Journal of Process Control最新文献

{"title":"Prescribed performance based direct data-driven model predictive control for continuous stirred tank reactor","authors":"Chengyu Zhou ,&nbsp;Li Jia ,&nbsp;Jianfang Li","doi":"10.1016/j.jprocont.2025.103609","DOIUrl":"10.1016/j.jprocont.2025.103609","url":null,"abstract":"<div><div>Continuous stirred tank reactor (CSTR) is the most important and widely used reaction equipment in the process industry. The use of an indirect data-driven model predictive control (MPC) plays an important role in controlling the key variable in the CSTR system. However, because of the complex nonlinear dynamics in the reaction process, the existing indirect data-driven MPC strategies are always unable to avoid the problem of unmodeled dynamics, resulting in the inability to ensure the control performance of the system. To this end, this paper designs a new direct data-driven model predictive control (DDMPC) method for the CSTR system under the prescribed performance control (PPC) framework. Using dynamic linearization technology, a converted-output-based equivalent data-driven prediction model in the input–output sense to the original CSTR system is first established to deal with the unknown system dynamics under performance constraints. Then, a prescribed performance function and the converted-output-based data-driven prediction model are directly incorporated into the criterion function to derive the constraint MPC control scheme, which achieves the prescribed performance requirements of the system. Furthermore, the stability of the tracking error and the bounded-input-bounded-output (BIBO) are rigorously proved based on the contractive mapping principle. As a result, the resulting DDMPC control scheme only requires the input and output data of the controlled CSTR system without any explicit model information. In the end, the effectiveness and superiority of the developed control method are demonstrated by a nonlinear CSTR system.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"157 ","pages":"Article 103609"},"PeriodicalIF":3.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145797809","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":"Dry biomass estimation in production of insects larvae using Interconnected Generalized Super-Twisting Observer","authors":"Rania Tafat ,&nbsp;Jaime A. Moreno ,&nbsp;Stefan Streif","doi":"10.1016/j.jprocont.2025.103615","DOIUrl":"10.1016/j.jprocont.2025.103615","url":null,"abstract":"<div><div>Alternative protein sources are becoming essential for achieving a sustainable food system. The Black Soldier Fly larvae (BSFL), a protein-rich insect capable of feeding on a wide range of organic materials, shows immense potential for use in bio-conversion. It is already being used in poultry and fish aquaculture and is currently under evaluation for human consumption. Consequently, the farming of this insect is of great interest, and advanced control methods could significantly optimize the process and improve resource efficiency. One of the main challenges in applying these advanced techniques is the lack of information about certain critical system states, particularly the estimation of dry biomass weight. Measuring the dry biomass weight of the larvae is a destructive process that can only be performed at the beginning and end of the cycle. This low sampling frequency is insufficient for the application of advanced control strategies. Thus, a non-invasive estimation method is required. This work addresses the observer design problem for estimating the dry biomass weight of BSFL. The objective is to obtain an online estimation of this weight before the larvae reach maturity. To achieve this, a reduced version of the existing BSFL full-fledged model is proposed, based on specific assumptions. A subsystem is extracted from this BSFL reduced model, for which, a necessary and sufficient condition is provided for its global strong observability. Moreover, an interconnection of Generalized Super-Twisting Observers is designed, and a comparison is made between this method and the high-gain observer.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"157 ","pages":"Article 103615"},"PeriodicalIF":3.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884228","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":"Real-time physical activity detection module during sensor augmented insulin pump therapy","authors":"Eleonora Manzoni ,&nbsp;Emilia Fushimi ,&nbsp;Eleonora M. Aiello ,&nbsp;Zoey Li ,&nbsp;Robin Gal ,&nbsp;Corby K. Martin ,&nbsp;Susana R. Patton ,&nbsp;Simone Del Favero ,&nbsp;Francis J. Doyle III","doi":"10.1016/j.jprocont.2025.103608","DOIUrl":"10.1016/j.jprocont.2025.103608","url":null,"abstract":"<div><div>Individuals living with type 1 diabetes (T1D) face important challenges when engaging in physical activity (PA), as it necessitates careful management of blood glucose levels often through insulin adjustments and carbohydrate intake. Integrating PA detection into sensor augmented insulin pumps (SAP) is a promising strategy to enhance glycemic control by suggesting basal insulin reduction or carbohydrate ingestion in the critical 24 h after the PA detection.</div><div>We have developed a real-time, model-based module for PA detection based solely on measured glucose levels, insulin infusion rates, and carbohydrate intake. The approach is based on the monitoring of the magnitude as well as various statistical properties of the prediction residuals, i.e., the discrepancies between actual sensor-measured glucose levels and model-predicted levels.</div><div>We tested our algorithm on the Type 1 Diabetes and Exercise Initiative (T1DEXI) dataset, which includes structured sessions of aerobic, resistance, and interval exercises. In a dataset containing all three activity types, the detection approach based on the median of the prediction residuals successfully detected an average of 59% of PA instances, while keeping the false alarms to 3.5 per considered timeframe, when considering models tailored to each participant. When using a population model identified on in-silico data from the UVa/Padova T1D simulator, the approach successfully detected 62% of PAs, while keeping the false alarms to 3.6 per considered timeframe.</div><div>These encouraging findings open the possibility of integrating PA detection into SAP systems without the need for additional physiological signals, thus enabling improved glucose management.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"157 ","pages":"Article 103608"},"PeriodicalIF":3.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884230","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 soft sensing with causal and injectivity-preserving Graph Neural Network","authors":"Warren Acheampong ,&nbsp;Om Prakash ,&nbsp;Biao Huang","doi":"10.1016/j.jprocont.2025.103614","DOIUrl":"10.1016/j.jprocont.2025.103614","url":null,"abstract":"<div><div>Graph Neural Networks (GNNs) excel in soft sensing by effectively modeling complex interdependencies among process variables. This study presents a graph-based framework for improved process quality prediction in nonlinear, dynamic industrial systems. We address two key challenges in chemical process soft sensing: (i) unknown graphs where the structure is not available <em>a priori</em>, and (ii) injectivity issues from scalar features. To resolve non-injective aggregation, where distinct neighborhoods become indistinguishable, we expand the input domain to preserve structural uniqueness in both undirected and directed graphs. We also propose a method for learning directed graphs using Sparse Debiased Dynamic Mode Decomposition, which captures temporal dynamics and produces sparse, interpretable, and noise-resilient representations. An end-to-end framework jointly learns the graph structure and GNN parameters, allowing the graph to adapt during training based on the prediction task. The proposed methods are validated through simulations under varying noise levels and a benchmark case study involving a Sulfur Recovery Unit, demonstrating strong robustness and predictive performance.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"157 ","pages":"Article 103614"},"PeriodicalIF":3.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145840842","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":"Least squares and marginal log-likelihood model predictive control using normalizing flows","authors":"Eike Cramer","doi":"10.1016/j.jprocont.2025.103593","DOIUrl":"10.1016/j.jprocont.2025.103593","url":null,"abstract":"<div><div>Real-world (bio)chemical processes often exhibit stochastic dynamics with non-trivial correlations and state-dependent fluctuations. Model predictive control (MPC) often must consider these fluctuations to achieve reliable performance. However, most process models simply add stationary noise terms to a deterministic prediction. This work proposes using conditional normalizing flows as discrete-time models to learn stochastic dynamics. Normalizing flows learn the probability density function (PDF) of the states explicitly, given prior states and control inputs. In addition to standard least squares (LSQ) objectives, this work derives a marginal log-likelihood (MLL) objective based on the explicit PDF and Markov chain simulations. In a reactor study, the normalizing flow MPC setpoint errors in open and closed-loop cases are competitive with a full model-based stochastic MPC. Furthermore, the chance constraints lead to fewer constraint violations than the benchmark controller. The MLL objective yields slightly more stable optimization results than the LSQ, particularly for small scenario sets.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"156 ","pages":"Article 103593"},"PeriodicalIF":3.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145571423","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":"Bioprocess model-predictive control with physics-informed neural networks: Driving microbiome evolution toward high polyhydroxyalkanoates production capacity","authors":"Marta Catalão ,&nbsp;José Pinto ,&nbsp;Cristiana A.V. Torres ,&nbsp;Filomena Freitas ,&nbsp;Maria A.M. Reis ,&nbsp;Rafael S. Costa ,&nbsp;Rui Oliveira","doi":"10.1016/j.jprocont.2025.103594","DOIUrl":"10.1016/j.jprocont.2025.103594","url":null,"abstract":"<div><div>Many previous studies have investigated the economic production of polyhydroxyalkanoates (PHA) by natural microbiomes. A key underlying strategy is the feast and famine (F/F) feeding regimen for bacteria selection. For this purpose, a sequencing batch reactor (SBR) is commonly operated in a sequence of F/F cycles until an evolved microbiome is attained with high PHA storage capacity. The effectiveness of this process is critically dependent on control parameters such as the hydraulic retention time (HRT), organic loading rate (OLR) and carbon-to-nitrogen ratio (C/N) applied at each cycle. This study evaluates for the first time a physics-informed neural network (PINN) for model predictive control (MPC) of microbiome evolution in a SBR. A PINN model was trained on historical data collected in a SBR operated over 93 days and 31 cycles. Carbon (acetate), Nitrogen (ammonium), Volatile Suspended Solids (VSS) and intracellular PHA concentration data were used to train and validate the PINN. Subsequently, a second SBR experiment was conducted under automatic control of the PINN over a period of 36 days and 12 cycles. A transfer learning method was implemented leverage on in-process data to minimize process-model mismatch. The results showed a systematic cycle-to-cycle prediction error decrease. The intracellular PHA concentration systematic increased from 0.51 % (w/w) to 16.5 % (w/w) at the 12th cycle (32-fold increase). The final evolved microbiome, collected at the 12th cycle, was inoculated in a production reactor yielding a final intracellular PHA content of 52.86 % (w/w) and volumetric concentration of 8.93 g PHA/L. Overall, the PINN-MPC method has shown high potential to efficiently explore the reactor design space and to implement in autonomy efficient strategies for natural microbiome evolution.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"156 ","pages":"Article 103594"},"PeriodicalIF":3.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145571421","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":"Dynamic compensation of the threading speed drop in rolling processes: Bayesian optimization of the roughing and finishing mill","authors":"Johannes Reinhard ,&nbsp;Klaus Löhe ,&nbsp;Sebastian Kallabis ,&nbsp;Knut Graichen","doi":"10.1016/j.jprocont.2025.103579","DOIUrl":"10.1016/j.jprocont.2025.103579","url":null,"abstract":"<div><div>This paper introduces an advanced approach for dynamic speed drop compensation during threading in rolling processes. The approach combines a data-driven machine learning procedure with a recently presented flatness-based feedforward control to robustly compensate for the speed drop. The feedforward control design accelerates both the rolls and the drivetrain, ensuring that the acceleration torque matches the rolling torque during threading, while maintaining the roll at the desired target speed. Ideally, this prevents the speed drop and enhances the quality and stability of the rolling process. The dynamic speed drop compensation approach is extended in this paper to optimize all stands of a rolling mill, finishing mill as well as roughing mill. To achieve this, the flatness-based feedforward trajectories are adapted to account for uncertainties in the threading event. Moreover, a cost function dependent on optimization parameters is established to optimize the dynamic speed drop compensation. This optimization is carried out using Bayesian Optimization with a Gaussian Process as surrogate model. Both the feedforward control and the Bayesian Optimization run in real-time on an industrial Programmable Logic Controller (PLC). Extensive experimental validation on a hot strip finishing mill, including both the roughing and finishing mill, demonstrates the superior performance of this approach across various key performance indicators in comparison to standard compensation methods.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"156 ","pages":"Article 103579"},"PeriodicalIF":3.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145420506","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 predictive control of pressure-swing distillation via closed-loop system identification","authors":"Daye Yang ,&nbsp;Jingcheng Wang ,&nbsp;Naiyi Ban ,&nbsp;Yanjiu Zhong ,&nbsp;David Shan-Hill Wong ,&nbsp;Abolhassan Razminia ,&nbsp;Chengtian Cui","doi":"10.1016/j.jprocont.2025.103589","DOIUrl":"10.1016/j.jprocont.2025.103589","url":null,"abstract":"<div><div>Pressure-swing distillation (PSD) is a proven technique for separating azeotropic mixtures by exploiting pressure-dependent shifts in azeotropic composition. Despite its efficacy, PSD systems present significant control challenges due to inherent nonlinearities, complex multivariable interactions, and internal recycle loops. This study proposes a model predictive control (MPC) framework for PSD systems, founded on closed-loop system identification. A comprehensive plantwide nonlinear dynamic model of a PSD process for separating a maximum-boiling azeotrope of acetone and chloroform is developed using Aspen Dynamics and interfaced with MATLAB/Simulink for controller design and testing. To address the limitations of open-loop excitation in systems with recycles, pseudo-random binary sequence (PRBS) signals are applied under closed-loop operation to sufficiently excite the process. Subsequently, linear state-space models are identified using the prediction error method. Based on these models, two MPC configurations are developed: temperature control (TC) and composition–temperature cascade control (CC–TC). Simulation results demonstrate that the proposed MPC strategies quantitatively outperform proportional–integral (PI) controllers. Specifically, under the TC strategy, the total integral of absolute error (IAE) values of <span><math><msub><mrow><mi>X</mi></mrow><mrow><mi>D</mi><mn>1</mn><mo>,</mo><mi>A</mi><mi>C</mi><mi>E</mi></mrow></msub></math></span> and <span><math><msub><mrow><mi>X</mi></mrow><mrow><mi>D</mi><mn>2</mn><mo>,</mo><mi>C</mi><mi>H</mi><mi>L</mi></mrow></msub></math></span> are reduced by approximately 10% and 3%, respectively; while under the CC–TC strategy, the reductions reach about 26% and 55%. Moreover, across four disturbance scenarios, the steady convergence times of both composition purities are shortened by more than 5 h compared with PI controllers. These results highlight the advantages of the proposed MPC strategies in disturbance rejection and transient product quality regulation. These findings underscore the effectiveness of closed-loop system identification as a basis for advanced control of PSD processes.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"156 ","pages":"Article 103589"},"PeriodicalIF":3.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520304","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":"Adaptive ensemble reinforcement learning for industrial process control","authors":"Soroush Rastegarpour,&nbsp;Hamid Reza Feyzmahdavian,&nbsp;Alf J. Isaksson","doi":"10.1016/j.jprocont.2025.103575","DOIUrl":"10.1016/j.jprocont.2025.103575","url":null,"abstract":"<div><div>Reinforcement learning (RL) has shown significant promise in optimizing control strategies for industrial processes. However, maintaining robustness and stability remains a critical challenge, especially in continuous-time systems affected by uncertainties and nonlinearities. This paper introduces an Adaptive Ensemble RL framework that improves control performance and robustness by dynamically integrating multiple process models. Instead of relying on a fixed model ensemble, we employ a deep neural network to predict optimal weighting factors for combining models based on input-state conditions during training. This adaptive strategy refines the representation of system dynamics, improving robustness against disturbances and model mismatches. We validate the proposed framework using a continuous stirred tank reactor (CSTR) benchmark, showing improved robustness, faster convergence, and reduced performance degradation under varying uncertainties compared to existing methods.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"156 ","pages":"Article 103575"},"PeriodicalIF":3.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145420505","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":"Exponential admissibility and control for singular LPV-PDE systems with application to chemical tubular reactor","authors":"Xiaona Song ,&nbsp;Ao Shang ,&nbsp;Shuai Song ,&nbsp;Danjing Zheng ,&nbsp;Choon Ki Ahn","doi":"10.1016/j.jprocont.2025.103590","DOIUrl":"10.1016/j.jprocont.2025.103590","url":null,"abstract":"<div><div>In this work, the issue of exponential admissibility analysis and controller designs for the singular linear parameter-varying partial differential equation (LPV-PDE) system is investigated. First, the reaction process in the chemical tubular reactor is described by a singular LPV-PDE, and the definition for the singular LPV-PDE system to be exponentially admissible is established by the Galerkin method, energy estimates, etc. Second, the parameter-dependent fuzzy dynamic event-triggered mechanism is developed to alleviate communication pressure by reducing the amount of transmitted data. Furthermore, a sliding surface is constructed, then a suitable control law is designed to ensure the reachability of system states, and sufficient conditions for the singular LPV-PDE system to be exponentially admissible are given during the sliding phase. Finally, the effectiveness of the proposed method is verified through simulation studies.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"156 ","pages":"Article 103590"},"PeriodicalIF":3.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145571422","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}