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A Monte Carlo and PSO based virtual sample generation method for enhancing the energy prediction and energy optimization on small data problem: An empirical study of petrochemical industries

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  • Gong, Hong-Fei
  • Chen, Zhong-Sheng
  • Zhu, Qun-Xiong
  • He, Yan-Lin

Abstract

Due to the imbalanced and uncompleted characteristics of complex petrochemical small datasets, it is a challenge to build an accurate prediction and optimization model of energy consumption of petrochemical systems. Therefore, this paper proposes a novel virtual sample generation (VSG) approach based on the Monte Carlo (MC) and Particle Swarm Optimization (PSO) algorithms to improve the accuracy of the energy efficiency analysis on small data set problems. The proposed approach utilizes the MC and PSO algorithms to generate appropriate virtual samples based on the underlying information extracted from the small datasets. An accurate prediction model is presented using the extreme machine learning (ELM) in view of the synthetic data. The performance of the proposed model is validated via an application using a purified Terephthalic acid (PTA) solvent system and an ethylene production system. The experiment results demonstrate that the accuracy of the prediction model can be improved, and guidance for the production department to improve the energy efficiency, energy savings and emission reduction is provided under the small data circumstance.

Suggested Citation

  • Gong, Hong-Fei & Chen, Zhong-Sheng & Zhu, Qun-Xiong & He, Yan-Lin, 2017. "A Monte Carlo and PSO based virtual sample generation method for enhancing the energy prediction and energy optimization on small data problem: An empirical study of petrochemical industries," Applied Energy, Elsevier, vol. 197(C), pages 405-415.
    • Handle: RePEc:eee:appene:v:197:y:2017:i:c:p:405-415
    DOI: 10.1016/j.apenergy.2017.04.007
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    1. Zhang, Xiao-Han & Zhu, Qun-Xiong & He, Yan-Lin & Xu, Yuan, 2018. "A novel robust ensemble model integrated extreme learning machine with multi-activation functions for energy modeling and analysis: Application to petrochemical industry," Energy, Elsevier, vol. 162(C), pages 593-602.
    2. Zhu, Qun-Xiong & Zhang, Chen & He, Yan-Lin & Xu, Yuan, 2018. "Energy modeling and saving potential analysis using a novel extreme learning fuzzy logic network: A case study of ethylene industry," Applied Energy, Elsevier, vol. 213(C), pages 322-333.
    3. do Carmo, Pedro R.X. & do Monte, João Victor L. & Filho, Assis T. de Oliveira & Freitas, Eduardo & Tigre, Matheus F.F.S.L. & Sadok, Djamel & Kelner, Judith, 2023. "A data-driven model for the optimization of energy consumption of an industrial production boiler in a fiber plant," Energy, Elsevier, vol. 284(C).
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    6. Yue, Meiling & Jemei, Samir & Zerhouni, Noureddine & Gouriveau, Rafael, 2021. "Proton exchange membrane fuel cell system prognostics and decision-making: Current status and perspectives," Renewable Energy, Elsevier, vol. 179(C), pages 2277-2294.
    7. Zhang, Xiao-Han & Zhu, Qun-Xiong & He, Yan-Lin & Xu, Yuan, 2018. "Energy modeling using an effective latent variable based functional link learning machine," Energy, Elsevier, vol. 162(C), pages 883-891.
    8. Fei-Yu Zhou & Ning-Jing Tao & Yu-Rong Zhang & Wei-Bin Yuan, 2023. "Prediction of Chloride Diffusion Coefficient in Concrete Based on Machine Learning and Virtual Sample Algorithm," Sustainability, MDPI, vol. 15(24), pages 1-17, December.
    9. Mafakheri, Aso & Sulaimany, Sadegh & Mohammadi, Sara, 2023. "Predicting the establishment and removal of global trade relations for import and export of petrochemical products," Energy, Elsevier, vol. 269(C).
    10. Sultana, U. & Khairuddin, Azhar B. & Sultana, Beenish & Rasheed, Nadia & Qazi, Sajid Hussain & Malik, Nimra Riaz, 2018. "Placement and sizing of multiple distributed generation and battery swapping stations using grasshopper optimizer algorithm," Energy, Elsevier, vol. 165(PA), pages 408-421.
    11. Azarpour, Abbas & Mohamadi-Baghmolaei, Mohamad & Hajizadeh, Abdollah & Zendehboudi, Sohrab, 2022. "Systematic energy and exergy assessment of a hydropurification process: Theoretical and practical insights," Energy, Elsevier, vol. 239(PC).
    12. Mochen Liao & Kai Lan & Yuan Yao, 2022. "Sustainability implications of artificial intelligence in the chemical industry: A conceptual framework," Journal of Industrial Ecology, Yale University, vol. 26(1), pages 164-182, February.
    13. Xu, Yuan & Zhang, Mingqing & Ye, Liangliang & Zhu, Qunxiong & Geng, Zhiqiang & He, Yan-Lin & Han, Yongming, 2018. "A novel prediction intervals method integrating an error & self-feedback extreme learning machine with particle swarm optimization for energy consumption robust prediction," Energy, Elsevier, vol. 164(C), pages 137-146.
    14. Song, Wanqing & Cattani, Carlo & Chi, Chi-Hung, 2020. "Multifractional Brownian motion and quantum-behaved particle swarm optimization for short term power load forecasting: An integrated approach," Energy, Elsevier, vol. 194(C).
    15. Wang, Yihan & Chen, Chen & Tao, Yuan & Wen, Zongguo & Chen, Bin & Zhang, Hong, 2019. "A many-objective optimization of industrial environmental management using NSGA-III: A case of China’s iron and steel industry," Applied Energy, Elsevier, vol. 242(C), pages 46-56.
    16. Zeng, Zhiqiang & Hong, Mengna & Li, Jigeng & Man, Yi & Liu, Huanbin & Li, Zeeman & Zhang, Huanhuan, 2018. "Integrating process optimization with energy-efficiency scheduling to save energy for paper mills," Applied Energy, Elsevier, vol. 225(C), pages 542-558.

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