An interpretable data-driven approach for process flowsheet convergence troubleshooting

IF 8 1区 工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Shifeng Qu, Xinjie Wang, Wenli Du, Feng Qian
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引用次数: 0

Abstract

Practitioners typically alleviate the convergence problem of process flowsheet models through manual adjustment of the convergence-related flowsheet inputs, which is labor-intensive and relies heavily on expert experience. This paper aims to realize fast troubleshooting for process flowsheets with convergence problems and proposes an interpretable approach for the adjustment of the flowsheets to liberate the manpower for process model maintenance. Specifically, the flowsheet convergence problem is addressed from a data-driven perspective for the first time. The correlation between flowsheet inputs selected according to expert knowledge and convergence status is modeled utilizing the tree-based framework to capture the flowsheet convergence behavior. In addition, a novel interpretable adjustment procedure based on an adaptive minimum mean strategy is constructed to automatically identify strongly convergence-related flowsheet inputs and provide them with quantitative adjustment suggestions. The proposed approach shows effectiveness on non-convergence flowsheets with a success rate of up to 92.5%.
一种可解释的数据驱动方法,用于工艺流程表衔接故障排除
实践者通常通过人工调整与收敛相关的流程表输入来缓解流程表模型的收敛问题,这种方法劳动密集且严重依赖专家经验。本文旨在实现对存在收敛问题的工艺流程表的快速故障诊断,并提出一种可解释的流程表调整方法,以解放工艺模型维护的人力。具体来说,这是首次从数据驱动的角度来解决流程表收敛问题。根据专家知识选择的流程表输入与收敛状态之间的相关性,利用基于树的框架进行建模,以捕捉流程表收敛行为。此外,还构建了基于自适应最小均值策略的新型可解释调整程序,以自动识别与收敛密切相关的流程表输入,并为其提供定量调整建议。所提出的方法在非收敛流程表上显示出了有效性,成功率高达 92.5%。
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来源期刊
Advanced Engineering Informatics
Advanced Engineering Informatics 工程技术-工程:综合
CiteScore
12.40
自引率
18.20%
发文量
292
审稿时长
45 days
期刊介绍: Advanced Engineering Informatics is an international Journal that solicits research papers with an emphasis on 'knowledge' and 'engineering applications'. The Journal seeks original papers that report progress in applying methods of engineering informatics. These papers should have engineering relevance and help provide a scientific base for more reliable, spontaneous, and creative engineering decision-making. Additionally, papers should demonstrate the science of supporting knowledge-intensive engineering tasks and validate the generality, power, and scalability of new methods through rigorous evaluation, preferably both qualitatively and quantitatively. Abstracting and indexing for Advanced Engineering Informatics include Science Citation Index Expanded, Scopus and INSPEC.
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