Journal of Process Control最新文献

{"title":"Robust PINN modeling via sensitivity-based adaptive sampling: Integration of optimal sensor placement and structural uncertainty handling","authors":"Shuji Chang ,&nbsp;Piyush Agarwal ,&nbsp;Chris McCready ,&nbsp;Luis Ricardez-Sandoval ,&nbsp;Hector Budman","doi":"10.1016/j.jprocont.2025.103493","DOIUrl":"10.1016/j.jprocont.2025.103493","url":null,"abstract":"<div><div>Physics-informed neural networks (PINNs) have emerged as promising surrogate models for systems governed by partial differential equations (PDEs), yet their practical implementation faces challenges in training efficiency and robustness to uncertainties. This study refines and improves a recently proposed sensitivity-based adaptive sampling (SBS) methodology to address these challenges specifically for twice-differentiable PDE systems. We first conduct a systematic investigation of SBS hyper-parameters, including prediction horizon and adaptation rate, revealing their crucial role in training performance. To enhance PINN model robustness facing uncertainties, we propose two approaches: (1) incorporating sensor measurements at sensitivity-identified locations into the loss function, and (2) augmenting the PINN architecture with direct sensor data inputs. Results show that our proposed approaches achieve superior generalization capabilities and robustness. Furthermore, we demonstrate that the SBS methodology can serve for optimal sensor placement by identifying locations that maximize information gain for model training.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"152 ","pages":"Article 103493"},"PeriodicalIF":3.3,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144557294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting transfer learning suitability in ANN-based control of FOPDT industrial processes","authors":"Pau Comas,&nbsp;Antoni Morell,&nbsp;Ramon Vilanova,&nbsp;Jose Lopez Vicario","doi":"10.1016/j.jprocont.2025.103476","DOIUrl":"10.1016/j.jprocont.2025.103476","url":null,"abstract":"<div><div>The application of Artificial Neural Networks (ANN) in industrial control has become a popular topic of research in recent years. The adoption of strategies showing satisfactory results in other domains, such as Transfer Learning, have been proposed to overcome scarce data limitations. However, there is a lack of studies specifically addressing the requirements of control environments, where applying unsuitable ANN-based controllers can have critical consequences. In this work, we conduct an analysis of Transfer Learning focusing on the control of First-Order plus Dead-Time (FOPDT) processes. In particular, we first provide an overview of state-of-the-art Transfer Suitability Metrics (TSM) along with an analysis of their applicability to control. To do that, we define two transference scenarios that can be found in practice. Based on the insights extracted from the analysis, we propose a novel learning-based metric aimed at estimating the transfer deterioration when applying a data-based controller to a target domain. This metric enables the quantification of transfer suitability, so that a low deterioration value indicates that training a new neural network specifically for this process would yield similar performance. The proposed metric shows a good performance, and a simplified version is also proposed to offer a balanced trade-off between complexity and predictive accuracy.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"152 ","pages":"Article 103476"},"PeriodicalIF":3.3,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144557293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A semi-supervised temporal modeling strategy integrating VAE and Wasserstein GAN under sparse sampling constraints","authors":"Yujie Hu ,&nbsp;Changrui Xie ,&nbsp;Xi Chen","doi":"10.1016/j.jprocont.2025.103497","DOIUrl":"10.1016/j.jprocont.2025.103497","url":null,"abstract":"<div><div>Time series network models are widely applied in process industries for soft sensing, fault monitoring, and real-time optimization, serving as a powerful tool to enhance the safety and efficiency of industrial production. Typically, time series networks require labeled data for supervised learning. However, labeled data often exhibits sparse sampling characteristics in industrial settings, which limits the model's performance. To address this issue, a semi-supervised modeling strategy based on Variational Autoencoder (VAE) and Wasserstein Generative Adversarial Network (WGAN) is proposed in this paper. The strategy consists of three steps. First, for the labeled samples, process data and labeled data are used as input to train a supervised VAE model (SVAE). Upon completion of the training, the posterior distribution of the latent variable <em><strong>z</strong></em>_{<em><strong>S</strong></em>} is obtained. Second, in all samples, only process data is used to train an unsupervised VAE model (UVAE) to extract the latent variable <em><strong>z</strong></em>_{<em><strong>U</strong></em>}, and the WGAN discriminator is introduced to distinguish between \"fake data\" (<em><strong>z</strong></em>_{<em><strong>U</strong></em>}) and \"real data\" (<em><strong>z</strong></em>_{<em><strong>S</strong></em>}). Through adversarial learning between the UVAE and WGAN discriminator, the posterior distribution of <em><strong>z</strong></em>_{<em><strong>U</strong></em>} is forced to approximate <em><strong>z</strong></em>_{<em><strong>S</strong></em>}. Finally, the encoder of UVAE and the decoder of SVAE are combined to form a Semi-Supervised Variational Autoencoder model (SS-VAE), which extracts the latent variable <em><strong>z</strong></em>_{<em><strong>SS</strong></em>} and the reconstructed labeled data from the decoder as inputs for the time series network. Long Short-Term Memory (LSTM) and Temporal Convolutional Network (TCN) are selected as two basic time series models, and their performance, both with and without the proposed semi-supervised approach, is compared to assess the effectiveness and robustness of the strategy. The improvements observed in two industrial case studies validate the efficiency of the proposed approach.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"152 ","pages":"Article 103497"},"PeriodicalIF":3.3,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144563215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to “Study of parabolic trough collector with crucial analysis on controller and estimator with variable mirror efficiency” [J. Process Control 143 (2024) 103317]","authors":"Dibyajyoti Baidya ,&nbsp;Surender Kannaiyan ,&nbsp;Neeraj Dhanraj Bokde","doi":"10.1016/j.jprocont.2025.103498","DOIUrl":"10.1016/j.jprocont.2025.103498","url":null,"abstract":"","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"152 ","pages":"Article 103498"},"PeriodicalIF":3.3,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144595386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of industrial controllers: Disturbance rejection in an up-and-running industrial flotation process","authors":"Frida Norlund ,&nbsp;Kristian Soltesz ,&nbsp;Margret Bauer","doi":"10.1016/j.jprocont.2025.103478","DOIUrl":"10.1016/j.jprocont.2025.103478","url":null,"abstract":"<div><div>Linear quadratic (LQ) control optimizes a quadratic cost function while following a linear model. It is commercially available in the process industry but often not labeled as such and infrequently used. Froth flotation is a process in the minerals industry that extracts precious metals from a slurry of finely ground rock in consecutive tanks called cells. Flotation cells are often arranged in two parallel streams and the main control task is to regulate the cell levels. In this work, a commercial solution is used to assess the performance of LQ control and compare it to existing PI/PID controllers. Comparing control solutions in the process industry in general and the minerals industry in particular is fraught with difficulty because operating conditions change frequently, especially when studying the capability to reject disturbances. In the flotation process studied here, the disturbances act on two parallel lines, one controlled by an LQ algorithm and the other with the existing PI/PID controllers. This work develops data assessment strategies to isolate events of interest and analyses them both with classical metrics such as integrated absolute error, and with metrics relevant for the operators, such as maximum level deviations. Both these metrics clearly show that the LQ controller performs significantly and consistently better than the PI/PID controllers.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"152 ","pages":"Article 103478"},"PeriodicalIF":3.3,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reducing false alarms in fault detection: A comparative analysis between conformal prediction and classical methods applied to PCA and autoencoders","authors":"Abdoul Rahime Diallo,&nbsp;Lazhar Homri,&nbsp;Jean-Yves Dantan","doi":"10.1016/j.jprocont.2025.103495","DOIUrl":"10.1016/j.jprocont.2025.103495","url":null,"abstract":"<div><div>Setting detection thresholds in data-driven fault detection is a critical challenge, particularly in ensuring a reliable balance between false alarm rate and fault detection capability. Although conformal prediction has been applied to various domains including medicine, finance, and the monitoring of physical systems, its use in industrial fault detection remains underexplored. This study compares conformal prediction methods with classical threshold-setting techniques used in Principal Component Analysis (PCA) and Autoencoder (AE) based fault detection, using extensive experiments on the Tennessee Eastman Process (TEP). The analysis considers conformal prediction strategies, with marginal and conditional validity alongside traditional parametric approaches for PCA and non-parametric methods for AE. The results highlight the sensitivity of false alarm rates to training data availability, with both traditional and marginal conformal methods often exceeding the targeted false alarm risk when training data are limited. In this context, approaches with conditional validity provide a reliable estimation of the uncertainty associated with the false alarm rate. When sufficient training data are available, conditional conformal methods, particularly those based on the Dvoretzky-Kiefer-Wolfowitz (DKW) and Simes adjustments, provide stricter false alarm rate control, systematically remaining below the predefined risk levels. While this comes at the cost of a slight decrease in fault detection rates, the trade-off is particularly relevant in industrial settings where normal operation is overwhelmingly more frequent than fault occurrences. Overall, conformal prediction demonstrates competitive performance compared to analytically established PCA-based thresholds and the widely used Kernel Density Estimation (KDE) for AE-based fault detection.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"152 ","pages":"Article 103495"},"PeriodicalIF":3.3,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A parametric sensitivity method for Arrival Cost design in nonlinear Moving Horizon Estimation","authors":"Simen Bjorvand,&nbsp;Johannes Jäschke","doi":"10.1016/j.jprocont.2025.103466","DOIUrl":"10.1016/j.jprocont.2025.103466","url":null,"abstract":"<div><div>Moving Horizon Estimation (MHE) is an optimization based state estimation algorithm where a fixed number of past measurements in a moving time horizon are used to infer the states. The strengths of MHE are the capability of directly incorporating nonlinear model equations without approximations, and the ease of incorporating physical knowledge like conservation of mass or limits on noise as inequality constraints. When a new measurement is available the oldest measurement in the horizon is discarded to make room for a new one. All discarded measurements are summarized in a term known as the Arrival Cost, which also acts as a prior for the initial state in the MHE. In this work we introduce a novel methodology for calculating the Arrival Cost based on parametric nonlinear programming sensitivities. This is done by interpreting the MHE as an approximation of the Full Information (FI) estimator, where all available measurements are used to estimate the states, by formulating an Ideal Arrival Cost such that the MHE and FI estimator becomes identical. This Ideal Arrival Cost is a parametric optimization problem, and the sensitivity of the optimal solution manifold of this problem is used to approximate the Ideal Arrival Cost. Our method incorporates inequality constraints elegantly into the Arrival Cost, and we show that the proposed method introduces a smaller approximation error of the Ideal Arrival Cost compared to similar methods in literature. In a distillation column simulation example we show that the approximation error of the Full Information Estimate by the MHE with the new Arrival Cost method is reduced to 0.35% from 8.06% by using the Extended Kalman Filter (EKF) as the Arrival Cost or 5.66% by using the Smoothed EKF (SEKF).</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"152 ","pages":"Article 103466"},"PeriodicalIF":3.3,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144522613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Charge process management of lithium-ion batteries based on digital twins: A new way to extend life","authors":"Chunhui Ji ,&nbsp;Guang Jin ,&nbsp;Ran Zhang","doi":"10.1016/j.jprocont.2025.103475","DOIUrl":"10.1016/j.jprocont.2025.103475","url":null,"abstract":"<div><div>Effective management of the charging process is not only crucial for reducing costs and improving the efficiency of electric vehicles and renewable energy systems but also essential for enhancing the stability and safety of energy systems. Consequently, it has become a key focus in battery management system research. However, there is a contradiction between improving battery charging efficiency and extending service life, and the diversified battery use needs make this contradiction more prominent. This paper proposes a lithium-ion battery charging process management framework based on digital twin technology and Bayesian principle. A hybrid model is used to establish the digital twin of the lithium-ion battery charging state and health state. At the same time, the model evaluation reward and maturity evaluation reward are comprehensively considered in the decision-making process to improve the effectiveness of decision-making. Furthermore, the charging strategy is optimized according to the distinct characteristics of different battery life stages. The case analysis demonstrates that, compared to existing charging strategies, the proposed method effectively extends battery lifespan while meeting various battery performance requirements.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"152 ","pages":"Article 103475"},"PeriodicalIF":3.3,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144513921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ECCBO: An inherently safe Bayesian optimization with embedded constraint control for real-time process optimization","authors":"Dinesh Krishnamoorthy","doi":"10.1016/j.jprocont.2025.103467","DOIUrl":"10.1016/j.jprocont.2025.103467","url":null,"abstract":"<div><div>This paper presents a model-free real-time optimization (RTO) framework that leverages unconstrained Bayesian optimization (BO) embedded with constraint control to achieve optimal steady-state operation of process systems without the need for detailed models. Leveraging the vertical decomposition of information flow with timescale separation, this paper proposes two approaches to BO with embedded constraint controllers that simplifies model-free RTO with unknown cost and constraints, while ensuring steady-state constraint feasibility. The first approach employs constraint controllers that controls the constraints to some feasible setpoint in the fast timescale, and an unconstrained BO finds the optimal setpoints to these controllers in the slower timescale. The second approach uses constraint controllers as safety filters, where BO searchers over the RTO degrees of freedom, which can be overridden by the constraint controller when necessary to ensure steady-state constraint feasibility. By embedding constraint controllers with Bayesian optimization, both approaches ensure zero cumulative constraint violation without depending on specific assumptions about the Gaussian process model used in Bayesian optimization, making it inherently safe. The proposed scheme is demonstrated on several illustrative benchmark examples.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"152 ","pages":"Article 103467"},"PeriodicalIF":3.3,"publicationDate":"2025-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144502844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust recursive transformed component statistical analysis for incipient industrial fault detection with missing data","authors":"Hanwen Zhang ,&nbsp;Qingqing Liu ,&nbsp;Jianxun Zhang ,&nbsp;Jun Shang","doi":"10.1016/j.jprocont.2025.103470","DOIUrl":"10.1016/j.jprocont.2025.103470","url":null,"abstract":"<div><div>In practical industrial processes, data integrity is often compromised by sensor malfunctions or issues in data management. Furthermore, incipient faults, which can escalate into severe accidents, are typically challenging to detect due to their subtle nature. This paper introduces a robust recursive transformed component statistical analysis method for detecting incipient faults in industrial processes with missing data. Within a sliding window, missing data are restored by minimizing the detection index in a recursive way, and the converged statistical model is then used for fault detection. The detectability of the proposed method is analyzed theoretically in scenarios with incomplete data. To validate the effectiveness of the proposed method, experiments are conducted on both a numerical case study and the Tennessee Eastman process. The results demonstrate robust performance under incomplete training and testing data, enabling accurate detection of incipient faults in industrial settings. Furthermore, compared to existing methods, the proposed approach achieves significant improvements in fault detection under missing-data conditions, attaining a detection rate close to 100% for most fault scenarios while maintaining a near-zero false alarm rate.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"152 ","pages":"Article 103470"},"PeriodicalIF":3.3,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144481118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}