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Resource optimization model using novel extreme learning machine with t-distributed stochastic neighbor embedding: Application to complex industrial processes

机译：具有T分布式随机邻居嵌入的新型极端学习机的资源优化模型：应用于复杂工业过程的应用

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摘要

Energy-saving and emission reduction are crucial to modern society, but the previous resource optimization methods based on traditional neural networks are complex and low accuracy. Therefore, this paper presented a novel extreme learning machine (ELM) method based on t-distributed stochastic neighbor embedding (t-SNE) to optimize the energy and reduce the carbon emission. In terms of mapping high-dimensional production data to low-dimensional space, the t-SNE can deal with the major factors affecting the energy efficiency, which are taken as inputs of the ELM. Then the resource optimization model for energy-saving is obtained based on the ELM to predict the output and achieve the optimal configuration. Finally, the proposed method is used to establish the resource optimization model for the ethylene and purified terephthalic acid (PTA) production processes in complex industrial processes. The experimental results demonstrate that the proposed model can improve the prediction accuracy of resource optimization models of complex industrial processes, and realize energy-saving and carbon emissions reduction. ? 2021 Elsevier Ltd. All rights reserved.
机译：节能减排对现代社会至关重要，但基于传统神经网络的先前资源优化方法是复杂的和低的准确性。因此，本文介绍了一种基于T分布式随机邻居嵌入（T-SNE）的新型极端学习机（ELM）方法，以优化能量并降低碳排放。在将高维生产数据映射到低维空间方面，T-SNE可以应对影响能量效率的主要因素，这被视为ELM的输入。然后基于ELM获得节能的资源优化模型，以预测输出并实现最佳配置。最后，所提出的方法用于建立乙烯和纯化对苯二甲酸（PTA）生产过程的资源优化模型在复杂的工业过程中。实验结果表明，该模型可以提高复杂工业过程资源优化模型的预测准确性，并实现节能和碳排放减少。还2021 Elsevier Ltd.保留所有权利。

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  • 来源
    《Energy》 |2021年第15期|120255.1-120255.11|共11页
  • 作者

    Han Yongming; Liu Shuang; Cong Di; Geng Zhiqiang; Fan Jinzhen; Gao Jingyang; Pan Tingrui;

  • 作者单位

    Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing 100029 Peoples R China|Minist Educ China Engn Res Ctr Intelligent PSE Beijing 100029 Peoples R China;

    Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing 100029 Peoples R China|Minist Educ China Engn Res Ctr Intelligent PSE Beijing 100029 Peoples R China;

    Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing 100029 Peoples R China|Minist Educ China Engn Res Ctr Intelligent PSE Beijing 100029 Peoples R China;

    Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing 100029 Peoples R China|Minist Educ China Engn Res Ctr Intelligent PSE Beijing 100029 Peoples R China;

    Genentech Inc Dept Stat Programming & Anal Prod Dev San Francisco CA USA;

    Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing 100029 Peoples R China;

    Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Resource optimization; Energy saving; Energy efficiency improvement; Extreme learning machine; Complex industrial processes; T-distributed stochastic neighbor embedding;

    机译：资源优化;节能;能效改进;极端学习机;复杂的工业过程;T分布式随机邻居嵌入;

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