Journal of Process Control最新文献_第2页

Robust to outlier image inpainting for interface detection in primary separation vessel 初级分离容器界面检测对离群图像的鲁棒性

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2025-04-09 DOI: 10.1016/j.jprocont.2025.103426

Parashara Kodati , Vamsi Krishna Puli , Ranjith Chiplunkar , Biao Huang

{"title":"Robust to outlier image inpainting for interface detection in primary separation vessel","authors":"Parashara Kodati , Vamsi Krishna Puli , Ranjith Chiplunkar , Biao Huang","doi":"10.1016/j.jprocont.2025.103426","DOIUrl":"10.1016/j.jprocont.2025.103426","url":null,"abstract":"<div><div>The Primary Separation Vessel (PSV) is integral to the bitumen extraction process in the oil sands industry. Effective control of the interface between the froth and middlings layers is critical for the PSV’s optimal operation. Computer vision techniques can monitor this interface using the images captured from the PSV sight glass. However, image-based models suffer from a lower performance when the image quality is inferior. This is evident in the case of the image data being affected by external degradations. Image inpainting addresses the task of removing unwanted objects and improving the quality of the images. Variational Autoencoder (VAE) can be trained to remove the degradations and restore the image quality. However, in the latent space of a standard VAE which uses a Gaussian distribution for the prior, the input information is spread across all the latent dimensions. This is not suitable particularly in scenarios where the input data consists of a limited number of salient features, without involving complex patterns. This under-regularization of latent space may impact the performance of inpainting when outliers are present in the training data. In this article, a Laplace VAE framework is proposed where the prior is modeled as a Laplace distribution to achieve a better regularization of the latent space and enhance robustness to the outliers in training data. Further, we demonstrate that the Laplace prior promotes sparsity in the latent representations, when there are limited features of interest in the input. This model is used to restore degraded images from a pilot-scale PSV and the interface level is predicted from the restored images using a region-based segmentation method.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"150 ","pages":"Article 103426"},"PeriodicalIF":3.3,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143799933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimal modeling of fermentation process using hybrid support vector regression 基于混合支持向量回归的发酵过程优化建模

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2025-04-07 DOI: 10.1016/j.jprocont.2025.103429

Kangwei Zhu, Shunyi Zhao, Xiaoli Luan, Fei Liu

{"title":"Optimal modeling of fermentation process using hybrid support vector regression","authors":"Kangwei Zhu, Shunyi Zhao, Xiaoli Luan, Fei Liu","doi":"10.1016/j.jprocont.2025.103429","DOIUrl":"10.1016/j.jprocont.2025.103429","url":null,"abstract":"<div><div>This study investigates the accuracy of concentration prediction in industrial fermentation, a critical factor for optimizing large-scale production. To address the limitations of single models in generalization, a hybrid support vector regression (H-SVR) model is proposed, combining the strengths of flexible and robust SVRs to enhance prediction accuracy. The algorithm segments fermentation data based on bacterial characteristics at different stages, emphasizing local phase-specific features, and uses weighted factors to construct the final hybrid model. The corresponding hyperparameters are optimized via grid search to ensure performance. Simulation results based on industrial penicillin fermentation data and succinic acid fermentation experiment demonstrate that the H-SVR model significantly reduces prediction error compared to models such as least squares support vector machines and some network models, while enabling real-time process monitoring. These findings highlight the potential of the H-SVR model in complex biological systems and demonstrate its effectiveness as a tool for optimizing fermentation processes.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"150 ","pages":"Article 103429"},"PeriodicalIF":3.3,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143785066","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}

引用次数: 0

A numerically stable unscented transformation with optimal tuning parameters for high-dimensional systems 针对高维系统的数值稳定无符号变换与优化调整参数

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2025-04-06 DOI: 10.1016/j.jprocont.2025.103406

H.A. Krog, J. Jäschke

引用次数: 0

A robust data-driven approach for modeling industrial systems with non-stationary and sparsely sampled data streams 具有非平稳和稀疏采样数据流的工业系统建模的鲁棒数据驱动方法

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2025-04-02 DOI: 10.1016/j.jprocont.2025.103425

Changrui Xie, Xi Chen

{"title":"A robust data-driven approach for modeling industrial systems with non-stationary and sparsely sampled data streams","authors":"Changrui Xie, Xi Chen","doi":"10.1016/j.jprocont.2025.103425","DOIUrl":"10.1016/j.jprocont.2025.103425","url":null,"abstract":"<div><div>Data-driven modeling has been widely applied to estimate quality variables in industrial processes, while several practical challenges hinder their applications. This paper is concerned with data-driven modeling for industrial plants with non-stationary and sparsely sampled data streams. To avoid overfitting from data scarcity, a Bayesian linear model is preferred and an associated identification algorithm based on variational inference is developed. The key contribution of this work lies in the development of an adaptation mechanism using streaming variational Bayes with power priors, enabling model identification and adaptation to non-stationary and sparsely sampled data within a full Bayesian framework. To enhance robustness against outliers in streaming batches, Student’s-<em>t</em> distribution is used to account for noise. Furthermore, a posterior predictive distribution is approximately derived, allowing the model to provide not only a point estimate but also the associated predictive uncertainty. The effectiveness of the proposed method is validated through a numerical example and an industrial application.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"150 ","pages":"Article 103425"},"PeriodicalIF":3.3,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143760818","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}

引用次数: 0

Fault detection and identification using a novel process decomposition algorithm for distributed process monitoring 基于分布式过程监控的新型过程分解算法故障检测与识别

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2025-03-29 DOI: 10.1016/j.jprocont.2025.103423

Enrique Luna Villagómez, Vladimir Mahalec

{"title":"Fault detection and identification using a novel process decomposition algorithm for distributed process monitoring","authors":"Enrique Luna Villagómez, Vladimir Mahalec","doi":"10.1016/j.jprocont.2025.103423","DOIUrl":"10.1016/j.jprocont.2025.103423","url":null,"abstract":"<div><div>Recent progress in fault detection and identification increasingly relies on sophisticated techniques for fault detection, applied through either centralized or distributed approaches. Instead of increasing the sophistication of the fault detection method, this work introduces a novel algorithm for determining process blocks of interacting measurements and applies principal component analysis (PCA) at the block level to identify fault occurrences. Additionally, we define a novel contribution map that scales the magnitudes of disparate faults to facilitate the visual identification of abnormal values of measured variables and analysis of fault propagation. Bayesian aggregate fault index and block fault indices vs. time pinpoint origins of the fault. The proposed method yields fault detection rates on par with most sophisticated centralized or distributed methods on the Tennessee Eastman Process (TEP) benchmark. Since the decomposition algorithm relies on the process flowsheet and control loop structures, practicing control engineers can implement the proposed method in a straightforward manner.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"149 ","pages":"Article 103423"},"PeriodicalIF":3.3,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improved gain conditioning for linear model predictive control 线性模型预测控制的改进增益调节

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2025-03-28 DOI: 10.1016/j.jprocont.2025.103413

Mouna Y. Harb , Stephen D. Sanborn , Andrew J. Thake , Kimberley B. McAuley

{"title":"Improved gain conditioning for linear model predictive control","authors":"Mouna Y. Harb , Stephen D. Sanborn , Andrew J. Thake , Kimberley B. McAuley","doi":"10.1016/j.jprocont.2025.103413","DOIUrl":"10.1016/j.jprocont.2025.103413","url":null,"abstract":"<div><div>Industrial practitioners who develop linear model predictive control (MPC) applications want to prevent undesirable controller behaviour caused by ill-conditioned gain matrices and model mismatch. In this work, we propose improvements to an existing orthogonalization-based method for gain conditioning. In this offline algorithm, manipulated variables (MVs) are ranked based on their influences on the controlled variables (CVs), so that problematic MVs with correlated effects can be identified. A constrained linear least-squares optimization problem is then solved to adjust columns in the gain matrix that correspond to problematic MVs. Our goal is to update this optimization problem to prevent the optimizer from switching the signs of some gains. The updated algorithm also permits control practitioners to hold key gains constant if their estimated values are trusted. Finally, we extend the methodology to condition gain submatrices, which arise when CVs are removed from the MPC problem. An industrial fluidized catalytic cracking case study is used to test the proposed method. The conditioned gains lead to improved controller performance and less aggressive movement of MVs when there is a plant-model mismatch.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"149 ","pages":"Article 103413"},"PeriodicalIF":3.3,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Terminal set-based cyberattack detection in model predictive control systems with zero false alarms 基于终端集的零虚警模型预测控制系统网络攻击检测

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2025-03-25 DOI: 10.1016/j.jprocont.2025.103409

Rahul Panicker, Aatam Gajjar, Nael H. El-Farra, Matthew J. Ellis

{"title":"Terminal set-based cyberattack detection in model predictive control systems with zero false alarms","authors":"Rahul Panicker, Aatam Gajjar, Nael H. El-Farra, Matthew J. Ellis","doi":"10.1016/j.jprocont.2025.103409","DOIUrl":"10.1016/j.jprocont.2025.103409","url":null,"abstract":"<div><div>The increased reliance of industrial control systems on networked components has made them more vulnerable to cyberattacks, necessitating cyberattack detection schemes specifically designed for detecting cyberattacks affecting industrial control systems. This work presents a set-membership-based detection scheme for systems under model predictive control (MPC). Specifically, we consider steady-state operation because many systems operate over long periods near a desired steady state. Provided the disturbances and measurement noise acting on the system are sufficiently small, we show that the closed-loop system under MPC is equivalent to the closed-loop system under a linear quadratic regulator, formulated with the same stage cost and weighting matrices, in a region containing the desired operating point. This equivalence is leveraged to show that the minimum robust positively invariant (mRPI) sets under both controllers are equivalent, enabling the calculation of the mRPI set for the closed-loop system under MPC. Using the mRPI set of the attack-free system, we present an attack detection scheme for systems under MPC and derive conditions under which the attack detection scheme applied to the attack-free closed-loop system does not raise an alarm. The detection scheme is applied to a simplified (linear) building space-cooling system to demonstrate that it does not raise false alarms during attack-free operation and that it successfully detects attacks when the system is subjected to a multiplicative false-data injection attack altering the data communicated over the sensor-controller link. Furthermore, the detection scheme’s applicability to nonlinear systems is assessed. Specifically, the detection scheme is applied to a nonlinear chemical process to demonstrate that the detection scheme does not raise false alarms during attack-free operation and successfully detects an attack when the process is subjected to a false-data injection cyberattack.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"149 ","pages":"Article 103409"},"PeriodicalIF":3.3,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143679492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cross-domain knowledge transfer in industrial process monitoring: A survey 工业过程监控中的跨领域知识转移研究

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2025-03-23 DOI: 10.1016/j.jprocont.2025.103408

Zheng Chai , Chunhui Zhao , Biao Huang

{"title":"Cross-domain knowledge transfer in industrial process monitoring: A survey","authors":"Zheng Chai , Chunhui Zhao , Biao Huang","doi":"10.1016/j.jprocont.2025.103408","DOIUrl":"10.1016/j.jprocont.2025.103408","url":null,"abstract":"<div><div>The last decades have witnessed rapid progress in machine learning and data analytics-based industrial process monitoring. However, the underlying assumption that the training and test data should have the same feature space and the same distribution is generally challenged in practical industrial applications due to varying working conditions, mechanical wear, feed changes, etc. To this end, knowledge transfer, which reduces the discrepancy between different data and facilitates the target model learning, has given rise to tremendous advances for mitigating this trap. Motivated by the success, in this survey, the state-of-the-art techniques are investigated and a review from a broad perspective in the field of cross-domain industrial process monitoring applications is provided, including fault detection and diagnosis, fault prognosis, and soft sensors. Owing to the extensive developments, the cross-domain knowledge transfer in process monitoring can be divided into three branches in this survey, i.e., the multivariate statistical analysis-based, the shallow neural networks-based, and the deep neural networks-based methods. Benefiting from the theoretical development and elaborately developed approaches, current challenges and instructive perspectives are further conceived for inspiring new directions in this exciting research field. The aim of this paper is to sketch the basic principles and frameworks for cross-domain knowledge transfer in process monitoring and provide inspiration for future studies in the process industry.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"149 ","pages":"Article 103408"},"PeriodicalIF":3.3,"publicationDate":"2025-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143679493","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}

引用次数: 0

Interpretable Dynamic Modelling and Prediction of Free Acid in Zinc Leaching Process 锌浸出过程中游离酸的可解释动态建模与预测

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2025-03-21 DOI: 10.1016/j.jprocont.2025.103407

Jainish Nareshkumar Rajput , Vamsi Krishna Puli , Graham Slot , Biao Huang

{"title":"Interpretable Dynamic Modelling and Prediction of Free Acid in Zinc Leaching Process","authors":"Jainish Nareshkumar Rajput , Vamsi Krishna Puli , Graham Slot , Biao Huang","doi":"10.1016/j.jprocont.2025.103407","DOIUrl":"10.1016/j.jprocont.2025.103407","url":null,"abstract":"<div><div>In the metallurgical processing industry, the leaching process converts a concentrated slurry of zinc sulphide to zinc sulphate solution. The leaching process occurs within a multi-compartment autoclave in the presence of sulphuric acid and oxygen at high temperatures and pressure. The amount of unreacted acid (free acid) within each autoclave compartment is crucial for achieving high zinc recovery but is not directly measured, necessitating an efficient model. This work involves developing a dynamic model utilizing both the first principles and machine learning techniques to predict the free acid, making the model physically interpretable. Due to the dependency of free acid on upstream process variables, several sub-models were built for each preceding unit. The main challenge was the unavailability of several measurements required for the mass balance model, while some available measurements were sampled at a slower rate. Moreover, bias correction was performed, considering delays in receiving laboratory analysis results and the lack of exact timestamps for samples provided by the field operator. The proposed model is validated with integrated zinc and lead smelter process data. The model successfully predicts free acid at a fast rate despite several practical constraints. It performs well under various process conditions, detects abnormalities, and enhances stability in the leaching process.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"149 ","pages":"Article 103407"},"PeriodicalIF":3.3,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143679482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Subspace identification of Hammerstein models with interval uncertainties 区间不确定性Hammerstein模型的子空间辨识

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2025-03-18 DOI: 10.1016/j.jprocont.2025.103412

Marcus Vinicius de Paula , Rodrigo Augusto Ricco , Bruno Otávio Soares Teixeira

引用次数: 0