Journal of Process Control最新文献

{"title":"P3LS: Partial Least Squares under privacy preservation","authors":"Du Nguyen Duy,&nbsp;Ramin Nikzad-Langerodi","doi":"10.1016/j.jprocont.2024.103229","DOIUrl":"https://doi.org/10.1016/j.jprocont.2024.103229","url":null,"abstract":"<div><p>Modern manufacturing value chains require intelligent orchestration of processes across company borders in order to maximize profits while fostering social and environmental sustainability. However, the implementation of integrated, systems-level approaches for data-informed decision-making along value chains is currently hampered by privacy concerns associated with cross-organizational data exchange and integration. We here propose Privacy-Preserving Partial Least Squares (P3LS) regression, a novel federated learning technique that enables cross-organizational data integration and process modeling with privacy guarantees. P3LS involves a singular value decomposition (SVD) based PLS algorithm and employs removable, random masks generated by a trusted authority in order to protect the privacy of the data contributed by each data holder. We demonstrate the capability of P3LS to vertically integrate process data along a hypothetical value chain consisting of three parties and to improve the prediction performance on several process-related key performance indicators. Furthermore, we show the numerical equivalence of P3LS and PLS model components on both a synthetic and a real-world dataset and provide a thorough privacy analysis of the former. Moreover, we propose privacy-preserving explained X- and Y-block variance computations for determining the contribution of each data holder to the federated process model as a basis to incentivize data federation and fair profit-sharing.</p></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"138 ","pages":"Article 103229"},"PeriodicalIF":4.2,"publicationDate":"2024-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140818689","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":"ANFIS and Takagi–Sugeno interval observers for fault diagnosis in bioprocess system","authors":"Esvan-Jesús Pérez-Pérez ,&nbsp;José-Armando Fragoso-Mandujano ,&nbsp;Julio-Alberto Guzmán-Rabasa ,&nbsp;Yair González-Baldizón ,&nbsp;Sheyla-Karina Flores-Guirao","doi":"10.1016/j.jprocont.2024.103225","DOIUrl":"https://doi.org/10.1016/j.jprocont.2024.103225","url":null,"abstract":"<div><p>This paper develops a data-driven approach for incipient fault diagnosis based on ANFIS and Takagi–Sugeno (TS) interval observers. First, the nonlinear bioreactor system is identified using an adaptive neuro-fuzzy inference system (ANFIS), which results in a set of polytopic TS models derived from measurement data. Second, a bank of TS interval observers is deployed to detect sensor and process faults using adaptive thresholds. Unlike other works that require training fault data, only fault-free data is considered for ANFIS learning in this work. Fault insolation is based on residual generation and evaluated on a fault signal matrix (FSM). Parametric uncertainty and measurement noise are considered to guarantee the method’s robustness. The effectiveness of the proposed method is tested on a well-known bioreactor Continuous stirred tank reactor system (CSTR) reference simulator.</p></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"138 ","pages":"Article 103225"},"PeriodicalIF":4.2,"publicationDate":"2024-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140807213","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":"The chemostat reactor: A stability analysis and model predictive control","authors":"Guilherme Ozorio Cassol ,&nbsp;Charles Robert Koch ,&nbsp;Stevan Dubljevic","doi":"10.1016/j.jprocont.2024.103223","DOIUrl":"https://doi.org/10.1016/j.jprocont.2024.103223","url":null,"abstract":"<div><p>This contribution develops the model predictive control for an unstable chemostat reactor. Initially, we analyze the system’s model — a nonlinear first-order hyperbolic partial integro-differential equation (PIDE) — and carry the model linearization around an unstable operating condition. Employing the structure-preserving Cayley–Tustin transformation, we obtain a discrete-time model representation of the continuous model. Subsequently, we solve the operator Ricatti equations in the discrete-time setting to derive a full state feedback controller that stabilizes the closed-loop and design a Luenberger observer for state reconstruction given the system output measures. Finally, we formulate a dual-mode MPC ensuring constraint satisfaction and optimality, integrating the gain-based unconstrained full-state feedback optimal control obtained from the Ricatti equation. This dual-mode strategy describes an optimization problem where the predictive controller acts only if constraints become active within the control horizon. Simulation studies validate the controller performance, where the MPC only takes action if the constraints are predicted to be active within the control horizon while also guaranteeing closed-loop stabilization under only output feedback. This type of controller can be easily implemented with other control strategies and significantly decreases the computational costs of solving the optimal control problems when compared to other MPC approaches.</p></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"138 ","pages":"Article 103223"},"PeriodicalIF":4.2,"publicationDate":"2024-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140647245","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":"Development and identification of a reduced-order dynamic model for wastewater treatment plants","authors":"Teo Protoulis ,&nbsp;Haralambos Sarimveis ,&nbsp;Alex Alexandridis","doi":"10.1016/j.jprocont.2024.103211","DOIUrl":"https://doi.org/10.1016/j.jprocont.2024.103211","url":null,"abstract":"<div><p>Wastewater treatment plants (WWTPs) employ a series of complex chemical and biological processes, to transform an influent stream of contaminated water to an effluent suitable for return to the water cycle. To optimize the performance of WWTP control schemes, appropriate mathematical models capable of accurately simulating the plant dynamic behavior are essential. However, the development of reliable dynamic representations for these large-scale plants is challenging, mainly because of the complex biological reactions taking place and the significant fluctuations in the disturbances that affect the operation of WWTPs. First-principles models, such as the well-known benchmark simulation model no. 1 (BSM1), may be capable of capturing the highly nonlinear nature of WWTPs, but this comes at the cost of employing complex, high-order representations of the reactive units and settling processes. This complexity leads to highly complicated configurations that cannot be efficiently integrated in advanced process control schemes, like model predictive controllers (MPCs). Furthermore, the large number of unknown parameters in these models, along with the non-convex nature of the underlying functions, renders the use of conventional system identification techniques insufficient. To remedy these issues, in this work we introduce a reduced-order first-principles model for WWTPs, incorporating low order mathematical models for the chemical phenomena of the reactive units and the settling procedure. Furthermore, we present a novel system identification scheme, which is based on a customized cooperative particle swarm optimization approach; the scheme effectively handles the high-dimensionality and multimodality of the underlying nonlinear optimization problem, enabling accurate estimation of the model parameters. Comparison results between the dynamic behavior of the original BSM1 and the identified reduced-order model, indicate that the proposed approach is capable of accurately and robustly capturing the highly nonlinear nature of WWTPs, while being simple enough for incorporation in the design of MPC and other advanced control schemes. This represents a significant advancement over traditional models, offering a more practical and efficient approach for WWTP management and control.</p></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"138 ","pages":"Article 103211"},"PeriodicalIF":4.2,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140643728","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":"Virtual unmodeled dynamic and data-driven nonlinear robust predictive control","authors":"Bo Peng ,&nbsp;Huiyuan Shi ,&nbsp;Ping Li ,&nbsp;Chengli Su","doi":"10.1016/j.jprocont.2024.103222","DOIUrl":"https://doi.org/10.1016/j.jprocont.2024.103222","url":null,"abstract":"<div><p>This study presents a novel approach for controlling an industrial process that exhibits uncertainty and significant nonlinear features. The proposed method utilizes a virtual unmodeled dynamic and data-driven nonlinear robust predictive control strategy. The representation of a controlled object involves a composite state space model that combines both linear and high-order nonlinear elements. Moreover, a robust model predictive controller is developed using the linear component. In addition, the notion of one-step optimal feedforward is used in combination with a compensating controller to handle the high-order nonlinear factor specifically. Subsequently, a compensation controller with incremental characteristics is developed for a modified version of the high-order nonlinear term. Furthermore, the stability conditions of the closed-loop system are derived, and an analysis is conducted on the stability and convergence of the proposed approach. The TTS20 three-capacity water tank was utilized in both simulations and practical scenarios. The study demonstrated that the suggested approach successfully reduces system output variations and enhances overall performance in response to unpredictable changes in the process’s dynamic features.</p></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"138 ","pages":"Article 103222"},"PeriodicalIF":4.2,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140633223","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":"PKG-DTSFLN: Process Knowledge-guided Deep Temporal–spatial Feature Learning Network for anode effects identification","authors":"Weichao Yue ,&nbsp;Jianing Chai ,&nbsp;Xiaoxue Wan ,&nbsp;Yongfang Xie ,&nbsp;Xiaofang Chen ,&nbsp;Weihua Gui","doi":"10.1016/j.jprocont.2024.103221","DOIUrl":"https://doi.org/10.1016/j.jprocont.2024.103221","url":null,"abstract":"<div><p>In the aluminum electrolysis process, the accurate identification of anode effect (AE) can improve production efficiency. However, the existing methods fail to effectively capture the features of the anode current signal (ACS) due to its complex dynamic characteristics and temporal–spatial dependence. To address this issue, we propose a Process Knowledge-guided Deep Temporal–spatial Feature Learning Network (PKG-DTSFLN). We believe that knowledge and production data are complementary. Knowledge has potential to deduce beyond observational conditions. Data can be used to detect unexpected patterns. The combination of data and knowledge is potential to improve the performance. Specifically, knowledge is utilized to construct the adjacency matrix to represent the spatial structure of ACS. Then, a deep learning model is constructed by integrating the 1D-CNN and GAT, which is used to capture the temporal–spatial features of ACS. The experimental results on ACS dataset show that the accuracy is more than 99% with low computational cost.</p></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"138 ","pages":"Article 103221"},"PeriodicalIF":4.2,"publicationDate":"2024-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140619468","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":"Velocity control design of hyperbolic distributed parameter systems using zeroing dynamics and zeroing-gradient dynamics methods","authors":"Ahmed Maidi ,&nbsp;Radoslav Paulen ,&nbsp;Jean-Pierre Corriou","doi":"10.1016/j.jprocont.2024.103210","DOIUrl":"https://doi.org/10.1016/j.jprocont.2024.103210","url":null,"abstract":"<div><p>Velocity control proves to be an effective and a more easily implementable actuation than boundary and distributed actuations for hyperbolic distributed parameter systems. However, the design of velocity control for these systems, following the late lumping approach, i.e., using the partial differential equations model, poses a challenging problem in control engineering. Noticeably, the velocity controller faces a control singularity issue, resulting in a loss of controllability that renders the controller impractical. In this paper, we demonstrate that the zeroing dynamics method is a viable alternative design approach for velocity control of hyperbolic distributed parameter systems following the late lumping approach. Thus, employing the partial differential equations model, a velocity state feedback forcing output tracking is developed based on the zeroing dynamic method. Furthermore, to address the control singularity problem, the zeroing gradient method is combined with the zeroing method to design a state feedback that achieves output tracking even when a singularity occurs. The tracking error convergence is demonstrated for both developed state feedbacks. The effectiveness of these design approaches is clearly demonstrated in the case of a steam-jacketed heat exchanger and a non-isothermal plug flow reactor.</p></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"138 ","pages":"Article 103210"},"PeriodicalIF":4.2,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140548123","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":"Monitoring method and application of transition process with nonstationary conditions based on stability factor partitioning and RSFA","authors":"Zhipeng Zhang,&nbsp;Libin Wei,&nbsp;Xiaochen Hao,&nbsp;Yunzhi Wang,&nbsp;Yuming Li,&nbsp;Jiahao Hu","doi":"10.1016/j.jprocont.2024.103209","DOIUrl":"https://doi.org/10.1016/j.jprocont.2024.103209","url":null,"abstract":"<div><p>It is common for the working conditions to change with time in actual industrial processes. However, the transition modes of complex industrial processes under different working conditions often have various degrees of dynamic nonstationarity, which makes the traditional process monitoring model based on the stationarity assumption ineffective. In this paper, a Recursive Slow Feature Analysis method based on Stability Factor Partitioning (SFP-RSFA) is proposed for fine process monitoring of transition modes under dynamic nonstationarity characteristics. First, we calculate the stability factor according to the different stationarity characteristics of the production process variables. Then, K-means clustering is carried out according to the stability factor of each variable, and the stability factor of the cluster center is mapped to the interval [0,1] as the smoothing coefficient of the exponential weighted moving average (EWMA), which is applied to each data subblock respectively to highlight the steady-state and dynamic characteristics of the monitoring data subblock. In the online monitoring stage, the monitored data are fed into the subblock recursive slow feature analysis (RSFA) monitoring model. Finally, a comprehensive statistic method is proposed to integrate the subblock monitoring statistics. The Tennessee Eastman (TE) process and actual cement clinker production process were tested and compared with existing RPCA, RCA and RSFA methods. The effectiveness and superiority of the proposed method in the problem of nonstationary transition mode process monitoring are verified.</p></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"138 ","pages":"Article 103209"},"PeriodicalIF":4.2,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140545662","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":"Primal–dual feedback-optimizing control with override for real-time optimization","authors":"Risvan Dirza,&nbsp;Sigurd Skogestad","doi":"10.1016/j.jprocont.2024.103208","DOIUrl":"https://doi.org/10.1016/j.jprocont.2024.103208","url":null,"abstract":"<div><p>Primal–dual feedback-optimizing control is a simple yet powerful approach to optimally handle active constraint changes at steady state. It is composed of two layers: Constraint control in the upper master layer and unconstrained optimization or gradient control in the layer below. The master constraint controllers operate on a slow time scale by updating the dual variables (Lagrange multipliers). This can result in too slow control of the constraints, for example, for hard constraints that cannot be violated dynamically. To address this issue, we propose introducing a third fast override constraint control layer. Additionally, to optimally coordinate the constraint handling between the master and override layers, we need to introduce <em>auxiliary</em> constraints for the master controllers. A gas-lift oil production optimization case study demonstrates the power of the proposed scheme.</p></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"138 ","pages":"Article 103208"},"PeriodicalIF":4.2,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140535312","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":"Computationally efficient solution of mixed integer model predictive control problems via machine learning aided Benders Decomposition","authors":"Ilias Mitrai,&nbsp;Prodromos Daoutidis","doi":"10.1016/j.jprocont.2024.103207","DOIUrl":"https://doi.org/10.1016/j.jprocont.2024.103207","url":null,"abstract":"<div><p>Mixed integer Model Predictive Control (MPC) problems arise in the operation of systems where discrete and continuous decisions must be taken simultaneously to compensate for disturbances. The efficient solution of mixed integer MPC problems requires the computationally efficient online solution of mixed integer optimization problems, which are generally difficult to solve. In this paper, we propose a machine learning-based branch and check Generalized Benders Decomposition algorithm for the efficient solution of such problems. We use machine learning to approximate the effect of the complicating variables on the subproblem by approximating the Benders cuts without solving the subproblem, therefore, alleviating the need to solve the subproblem multiple times. The proposed approach is applied to a mixed integer economic MPC case study on the operation of chemical processes. We show that the proposed algorithm always finds feasible solutions to the optimization problem, given that the mixed integer MPC problem is feasible, and leads to a significant reduction in solution time (up to 97% or <span><math><mrow><mn>50</mn><mo>×</mo></mrow></math></span>) while incurring small error (in the order of 1%) compared to the application of standard and accelerated Generalized Benders Decomposition.</p></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"137 ","pages":"Article 103207"},"PeriodicalIF":4.2,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140343725","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}