Journal of Process Control最新文献

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) and targeted re-identification (TR) is proposed. In TE, only the manipulated variables corresponding to problematic transfer functions that have significant PMM are excited during data acquisition. On the other hand, the other manipulated variables are optimized to suppress the variations of the controlled variables. After data is acquired using TE, the TR method re-identifies only the problematic transfer functions by using the other transfer-function models without large PMM. The validity of the proposed framework is examined by theoretical analysis and numerical case studies. In the theoretical analysis, the stability during data acquisition using TE and the asymptotic bias of the parameters re-identified using TR were considered. In the numerical case studies, the applicability of the proposed framework to several processes including a fluid catalytic cracking (FCC) process was examined. As a result, it was shown that, for all the processes, the proposed framework can improve both the control performance during data acquisition and the model accuracy after re-identification, compared to an existing method that excites all the inputs during data acquisition.

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 on this model, an appropriate utility model has been derived for estimation purposes. The internal dynamics of the model have been burdened with unstructured and parametric uncertainty due to the unknown reaction kinetics functions. Taking this uncertainty into account, an analysis of the observability and detectability of the utility model has been carried out. The utility model and the available set of inputs and measured outputs have been used to design a new robust non-linear observer that allows the estimation of state variables in the presence of uncertainty. In the synthesis of the observer, the asymptotic observer methodology has been combined with a second-order sliding mode observer, a so-called super twisting algorithm. The developed observer generates not only robust and stable estimates of the state variables but also enables the reconstruction of unknown kinetic functions. The stability of the designed observer has been proven using the Lyapunov stability theory. The observer has been implemented in the Matlab/Simulink environment. The comprehensive simulation studies carried out show the high efficiency of the estimation process using the developed state observer.

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, and (ii) the softening of this constraint to ensure closed-loop stability for an enlarged domain of attraction. Additionally, configuration methods are proposed for the insulin on board constraint based on carb counting regression models and glycemia rate of change information. In-silico results in the FDA-approved UVA/Padova simulator, under over/underestimation of the meal amounts, and under insulin sensitivity variability, show a superiority of the proposed glucose control over postprandial periods.

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 with this, general conditions are established for the adapted nominal model for use on the basic trust-region algorithm. The proposed modifiers are then incorporated into an iterative algorithm of the real-time modifier adaptation scheme complemented with a trust-region framework. The effectiveness of the approach is demonstrated in simulation for the optimization of a continuous stirred tank reactor.

In this paper, considering the uncertainties of the controller and the model, a non-fragile $H_{\infty }$ 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 continuous-time delayed model, the stabilization around equilibrium points and the rejection of the exogenous disturbance are discussed via the non-fragile robust $H_{\infty }$ controller. Such an aim is realized by checking the feasibility of the LMI. Then, through solving an LMI, the controller’s parameters would be found and updated directly. To show the superiority of the tactic in process control, the reported strategy is employed in a two-stage chemical reactor. The figurative results along with the quantitative findings consistently disclose the advantages of the addressed controller over conventional techniques.

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 control actions among agents, the constraints of the process, and energy consumption. According to the proposed coordinated cooperative control mechanism, each agent must accomplish three tasks at each time step. First, each agent proposes its own “winner-takes-all” control scheme and calculates its payoff. Then, adjacent agents determine their own priorities at the current time step according to the payoffs of the “winner-takes-all” scheme by using a designed pairwise game mechanism. Next, each agent, in order of priority, calculates its sequence of actual optimal control input increments by applying the dynamic predictive control approach based on the dynamic model of the system. Finally, the effectiveness of the proposed coordinated cooperative control mechanism is verified by comparing it with the networked coordination-based distributed model predictive control approach and the centralized model predictive control approach based on the temperature control process of heating an alumina ceramic block in a high-power microwave reactor with six microwave sources.

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 between the two methods is based on a metric quantifying the capability of the estimators to predict the future behavior of the system. The metric is also used as a performance measure for tuning the hyperparameters of the estimators. While the results on simulated data are similar, the MEKF method outperforms MHE on clinical data. Tests show that the MEKF method can better predict the future trajectory of the system during the whole induction, on average for all the patients but also for the worst scenario.

To reduce the cost of quality and equipment management in high quality process, this paper uses Variable Sampling Interval $T_r$ (VSI $T_r$) control chart to monitor the Time Between Events (TBE) and constructs a joint model of VSI $T_r$ 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 delay monitoring strategies to divide the system update scenario and calculate the Expected Cost of unit Time (ECT). It verifies the accuracy of the model probability calculation and the effectiveness of the model in the high-quality process. This study analyzes the sensitivity of the model and studies the impact of process parameters on ECT. The results show that the joint model of VSI $T_r$ control chart and equipment maintenance can reduce the ECT of the system compared with the two contrasting models. Accordingly, it is more suitable for quality and equipment management of high quality processes.

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 a quasi-online methodology that incorporates Exponentially Weighted Moving Average (EWMA) charts for immediate failure detection. When a failure is detected via the EWMA charts, the Conjugate Gradient Method, traditionally developed for offline applications, is activated. This method is adapted to a quasi-online framework, facilitating a more rapid and precise identification of malfunctioning heat sources, the exact time of their failures, and the possibility of restoring normal operations. To assess the performance and reliability of this approach, it is compared with a Bayesian filter-based method, particularly using the Kalman filter for this purpose. Monte Carlo simulations are employed to evaluate the resilience and effectiveness of the quasi-online method, focusing on the system’s sensitivity to the accuracy of sensor measurements.

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 traditional data-driven methods. To address these challenges, this paper proposes a Trend-Value Fusion Process Mining (TVPM) approach based on the representation of industrial process events to tackle anomaly detection in industrial processes. Firstly, TVPM introduces a fusion measure that combines data trends and mean-value to provide a comprehensive description of operating events in industrial processes. This fusion measure serves as a vital tool for capturing the complex details and features of industrial events. Additionally, a novel process discovery algorithm is developed to extract behavioral patterns and sequence information from events. Finally, a process abnormal state detection model is constructed by leveraging the comprehensive and in-depth information obtained from TVPM. The proposed method is applied to an industrial coal gasification process, yielding satisfactory results.