Journal of Process Control最新文献

A novel big data-driven predictive control (BDPC) approach for nonlinear processes is proposed. To deal with nonlinear process behaviours, the process behaviour space, represented by a set of input–output variable trajectories, is partitioned into linear sub-behaviour spaces (clusters), based on linear inclusion of nonlinear behaviours. A behaviour space (represented using Hankel matrices) partitioning approach is developed based on subspace angles. During online control, the BDPC controller locates the most relevant linear sub-behaviour based on the current online trajectory, which is then used to determine predictive control actions using receding horizon optimisation. The incremental stability and dissipativity conditions are developed to attenuate the effect of the error of approximating linear sub-behaviours on the output and guarantee closed-loop stability. These conditions are implemented as additional constraints during online data-driven predictive control. An example of controlling the Hall–Héroult process is used to illustrate the proposed approach.

Behavioral interventions (such as those developed to increase physical activity, achieve smoking cessation, or weight loss) can be represented as dynamic process systems incorporating a multitude of factors, ranging from cognitive (internal) to environmental (external) influences. This facilitates the application of system identification and control engineering methods to address questions such as: what drives individuals to improve health behaviors (such as engaging in physical activity)? In this paper, the goal is to efficiently estimate personalized, dynamic models which in turn will lead to control systems that can optimize this behavior. This problem is examined in system identification applied to the Just Walk study that aimed to increase walking behavior in sedentary adults. The paper presents a Discrete Simultaneous Perturbation Stochastic Approximation (DSPSA)-based modeling of the Goal Attainment construct estimated using AutoRegressive with eXogenous inputs (ARX) models. Feature selection of participants and ARX order selection is achieved through the DSPSA algorithm, which efficiently handles computationally expensive calculations. DSPSA can search over large sets of features as well as regressor structures in an informed, principled manner to model behavioral data within reasonable computational time. DSPSA estimation highlights the large individual variability in motivating factors among participants in Just Walk, thus emphasizing the importance of a personalized approach for optimized behavioral interventions.

In this paper, we present results with clinical data to enable a 2x2 input–output multivariable patient model for hypnosis and analgesia. Nonlinear multi-drug interaction models are identified from data recorded from 70 patients undergoing surgery during total intravenous anesthesia (TIVA) with several medical monitors for variables such as Bispectral Index, Nociception level (Medasense), skin conductance (Medstorm) and advanced spectral analysis conductance (AnspecPro). Bispectral index measures the depth of hypnosis (lack of consciousness), while nociception related indices from Medasense, Medstorm, and AnspecPro devices measure levels related to analgesia (lack of reaction to noxious stimuli). A comparison is given among three response surface model (RSM) structures – Minto, Greco, and Reduced Greco – for hypnotic and analgesic states during Propofol–Remifentanil interaction. The identified models capture the pharmacodynamic properties of dose–effect concentrations of Propofol/Remifentanil while the pharmacokinetic part of the patient model is calculated from patient’s biometric values using Schnider/Minto (SM), and Eleveld/Eleveld (EE) models. In presence of strict clinical protocols delivering data under poor identifiability conditions, we propose two methods of identification: (i) based on steady-state gains, and (ii) using all available data which includes part of the dynamic transient. The model parameters are optimized with Genetic Algorithm based on a goodness of fit performance measure complemented with root mean square error. The results suggest that the EE model combination is advantageous for Bispectral index pharmacokinetic modeling at the cost of numerical complexity, therefore reducing the uncertainty left to be identified in the pharmacodynamic part of the patient model. By contrast, the SM model combination is less computationally demanding and provides some improvement in the RSM accuracy for nociception level indicators. The comparison of three devices for nociception levels evaluation suggests that clinical data captured with the Medasense monitor provides best fitted RSMs with the Reduced Greco RSM structure, despite having fewer parameters.

Industrial scheduling problems (ISPs), especially industrial production workshop scheduling problems (IPWSPs) in various sectors like manufacturing, and power require allocating resources to tasks within multiple constraints. Addressing these complex challenges involves computationally solving problems with numerous variables and constraints. Lagrangian relaxation-based algorithms (LRAs) effectively tackle ISPs (particularly IPWSPs) by relaxing constraints through multipliers. While LRAs may violate original constraints, heuristic algorithms rectify this by crafting feasible solutions. LRAs offer efficient problem-solving and yield high-quality results with manageable computational effort. This review presents the cutting-edge LRAs for ISPs, based on 200 papers from the fields of supply chain, manufacture, power, etc. It exemplifies LRAs’ principles through practical cases, and provides a classification of problems in detail, along with LRA’s problem-solving process. We discuss strengths, weaknesses, and future research prospects, offering comprehensive guidance. Summarizing these findings, the review enhances understanding of LRAs’ application potential in solving ISPs for practitioners and researchers.

Within the crucial domain of blast furnace ironmaking and sintering, the quality of sinter ore and molten iron holds supreme importance, with direct implications for downstream processes. However, the complexities of utilizing operational experience, understanding mechanisms, leveraging extensive data for precise modeling, and optimizing multiple objectives have persistently posed challenges for engineers. In this research, we propose an novel data/mechanism hybrid-driven modeling and global sequential optimization framework, with three core contributions: (1) Synthesizing field operation insights and mechanistic principles to construct models for molten iron production and energy consumption in ironmaking. (2) Crafting the broad learning approximate-aided subspace identification method (BLASIM), encapsulating the system’s dynamic and nonlinear characteristics. This method pioneers a parametric modeling strategy predicated on correlation error for dynamic nonlinear system identification, with its feasibility robustly underpinned by theoretical verification. (3) Streamlining the optimization process by applying expert knowledge to deconstruct a complex multi-objective optimization problem into manageable single-objective tasks. These tasks are addressed sequentially, reflecting operational chronology, and are adeptly resolved using gray wolf optimization algorithm with a sequence relaxant factor. To conclude, the proposed methods are thoroughly validated using real-world blast furnace smelting data, affirming the feasibility and efficiency of modeling accuracy and optimization performance.

Surge is a form of dynamic instability created as an unstable pattern in the flow of fluid and can severely affect centrifugal compressor performance by causing fluctuations in flow and pressure parameters. Due to the heavy and costly damage that the surge may cause in various industrial processes such as petrochemical plants, it is necessary to design an appropriate control system to reduce the effect of this phenomenon. The problem of active surge control of a centrifugal compressor using the throttle control valve (TCV) in the presence of compressor parametric uncertainties as well as large demands on upstream and downstream loads is investigated in this work. The control objective was to design a robust control system that can stabilize the compressor over a wide operating range without knowing the upper bound for the uncertainties and load demand. The controller should also react quickly by generating a smooth control signal without saturating the control input. These objectives are achieved by designing a sliding mode controller along with a nonlinear disturbance observer. The performance of the proposed disturbance observer-based controller is evaluated under various operational and load conditions and the results are compared against fuzzy type 1, conventional sliding mode, and wavelet-based neural network robust adaptive controllers. The results show that the proposed method can tolerate large disturbances without any knowledge on the upper bound of the incident disturbance, both on the downstream pressure and upstream mass flow which is highly desirable in practice. The comparative study proves the efficacy of the proposed method using various performance measures. The study also confirms the superior robust performance and stability of the proposed method in front of matched and mismatched disturbances as well as model uncertainties especially close to the instability boundary. Although choosing a TCV actuator has made the control system design easier, the sensitivity of the control valve to flow coefficient and zero calibration under different operating ranges of the compression system is studied carefully and some recommendations for the users are provided.

In the continuous casting of steel slabs, the ferrostatic pressure in the liquid core of the strand leads to bending, i.e., bulging, of the strand shell between the guiding rolls. Unsteady bulging means that this bending process is time-varying. During the continuous casting process, the emergence of unsteady bulging can be observed, leading to unwanted mold level fluctuations and lowering the quality of the cast strand. This work presents a detailed nonlinear beam model, an approximated nonlinear beam model, and a control-oriented linear model to gain new insights into the mechanism of unsteady bulging. Simulation results show that the linear model allows real-time computation, making it feasible to design advanced model-based control and state estimation strategies. The developed models are validated using measurements from the literature and an industrial continuous casting plant. More specifically, these models permit, for the first time, a detailed stability analysis of the overall mold level control loop, which gives a system-theoretic explanation for the root cause of unsteady bulging and why it is tied to particular frequencies of mold level fluctuations. This analysis shows that the emergence of unsteady bulging is related to an unstable closed-loop system and opens up different strategies to eliminate the observed instability.

In this paper, we address the economic performance of Model Predictive Control (MPC) while operating at a backed-off operating point. Operating the plant at a constrained optimal point will often cause constraint violations due to uncertainties such as disturbances and measurement errors, etc. To ensure dynamic feasibility, the concept of economic back-off is used. In this work, we select the set point as the economic back-off point such that the dynamic operating region should have the least variability in the active constrained variables while ensuring the feasibility of other variables. In other words, the dynamic operating region is oriented by the proper design of a controller such that variability in active constrained variables is as low as possible. This controller design can be transformed into equivalent objective function weights of the MPC controller. In this study, we demonstrate that the determined back-off point is optimal for both linear controller and MPC controller when there are no unconstrained degrees of freedom. For the case with unconstrained degrees of freedom, the back-off point determined using the presented approach is optimal only for a linear controller but suboptimal for an MPC controller. Demonstrative case studies are presented to illustrate the economic performance of the MPC controller at the determined economic back-off point.

Time delay is a ubiquitous phenomenon in chemical engineering. In the development of a practical rigorous model for process system, the influence of time delay cannot be ignored. A simultaneous approach for estimating time delays and model parameters is proposed in this work. The critical issue is how to address the approximation for state variable with time delay, which depends on the quotient and remainder obtained by dividing time delay by length of finite element. The strategies for handling the unknown quotient and remainder are designed. Finally, two case studies are considered. The effectiveness of the proposed approach is verified. Moreover, the proposed approach exhibits better performance compared with the gradient-based sequential approach.

We consider the problem of dynamic regulation with an economic cost function to control unknown linear systems, in which improving the economic performance and guaranteeing the stability of economical optimal equilibrium point are control objectives. A data-driven economic MPC scheme is presented using measured input-output trajectories without a prior system identification step. Our method uses Hankel matrices which include one input-output data trajectory for prediction in economic MPC, while persistently exciting of the input generating the data is needed. One of the novelties of the presented framework is directly verifying the strong duality property from input-output trajectory with the general cost function, considered as the supply rate. This is used to find a Lyapunov function for data-driven economic MPC. Under the strong duality assumption, asymptotic stability of the economical optimal equilibrium point for the closed-loop system with terminal equality constraint is guaranteed. The proposed data-driven economic MPC approach needs only persistently exciting data trajectory along with an upper bound on the system order and need no model description and no online parameter estimation. The proposed scheme applicability compared to the existing model-based economic MPC and data-driven MPC is illustrated for continuous stirred tank reactor (CSTR) and a numerical example and the robustness of the proposed scheme is evaluated in the case of measurement noise, as well as nonlinear model for CSTR system.