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Energy efficiency and savings analysis with multirate sampling for petrochemical process using convolutional neural network-based transfer learning

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Energy and AI 2023年第4期14卷 43-59页

作者： Chanin Panjapornpon Santi Bardeeniz Mohamed Azlan Hussain Kanthika Vongvirat Chayanit Chuay-ock Department of Chemical Engineering Center of Excellence on Petrochemicals and Materials TechnologyFaculty of EngineeringKasetsart UniversityBangkok10900Thailand Department of Chemical Engineering Faculty of EngineeringUniversity of Malaya50603Kuala LumpurMalaysia

Energy efficiency in the petrochemical industry is crucial in reducing energy consumption and environmental *** accurate energy efficiency model will provide valuable insight for supporting operational adjustment *** practice,due to inconsistent sampling intervals in the petrochemical industry,the traditional approach for obtaining energy efficiency may be unreliable and difficult to handle these multirate data ***,in this paper,a multi-channel convolutional neural network model integrating a model parameter-based transfer learning approach is proposed to improve the prediction of energy efficiency under inconsistent sampling *** multi-channel structure aims to recognize a different pattern from the dataset by convolving the information along the time ***,transfer learning allows the model to learn a new pattern of input after the model is fully ***,the performance for energy ef-ficiency prediction and saving analysis is validated by applying it to the vinyl chloride monomer production case *** result shows that the proposed model outperformed traditional models and typical convolutional neural network structures in terms of accuracy and reproducibility,with an r-square of *** utilization of transfer learning prevents a significant drop in performance and enhances adaptability in model learning on real-time energy ***,the energy gap analysis of the prediction result identified a significant energysaving potential,which would decrease annual energy consumption by 7.25%on average and a 5,709-ton reduction in carbon dioxide emissions.

关键词： Energy efficiency prediction Transfer learning petrochemical process Multirate prediction Convolutional neural network

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Explainable deep transfer learning for energy efficiency prediction based on uncertainty detection and identification

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Energy and AI 2023年第2期12卷 44-61页

作者： Chanin Panjapornpon Santi Bardeeniz Mohamed Azlan Hussain Patamawadee Chomchai Department of Chemical Engineering Center of Excellence on Petrochemicals and Materials TechnologyFaculty of EngineeringKasetsart UniversityBangkok 10900Thailand Department of Chemical Engineering Faculty of EngineeringUniversity of Malaya50603 Kuala LumpurMalaysia

Energy efficiency is an important aspect of increasing production capacity, minimizing environmental impact, and reducing energy usage in the petrochemical industries. However, in practice, data quality can be degraded by measurement malfunction throughout the operation, leading to unreliable and inaccurate prediction results. Therefore, this paper presents a transfer learning fault detection and identification-energy efficiency predictor (TFDI-EEP) model formulated using long short-term memory. The model aims to predict the energy efficiency of the petrochemical process under uncertainty by using the knowledge gained from the uncertainty detection task to improve prediction performance. The transfer procedure resolves weight initialization by applying partial layer freezing before fine-tuning the additional part of the model. The performance of the proposed model is verified on a wide range of fault variations to thoroughly examine the maximum contribution of faults that the model can tolerate. The results indicate that the TFDI-EEP achieved the highest r-squared and lowest error in the testing step for both the 10% and 20% fault variation datasets compared to other conventional methods. Furthermore, the revelation of interconnection between domains shows that the proposed model can also identify strong fault-correlated features, enhancing monitoring ability and strengthening the robustness and reliability of the model observed by the number of outliers. The transfer parameter improves the prediction performance by 9.86% based on detection accuracy and achieves an r-squared greater than 0.95 on the 40% testing fault variation.

关键词： Energy efficiency prediction Transfer learning petrochemical process Measurement reliability Fault detection and identification

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Problems and Strategies in Applications of Advanced process Control in China

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Petroleum Science 2005年第3期2卷 44-49页

作者： Luo Xionglin Zuo Xin Research Institute of Automation China University of Petroleum ChinaBeijing 102249

Advanced process Control （APC） is necessary for oil refining and chemical process in China, but some problems have emerged in the application of APC techniques in this field. This paper discusses the conditions of APC application concerning process design, distributed control system （DCS） choice and regular control. It analyzes the problems and strategies in APC application. Some suggestions are proposed for the enterprise to benefit from APC application.

关键词： China petrochemical process advanced process control application problem strategy

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