利用人工智能优化化工过程实时软分析仪

M. M. Karimi, A. Fatehi, R. Ebrahimpour, Ali Shamsaddinlou
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引用次数: 0

摘要

本文研究了数据衍生方法在化工过程仪表分析与监控、产品信息提取、主要过程变量或难以实时测量的变量估计模型设计等方面的应用。讨论了用优化后的经典神经网络对过程进行建模的多层感知器。田纳西州伊士曼过程,一个著名的工厂范围内的过程基准,被用于验证所提出的方法。采用了阶跃响应检验、利普希茨数法和正向选择等算法。本文的主要进展是采用一种分层负责策略来选择输入变量和各自的有效时滞,以达到工业过程识别神经网络模型的最高预测精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimized real-time soft analyzer for chemical process using artificial intelligence
This paper concerns application of data-derived approaches for analyzing and monitoring chemical process instruments, extracting product information, and designing estimation models for primary process variables, or difficult to measure in real-time variables. Modeling of process with an optimized classical neural network, the multi-layer perceptron (MLP) is discussed. Tennessee Eastman Process, a well-known plant wide process benchmark, is applied to validate the proposed approach. Investigations and several algorithms as step response test, Lipschitz number method and forward selection are used. The main advancement introduced here is that a hierarchical level responsible strategy is applied for selection of input variables and respective efficient time delays to attain the highest possible prediction accuracy of the neural network model for industrial process identification.
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