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Control of cascaded series dead-time processes with ideal achievable disturbance attenuation using a predictors-based structure J. Process Control (IF 4.2) Pub Date : 2024-03-12 Bismark C. Torrico, Juliana S. Barros, Felipe J.S. Vasconcelos, Fabrício G. Nogueira, Julio E. Normey-Rico
This paper proposes a cascade series control structure and design for two series processes represented by first-order plus dead-time FOPDT models. The proposed controller uses two series predictors, one for each process, and can deal with stable, unstable, or integrative processes. The design follows similar principles of the simplified filtered Smith Predictor (SFSP) for a single-loop dead-time system
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Targeted excitation and re-identification methods for multivariate process and model predictive control J. Process Control (IF 4.2) Pub Date : 2024-03-10 Masanori Oshima, Sanghong Kim, Yuri A.W. Shardt, Ken-Ichiro Sotowa
A process controlled using model predictive control is required to be re-identified when significant plant-model mismatch (PMM) occurs. During data acquisition for re-identification, the process is excited to enable accurate re-identification. However, the process excitation worsens the control performance. To prevent this problem, a new model-update framework that consists of targeted excitation (TE)
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Robust asymptotic super twisting sliding mode observer for non-linear uncertain biochemical systems J. Process Control (IF 4.2) Pub Date : 2024-03-05 Mateusz Czyżniewski, Rafał Łangowski
The problem of state estimation (reconstruction of the state vector) for a given class of biochemical systems under uncertain system dynamics has been addressed in this paper. In detail, the bioreactor at a water resource recovery facility represents the considered biochemical systems. The biochemical processes taking place in the bioreactor have been modelled using an activated sludge model. Based
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Pulsatile Zone MPC with asymmetric stationary cost for artificial pancreas based on a non-standard IOB constraint J. Process Control (IF 4.2) Pub Date : 2024-03-04 Pablo Abuin, Antonio Ferramosca, Chiara Toffanin, Lalo Magni, Alejandro H. González
This paper presents a novel pulsatile Zone Model Predictive Control (pZMPC) for glycemic control in type 1 diabetic patients, which is an extension of the one presented in literature. Its main characteristics are (i) the explicit inclusion of a time-varying insulin on board constraint to promote a non-zero insulin delivery after a standard bolus infusion and to increase the postprandial system controllability
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Real-time optimization modifier adaptation approach using quadratic approximation of the plant-model mismatch function J. Process Control (IF 4.2) Pub Date : 2024-02-24 Agustín Bottari
The present work proposes a real-time optimization modifier adaptation scheme (RTO MA) in which the modifiers are obtained through a quadratic approximation of the plant-model mismatch function. The work also highlights the links between the model used in the trust-region framework, the model used in bias, gradient, and Hessian-based RTO MA schemes, and the model used in function-based RTO MA. In line
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Joint design of VSI Tr control chart and equipment maintenance in high quality process J. Process Control (IF 4.2) Pub Date : 2024-02-20 Ying Zhang, Mengyao Yan, Chuxian Ke
To reduce the cost of quality and equipment management in high quality process, this paper uses Variable Sampling Interval (VSI ) control chart to monitor the Time Between Events (TBE) and constructs a joint model of VSI chart and equipment maintenance to stabilize product quality and reduce process costs. Considering the state changes of the equipment, this paper uses four maintenance methods and
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Comparison of multiple Kalman filter and moving horizon estimator for the anesthesia process J. Process Control (IF 4.2) Pub Date : 2024-02-20 Bob Aubouin-Pairault, Mirko Fiacchini, Thao Dang
In this paper, a new method to estimate the states and the parameters of the anesthesia process is proposed and compared to a Moving Horizon Estimator (MHE) approach. The proposed method makes use of multiple extended Kalman filters (MEKF) where each EKF uses a different set of system parameters whose selection is based on a predictive performance criterion. In view of usage in a closed loop, the comparison
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Model predictive coordinated cooperative control mechanism for multiagent systems based on priority negotiation J. Process Control (IF 4.2) Pub Date : 2024-02-19 Cheng Cheng, Biao Yang, Binhua Li, Zemin Han, Feiyun Peng
Aiming at the control requirements in complex industrial processes and the problem of poor decision-making flexibility of agents in state/output difference-based consensus strategies, a novel model predictive coordinated cooperative control mechanism is proposed using multiagent systems as the computational paradigm. The proposed mechanism considers the global state of the system, the interaction of
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A non-fragile robust H∞ controller for continuous-time delayed models J. Process Control (IF 4.2) Pub Date : 2024-02-19 Iran Akbarpur, Valiollah Ghaffari
In this paper, considering the uncertainties of the controller and the model, a non-fragile control law is robustly developed for time-delay equations with additive uncertainty. Hence, exploiting the linear matrix inequality (LMI), the robust compensator design is translated to an LMI one. In a continuoustime delayed model, the stabilization around equilibrium points and the rejection of the exogenous
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Quasi-online failure times identification of mobile heat sources in 2D geometry J. Process Control (IF 4.2) Pub Date : 2024-02-17 M.S. Bidou, L. Perez, S. Verron, L. Autrique
Identifying the failure instants in thermal systems subject to 2D parabolic partial differential equations presents a significant challenge, especially when the systems involve mobile heat sources. In the context of this study, mobile heat sources are examined, along with a set of stationary sensors, while assuming known and constant-velocity trajectories for the heat sources. This research introduces
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Novel approach for industrial process anomaly detection based on process mining J. Process Control (IF 4.2) Pub Date : 2024-02-15 Yilin Shi, Ning Zhang, Xiaolu Song, Hongguang Li, Qunxiong Zhu
Anomaly detection plays a critical role in ensuring the quality and safety of industrial processes. Process mining, as an emerging technology, has proven effective in extracting knowledge and process rules inherent in process events. However, industrial time series data possess characteristics such as high noise, and data redundancy, posing challenges for accurately assessing system anomalies using
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Data-based decomposition plant for decentralized monitoring schemes: A comparative study J. Process Control (IF 4.2) Pub Date : 2024-02-09 M.J. Fuente, M. Galende-Hernández, G.I. Sainz-Palmero
The complexity of the industrial processes, large-scale plants and the massive use of distributed control systems and sensors are challenges which open ways for alternative monitoring systems. The decentralized monitoring methods are one option to deal with these complex challenges. These methods are based on process decomposition, i.e., dividing the plant variables into blocks, and building statistical
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Reordered short-term autocorrelation-driven long-range discriminative convolutional autoencoder for dynamic process monitoring J. Process Control (IF 4.2) Pub Date : 2024-02-09 Kai Wang, Daojie He, Gecheng Chen, Xiaofeng Yuan, Yalin Wang, Chunhua Yang
Deep neural networks (DNNs) can result in suboptimal monitoring performance due to nonlinearity, dynamics, and local characteristics in modern complex industrial processes. To surmount these limitations, this paper first proposes a novel data construction method to model the short-term autocorrelation and spatial correlations as a three-dimensional matrix and then reorder the elements of it to better
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Enhanced pressure control system for the vacuum vessel of Damavand Tokamak using PID and multiple model control J. Process Control (IF 4.2) Pub Date : 2024-02-02 Mahdi Amini, Mahdi Aliyari Shoorehdeli, Hossein Rasouli
This paper presents the implementation of the pressure control system for the vacuum vessel of Damavand Tokamak. PID controllers within the framework of multiple-model control are utilized for controller design, aiming to safely achieve the desired setpoint for the pressure of the vacuum vessel. The chamber pressure is measured in real-time using a Cold Cathode Pirani gauge and transferred as feedback
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Double bagging trees with weighted sampling for predictive maintenance and management of etching equipment J. Process Control (IF 4.2) Pub Date : 2024-02-02 Gyeong Taek Lee, Hyeong Gu Lim, Tianhui Wang, Gejia Zhang, Myong Kee Jeong
Proper maintenance and management of equipment are essential for producing high-quality wafers. Anomalies in equipment lead to the production of low-quality wafers. This study proposes a method to maintain and manage etching equipment in semiconductor manufacturing utilizing a virtual metrology (VM) model. Leveraging acquired equipment data, the VM model predicts electrical resistance measurement values
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A novel parallel feature extraction-based multibatch process quality prediction method with application to a hot rolling mill process J. Process Control (IF 4.2) Pub Date : 2024-01-25 Kai Zhang, Xiaowen Zhang, Kaixiang Peng
In a hot strip rolling mill (HSRM) process, the prediction of the steel crown is a key factor in improving the quality of the strip steel. In this paper, a new multibatch feature extraction-based method is proposed for predicting the steel crown. Different from the cascaded feature extraction-based method which cannot extract both temporal and local features well, this method parallelly captures the
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Dynamic fault detection and diagnosis for alkaline water electrolyzer with variational Bayesian Sparse principal component analysis J. Process Control (IF 4.2) Pub Date : 2024-01-25 Qi Zhang, Weihua Xu, Lei Xie, Hongye Su
Electrolytic hydrogen production serves as not only a vital source of green hydrogen but also a key strategy for addressing renewable energy consumption challenges. For the safe production of hydrogen through Alkaline water electrolyzer (AWE), dependable process monitoring technology is essential. However, random noise can easily contaminate the AWE process data collected in industrial settings, presenting
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Advanced embedded generalized predictive controller based on fuzzy gain scheduling for agricultural sprayers with dead zone nonlinearities J. Process Control (IF 4.2) Pub Date : 2024-01-23 Deniver R. Schutz, Heitor V. Mercaldi, Elmer A.G. Peñaloza, Lucas J.R. Silva, Vilma A. Oliveira, Paulo E. Cruvinel
Variable rate application of pesticides in agriculture can improve pest control and also increase food production. Nevertheless, incorrect spraying poses risks to the environment and human health, as well as may increase the total cost of production. Nowadays, it is quite known the importance of innovation in techniques and technologies to improve the spraying process in a variable rate application
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Quickest detection of bias injection attacks on the glucose sensor in the artificial pancreas under meal disturbances J. Process Control (IF 4.2) Pub Date : 2024-01-13 Fatih Emre Tosun, André M.H. Teixeira, Mohamed R.-H. Abdalmoaty, Anders Ahlén, Subhrakanti Dey
Modern glucose sensors deployed in closed-loop insulin delivery systems, so-called artificial pancreas use wireless communication channels. While this allows a flexible system design, it also introduces vulnerability to cyberattacks. Timely detection and mitigation of attacks are imperative for device safety. However, large unknown meal disturbances are a crucial challenge in determining whether the
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Optimal control of viscous fingering J. Process Control (IF 4.2) Pub Date : 2024-01-12 Nicolas Petit
The paper considers the problem of optimally filling a Hele-Shaw cell. The system is subject to viscous fingering effect. It is shown that, despite the threshold terms appearing on the right-hand side of the governing equations, the dynamics can be rewritten using several prime integrals. This allows reforming optimal control problems for the Fourier modes describing the fluid interface into smooth
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Data-driven two-dimensional integrated control for nonlinear batch processes J. Process Control (IF 4.2) Pub Date : 2024-01-12 Chengyu Zhou, Li Jia, Jianfang Li, Yan Chen
Two-dimensional control has been considered as an effective strategy to accomplish high-accuracy tracking for batch processes because of its excellent learning ability and time-domain stability. However, being a model-based control method, the performance of the two-dimensional control system will inevitably decrease due to unknown uncertainties or unmodeled dynamics. In addition, the high computational
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Global self-optimizing control of batch processes J. Process Control (IF 4.2) Pub Date : 2024-01-13 Chenchen Zhou, Hongxin Su, Xinhui Tang, Yi Cao, Shuang-hua Yang
This work considers to achieve near-optimal operation for a class of batch processes by employing self-optimizing control (SOC). Comparing with a continuous one, a batch process exhibits stronger nonlinearity with dynamics because of the non-steady operation condition. This necessitates a global version of SOC to achieve satisfactory performance. Meanwhile, it also makes the existing global SOC (gSOC)
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Geometric constructive network with block increments for lightweight data-driven industrial process modeling J. Process Control (IF 4.2) Pub Date : 2024-01-10 Jing Nan, Wei Dai, Haijun Zhang
Industrial data-driven models may require frequent reconstruction to maintain model performance due to the dynamics, uncertainty, and complexity of industrial processes. The infrastructure of the industrial processes is usually distributed control systems (DCS) with energy-sensitive and resource-constrained. In this context, this article proposes a geometric constructive network with block increments
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Distributed partial output consensus optimization for constrained chain interconnected systems J. Process Control (IF 4.2) Pub Date : 2024-01-08 Zidong Liu, Dongya Zhao, Shuzhan Zhang, Xindong Wang, Sarah K. Spurgeon
For chain interconnected systems with state and input constraints, a partial output consensus (POC) optimization problem is studied when the set-points are infeasible. In this case, outputs with and without consensus requirements cannot converge to the set-points achieved from real-time optimization. For this case, a novel set-point optimization method is developed, which is called distributed partial
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Efficient model predictive control of boiler coal combustion based on NARX neutral network J. Process Control (IF 4.2) Pub Date : 2024-01-05 Zongyang Hu, Jiuwen Fang, Ruixiang Zheng, Mian Li, Baosheng Gao, Lingcan Zhang
During coal-fired power generation, uniform combustion temperature in the boiler is desired which will benefit both economical efficiency and pollution reduction. To this end, a model predictive control (MPC) algorithm based on the Nonlinear Auto-Regressive Exogenous Inputs (NARX) neural network and KS-function is proposed, and the uniform combustion in the boiler is realized by controlling the opening
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Markov Chain approach to get control limits for a Shewhart Control Chart to monitor the mean of a Discrete Weibull distribution J. Process Control (IF 4.2) Pub Date : 2023-12-26 Leandro Alves da Silva, Linda Lee Ho, Roberto da Costa Quinino
Typically, failure time is modeled using continuous distributions such as the Weibull or Gamma distributions. In many practical scenarios, data is recorded in terms of discrete counts, such as the number of days or cycles, therefore the Discrete Weibull distribution is employed to model such cases. In this paper, we propose the use of a Shewhart X¯ control chart to monitor the mean of a Discrete Weibull
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Hybrid method for multi-rate refined oil pumping station system unsteady state estimation with bad data attacks J. Process Control (IF 4.2) Pub Date : 2023-12-21 Lei He
With the recent advancement of products pipelines digitization, a large number of sensors have been installed in pumping stations for real-time flow parameters measurement. In these asynchronous multi-sensor systems, data missing and false data attacks are likely to occur when performing online operation monitoring of the oil pipeline system. In this paper, a hybrid state estimation method is proposed
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Data-driven inference of bioprocess models: A low-rank matrix approximation approach J. Process Control (IF 4.2) Pub Date : 2023-12-21 Guilherme A. Pimentel, Laurent Dewasme, Alain Vande Wouwer
Following the recent advent of Process Analytical Technologies, dataset production has undergone significant leverage. In this new abundance of data, isolating meaningful, informative content is critical for process dynamic modeling. This paper proposes a data-driven algorithm based on low-rank matrix approximation, the so-called successive projection algorithm, to retrieve a minimal set of macroscopic
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Nonferrous metal price forecasting based on signal decomposition and ensemble learning J. Process Control (IF 4.2) Pub Date : 2023-12-14 Peng Kong, Bei Sun, Hui Yang, Xueyu Huang
Nonferrous metals are indispensable raw materials for modern industry. The price forecasting of nonferrous metals is vital for business operators and investors. Based on the decomposition-integration framework, we propose a signal decomposition model combining variational mode decomposition (VMD) and an improved long-short time memory (LSTM) network. Using the MAE metric as a benchmark, the improved
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Constrained model predictive control of an industrial high-rate thickener J. Process Control (IF 4.2) Pub Date : 2023-12-14 Ridouane Oulhiq, Khalid Benjelloun, Yassine Kali, Maarouf Saad, Hafid Griguer
High-rate thickeners are used in the mining industry to improve water recovery from slurries and increase their solids ratio. High-rate thickeners operate under strict constraints and several disturbances. To control this process, a constrained model predictive control (MPC) is developed in this paper. For process identification, a historical data-driven methodology is used and a vector autoregressive
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Closed-loop optimisation of neural networks for the design of feedback policies under uncertainty J. Process Control (IF 4.2) Pub Date : 2023-12-13 Evren Mert Turan, Johannes Jäschke
Solving model predictive control (MPC) problems online can be computationally intractable, especially when considering uncertainty and nonlinear systems. One approach to avoid this is to train a neural network on a data-set of solutions of MPC problems (potentially nonlinear) offline, and to evaluate the trained control policy online. However, due to the separation of these optimisation problems the
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Optimization of crude oil operations scheduling by applying a two-stage stochastic programming approach with risk management J. Process Control (IF 4.2) Pub Date : 2023-12-13 Tomas Garcia Garcia-Verdier, Gloria Gutierrez, Carlos A. Méndez, Carlos G. Palacín, Cesar de Prada
This paper focuses on the problem of crude oil operations scheduling carried out in a system composed of a refinery and a marine terminal, considering uncertainty in the arrival date of the ships that supply the crudes. To tackle this problem, we develop a two-stage stochastic mixed-integer nonlinear programming (MINLP) model based on continuous-time representation. Furthermore, we extend the proposed
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Improved fault detection based on kernel PCA for monitoring industrial applications J. Process Control (IF 4.2) Pub Date : 2023-12-08 Khadija Attouri, Majdi Mansouri, Mansour Hajji, Abdelmalek Kouadri, Abderrazak Bensmail, Kais Bouzrara, Hazem Nounou
The conventional Kernel Principal Component Analysis (KPCA) -based fault detection technique requires more computation time and memory storage space to analyze large-sized datasets. In this context, two techniques, Spectral Clustering (SpC) and Random Sampling (RnS), are developed to reduce the dataset size by retaining the more relevant observations while preserving the main statistical characteristics
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A two-dimensional model predictive iterative learning control based on the set point learning strategy for batch processes J. Process Control (IF 4.2) Pub Date : 2023-12-05 Haisheng Li, Jianjun Bai, Hongbo Zou, Xunyuan Yin, Ridong Zhang
Although conventional two-dimensional model predictive iterative learning control (2D-MPILC) based on an extended non-minimum state space (ENMSS) model can avoid designing an observer, it only relies on feedback to passively deal with time delay. This passive treatment for time delay hinders the further improvement of control performance. To address this shortcoming, a two-dimensional model predictive
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Streaming variational probabilistic principal component analysis for monitoring of nonstationary process J. Process Control (IF 4.2) Pub Date : 2023-12-05 Cheng Lu, Jiusun Zeng, Yuxuan Dong, Xiaobin Xu
Modern industrial processes are characteristic of nonstationary and uncertainty. To address these issues, this paper proposes a probabilistic principal component analysis based model that utilizes streaming variational inference for online monitoring of nonstationary process. The model parameters are updated in a streaming/batch-wise manner to track the nonstationary behavior in the process. In the
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Optimal control strategies for water, sanitation, and hygiene in mitigating spread of waterborne diseases J. Process Control (IF 4.2) Pub Date : 2023-12-02 Rujira Chaysiri, Wirawan Chinviriyasit, Garrick E. Louis
Improving access to water, sanitation and hygiene (WASH) can help to eliminate the root cause of waterborne disease transmission. WASH has the potential to function as a sustained and effective mechanism for controlling enteric diarrheal disease (EDD) over the long term. To address this question, we formulate a mathematical model to study the impact of WASH services on reducing the transmission of
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A globally convergent estimator of the parameters of the classical model of a continuous stirred tank reactor J. Process Control (IF 4.2) Pub Date : 2023-11-27 Anton Pyrkin, Alexey Bobtsov, Romeo Ortega, Jose Guadalupe Romero, Denis Dochain
In this paper we provide the first solution to the challenging problem of designing a globally convergent estimator for the parameters of the standard model of a continuous stirred tank reactor. Because of the presence of non-separable exponential nonlinearities in the system dynamics that appear in Arrhenius law, none of the existing parameter estimators is able to deal with them in an efficient way
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Robust multi-mode probabilistic slow feature analysis with application to fault detection J. Process Control (IF 4.2) Pub Date : 2023-11-27 Alireza Memarian, Rahul Raveendran, Biao Huang
This paper proposes a robust multi-mode dynamic data-driven model to identify complex industrial processes and study its application in detecting incipient faults. To model the process dynamics, a robust multi-mode probabilistic slow feature analysis (RMPSFA) is developed. A multiple switching conditional random field (MSCRF) is utilized to handle the multi-modality in the process. In order to make
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Visual analytics for process monitoring: Leveraging time-series imaging for enhanced interpretability J. Process Control (IF 4.2) Pub Date : 2023-11-25 Ibrahim Yousef, Aditya Tulsyan, Sirish L. Shah, R. Bhushan Gopaluni
In the era of big data driven by the advent of the Internet of Things (IoT), process industries face the challenge of analyzing massive and complex data to extract relevant information for effective process monitoring. Despite exploring various approaches, scalability and interpretability continue to present practical limitations. To address these limitations, we propose a new framework called visual
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On positive systems with multivariable positive control: Process stabilization J. Process Control (IF 4.2) Pub Date : 2023-11-21 Horacio Leyva, Ricardo Femat, Francisco A. Carrillo, Griselda Quiroz-Compean
The process stabilization is tackled from the perspective of a family of positive systems with multivariable positive control. This framework is consistent because each of the n state variables and m control inputs in chemical processes is in R+. The aim is to provide multivariable positive feedback control schemes which take values in the hyperbox U∈R+m; while sufficient conditions are satisfied to
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Systematic model bank determination approach for nonlinear systems using gap metric and stability margin J. Process Control (IF 4.2) Pub Date : 2023-11-21 Elkhalil Khouloud, Zribi Ali
This paper introduces a novel systematic approach for designing multimodel controller tailored to nonlinear systems. Our methodology focuses on the precise selection of local controllers, aligning their performance with specific requirements while minimizing redundancy. We achieve this through a two-fold process: first, the creation of an initial multimodel bank using the Self-Organizing Map (SOM)
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SEAG: A novel dynamic security risk assessment method for industrial control systems with consideration of social engineering J. Process Control (IF 4.2) Pub Date : 2023-11-22 Kaixiang Liu, Yongfang Xie, Shiwen Xie, Limin Sun
The development of information and communication technology and its wide application in industrial control systems (ICSs) has brought a growing number of security risks to ICSs. Quantifying and dynamically assessing the security risks of ICSs is of great significance to protect ICSs from cyber attacks. Current risk assessment methods, however, do not take into account social engineering (SE) attacks
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Dynamic optimal control of flow front position in injection molding process: A control parameterization-based method J. Process Control (IF 4.2) Pub Date : 2023-11-17 Zhigang Ren, Jianghao Lin, Zongze Wu, Shengli Xie
High dynamic and precise control of process variables within permissible limits is the key to high product quality in the injection molding industry. Among these variables, the injection flow rate of molten polymer is particularly significant. In this paper, an effective optimal open-loop and state feedback controller design method is designed to realize the optimal tracking control of the melt polymer
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Model predictive control simulations with block-hierarchical differential–algebraic process models J. Process Control (IF 4.2) Pub Date : 2023-11-16 Robert B. Parker, Bethany L. Nicholson, John D. Siirola, Lorenz T. Biegler
Hierarchical optimization modeling in an algebraic modeling environment facilitates construction of large models with many interchangeable sub-models. However, for dynamic simulation and optimization applications, a flattened structure that preserves time indexing is preferred. To convert from a structure that facilitates model construction to a structure that facilitates dynamic optimization, the
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Control of microfluidic separation processes governed by the Zweifach-Fung effect J. Process Control (IF 4.2) Pub Date : 2023-11-15 Nicolas Petit
This paper addresses several control problems for a prototypical microfluidic process designed for the separation operations of a fluid containing particles. The device is composed of one or several cascaded bifurcations that are traveled by the fluid. The volume fraction of particles in the flow is modified at each bifurcation. Fractionation is caused by the Zweifach–Fung effect, which governs the
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Frequency-based multivariable control design with stability margin constraints: A linear matrix inequality approach J. Process Control (IF 4.2) Pub Date : 2023-11-10 Anna Paula V. de A. Aguiar, George Acioli, Péricles Rezende Barros
In this paper, a proportional–integral–derivative controller design problem for stable multivariable process is considered. A frequency-based control technique is formulated as a convex optimization problem with linear matrix inequality constraint. The multivariable proportional–integral–derivative controller is designed so that H∞ norm of the difference between the designed loop gain function and
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Process optimization of microbial fermentation with parameter uncertainties via distributionally robust discrete control J. Process Control (IF 4.2) Pub Date : 2023-11-06 Juan Wang, Chihua Chen, Feiyan Zhao, Jichao Wang, An Li
There are some uncertain kinetic parameters in microbial fermentation system because of the unclear intracellular metabolic mechanisms. Considering the affection of these uncertain parameters on system performance, dynamic process optimization of the fermentation system can be modeled as a distributionally robust discrete control problem under moment uncertainty, which aims to maximize the mean productivity
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Predictive control strategies for solar furnace systems on the basis of practical constrained solutions J. Process Control (IF 4.2) Pub Date : 2023-11-06 Igor M.L. Pataro, Juan D. Gil, José L. Guzmán, Manuel Berenguel, Inmaculada Cañadas
Controlling solar furnace systems presents significant challenges due to their nonlinear dynamics and uncertainties in model parameters. Therefore, this paper provides a comprehensive study of four predictive control strategies specifically tailored for solar furnaces: linear generalized predictive control (GPC), nonlinear GPC (NGPC), nonlinear model predictive control (NMPC), and practical NMPC (PNMPC)
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Steady-state real-time optimization using transient measurements and approximated Hammerstein dynamic model: A proof of concept in an experimental rig J. Process Control (IF 4.2) Pub Date : 2023-10-31 Pedro de Azevedo Delou, José Matias, Johannes Jäschke, Maurício B. de Souza, Argimiro R. Secchi
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Repetitive process based indirect-type iterative learning control for batch processes with model uncertainty and input delay J. Process Control (IF 4.2) Pub Date : 2023-10-30 Hongfeng Tao, Junhao Zheng, Junyu Wei, Wojciech Paszke, Eric Rogers, Vladimir Stojanovic
This paper develops an indirect iterative learning control scheme for batch processes with time-varying uncertainties, input delay, and disturbances. In this paper, a predictor based on a state observer is designed to estimate the future state and to compensate for the input delay. Then a feedback controller based on the estimated state and the set-point error is used to track the specified reference
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Enhanced robust multimode process monitoring under dirty data via difference-based decomposition of matrix J. Process Control (IF 4.2) Pub Date : 2023-10-26 Yang Wang, Ying Zheng, Qilin Qu, David Shan-Hill Wong
Traditional data-driven methods generally suppose the training dataset is not corrupted by outliers. However, outliers are inevitable in the real industrial processes even with a relatively high ratio, which degrades the accuracy of data-based models. For multimode process monitoring, outliers may deteriorate the accuracy of both mode identification and fault detection. However, the existing robust
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A fault-injection-based approach to leak localization in water distribution networks using an ensemble model of Bayesian classifiers J. Process Control (IF 4.2) Pub Date : 2023-10-24 Azim Farghadan, Morteza Saheb Zamani, Mohammadreza Jalili Ghazizadeh
Leak localization in water distribution networks (WDNs) is essential to water management systems. Developing a reliable and robust leak localization technique is crucial for reducing water losses in large-scale WDNs. The challenge of leak localization in WDNs can be addressed using a model-based data-driven approach. Each node of a WDN is used as a category label by the classifier model for identifying
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Robust data-based predictive control of systems with parametric uncertainties: Paving the way for cooperative learning J. Process Control (IF 4.2) Pub Date : 2023-10-20 Eva Masero, José M. Maestre, José R. Salvador, Daniel R. Ramirez, Quanyan Zhu
This article combines data and tube-based predictive control to deal with systems with bounded parametric uncertainty. This integration generates robustly feasible control sequences that can also be exploited in cooperative scenarios where controllers learn from each other’s data. In particular, the approach is based on a database that contains information from previous executions of the same and other
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Cascade multi-resonant disturbance observer design. Application to a distillation column J. Process Control (IF 4.2) Pub Date : 2023-10-19 I. Peñarrocha-Alós, D. Tena, R. Sanchis
In this work, we present a novel approach for input disturbance estimation design and implementation for dynamical processes under the influence of unknown disturbances that present a clear periodical behaviour of a known frequency. Both the design and the implementation are focused on simplicity. The observer consists of a set of transfer functions fed by the process manipulated variable and the sensor
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A model-free-based control chart for batch process using U-statistics J. Process Control (IF 4.2) Pub Date : 2023-10-19 Renan Faraon Cintra, Marcio Valk, Danilo Marcondes Filho
Batch processes are known to generate time series of successive measurements of many process variables in each run and the main challenge is to capture and accommodate the variability in the batch domain (batch-to-batch variability) and in the time domain (data with serial correlation). Classical approaches are focused on the first goal by applying multivariate techniques in the columns of a data matrix
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An alternative method for estimating Hurst exponent of control signals based on system dynamics J. Process Control (IF 4.2) Pub Date : 2023-10-19 Maryam Khosroshahi, Javad Poshtan, Yousef Alipouri
One of the indices for evaluating the performance of control systems based on the routine operation data is the Hurst index. It is shown that the Hurst index value is very close to that of the minimum variance index which measures the performance of a control loop in terms of minimum variance benchmark. The advantage of Hurst index over minimum variance index is that it does not require the system
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Modified performance-enhanced PCA for incipient fault detection of dynamic industrial processes J. Process Control (IF 4.2) Pub Date : 2023-10-16 Hongquan Ji, Qingsen Hou, Dehao Wu
Timely fault detection plays a critical role in modern complex industrial processes. While statistical process monitoring has gained significant practical application in recent years, traditional data-driven multivariate statistics often lack sensitivity in detecting incipient faults. To this end, this paper proposes a novel approach for fault detection of dynamic industrial processes. First, the process
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Intelligent state estimation for fault tolerant integrated frequent RTO and adaptive nonlinear MPC J. Process Control (IF 4.2) Pub Date : 2023-10-11 Giriraj Bagla, Sachin C. Patwardhan, Jayaram Valluru
Combinations of real-time optimization (RTO) and model predictive control (MPC) have been widely employed in the process industry for tracking the economic optimum in the face of drifting disturbances and parameters. Online update of model parameters is a critical step in the implementation of RTO. In this work, an intelligent state and parameter estimation approach is developed by combining a fault