véliz, Marcos & Jurado, Francisco, 2023. "An interval-based nested optimization framework for deriving flexibility from smart buildings and electric vehicle fleets in the TSO-DSO coordination," Applied Energy, Elsevier, vol. 341(C).
    12. Geng, Zhiqiang & Zhang, Yanhui & Li, Chengfei & Han, Yongming & Cui, Yunfei & Yu, Bin, 2020. "Energy optimization and prediction modeling of petrochemical industries: An improved convolutional neural network based on cross-feature," Energy, Elsevier, vol. 194(C).
    13. Panjapornpon, Chanin & Bardeeniz, Santi & Hussain, Mohamed Azlan, 2023. "Improving energy efficiency prediction under aberrant measurement using deep compensation networks: A case study of petrochemical process," Energy, Elsevier, vol. 263(PC).
    14. Zhang, Xinru & Hou, Lei & Liu, Jiaquan & Yang, Kai & Chai, Chong & Li, Yanhao & He, Sichen, 2022. "Energy consumption prediction for crude oil pipelines based on integrating mechanism analysis and data mining," Energy, Elsevier, vol. 254(PB).
    15. Mansouri, Seyed Amir & Nematbakhsh, Emad & Ahmarinejad, Amir & Jordehi, Ahmad Rezaee & Javadi, Mohammad Sadegh & Marzband, Mousa, 2022. "A hierarchical scheduling framework for resilience enhancement of decentralized renewable-based microgrids considering proactive actions and mobile units," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
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