流化床反应器中气相丙烯聚合反应的动力学和预测控制(英文)

A two-phase dynamic model, describing gas phase propylene polymerization in a fluidized bed reactor, was used to explore the dynamic behavior and process control of the polypropylene production rate and reactor temperature. The open loop analysis revealed the nonlinear behavior of the polypropylene fluidized bed reactor, jus- tifying the use of an advanced control algorithm for efficient control of the process variables. In this case, a central- ized model predictive control （MPC） technique was implemented to control the polypropylene production rate and reactor temperature by manipulating the catalyst feed rate and cooling water flow rate respectively. The corre- sponding MPC controller was able to track changes in the setpoint smoothly for the reactor temperature and pro- duction rate while the setpoint tracking of the conventional proportional-integral （PI） controller was oscillatory with overshoots and obvious interaction between the reactor temperature and production rate loops. The MPC was able to produce controller moves which not only were well within the specified input constraints for both control vari- ables, but also non-aggressive and sufficiently smooth for practical implementations. Furthermore, the closed loop dynamic simulations indicated that the speed of rejecting the process disturbances for the MPC controller were also acceotable for both controlled variables.     

Controlling of Proton Exchange Membrane Fuel Cell by Model Predictive Controller Based on ANFIS Model

Proton exchange fuel cell is one of the most promising new technologies in electrical energy production. Due to slow dynamic, nonlinearity and dependency of time changing variables of proton exchange membrane fuel cell (PEMFC), its control issue is a challenging problem. In this paper, model predictive controller (MPC) based on the adaptive neuro‐fuzzy interface model of the PEMFC is proposed to control the output voltage. First the adaptive neuro‐fuzzy interference system (ANFIS) model is identified to approximate the dynamic behavior of the PEMFC system with a set of data which are taken from a physical model of a 5 kW PEMFC setup plant. Then the branch‐and‐bound method and the greedy algorithm are used to solve the constrained optimization function of the predictive control problem. The results reveal that the ANFIS model can effectively approximate the dynamic behavior of the PEMFC and the predictive controller based on this model can successfully control the output and satisfy the constraints.     

Dynamic recurrent radial basis function network model predictive control of unstable nonlinear processes

A multistep model predictive control (MPC) strategy based on dynamically recurrent radial basis function networks (RBFNs) is proposed for single-input single-output (SISO) control of uncertain nonlinear processes. The control system consists of two automatically configured RBFNs, a trained network representing the plant model and a network with on-line learning to function as controller. The automatic configuration and learning of the networks is carried out by using a hierarchically self-organizing learning algorithm. This control strategy is structurally simple and computationally efficient since a single output node of each RBFN is configured to provide multistep predictions for plant output and controller. The performance of the proposed RBFNMPC strategy is evaluated by applying to two unstable nonlinear chemical processes, a chemical reactor and a biochemical reactor, and also a stable polymerization reactor. Further, the results of the RBFNMPC is compared with similar RBFN model based control strategies and also with well tuned PID/PI controller. The results show the better performance of the proposed RBFNMPC for the control of open-loop unstable nonlinear processes that exhibit multiple steady-state behavior.     

新一代的自适应模型预测控制器

提出了新一代的自适应模型预测控制器,自适应MPC控制器由MPC控制模块、在线辨识模块、性能监控模块3个模块组成,相互协调配和来实现自适应MPC控制。除了控制器功能设计以外,其余过程均可自动进行。对于新建MPC应用,首先进行多变量测试与辨识,在模型符合控制要求时,自动进入控制器投运。通过控制器性能监视发现模型不满足控制要求精度时,触发一次多变量模型测试与辨识过程,替换原有模型进行控制,保证控制器性能始终处于最佳状态。自适应MPC控制器在PTA装置上的应用表明了算法的有效性。     

A MODEL-BASED DIRECT ADAPTIVE PI CONTROLLER FOR NONLINEAR PROCESS CONTROL

This article proposes a model-based direct adaptive proportional-integral (PI) controller for a class of nonlinear processes whose nominal model is input-output linearizable but may not be accurate enough to represent the actual process. The proposed direct adaptive PI controller is composed of two parts: the first is a linearizing feedback control law that is synthesized directly based on the process's nominal model and the second is an adaptive PI controller used to compensate for the model errors. An effective parameter-tuning algorithm is devised such that the proposed direct adaptive PI controller is able to achieve stable and robust control performance under uncertainties. To show the robust stability and performance of the direct adaptive PI control system, a rigorous analysis involving the use of a Lyapunov-based approach is presented. The effectiveness and applicability of the proposed PI control strategy are demonstrated by considering the time-dependent temperature trajectory tracking control of a batch reactor in the presence of plant/model mismatch, unanticipated periodic disturbances, and measurement noises. Furthermore, for use in an environment that lacks full-state measurements, the integration of a sliding observer with the proposed control scheme is suggested and investigated. Extensive simulation results reveal that the proposed model-based direct adaptive PI control strategy enables a highly nonlinear process to achieve robust control performance despite the existence of plant/model mismatch and diversified process uncertainties.     

Some manifold learning considerations toward explicit model predictive control

Model predictive control (MPC) is a de facto standard control algorithm across the process industries. There remain, however, applications where MPC is impractical because an optimization problem is solved at each time step. We present a link between explicit MPC formulations and manifold learning to enable facilitated prediction of the MPC policy. Our method uses a similarity measure informed by control policies and system state variables, to “learn” an intrinsic parametrization of the MPC controller using a diffusion maps algorithm, which will also discover a low-dimensional control law when it exists as a smooth, nonlinear combination of the state variables. We use function approximation algorithms to project points from state space to the intrinsic space, and from the intrinsic space to policy space. The approach is illustrated first by “learning” the intrinsic variables for MPC control of constrained linear systems, and then by designing controllers for an unstable nonlinear reactor.     

Lexicographic optimization based MPC: Simulation and experimental study

Multi-variable prioritized control study is carried out using model predictive control (MPC) algorithms. The conventional MPC algorithm implements multi-variable control through one augmented objective function and requires weights adjustment for required performance. In order to implement explicit prioritization in multiple control objectives, we have used lexicographic MPC. To achieve better tracking performance, we have used a new MPC algorithm, by modifying the lexicographic constraint, referred to as MLMPC, where tuning of weights is not required. The effectiveness of MLMPC algorithm is demonstrated on a PMMA reactor for controlling the number average molecular weight and the reactor temperature. We have also verified the benefits of proposed algorithm on an experimental single board heater system (SBHS) for controlling temperature of a thin metal plate. These simulation and experimental studies demonstrate the superiority of the proposed method over conventional MPC and lexicographic MPC. Finally, we have presented generalized mathematical solutions to the optimization problem in MLMPC.     

Model predictive control of an industrial pyrolysis gasoline hydrogenation reactor

This study focuses on the implementation of a nonlinear model predictive control (MPC) algorithm for controlling an industrial fixed-bed reactor where hydrogenations of raw pyrolysis gasoline occur. An orthogonal collocation method is employed to approximate the original reactor model consisting of a set of partial differential equations. The approximate model obtained is used in the synthesis of a MPC controller to control the temperature rising across a catalyst bed within the reactor. In the MPC algorithm, a sequential optimization approach is used to solve an open-loop optimal control problem. Feedback information is incorporated in the MPC to compensate for modeling error and unmeasured disturbances. The control studies are demonstrated in cases of set point tracking and disturbance rejection.     

A robustly stabilizing fault-tolerant MPC: A performance improvement-based approach

In terms of model predictive control (MPC) performance degradation caused by operational faults, in this article, a robust MPC strategy with active fault tolerance properties is proposed. The proposed strategy incorporates a fault supervision layer into the structure of conventional cost-contracting formulation-based robust MPC for the online update of the nominal controller model in the event of faults. The robust MPC is based on multiplant uncertainty, while the supervisory layer consists of a bank of unknown input observers and a decision-making algorithm. Simulation results in a nonlinear polymerization reactor subject to process faults demonstrate that the proposed approach offers superior performance compared to the conventional strategy.     

Nonlinear temperature control of a batch suspension polymerization reactor

This paper concerns nonlinear temperature control of a batch polymerization reactor where suspension polymerization of methyl methacrylate (MMA) takes place. For this purpose, four control algorithms, namely, a fix proportional‐integral (PI) controller, an adaptive proportional‐integral‐derivative (PID) controller and two globally linearizing control (GLC) schemes, one for known kinetic model (GLC‐I) and the other for unknown kinetic model (GLC‐II), are selected. The performances of these controllers are compared through simulation and real‐time studies in the presence of different levels of parameter uncertainty. The results indicate that GLCI and GLC‐II have better performances than fix PI and adaptive PID, especially in case of strong gel effect. The worst performance belongs to adaptive PID because of rapid model changes in gel effect region. GLC‐II has a simpler structure than GLC‐I and can be used without requiring the kinetic model. In implementation of GLC‐I the closed loop observer should be used because of model uncertainties.     

Dynamics and Control of a Biodiesel Transesterification Reactor

Biodiesel transesterification reactors resemble the heart of any biodiesel manufacturing plant. These reactors involve a highly complex set of chemical reactions and heat transfer characteristics. The high nonlinearity inherent in the dynamics of these reactors requires an efficient process control algorithm to handle the variation of operational process parameters and the effect of process disturbances efficiently. In this work, a multi‐model adaptive control strategy is considered for achieving the goal mentioned above. In order to implement the adaptive controller, a rigorous mechanistic model of the biodiesel transesterification reactor was developed and validated with published experimental results. The validated model was analyzed for stability and nonlinearity. The analysis revealed that the system is stable. However, its high nonlinearity necessitates an advanced control strategy to be considered. The input‐output relationship between the effective process variables was studied and the control system synthesis revealed a two‐by‐two control system. Two adaptive control loops were then designed and tuned to optimize the performance of the controller. Finally, a comparison with conventional controllers revealed the superiority of the new control system in terms of set‐point tracking and disturbance rejection. The results of this work prove that an adequately designed adaptive control system can be used to improve the performance of the transesterification reactor.     

Characteristics‐Based Model Predictive Control of a Catalytic Flow Reversal Reactor

This paper describes the formulation and tuning of a model‐based controller for a catalytic flow reversal reactor (CFRR). A plug flow non‐linear pseudo‐homogeneous mathematical representation of the process is used to model the mass and energy transport phenomena for the model‐based controller. A combination of the method of characteristics and model predictive control (MPC) technology is used to formulate the controller (Shang et al., Ind. Eng. Chem. Res. 43 (9) 2140–2149 (2004)). Mass extraction from the midsection of the reactor is used as the manipulated variable. Numerical simulations are used to show the performance of the formulated controller. The performance of the controller is evaluated on a simulated catalytic flow reversal reactor unit for combustion of lean methane streams for reduction of greenhouse gases emissions.     

Dynamic Modelling and Process Control of ZnS Precipitation

Abstract

This paper presents the dynamic modelling and design of a control strategy for the ZnS precipitation process. During lab‐scale experiments, the sulfide concentration in a precipitator was controlled at a prespecified pS value by manipulating the flow from a buffer vessel. Batch tests showed that the optimal condition for zinc sulfide precipitation is at a sulfide concentration of 10^?15 mole/l (pS 15). Experiments with the precipitator showed that the sulfide concentration highly deviates from a given setpoint when proportional (P) control is used, but this deviation can be decreased using a Proportional Integral (PI) controller. Moreover, the PI controller was able to handle sudden disturbances in the process conditions (pH, influent flow rate, or zinc and sulfide concentration). Additional precipitation experiments were conducted using effluent from a sulfate reducing gas‐lift reactor to determine if the compounds present in the effluent influence the control process. With the gas‐lift reactor effluent and a PI controller, the desired sulfide concentration was reached almost instantaneously (within 15 minutes) within acceptable margins (2–5%).     

APPLICATION OF ADAPTIVE PID CONTROL WITH GENETIC ALGORITHM TO A POLYMERIZATION REACTOR

This article describes the application of adaptive PID control with genetic algorithm (GA) to a jacketed batch polymerization reactor. This method was used to keep the polymerization reactor temperature at the desired optimal path, which was determined by the Hamiltonian maximum principle method. The reactor was simulated and the model equations of this jacketed polymerization reactor were solved by means of Runge-Kutta-Felthberg methods. A genetic algorithm can be a good solution for finding the optimum PID parameters because unlike other techniques it does not impose many limitations and it is simple. In this research, suitability of these parameters was checked by the integral absolute error (IAE) criterion. The control parameters in the PID algorithm were changed with time during the control of a polymerization reactor. It was seen that the genetic algorithm was able to tune the PID controller used in this system in terms of higher robustness and reliability by changing the parameters continuously.     

基于ARX-PLS解耦框架下的多回路约束迭代模型预测控制方法(英文)

A multi-loop constrained model predictive control scheme based on autoregressive exogenous-partial least squares （ARX-PLS） framework is proposed to tackle the high dimension, coupled and constraints problems in industry processes due to safety limitation, environmental regulations, consumer specifications and physical restric-tion. ARX-PLS decoupling character enables to turn the multivariable model predictive control （MPC） controller design in original space into the multi-loop single input single output （SISO） MPC controllers design in latent space. An idea of iterative method is applied to decouple the constraints latent variables in PLS framework and recursive least square is introduced to identify ARX-PLS model. This algorithm is applied to a non-square simulation system and a stirred reactor for ethylene polymerizations comparing with adaptive internal model control （IMC） method based on ARX-PLS framework. Its application has shown that this method outperforms adaptive IMC method based on ARX-PLS framework to some extent.     

化学反应器的神经元自适应观测和非线性控制

提出了一种基于神经网络的自适应观测和非线性控制策略,证明了自适应观测器的收敛件和非线性控制系统的稳定性,将其用于连续搅拌釜式放热反应器的浓度控制。根据可在线测量的反应温度,在线估计不可在线测量的反应物浓度和辨识Arrhenius指前因子,并利用重构的状态信息设计出带约束的非线性控制策略。仿真结果表明,观测器/控制器的组合提供了满意的闭环特性,证实了本文方法的有效性。     

Flexible on‐line Modeling and Control of pH in Waste Neutralization Reactors

The control of pH in waste neutralization processes presents a challenging highly nonlinear and time‐varying problem in which the reactor also suffers from inaccessible state information. The ability to characterize the changing dynamics of such reactors is essential to the success of advanced control schemes for these applications. In this work, flexible on‐line modeling of a pH reactor simulating nonstationary behavior was studied. This entailed a comparison of the most popular connectionist learning algorithm, the “Widrow‐Hoff delta rule”, with a classical tool in adaptive identification and control, recursive least squares (RLS). The modeling was pursued within the framework of neural networks using the ADALINE neural network. Further, two heuristically defined first‐principles‐based transforms were investigated for providing “general globally linearizing” information to the ADALINE. The comparisons of the learning algorithms for different neural network information vectors has led to a critical understanding of the flexibility of each algorithm for on‐line learning of the diverse process gain characteristics encountered in pH reactors.     

Multivariable model predictive control of a novel rapid pressure swing adsorption system

A multivariable model predictive control (MPC) algorithm is developed for the control and operation of a rapid pressure swing adsorption (RPSA)‐based medical oxygen concentrator. The novelty of the approach is the use of all four step durations in the RPSA cycle as independent manipulated variables in a truly multivariable context. The RPSA has a complex, cyclic, nonlinear multivariable operation that requires feedback control, and MPC provides a suitable framework for controlling such a multivariable system. The multivariable MPC presented here uses a quadratic optimization program with integral action and a linear model identified using subspace system identification techniques. The controller was designed and tested in simulation using a complex, highly coupled, nonlinear RPSA process model. The model was developed with the least restrictive assumptions compared to those reported in the literature, thereby providing a more realistic representation of the underlying physical phenomena. The resulting MPC effectively tracks set points, rejects realistic process disturbances and is shown to outperform conventional PID control. © 2017 American Institute of Chemical Engineers AIChE J, 64: 1234–1245, 2018     

Model predictive control for the reactant concentration control of a reactor

The reactant concentration control of a reactor using Model Predictive Control (MPC) is presented in this paper. Two major difficulties in the control of reactant concentration are that the measurement of concentration is not available for the control point of view and it is not possible to control the concentration without considering the reactor temperature. Therefore, MIMO control techniques and state and parameter estimation are needed. One of the MIMO control techniques widely studied recently is MPC. The basic concept of MPC is that it computes a control trajectory for a whole horizon time minimising a cost function of a plant subject to a dynamic plant model and an end point constraint. However, only the initial value of controls is then applied. Feedback is incorporated by using the measurements/estimates to reconstruct the calculation for the next time step. Since MPC is a model based controller, it requires the measurement of the states of an appropriate process model. However, in most industrial processes, the state variables are not all measurable. Therefore, an extended Kalman filter (EKF), one of estimation techniques, is also utilised to estimate unknown/uncertain parameters of the system. Simulation results have demonstrated that without the reactor temperature constraint, the MPC with EKF can control the reactant concentration at a desired set point but the reactor temperator is raised over a maximum allowable value. On the other hand, when the maximun allowable value is added as a constraint, the MPC with EKF can control the reactant concentration at the desired set point with less drastic control action and within the reactor temperature constraint. This shows that the MPC with EKF is applicable to control the reactant concentration of chemical reactors.     

Neurocontrol of a multi-effect batch distillation pilot plant based on evolutionary reinforcement learning

The time cost of first-principles dynamic modelling and the complexity of nonlinear control strategies may limit successful implementation of advanced process control. The maximum return on fixed capital within the processing industries is thus compromised. This study introduces a neurocontrol methodology that uses partial system identification and symbiotic memetic neuro-evolution (SMNE) for the development of neurocontrollers. Partial system identification is achieved using singular spectrum analysis (SSA) to extract state variables from time series data. The SMNE algorithm uses a symbiotic evolutionary algorithm and particle swarm optimisation to learn optimal neurocontroller weights from the partially identified system within a reinforcement learning framework. A multi-effect batch distillation (MEBAD) pilot plant was constructed to demonstrate the real world application of the neurocontrol methodology, motivated by the nonsteady state operation and nonlinear process interaction between multiple distillation columns. Multi-loop proportional integral (PI) control was implemented as a reduced model, reflecting an approach involving no modelling or significant controller tuning. Rapid multiple input multiple out nonlinear controller development was achieved using SSA and the SMNE algorithm, demonstrating comparable time and cost to implementation in relation to the reduced model. The optimal neurocontroller reduced the batch time and therefore the energy consumption by 45% compared to conventional multi-loop SISO PI control.