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A machine learning methodology for reliability evaluation of complex chemical production systems

机译：复杂化学生产系统可靠性评估的机器学习方法

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摘要

System reliability evaluation is very important for safe operation and sustainable development of complex chemical production systems. This paper proposes a hybrid model for the reliability evaluation of chemical production systems. First, the influential factors in system reliability are categorized into five aspects: Man, Machine, Material, Management and Environment (4M1E), each of which represents a component subsystem of a complex chemical production process. Second, the Support Vector Machine (SVM) algorithm is used to develop machine learning models for the reliability evaluation of each subsystem, during which Particle Swarm Optimization (PSO) is applied for model parameter optimization. Third, the Random Forest (RF) algorithm is employed to correlate the reliability of the five subsystems with the reliability of the corresponding whole chemical production system. Then, Markov Chain Residual error Correction (MCRC) is adopted to improve the predictive accuracy of the machine learning model. The efficacy of the proposed hybrid model is tested on a case study, and the results indicate that the proposed model is capable of delivering satisfactory prediction results.
机译：系统可靠性评估对于复杂化学生产系统的安全运行和可持续发展非常重要。本文提出了一种用于化学生产系统可靠性评估的混合模型。首先，在系统的可靠性的影响因素分为五个方面：人，机器，材料，管理和环境（4M1E），其中每一个代表一个复杂的化学生产过程的一个组成部分子系统。其次，支持向量机（SVM）算法用于为每个子系统的可靠性评估开发机器学习模型，在此期间应用粒子群优化（PSO）进行模型参数优化。第三，采用随机森林（RF）算法以将五个子系统的可靠性与相应的整个化学生产系统的可靠性相关联。然后，采用马尔可夫链剩余纠错（MCRC）来提高机器学习模型的预测精度。在案例研究中测试了所提出的杂种模型的功效，结果表明该模型能够提供令人满意的预测结果。

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  • 来源
    《RSC Advances》 |2020年第34期|共11页
  • 作者

    Zhao Fanrui; Wu Jinkui; Zhao Yuanpei; Ji Xu; Zhou Li; Sun Zhongping;

  • 作者单位

    Sichuan Univ Coll Chem Engn Dept Chem Engn Chengdu 610065 Peoples R China;

    Sichuan Univ Coll Chem Engn Dept Chem Engn Chengdu 610065 Peoples R China;

    Chongqing Univ State Key Lab Power Transmiss Equipment &

    Syst Se Sch Elect Engn Chongqing 400044 Peoples R China;

    Sichuan Univ Coll Chem Engn Dept Chem Engn Chengdu 610065 Peoples R China;

    Sichuan Univ Coll Chem Engn Dept Chem Engn Chengdu 610065 Peoples R China;

    Sichuan Univ Coll Chem Engn Dept Chem Engn Chengdu 610065 Peoples R China;

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  • 正文语种 eng
  • 中图分类 化学;
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