Variable Cascade Control Structure for Tubular Reactors

The application of a variable control structure for tubular reactors, based on multiple temperature measurements, is explored. The proposed structure allows the controller to adapt to temperature error variations along the tubular reactor. Controllers can be designed from simple input‐output first‐order dynamical models obtained from step responses and are coupled with a dynamic law for assigning a variable weight to conventional feedback cascade control loops. The simulation results show that the variable structure based on linear proportional‐integral controllers enhances the performance and robustness properties of conventional cascade schemes for controlling the outlet reactor concentration. Major advantages are shown in the face of a variety of concentration and temperature feed disturbances.     

Modelling of unsteady-state hydrodynamics in periodically operated trickle-bed reactors:Influence of the liquid-phase physical properties

Process intensification using periodic operation of trickle bed reactors (TBRs) is still a long way from replacing conventional steady-state operation in industrial use, despite the numerous benefits described in the literature. Complex interactions between hydrodynamics, mass transfer and reaction phenomena make the design of periodically operated TBRs an almost insurmountable challenge. The development of hydrodynamic models able to provide reliable quantitative predictions of flow behaviour and possessing a sound physical basis, is an essential prerequisite for obtaining the necessary insights into this complexity. In this work, the two-phase pressure drop and dynamic liquid hold-up during max/min and on/off periodical operation were predicted using a model based on the relative permeability concept. In order to demonstrate the utility of this approach, a systematic investigation of the quantitative influence of the liquid-phase physical properties was carried out. The results obtained show that the modelling of the hydrodynamics in periodically operated TBRs using the relative permeability concept is feasible. By selecting suitable permeability parameters, unsteady-state hydrodynamics for different periodic operating modes can be predicted successfully.     

Robust discrete control of nonlinear processes: Application to chemical reactors

Trajectory tracking or rejecting persistent disturbances with digital controllers in nonlinear processes is a class of problems where classical control methods breakdown since it is very difficult to describe the dynamic behavior over the entire trajectory. In this paper, a model-based robust control scheme is proposed as a potential solution approach for these systems. The proposed control algorithm is a robust error feedback controller that allows us to track predetermined operation profiles while attenuating the disturbances and maintaining the stability conditions of the nonlinear processes. Various numerical simulation examples demonstrate the effectiveness of this robust scheme. Two examples deal with effective trajectory tracking in chemical reactors over a wide range of operating conditions. The third example analyses the attenuation of periodic load in a biological reactor. All examples illustrate the ability of the robust control scheme to provide good control in the face of parameter uncertainties and load disturbances.     

MULTIRATE MULTIVARIABLE SELF-TUNING CONTROL OF DISTILLATION COLUMNS USING KALMAN PREDICTOR

A novel method for designing Kalman Predictor (KP) based multivariable self-tuning controllers for Multi Input Multi Output (MIMO) systems has been proposed, and also applied to the control of two distillation columns. The objective is to maintain constant terminal compositions despite disturbances entering the system even when the controlled variables are not measured at the same sampling rate. The KP generates minimum variance estimates of the output variables. Simulation results show that KP based multirate multivariable self-tuning controller exhibits better performance than the earlier reported multirate controller for set point tracking, even in the presence of nonstationary disturbances. KP based self-tuning controller is, therefore, suitable for industrial applications.     

Robust and optimal control approach for exothermic reactor stabilization

In this paper, the possibility to stabilise open-loop unstable exothermic reactors with temperature measurements is studied. Moreover, the reactors are considered to be multi-input multi-output systems with parametric uncertainties. Robust static output feedback and optimal controllers are designed for stabilization of the exothermic reactors into their open-loop unstable steady states. Stabilization of reactors is simulated using designed controllers. The possibility of using both types of controllers for energy savings is studied and measured by coolant consumption. The approach is tested with a representative example. Obtained simulations results confirm that the robust static output feedback controllers outperform optimal controllers when systems with parametric uncertainties are controlled.     

Optimization-based strategies for the operation of low-density polyethylene tubular reactors: nonlinear model predictive control

In this work, we present a general nonlinear model predictive control (NMPC) framework for low-density polyethylene (LDPE) tubular reactors. The framework is based on a first-principles dynamic model able to capture complex phenomena arising in these units. We first demonstrate the potential of using NMPC to simultaneously regulate and optimize the process economics in the presence of persistent disturbances such as fouling. We then couple the NMPC controller with a compatible moving horizon estimator (MHE) to provide output feedback. Finally, we discuss computational limitations arising in this framework and make use of recently proposed advanced-step MHE and NMPC strategies to provide nearly instantaneous feedback.     

A method for robustness analysis of controlled nonlinear systems

This paper presents an approach to analyzing robustness properties of nonlinear systems under feedback control. The core idea is to apply numerical bifurcation analysis to the closed-loop process, using the controller/observer tuning parameters, the set points, and parameters describing model uncertainty (parametric as well as unmodeled dynamics) as bifurcation parameters. By analyzing the Hopf bifurcation and saddle-node bifurcation loci with respect to these parameters, bounds on the controller tuning are identified which can serve as a measure for the robustness of the controlled system. These bounds depend upon the type as well as the degree of mismatch that exists between the plant and the model used for controller design.The method is illustrated by analyzing three control systems which are applied to a continuously operated stirred tank reactor: a state feedback linearizing controller and two output feedback linearizing controllers. While model uncertainty has only a minor effect on the tuning of the state feedback linearizing controller, this does not represent a very realistic scenario. However, when an observer is implemented in addition to the controller and an output feedback linearizing scheme is investigated, it is found that the plant-model mismatch has a much more profound impact on the tuning of the observer than it has on the controller tuning. In addition, two observer designs with different level of complexity are investigated and it is found that a scheme which makes use of additional knowledge about the system will not necessarily result in better stability properties as the level of uncertainty in the model increases. These investigations are carried out using the robustness analysis scheme introduced in this paper.     

Modeling and control of batch-to-batch polymer processes

The modeling and control of a typical cyclic polymer process, such as injection molding or thermoforming, are considered. The purpose of control is to achieve a specified product quality for a sequence of parts. First, conventional feedback controllers are compared to statistically based controllers with respect to random noise disturbances. It is shown that conventional controllers may not react quickly to load disturbances without magnifying background noise and reducing product yield. It is demonstrated that statistically based controllers are able to differentiate between noise and load disturbances, proving them superior when part quality tolerance is tight and noise level relatively large. Next, the responsiveness of conventional controllers (proportional, integral, and proportionalintegral) is compared with several statistically based controllers (CUSUM, Western Electric runs rules, and simple Shewhart) when subjected to load disturbances. Three load disturbances were modeled; steps, ramps, and sinusoids. Again, statistically based methods generally prove superior to and, at worst, comparable to conventional controllers.     

Robust temperature control for batch chemical reactors

Since batch chemical reactors exhibit an integrating response, temperature control for these systems can be a real problem for conventional PID controllers. Tuning can be extremely difficult due to the reduced stability margins proved for this type of processes. In this work, a simple robust control strategy for temperature regulation in batch and semi-batch chemical reactors is proposed. The feedback controller is composed by an approximate I/O linearizing feedback equipped with a calorimetric balance estimator. Based on standard results from singular perturbations, it is proven that the proposed feedback controller (i) can track a bounded temperature trajectory as close as desired (i.e., practical stability) by adjusting a single estimation parameter, and (ii) after a short transient, the performance of the exact I/O linearizing feedback can be recovered as the calorimetric balance estimation rate is increased.     

Design and Analysis of Integrated Predictive Iterative Learning Control for Batch Process Based on Two-dimensional System Theory

Based on the two-dimensional (2D) systemtheory, an integrated predictive iterative learning control (2D-IPILC) strategy for batch processes is presented. First, the output response and the error transition model predictions along the batch index can be calculated analytically due to the 2D Roesser model of the batch process. Then, an integrated framework of combining iterative learning control (ILC) andmodel predictive control(MPC) is formed reasonably. The output of feedforward ILC is estimated on the basis of the predefined process 2D model. By minimizing a quadratic objective function, the feedback MPC is introduced to obtain better control performance for tracking problem of batch processes. Simulations on a typical batch reactor demonstrate that the satisfactory tracking performance as well as faster convergence speed can be achieved than traditional proportion type (Ptype) ILC despite the model error and disturbances.     

ARTIFICIAL NEURAL NETWORKS: PRINCIPLE AND APPLICATION TO MODEL BASED CONTROL OF DRYING SYSTEMS - A REVIEW

Abstract

This paper reviews the developments in the model based control of drying systems using Artificial Neural Networks (ANNs). Survey of current research works reveals the growing interest in the application of ANN in modeling and control of non-linear, dynamic and time-variant systems. Over 115 articles published in this area are reviewed. All landmark papers are systematically classified in chronological order, in three distinct categories; namely, conventional feedback controllers, model based controllers using conventional methods and model based controllers using ANN for drying process. The principles of ANN are presented in detail. The problems and issues of the drying system and the features of various ANN models are dealt with upto-date. ANN based controllers lead to smoother controller outputs, which would increase actuator life. The paper concludes with suggestions for improving the existing modeling techniques as applied to predicting the performance characteristics of dryers. The hybridization techniques, namely, neural with fuzzy logic and genetic algorithms, presented, provide, directions for pursuing further research for the implementation of appropriate control strategies. The authors opine that the information presented here would be highly beneficial for pursuing research in modeling and control of drying process using ANN     

ARTIFICIAL NEURAL NETWORKS: PRINCIPLE AND APPLICATION TO MODEL BASED CONTROL OF DRYING SYSTEMS - A REVIEW

This paper reviews the developments in the model based control of drying systems using Artificial Neural Networks (ANNs). Survey of current research works reveals the growing interest in the application of ANN in modeling and control of non-linear, dynamic and time-variant systems. Over 115 articles published in this area are reviewed. All landmark papers are systematically classified in chronological order, in three distinct categories; namely, conventional feedback controllers, model based controllers using conventional methods and model based controllers using ANN for drying process. The principles of ANN are presented in detail. The problems and issues of the drying system and the features of various ANN models are dealt with upto-date. ANN based controllers lead to smoother controller outputs, which would increase actuator life. The paper concludes with suggestions for improving the existing modeling techniques as applied to predicting the performance characteristics of dryers. The hybridization techniques, namely, neural with fuzzy logic and genetic algorithms, presented, provide, directions for pursuing further research for the implementation of appropriate control strategies. The authors opine that the information presented here would be highly beneficial for pursuing research in modeling and control of drying process using ANN     

Feedback control design for an anaerobic digestion process

An approach for the design of linear feedback controllers for anaerobic digestion systems is presented. The effluent chemical oxigen demand (COD) concentration and the dilution rate are taken respectively as the regulated and the manipulated variables. The control design is based on simple step‐response models of the process endowed with an input delay to account for dead‐times induced by measurement devices. The resulting feedback controller has a traditional proportional‐integral (PI) control structure, so it can be easily implemented with conventional control technologies. Since the concentration of volatile fatty acids can be easily and quickly measured as compared with COD concentration, it is used as a secondary measurement that is incorporated into the feedback loop scheme to enhance the robustness of the control scheme with respect of influent disturbances. The performance of the proposed control scheme is illustrated via numerical simulations and experimental work. © 2002 Society of Chemical Industry     

Process control of polymer extrusion. Part II: Feedforward control

A feedforward plus feedback control method (FFC) and an adaptive feedforward plus feedback control method (AFFC) were proposed in this study to control the extrudate thickness of polymer extrusion. They were tested by step changes of screw speed and feedstock, and square wave type of screw speed changes. It is found that these feedforward control methods worked very well for various load disturbances but they required a good process model and accurate on-line measurements of manipulated variables and load variables. The feedback function was necessary to compensate the over- or under-corrections of the feedforward controllers and to handle other disturbances which were not considered in the feedforward model.     

Optimal discrete stochastic control theory for process application

The design of discrete feedback controllers which minimize some linear function of the variances of the output deviations from target subject to possible constraints on the variances of the inputs, for linear systems subject to stochastic disturbances, is treated from two points of view: (1) using transfer function models to characterizing the process dynamics and autoregres-sive-moving-average models to characterize the stochastic disturbances, and then solving the optimal control problem using an approach due to Box and Jenkins and a discrete version of the Wiener-Newton theory; and (2) using state variable models to characterize both the dynamic and stochastic parts of the system, and then solving the optimal control problem using the results of dynamic programming and Kalman filtering. Practical considerations such as model forms, their identification and estimation, and the development of variance relationships that are necessary for the application of these two approaches in the process industries are discussed. The relationship between and a comparison of these two approaches is made.     

Iterative learning reliable control of batch processes with sensor faults

An iterative learning reliable control (ILRC) scheme is developed in this paper for batch processes with unknown disturbances and sensor faults. The batch process is transformed into and treated as a two-dimensional Fornasini-Marchesini (2D-FM) model. Under the proposed control law, the closed-loop system with unknown disturbances and sensor faults not only converges along both the time and the cycle directions, but also satisfies certain H_∞ performance. For performance comparison, a traditional reliable control (TRC) law based on dynamic output feedback is also developed by considering the batch process in each cycle as a continuous process. Conditions for the existence of ILRC scheme are given as biaffine and linear matrix inequalities. Algorithms are given to solve these matrix inequalities and to optimize performance indices. Applications to injection packing pressure control show that the proposed scheme can achieve the design objectives well, with performance improvement along both time and cycle directions, and also has good robustness to uncertain initialization and measurement disturbances.     

Two-degree-of-freedom output feedback controllers for nonlinear processes

In this work, a nonlinear output feedback control algorithm is proposed, in the spirit of model-state feedback control. The structure provides state estimates using a process model, the measured output, and the residual between the model output and the measured output. These estimates will track the process states at a rate determined by a set of tunable parameters. An algebraic transformation of the state estimates is incorporated in the control structure to ensure that the input/output gain of the observer matches the model upon which the static state feedback control law is based. The transformed states are then used in the control law. This leads to a controller of minimal order possessing integral action. The control structure is shown to have the same properties as the standard model-state feedback structure. The resulting algorithm is a two-degree of freedom control law, in the sense that the control action is not a function of the error only, but the output and the set point are processed in different ways. Finally, a simulation example using an exothermic CSTR operating at an open-loop unstable steady state is used to demonstrate the closed-loop performance of the proposed method.     

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.     

Dynamic modeling and control of soybean meal drying in a direct rotary dryer

This paper presents the analysis of the dynamic model and the application of Proportional, Integral and Derivative (PID) control in the soybean meal drying in a industrial direct rotary dryer by computational tests. Therefore, load disturbances as step, pseudorandom, and impulse were applied in the inlet speed and in the inlet moisture of soybean meal in the dryer. Later, the output responses for perturbations were investigated with control systems. The application of feedback PID control showed satisfactory results, returning expected output moisture. According to the Integral Square Error (ISE) the manipulated variables that showed better controllability were the inlet speed, when the perturbation was in the soybean meal inlet moisture; and the inlet temperature of drying air, when the perturbation was in the inlet speed of soybean meal. These results are coherent with literature and conclude that, the tuning feedback PID control keeps the output moisture of soybean meal inside the specifications with fast results.