Soft Sensor Development for Nonlinear Industrial Processes Based on Ensemble Just-in-Time Extreme Learning Machine through Triple-Modal Perturbation and Evolutionary Multiobjective Optimization

Pan, Bei; Jin, Huaiping; Yang, Biao; Qian, Bin; Zhao, Zhengang

doi:10.1021/acs.iecr.9b03702

Cited by 16 publications

(5 citation statements)

References 48 publications

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“…During the past few years, the JITL method has been extensively used for the design of adaptive controllers, [36][37][38] process monitoring, [39,40] and soft sensors [41,42] for chemical processes. The JITL method uses a set of onlineobtained local models to describe a nonlinear process.…”

Section: Jitl Modelling For Batch Chemical Processesmentioning

confidence: 99%

High‐order iterative learning model predictive control for batch chemical processes

2023

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In order to address two‐dimensional (2D) control issue for a class of batch chemical processes, we propose a novel high‐order iterative learning model predictive control (HILMPC) method in this paper. A set of local state‐space models are first constructed to represent the batch chemical processes by adopting the just‐in‐time learning (JITL) technique. Meanwhile, a pre‐clustered strategy is used to lessen the computational burden of the modelling process and improve the modelling efficiency. Then, a two‐stage 2D controller is designed to achieve integrated control by combining high‐order iterative learning control (HILC) on the batch domain with model predictive control (MPC) on the time domain. The resulting HILMPC controller can not only guarantee the convergence of the system on the batch domain, but also guarantee the closed‐loop stability of the system on the time domain. The convergence of the HILMPC method is ensured by rigorous analysis. Two examples are presented in the end to demonstrate that the developed method provides better control performance than its previous counterpart.

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Section: Jitl Modelling For Batch Chemical Processesmentioning

confidence: 99%

High‐order iterative learning model predictive control for batch chemical processes

2023

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“…In recent years, ELM has gained growing popularity in soft sensor applications, due to its fast learning speed and good generalization performance [63][64][65][66]. However, ELM often produces unstable predictions, due to the uncertainties caused by the random assignments of input weights and biases in the learning process.…”

Section: Nclelmmentioning

confidence: 99%

Pseudo-Labeling Optimization Based Ensemble Semi-Supervised Soft Sensor in the Process Industry

You-wei

Jin

Dong

et al. 2021

Sensors

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Nowadays, soft sensor techniques have become promising solutions for enabling real-time estimation of difficult-to-measure quality variables in industrial processes. However, labeled data are often scarce in many real-world applications, which poses a significant challenge when building accurate soft sensor models. Therefore, this paper proposes a novel semi-supervised soft sensor method, referred to as ensemble semi-supervised negative correlation learning extreme learning machine (EnSSNCLELM), for industrial processes with limited labeled data. First, an improved supervised regression algorithm called NCLELM is developed, by integrating the philosophy of negative correlation learning into extreme learning machine (ELM). Then, with NCLELM as the base learning technique, a multi-learner pseudo-labeling optimization approach is proposed, by converting the estimation of pseudo labels as an explicit optimization problem, in order to obtain high-confidence pseudo-labeled data. Furthermore, a set of diverse semi-supervised NCLELM models (SSNCLELM) are developed from different enlarged labeled sets, which are obtained by combining the labeled and pseudo-labeled training data. Finally, those SSNCLELM models whose prediction accuracies were not worse than their supervised counterparts were combined using a stacking strategy. The proposed method can not only exploit both labeled and unlabeled data, but also combine the merits of semi-supervised and ensemble learning paradigms, thereby providing superior predictions over traditional supervised and semi-supervised soft sensor methods. The effectiveness and superiority of the proposed method were demonstrated through two chemical applications.

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“…Currently, most of the reported local dynamic JITL-ELM methods are used to nonlinear processes modeling. [24][25][26][27][28] To the author's knowledge, the related reports about the local dynamic JITL-ELM model based optimal ILC control methods for nonlinear batch process have not previously been published. The main reason is that updating a local dynamic JITL-ELM model is more computationally expensive than updating a fixed global model, which leads to the control performance of optimal ILC strategy degradation because of the real-time problem of the JITL-ELM model.…”

Section: Introductionmentioning

confidence: 99%

“…Currently, most of the reported local dynamic JITL‐ELM methods are used to nonlinear processes modeling 24‐28 . To the author's knowledge, the related reports about the local dynamic JITL‐ELM model based optimal ILC control methods for nonlinear batch process have not previously been published.…”

Section: Introductionmentioning

confidence: 99%

A local dynamic extreme learning machine based iterative learning control of nonlinear batch process

Zhou

Jia

2021

Optim Control Appl Methods

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This article deals with the optimal control issue of nonlinear batch process. First, in order to derive high efficiency and accuracy process model, a novel hierarchical searching mechanism local dynamic nonlinear model is constructed which is composed of just‐in‐time learning and extreme learning machine (JITL‐ELM). Then, based on the local dynamic JITL‐ELM model, an optimal quadratic‐criterion‐based iterative learning control (Q‐ILC) algorithm is presented, where the control input trajectory can be obtained by solving a quadratic programming problem. Moreover, on the basis of inverse model system, the initial batch control input trajectory of the Q‐ILC algorithm can be obtained by the use of JITL method. As a result, not only the issue of model‐plant mismatch and real‐time disturbance can be solved, but also obtain faster system convergence rate and smaller tracking error. Besides, the convergence properties of control input and tracking error are analyzed. Finally, a typical batch process is presented to demonstrate the feasibility and superiority.

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Soft Sensor Development for Nonlinear Industrial Processes Based on Ensemble Just-in-Time Extreme Learning Machine through Triple-Modal Perturbation and Evolutionary Multiobjective Optimization

Cited by 16 publications

References 48 publications

High‐order iterative learning model predictive control for batch chemical processes

High‐order iterative learning model predictive control for batch chemical processes

Pseudo-Labeling Optimization Based Ensemble Semi-Supervised Soft Sensor in the Process Industry

A local dynamic extreme learning machine based iterative learning control of nonlinear batch process

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