Abstract Recent decades have witnessed a trend that soft sensing, instead of hard sensing, has been extensively applied to estimate the key performance indices under the circumstances that practical measurements are hardly to be achieved at a reasonable cost. However, due to the existence of nonlinearities and time-varying characteristics in the practical industrial processes, the conventional soft sensor models probably suffer from severe performance degradations when the original designed models are mismatched. Although many novel methodologies have been employed to alleviate this problem, each of them merely focuses on certain aspect of model features, a comprehensive framework combining these features is needed. Therefore, this study proposes an online predictive methodology based on an integration of ensemble learning based on a novel adaptive locally weighted partial least squares. Specifically, sub-models established on the respective dataset are generated by moving window model, time difference model and just-in-time learning model for the sake of different properties in processes. The effectiveness of the proposed model is validated on the practical nonlinear processes represented by a benchmark simulation model No.1 (BSM1), in wastewater treatment plants (WWTP), and a real industrial catalytic reforming process.

中文翻译：

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基于集成局部加权偏最小二乘法的关键性能指标估计及其在工业非线性过程中的应用

摘要 近几十年来，在难以以合理的成本实现实际测量的情况下，软感知代替硬感知被广泛应用于估计关键性能指标的趋势。然而，由于实际工业过程中非线性和时变特性的存在，当原始设计的模型不匹配时，传统的软传感器模型可能会遭受严重的性能下降。尽管已经采用了许多新颖的方法来缓解这个问题，但它们中的每一个都只关注模型特征的某些方面，因此需要一个结合这些特征的综合框架。所以，本研究提出了一种基于集成学习的在线预测方法，该方法基于一种新颖的自适应局部加权偏最小二乘法。具体而言，针对过程中的不同属性，通过移动窗口模型、时差模型和即时学习模型生成在各个数据集上建立的子模型。所提出模型的有效性在以基准模拟模型 No.1 (BSM1)、废水处理厂 (WWTP) 和实际工业催化重整过程为代表的实际非线性过程中得到验证。