Enhanced industrial process modeling with transfer-incremental-learning: A parallel SAE approach and its application to a sulfur recovery unit

IF 5.4 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Tianhao Mou , Jinfeng Liu , Yuanyuan Zou , Shaoyuan Li , Maria Gabriella Xibilia
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引用次数: 0

Abstract

In industrial processes, quality variable prediction is important for process control and monitoring. Deep learning (DL) methods offer excellent prediction performance and potential paradigm shifts in quality variable modeling. However, in real-world production, the lack of offline labeled data and time-varying data distributions commonly exist, which seriously prohibits practical applications of DL-based predictive models. This paper introduces an enhanced quality variable prediction framework, Transfer-Incremental-Learning Parallel Stacked Autoencoders (TIL-PSAE), to address this challenge. TIL-PSAE integrates three key components: a parallel model structure, a transfer-learning (TL)-based offline training strategy that accumulates knowledge from multiple similar but different processes, and an incremental-learning (IL)-based online adaptation strategy. The model structure comprises two parallel SAEs for extracting process-invariant and target-process-specific features. Offline training involves sequential training using data from different processes, facilitating knowledge accumulation into different parts of model. During online adaptation, the accumulated knowledge remains unchanged while a new combination of knowledge is learned, thus improving online prediction accuracy and avoiding knowledge forgetting. The proposed model is applied to a sulfur recovery unit with four parallel sub-units. Experimental results demonstrate the effectiveness of the proposed model in both offline and online prediction performance.

利用迁移-渐进学习增强工业过程建模:并行 SAE 方法及其在硫磺回收装置中的应用
在工业流程中,质量变量预测对于流程控制和监测非常重要。深度学习(DL)方法具有出色的预测性能,并有望改变质量变量建模的模式。然而,在实际生产中,普遍存在缺乏离线标注数据和数据分布时变的问题,这严重阻碍了基于深度学习的预测模型的实际应用。本文介绍了一种增强型质量变量预测框架--转移增量学习并行堆叠自动编码器(TIL-PSAE),以应对这一挑战。TIL-PSAE 集成了三个关键部分:并行模型结构、基于迁移学习 (TL) 的离线训练策略(从多个相似但不同的过程中积累知识)和基于增量学习 (IL) 的在线适应策略。模型结构包括两个并行的 SAE,用于提取流程不变特征和目标流程特定特征。离线训练包括使用来自不同流程的数据进行连续训练,促进知识积累到模型的不同部分。在在线适应过程中,积累的知识保持不变,同时学习新的知识组合,从而提高在线预测的准确性,避免知识遗忘。所提出的模型被应用于具有四个并行子单元的硫磺回收装置。实验结果证明了所提模型在离线和在线预测性能方面的有效性。
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来源期刊
Control Engineering Practice
Control Engineering Practice 工程技术-工程:电子与电气
CiteScore
9.20
自引率
12.20%
发文量
183
审稿时长
44 days
期刊介绍: Control Engineering Practice strives to meet the needs of industrial practitioners and industrially related academics and researchers. It publishes papers which illustrate the direct application of control theory and its supporting tools in all possible areas of automation. As a result, the journal only contains papers which can be considered to have made significant contributions to the application of advanced control techniques. It is normally expected that practical results should be included, but where simulation only studies are available, it is necessary to demonstrate that the simulation model is representative of a genuine application. Strictly theoretical papers will find a more appropriate home in Control Engineering Practice''s sister publication, Automatica. It is also expected that papers are innovative with respect to the state of the art and are sufficiently detailed for a reader to be able to duplicate the main results of the paper (supplementary material, including datasets, tables, code and any relevant interactive material can be made available and downloaded from the website). The benefits of the presented methods must be made very clear and the new techniques must be compared and contrasted with results obtained using existing methods. Moreover, a thorough analysis of failures that may happen in the design process and implementation can also be part of the paper. The scope of Control Engineering Practice matches the activities of IFAC. Papers demonstrating the contribution of automation and control in improving the performance, quality, productivity, sustainability, resource and energy efficiency, and the manageability of systems and processes for the benefit of mankind and are relevant to industrial practitioners are most welcome.
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