Journal of Process Control最新文献

{"title":"A simple and fast robust nonlinear model predictive control heuristic using n-steps-ahead uncertainty predictions for back-off calculations","authors":"H.A. Krog,&nbsp;J. Jäschke","doi":"10.1016/j.jprocont.2024.103270","DOIUrl":"10.1016/j.jprocont.2024.103270","url":null,"abstract":"<div><p>A new robust nonlinear model predictive control (RNMPC) heuristic is proposed, specifically developed to be i) easy to implement, ii) robust against constraint violations and iii) fast to solve. Our proposed heuristic samples from the disturbance distributions and performs <span><math><mi>n</mi></math></span>-steps-ahead Monte Carlo (MC) simulations to calculate the back-off where <span><math><mi>n</mi></math></span> is a small number, typically one. We show two implementations of our heuristic. The Automatic Back-off Calculation NMPC (ABC-NMPC) uses MC simulations on a process model to calculate the back-off, and <em>explicitly</em> states the back-off in a standard NMPC problem. Our second implementation, the MC Single-Stage NMPC (MCSS-NMPC), directly includes the disturbance distribution in the optimization problem, making it an <em>implicit</em> back-off method. Our methods are robust against constraint violation in the next time-step, under certain assumptions. In the presented case-study, our proposed RNMPC methods outperform the popular multi-stage NMPC in terms of robustness and/or computational cost. We suggest several further modifications to our RNMPC methods to improve performance, at the cost of increased complexity.</p></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"141 ","pages":"Article 103270"},"PeriodicalIF":3.3,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141934414","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":"Autoregressive double latent variables probabilistic model for higher-order dynamic process monitoring","authors":"Ze Ying ,&nbsp;Yuqing Chang ,&nbsp;Yuchen He ,&nbsp;Fuli Wang","doi":"10.1016/j.jprocont.2024.103281","DOIUrl":"10.1016/j.jprocont.2024.103281","url":null,"abstract":"<div><p>The application of multivariate statistical analysis in process monitoring has emerged as a significant research topic, with a focus on consideration of data correlations. The present study investigates an anomaly detection method based on autoregressive double latent variables probabilistic (ADLVP) model for industrial dynamic processes. Specifically, the ADLVP model incorporates two distinct types of latent variables (LVs) to capture the internal relationships within the data from both quality-correlated and uncorrelated perspectives. Moreover, the model employs autoregressive modeling to characterize the double latent variables with time-dependence, enabling them to unveil more intricate higher-order autocorrelations between industrial measurements. The model parameters and the double latent variables can be iteratively determined using the expectation maximization (EM) algorithm, upon which the statistics for process monitoring are devised. Finally, the proposed method is validated in two industrial studies, and experimental results demonstrate that the ADLVP model outperforms its counterparts in dynamic processes monitoring.</p></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"141 ","pages":"Article 103281"},"PeriodicalIF":3.3,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141934415","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":"Category-tree-guided hierarchical knowledge transfer framework for zero-shot fault diagnosis","authors":"Baolin Zhang ,&nbsp;Jiancheng Zhao ,&nbsp;Xu Chen ,&nbsp;Jiaqi Yue ,&nbsp;Chunhui Zhao","doi":"10.1016/j.jprocont.2024.103267","DOIUrl":"10.1016/j.jprocont.2024.103267","url":null,"abstract":"<div><p>Zero-shot learning (ZSL) can diagnose unseen faults without corresponding training data, which has aroused the researchers’ interest. However, a prevailing challenge in most existing ZSL approaches is their limited effectiveness in distinguishing similar unseen faults. This paper proposed a category-tree-guided hierarchical knowledge transfer zero-shot fault diagnosis (CTZSD) method, which is a coarse-to-fine zero-shot fault diagnosis framework to alleviate this problem. To embody the similarities between fault categories, the concept of fault category tree is proposed, for which a data-attribute collaborative tree construction mechanism (DATC) is designed. Rather than relying solely on semantic knowledge, DATC involves data, which carries richer information, to complement the category similarity evaluation. A hierarchical knowledge transfer zero-shot fault diagnosis mechanism (HKT) is subsequently developed, utilizing the established category tree to gradually narrow down the options, thereby promoting the recognition of similar unseen faults. The mechanism treats the diagnostic outcomes and model parameters from coarse-grained tasks as knowledge and transfers them to fine-grained tasks for guidance, realizing a coarse-to-fine diagnosis. Aiming at providing discriminative information to further distinguish similar unseen faults, attention modules are integrated within HKT. These modules assess attribute weight, thereby directing the model’s focus toward the discriminative attributes of similar unseen faults. Experiments on a real TPP industrial process demonstrate that the proposed CTZSD outperforms other traditional ZSL methods in distinguishing similar unseen faults, improving the average accuracy by at least 19.7%.</p></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"141 ","pages":"Article 103267"},"PeriodicalIF":3.3,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141934417","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":"SA-MSIFF: Soft sensing the cement f-CaO content with a self-adaptive multisource information fusion framework in clinker burning process","authors":"Yuchen Zhao ,&nbsp;Chunjie Yang ,&nbsp;Yaoyao Bao ,&nbsp;Siwei Lou ,&nbsp;Genius B. Machingura ,&nbsp;Hang Xiao ,&nbsp;Zhe Liu ,&nbsp;Bo Huang ,&nbsp;Jiayun Mao ,&nbsp;Pengwei Tian","doi":"10.1016/j.jprocont.2024.103282","DOIUrl":"10.1016/j.jprocont.2024.103282","url":null,"abstract":"<div><p>The accurate soft sensing of f-CaO content in cement clinker is crucial for the cement industry. However, existing methods require improvements in terms of effectiveness, practicality, and computational efficiency for industrial applications. Responding to these needs, this paper proposes a self-adaptive multisource information fusion framework (SA-MSIFF) for f-CaO content soft sensing. The SA-MSIFF utilizes a dynamic rotary kiln model for independent and real-time calcination state estimation and mechanistic feature generation, along with a dilated 3D convolution and attention-based network for direct feature extraction from flame image sequences. Subsequently, a temporal–spatial feature extraction and fusion (TSFE&amp;F) network is introduced to utilize the multisource feature series for f-CaO content soft sensing. Offline experiments validate the SA-MSIFF’s ability to adaptively extract features from multisource information. Compared to its previous version, MSIFF, the SA-MSIFF achieves a considerable 89.65% reduction in framework training time and an 8.22% decrease in soft sensing error. The SA-MSIFF’s effectiveness is also demonstrated in its engineering applications.</p></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"141 ","pages":"Article 103282"},"PeriodicalIF":3.3,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141934416","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":"Concurrent analysis of static deviation and dynamic oscillation for momentum wheel bearing health monitoring and prognostication","authors":"Shumei Zhang ,&nbsp;Sirui Du ,&nbsp;Feng Dong","doi":"10.1016/j.jprocont.2024.103278","DOIUrl":"10.1016/j.jprocont.2024.103278","url":null,"abstract":"<div><p>Momentum wheel bearing is a critical component within satellite systems, and its condition monitoring not only extends the operational lifespan of the satellite but also ensures the seamless fulfillment of its mission objectives. Various data-driven techniques have been introduced to assimilate health-related information. However, these techniques neglect the significant challenges posed by robust disturbance and volatility of degradation process, resulting in suboptimal evaluation performance. To address these issues comprehensively, this paper proposes a novel approach named canonical variable fluctuation analysis (CVFA) to facilitate precise health monitoring of momentum wheel bearings by concurrent analysis of static deviation and dynamic oscillation. Firstly, three quantifiable standards of consistency, accuracy and sensitivity are defined to select the degradation trend-related indices from multi-domain features, which provides an automatic and objective feature selection method. Subsequently, CVFA is developed to realize feature reduction and extracts the dynamic information from the features with strong disturbance and high fluctuation. Two Fluctuation (<em>F</em>) statistics are defined to characterize the health degradation trend by integrating both static deviation and dynamic volatility within a sliding window. Afterwards, autoregressive moving average (ARMA) model is constructed on the basis of <em>F</em> statistics for short-term prognostication, which enables proactive detection of degradation trends. Lastly, by integrating two <em>F</em> statistics, a health degree (HD), which is independent of parameter adjustments, is defined to intuitively represent bearing health status. The efficacy and superiority of the proposed method are substantiated through validation and analysis conducted using accelerated life tests of bearings.</p></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"141 ","pages":"Article 103278"},"PeriodicalIF":3.3,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141731940","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":"Switching probabilistic slow feature extraction for semisupervised industrial inferential modeling","authors":"Chao Jiang ,&nbsp;Xin Peng ,&nbsp;Biao Huang ,&nbsp;Weimin Zhong","doi":"10.1016/j.jprocont.2024.103277","DOIUrl":"10.1016/j.jprocont.2024.103277","url":null,"abstract":"<div><p>Predicting quality-relevant process variables is of paramount importance in optimizing and controlling chemical processes. Probabilistic Slow Feature Analysis (PSFA), a potent data-driven technique, plays a pivotal role in deducing quality indices by abstracting gradual variations in processes distinctly characterized by pronounced inertia. Nevertheless, PSFA’s predictive efficacy encounters a substantial bottleneck due to the assumption of a single operating condition, compromising its accuracy, particularly in industries represented by switching operating conditions. To surmount this limitation, this study proposes an innovative approach that enriches PSFA with multi-operating condition process data and limited labels within a Bayesian framework, effectively combining continuous and discrete first-order Markov chains to capture the processes’ inertia and dynamic shifts. The proposed method updates latent posterior distributions and model parameters iteratively via the Expectation–Maximization algorithm. The effectiveness of the proposed methodology is verified through a numerical case and industrial hydrocracking process data.</p></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"141 ","pages":"Article 103277"},"PeriodicalIF":3.3,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141639274","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 temperature model for microalgae cultivation systems","authors":"A. Gharib,&nbsp;W. Djema,&nbsp;F. Casagli,&nbsp;O. Bernard","doi":"10.1016/j.jprocont.2024.103280","DOIUrl":"10.1016/j.jprocont.2024.103280","url":null,"abstract":"<div><p>Microalgae cultivation for energy production is a promising avenue for converting solar light into sustainable biofuel. Solar processes are however subjected to the permanent fluctuations of light and medium temperature. Accurate temperature prediction of the culture medium turns out to be critical for optimising growth conditions. In this study, we introduce a reduced-model approach derived from existing models, turning the complex heat transfer modelling problem into an identification problem. The resulting generic model, called the Simplified Auto Tuning Heat Exchange (SATHE) model, has a clear and simple structure, offering a balance between accuracy and computational complexity. The SATHE model is versatile and contains the necessary terms to catch a large variety of heat transfer problems, while the parameters can be identified from experimental data. We first prove the parameter identifiability and then propose an identification strategy, based on the gradient computation, to identify the model’s underlying parameters. We further validate the SATHE model performance in two distinct reactors across various seasons. Finally, we discuss the potential of online applications with a continuous self-tuning strategy to keep optimal predictive performances. This work lays the foundation for enhanced control strategies in large-scale cultivation systems.</p></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"141 ","pages":"Article 103280"},"PeriodicalIF":3.3,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141639271","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":"Estimation-based model predictive control with objective prioritization for mutually exclusive objectives: Application to a power plant","authors":"Daniel Beahr ,&nbsp;Vivek Saini ,&nbsp;Debangsu Bhattacharyya ,&nbsp;Steven Seachman ,&nbsp;Charles Boohaker","doi":"10.1016/j.jprocont.2024.103268","DOIUrl":"10.1016/j.jprocont.2024.103268","url":null,"abstract":"<div><p>This work presents an algorithm for estimation-based model predictive control with objective prioritization such that distinct objectives may be defined for mutually exclusive operational regions. The objective prioritization algorithm is built by using logical conditions that define regions of operation which are incorporated into the objective function, thus allowing smooth transitions between a bank of objectives. The control objective prioritization is cast in the framework of model predictive control that is coupled with an extended Kalman filter for estimation of critical yet unmeasured state variables. The algorithm is applied to the challenging control problem of an industrial superheater (SH)-reheater (RH) system of a natural gas combined cycle plant under load following operation where smooth transitions among various control objectives is desired – operation under nominal conditions, avoidance of spraying to saturation at the inlet of the SH and RH systems, and avoidance of main steam temperature excursions. The results from the estimator framework are compared with the industrial data from an operating power plant. The control algorithm is evaluated by simulating a servo control problem and disturbance rejection scenarios as expected under load-following operation of the power plant. This algorithm is generic and can be applied to accomplish local control policies for safety, economics, quality control, state constraints, and others.</p></div><div><h3>Topical Heading</h3><p>Process Systems Engineering</p></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"141 ","pages":"Article 103268"},"PeriodicalIF":3.3,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141639273","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 control of torch height in NG-GMAW process based on passive vision sensing technology","authors":"Lei Xia,&nbsp;Ruilei Xue,&nbsp;Jianping Zhou,&nbsp;Hongsheng Liu,&nbsp;Tongwei Ma,&nbsp;Yong Shen","doi":"10.1016/j.jprocont.2024.103279","DOIUrl":"10.1016/j.jprocont.2024.103279","url":null,"abstract":"<div><p>In narrow gap gas-shielded arc welding (NG-GMAW) for pipelines, maintaining a stable welding process and ensuring weld quality necessitates controlling the extension length of the welding wire (WWEL) within a specific range. However, when dealing with three-dimensional weld workpieces featuring height variations, welding defects are prone to occur due to changes in welding wire extension length. Therefore, real-time adjustment of the distance between the contact tip and workpiece (CTWD) is crucial during the welding process. To address this challenge, this paper proposes a welding torch height (WTH) control method based on passive vision sensing. The proposed method utilizes a wide dynamic range (WDR) camera to acquire distinct real-time welding images. An adaptive region of interest extraction method for the welding wire is then proposed based on the position relationship between the welding wire and arc. To address false edge issues in the welding wire profile, a cellular neural network (CNN) edge detection algorithm, optimized by particle swarm optimization, is employed to eliminate false edges. The extended length of the welding wire is subsequently extracted using an adaptive mask kernel morphology and corner detection method. Accordingly, a model predictive control (MPC) technique is developed to govern the height of the welding torch with the WWEL as input. The proposed MPC algorithm's tracking performance and robustness are validated through feedback control experiments. The results indicate that the tracking error of the WTH trajectory can be controlled within±0.41 mm, meeting the requirements of NG-GMAW welding torch height control for welding robots.</p></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"141 ","pages":"Article 103279"},"PeriodicalIF":3.3,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141639272","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":"Modified ESO based disturbance rejection for dynamical systems: An experimental study","authors":"Sonali Singh ,&nbsp;Jitendra Kumar Goyal ,&nbsp;Ankit Sachan ,&nbsp;Amutha Prabha N. ,&nbsp;Awaneendra Kumar Tiwari ,&nbsp;Shyam Kamal ,&nbsp;Sandip Ghosh ,&nbsp;Shubhi Purwar ,&nbsp;Xiaogang Xiong","doi":"10.1016/j.jprocont.2024.103263","DOIUrl":"https://doi.org/10.1016/j.jprocont.2024.103263","url":null,"abstract":"<div><p>This paper introduces a new approach to designing a disturbance observer called a modified extended state observer (ESO). The existing ESO technique ensures that the trajectories of estimation error dynamics globally asymptotically converge to zero in the absence of time varying disturbances. However, for perturbed systems, where time varying disturbances affect system behavior, these trajectories never reach zero but rather remain bounded around the origin within a constant value. Consequently, this discrepancy leads to challenges in accurately estimating state trajectories and discerning information about disturbances. This, in turn, complicates the precise estimation of state dynamics and disturbances and poses difficulties in designing control laws for stability analysis of the system. Unlike existing ESO methods, the distinguishing characteristic of this modified ESO is its capability to achieve global and asymptotic convergence of observation error in the presence of unknown bounded time varying disturbances. This unique property enables the exact estimation of state trajectories. Information about the bounded time varying disturbances is obtained and significantly attenuate more efficiently compared to existing ESO techniques. Based on the estimated disturbances, any classical controller can be designed for the system to achieve set-point tracking subject to time varying disturbances. To validate the performance of the proposed modified ESO, the model of a coupled-tank setup is simulated for the stabilization problem and its experimental setup is demonstrated for the tracking problem. For carrying out the experiment on a real-time hardware setup, an input of step change at every 60 s with water level variation of <span><math><mo>±</mo></math></span>2 cm from initial set value of 15 cm to achieve the set-point tracking of the water levels in the both the tanks along with good transient performance in the presence of time-varying external disturbances.</p></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"141 ","pages":"Article 103263"},"PeriodicalIF":3.3,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141593196","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}