Journal of Process Control最新文献

{"title":"Closed-loop control framework for optimal startup of cryogenic air separation units","authors":"Anthony W.K. Quarshie ,&nbsp;Jose Matias ,&nbsp;Christopher L.E. Swartz ,&nbsp;Yanan Cao ,&nbsp;Yajun Wang ,&nbsp;Jesus Flores-Cerrillo","doi":"10.1016/j.jprocont.2025.103525","DOIUrl":"10.1016/j.jprocont.2025.103525","url":null,"abstract":"<div><div>Current volatile electricity market conditions incentivize the adaptation of the operation, including the startup, of cryogenic air separation units (ASUs) which are large consumers of electricity. Improvement in ASU startups using earlier proposed open-loop control strategies may not be fully realized in the presence of uncertainties and disturbances. This paper assesses the potential benefit of using a proposed closed-loop control framework to address uncertainty and disturbances. A rolling-horizon economic nonlinear model predictive control (ENMPC) approach is considered, for which strategies are proposed to improve computation time. Online parameter estimation is performed using a computationally efficient method that is easy to implement. Through the case studies presented, it is shown that the proposed framework outperforms the use of offline pre-computed optimal inputs in response to the disturbance and uncertainty considered.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"154 ","pages":"Article 103525"},"PeriodicalIF":3.9,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144889318","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 new method for monitoring flotation performance using a performance-guided autoencoder with the self-attention mechanism","authors":"Hao Yan ,&nbsp;Haoyu Shang ,&nbsp;Guangyu Zhu ,&nbsp;Fuli Wang","doi":"10.1016/j.jprocont.2025.103530","DOIUrl":"10.1016/j.jprocont.2025.103530","url":null,"abstract":"<div><div>Performance indicators are the core for reflecting the quality of flotation products. Real-time monitoring of these indicators is of great significance to enterprises. It remains challenging to capture the multimodal dynamic characteristics of time series data of the flotation process and effectively apply them to the monitoring task. To address this issue, this paper proposes a flotation performance monitoring method based on a performance-guided autoencoder that merges the self-attention mechanism. Firstly, the autoencoder takes the long short-term memory and the one-dimensional convolutional layer as its internal structure to parallel extract the long-term and local features of the time series data. Then, the self-attention mechanism is utilized to dynamically allocate weights to the fused features. The performance-guided autoencoder is based on unsupervised and supervised learning. The prediction error is incorporated into the loss function of the autoencoder to enhance the extracted features, making them more relevant to the monitoring task. Finally, the features extracted by the autoencoder are sent to the predictor module for real-time monitoring of performance indicators. The MAE, RMSE, and R² of the proposed method on the zinc flotation test data are 0.2475, 0.3433, and 0.7643, respectively, outperforming other existing advanced techniques. The experimental results verify the effectiveness and superiority of this method.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"154 ","pages":"Article 103530"},"PeriodicalIF":3.9,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144879537","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":"Non-linear control of a fuel gas blending benchmark problem with added consumer dynamics","authors":"M.D. Sibiya,&nbsp;A.J. Wiid,&nbsp;J.D. le Roux,&nbsp;I.K. Craig","doi":"10.1016/j.jprocont.2025.103527","DOIUrl":"10.1016/j.jprocont.2025.103527","url":null,"abstract":"<div><div>This paper contributes to existing literature on fuel gas control by providing a feasible control solution with improved economic performance for an existing fuel gas control benchmark problem. Improved economic performance is achieved by implementing a non-linear model predictive controller (NMPC) that uses state estimates provided by a moving horizon estimator (MHE) and extended Kalman filter (EKF) for the fuel gas composition and flame speed index (FSI) to provide continuous inputs for the controller. Furthermore, the original fuel gas benchmark model is expanded to include consumer dynamics affecting fuel gas demand due to changes in the fuel gas heating value, making the model more representative of real industrial plants. The behaviour of an NMPC that neglects consumer dynamics (NMPC<span><math><msub><mrow></mrow><mrow><mn>1</mn></mrow></msub></math></span>) was compared against an NMPC that includes consumer dynamics (NMPC<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span>).</div><div>The aim of the benchmark problem is to reduce the time-weighted average cost of fuel gas for three 46-hour cases, accounting for purchase costs and penalties for fuel gas specification violations. An optimal cost for each case is determined assuming ideal conditions and perfect control. The benchmark controller is a conventional multi-loop feedforward/feedback system and has an average cost for the three cases which is 38.5% higher than the optimal cost. The NMPC<span><math><msub><mrow></mrow><mrow><mn>1</mn></mrow></msub></math></span> controller has an average cost which is 33.9% higher than the optimal cost and better than the benchmark controller.</div><div>A new benchmark scenario was developed which includes the consumer dynamics. For the new scenario, NMPC<span><math><msub><mrow></mrow><mrow><mn>1</mn></mrow></msub></math></span> could not find a feasible solution, resulting in oscillations and specification violations. The oscillations would result in site-wide instabilities for all equipment using fuel gas. NMPC<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> was able to keep the process stable during these scenarios and maintain all specifications. This shows the necessity to include consumer dynamics for effective fuel gas blending control.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"154 ","pages":"Article 103527"},"PeriodicalIF":3.9,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144864509","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":"Fault diagnosis of air handling units based on an MCNN-Transformer ensemble learning","authors":"Yin Xia,&nbsp;Danhong Zhang,&nbsp;Chenyu Liu,&nbsp;Zhiqiang Cao,&nbsp;Yixin Su,&nbsp;Yuhang Chen","doi":"10.1016/j.jprocont.2025.103526","DOIUrl":"10.1016/j.jprocont.2025.103526","url":null,"abstract":"<div><div>The Air Handling Units (AHU) in Heating Ventilation and Air Conditioning (HVAC) systems regulates air temperature and humidity to ensure indoor air quality and thermal comfort. Fault diagnosis of AHU is critical for reducing energy consumption and maintaining system performance. However, data noise and missing values introduce considerable uncertainty into AHU fault diagnosis, while most existing methods do not utilize time-series models and thus neglect the extraction of temporal features and the modeling of long-range dependencies. This limitation hinders the effective capture of fault evolution and long-term correlations, making it difficult to meet dynamic real-time requirements under complex operating conditions. To address these challenges, this paper proposes an ensemble learning framework that integrates Dempster–Shafer (DS) theory with a Multi-Channel Convolutional Neural Network and Transformer (MCNN-Transformer) model, aiming to enhance generalization and improve diagnostic performance. The DS theory combines the strengths of Random Forest, Pearson Correlation, and Mutual Information, effectively mitigating uncertainty and noise in fault feature data by fusing multi-source information. The MCNN-Transformer integrates multi-scale convolutional layers with a self-attention mechanism, enabling effective extraction of features across multiple temporal scales and modeling of long-range dependencies. Experimental results show that the proposed MCNN-Transformer framework achieves high efficiency and strong generalization capability, reaching a fault diagnosis accuracy of 99.2%, a precision of 0.992, a recall of 0.992, and an F1 score of 0.991, significantly outperforming traditional models. Moreover, the improved stability of the model’s accuracy curve further demonstrates its robustness.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"154 ","pages":"Article 103526"},"PeriodicalIF":3.9,"publicationDate":"2025-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144852365","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":"Performance grade similarity-based generalized zero-shot operating performance assessment of industrial processes with insufficient samples","authors":"Siqi Wang ,&nbsp;Yan Liu ,&nbsp;Lulu Fu ,&nbsp;Fei Chu ,&nbsp;Fuli Wang ,&nbsp;Chenhui Bao","doi":"10.1016/j.jprocont.2025.103523","DOIUrl":"10.1016/j.jprocont.2025.103523","url":null,"abstract":"<div><div>The process operating performance assessment (POPA) is vital for enhancing economic production in industrial processes. This study addresses the challenge in POPA of assessment unseen performance grades with zero samples, while also dealing with insufficient data for seen performance grades. We propose PGSGZSIS, a performance grade similarity-based generalized zero-shot method that integrates accessible superficial expert knowledge with a multi-expert voting mechanism to construct a performance grade similarity matrix (PGSM). The PGSM is validated by seen-data-driven expert reliability calculation, reducing dependency on deep expert knowledge while enhancing objectivity through data quantification. Additionally, an auxiliary set augmentation strategy based on feature similarity is introduced, constructing an auxiliary dataset by screening samples from similar operational conditions to address scarce seen samples. By constructing the PGSM and augmenting seen samples with auxiliary data, our approach not only alleviates the issue of insufficient seen samples but also tackles the generalized zero-shot learning (GZSL) problem for POPA. Experimental results validate the effectiveness of the proposed method in a hydrometallurgical process.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"154 ","pages":"Article 103523"},"PeriodicalIF":3.9,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144841765","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 dynamic programming-inspired approach for Mixed Integer Optimal Control Problems with dwell time constraints","authors":"Ramin Abbasi-Esfeden ,&nbsp;Christoph Plate ,&nbsp;Sebastian Sager ,&nbsp;Jan Swevers","doi":"10.1016/j.jprocont.2025.103522","DOIUrl":"10.1016/j.jprocont.2025.103522","url":null,"abstract":"<div><div>This paper introduces a dynamic programming-inspired approach for solving the Combinatorial Integral Approximation (CIA) problem within the CIA decomposition approach for Mixed-Integer Optimal Control Problems (MIOCPs). Additionally, we incorporate general dwell time constraints into this framework. The proposed method is tested on four MIOCPs with a minimum dwell time constraint, and its performance is compared to that of the state-of-the-art general purpose solver GuRoBi (MILP) and to the tailored branch-and-bound (BnB) solver from the pycombina package. The results show that the proposed approach is more computationally efficient, and its flexible cost-to-go function formulation makes it suitable for handling cases where simple approximations of the relaxed solution are insufficient.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"154 ","pages":"Article 103522"},"PeriodicalIF":3.9,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144810228","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":"Addressing external distorted heterogeneity: Input–output disentangled causal representation for mixed time series","authors":"Liujiayi Zhao ,&nbsp;Baoxue Li ,&nbsp;Chunhui Zhao","doi":"10.1016/j.jprocont.2025.103521","DOIUrl":"10.1016/j.jprocont.2025.103521","url":null,"abstract":"<div><div>In causal analysis, it is common for industrial systems to have a mixture of continuous and discrete variables, called distribution heterogeneity. In fact, discrete variables typically serve as external inputs to modulate continuous variables in these systems. Existing methods for causal discovery encounter the External Distorted Heterogeneity challenge. The challenge is defined as the difficulty of correcting the statistical relationships distorted by discrete inputs, interfering with the identification of causality within systems. To overcome the challenge, we propose a method called Input–Output Disentangled Causal Representation. The key idea is to reveal the continuous external control effects from discrete inputs and exclude the control effects from observed outputs to decouple the inference of causality. Technically, a reversible external control converter is designed to recover the continuous control effects from discrete input signals through affine processes, bridging the heterogeneity. In addition, we construct an additive causal model to distinguish between causal effects from inputs and outputs, capturing disentangled representations in a unified space through feature distribution alignment and discrimination. Dual predictions are designed to exclude the regulatory influences from observed outputs using gradient truncation, thereby decoupling the inference of causality. The proposed method demonstrates robust causal identification accuracy across diverse datasets and scenarios, outperforming existing approaches in high-dimensional input–output systems. These results highlight its potential for industrial applications in the causal discovery of input–output systems.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"154 ","pages":"Article 103521"},"PeriodicalIF":3.9,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144809632","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":"Harnessing monotonicity to design an adaptive PI passivity-based controller for a fuel-cell system","authors":"Carlo A. Beltrán ,&nbsp;Rafael Cisneros ,&nbsp;Diego Langarica-Cordoba ,&nbsp;Romeo Ortega ,&nbsp;Luis H. Díaz-Saldierna","doi":"10.1016/j.jprocont.2025.103511","DOIUrl":"10.1016/j.jprocont.2025.103511","url":null,"abstract":"<div><div>In this paper, a controller is designed to regulate the output voltage of a fuel-cell (FC) system comprising a proton-exchange membrane FC feeding a purely resistive load through a boost converter. The controller aims to maintain voltage regulation regardless of uncertainties in the resistive load. Leveraging the monotonicity of the FC polarization curve, it is demonstrated that the non-linear system can be controlled with a simple proportional–integral (PI) action through the PI-passivity-based control (PI-PBC) methodology. The result is subsequently extended to an adaptive version, enabling it to address parametric uncertainties, including inductor parasitic resistance, load variations, and fuel cell polarization curve parameters. The overall system is proved to be stable by regulating the output voltage under parametric uncertainty. Experimental results validate the proposed controller.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"154 ","pages":"Article 103511"},"PeriodicalIF":3.9,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144780234","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":"An integrated performance degradation detection and recovery scheme incorporating 2-DOF controllers for feedback control systems","authors":"Jingjing Gao ,&nbsp;Xu Yang ,&nbsp;Linlin Li ,&nbsp;Steven X. Ding ,&nbsp;Jian Huang ,&nbsp;Kaixiang Peng","doi":"10.1016/j.jprocont.2025.103510","DOIUrl":"10.1016/j.jprocont.2025.103510","url":null,"abstract":"<div><div>This paper investigates an integrated performance degradation detection and recovery (PDDR) scheme for feedback control systems. In this context, offline and online data-driven realization of stable image representation (SIR) in a feedback control loop are derived first. Building on this foundation, online data-driven computation of the gap metric is addressed, and associated with it, a real-time performance degradation detection method is proposed. It is followed by the data-driven configuration of a two-degrees-of-freedom (2-DOF) controller including an observer-based state feedback controller and an image subspace predictive feed-forward controller for system performance degradation recovery. The effectiveness of the proposed performance degradation detection and recovery scheme is validated through an experimental case study on a laboratory three-tank system.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"153 ","pages":"Article 103510"},"PeriodicalIF":3.9,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144771452","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":"Model selection and parameter optimization of model predictive control for building radiant systems","authors":"Qiong Chen ,&nbsp;Wenjing Wang ,&nbsp;Nan Li","doi":"10.1016/j.jprocont.2025.103512","DOIUrl":"10.1016/j.jprocont.2025.103512","url":null,"abstract":"<div><div>In this work, we present a comprehensive parametric investigation quantifying the influence of reduced-order models (ROMs) of varying fidelity on both dynamic and steady-state performance of a Model Predictive Control (MPC) loop for building thermal regulation. Through simulations using the full-order model and ROMs of orders 1–6, we systematically determined that ROMs of order 4 or higher achieve temperature overshoots within 0.2 °C, settling times under 15 min, and steady-state errors below 0.5 °C when paired with prediction horizons of 12–24 steps and control horizons ≥ 2, thus matching full-order MPC performance while reducing computation by up to 70 %. In contrast, the lowest-order ROM (ROM1) requires a prediction horizon ≤ 12 and a control horizon ≥ 3 to limit overshoot to 1.0 °C and static error to 1.2 °C. Furthermore, the original model and high-order ROMs maintain robust control (overshoot &lt; 0.5 °C, settling time &lt; 10 min) across manipulated-variable rate weights from 0.1 to 1.0 and manipulated-output weights from 0.5 to 2.0, whereas ROM1 exhibits strong sensitivity, operating acceptably only near MV-rate ≈ 0.2 and MO weight ≈ 1.0. These quantitative guidelines enable practitioners to balance computational cost and control accuracy by selecting an appropriately ordered ROM and tuning horizons and weightings within the identified numerical ranges.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"154 ","pages":"Article 103512"},"PeriodicalIF":3.9,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144770853","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}