Adaptive weighted relevant sample selection of just-in-time learning soft sensor for chemical processes

Kun Chen, Yi Liu
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引用次数: 1

Abstract

A new just-in-time learning (JITL) method using adaptive relevant sample selection strategy is proposed for online prediction of product quality in chemical processes. To overcome certain shortcomings in traditional JITL, such as the incomplete usage of primary variable information and inaccurate feature weights assignment, an adaptive sample selection approach is introduced. First, to keep both input and output information, a dual-objective optimization scheme with an adaptive parameter is considered. Then, an adaptive feature weight assignment strategy is present to construct a suitable similarity criterion for JITL. To illustrate the performance of the proposed method, it is applied to online prediction of the crude oil endpoint in an industrial fluidized catalytic cracking unit. The experimental results demonstrate that the proposed method can help improve the prediction performance.
化工过程实时学习软传感器的自适应加权相关样本选择
提出了一种基于自适应相关样本选择策略的实时学习(jit)方法,用于化工过程产品质量在线预测。为了克服传统JITL方法对主变量信息利用不充分、特征权值分配不准确等缺点,提出了一种自适应样本选择方法。首先,为了同时保留输入和输出信息,考虑了一种带自适应参数的双目标优化方案。然后,提出一种自适应特征权值分配策略,构建适合JITL的相似度准则。为验证该方法的有效性,将其应用于某工业流化催化裂化装置原油端点的在线预测。实验结果表明,该方法可以提高预测性能。
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