Sliding mode control of a bioreactor

In this paper, sliding mode control (SMC) of a bioreactor is considered and is compared with PID control. The magnitude of the error in SMC is found to be lower than that in PID control. Moreover, the magnitudes of cells and nutrients were very close to the selected reference values in SMC, whereas they were quite different in PID control. Overall, SMC was more robust against disturbances and had better performance than PID control.     

LINEAR FEEDBACK EQUIVALENCE AND CONTROL OF AN UNSTABLE BIOLOGICAL REACTOR

A novel method for the feedback control of an unstable, continuous stirred tank bioreactor is described in this paper. The models considered describe the growth of a single microorganism on a single, rate limiting substrate. A theoretical discussion demonstrates that for models of this type, a global feedback transformation exists which linearizes the state response. Such a transformation is computed using the methods of Hunt, Su and Meyer (1983). A controller is computed using linear state feedback control, and an extended Kalman filter is used to estimate unmeasured state variables. Simulation results demonstrate satisfactory control behavior over a wide region of the phase plane.     

LINEAR FEEDBACK EQUIVALENCE AND CONTROL OF AN UNSTABLE BIOLOGICAL REACTOR

A novel method for the feedback control of an unstable, continuous stirred tank bioreactor is described in this paper. The models considered describe the growth of a single microorganism on a single, rate limiting substrate. A theoretical discussion demonstrates that for models of this type, a global feedback transformation exists which linearizes the state response. Such a transformation is computed using the methods of Hunt, Su and Meyer (1983). A controller is computed using linear state feedback control, and an extended Kalman filter is used to estimate unmeasured state variables. Simulation results demonstrate satisfactory control behavior over a wide region of the phase plane.     

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.     

Model-based control strategies for a chemical batch reactor with exothermic reactions

Batch reactor control provides a very challenging problem for the process control engineer. This is because a characteristic of its dynamic behavior shows a high nonlinearity. Since applicability of the batch reactor is quite limited to the effectiveness of an applied control strategy, the use of advanced control techniques is often beneficial. This work presents the implementation and comparison of two advanced nonlinear control strategies, model predictive control (MPC) and generic model control (GMC), for controlling the temperature of a batch reactor involving a complex exothermic reaction scheme. An extended Kalman filter is incorporated in both controllers as an on-line estimator. Simulation studies demonstrate that the performance of the MPC is slightly better than that of the GMC control in nominal case. For model mismatch cases, the MPC still gives better control performance than the GMC does in the presence of plant/model mismatch in reaction rate and heat transfer coefficient.     

Nonlinear model based control of two-product reactive distillation column

Nonlinear feedback control scheme for reactive distillation column has been proposed. The proposed control scheme is derived in the framework of Nonlinear Internal Model Control. The product compositions and liquid and vapor flow rates in sections of the reactive distillation column are estimated from selected tray temperature measurements by an observer. The control scheme is applied to an example reactive distillation column in which two products are produced in a single column and the reversible reaction A+B=C+D occurs. The relative volatilities are favorable for reactive distillation so that the reactants are intermediate boilers between the light product C and the heavy product D. Ideal physical properties, kinetics, and vapor-liquid equilibrium are also assumed. It is shown that the proposed control scheme keeps tight product composition control.     

Nonlinear control of a batch crystallizer

A nonlinear geometric feedback controller with and without state estimation (the extended continuous-discrete Kalman filter) is developed and applied to a 0.027 m 3 potash alum batch crystallizer. The manipulated variable isthetemperature of the inlet cooling water supplied to the jacket of the crystallizer, and the controlled variable is the supersaturation. It is shown that the controller eliminates the large initial peak in the supersaturation (which results in excessive nucleation) and maintains the supersaturation at its set-point, provided that themanipulated variable does not reach its constraints. The controller performs well with only two measured states (the crystallizer temperature and the soluteconcentration) and results in larger terminal crystal mean size in com-parison with natural cooling and linear cooling policies with fines dissolution.     

Nonlinear inferential control for an exothermic packed-bed reactor: geometric approaches

This article deals with the low-order output regulator designs for a commercial-scale packed-bed reactor. Under the static interpolation-based technique for data reconciliation mechanism, the optimum-based two-input control scheme by exploiting the secondary output information can almost attenuate measurable disturbances on the primary output. The steady-state approach not only reduces the critical hot spot and/or thermal runaway but it also dominates the desirable conversion rate in the exit. Moreover, the piece-wise iterative procedure connected with feasible programming evolution for control computation is proposed such that the flexible output regulator with the aid of the ‘intelligent’ algorithm can effectively improve the transient performance. Simulation results have shown that the non-distributed, piece-wise feedback control scheme turns out to be robust against unknown perturbations.     

Designing control system with entropic modeling

Contributions of entropic modeling to the performance of reactive process control have been investigated. Modeling has been developed based on mass, energy and entropy balances and thermodynamic relations, resulting in a model for the entropy production rate. Using the conventional optimization technique, a minimum for the entropy production rate was found when a given relationship between the temperatures of the inlet stream and of the reaction is satisfied for a particular residence time in the reactor. A new class of nonlinear controller is proposed by means of introducing entropic models into the classical algorithms designed from a synthesis of the reference system. The results indicate that such a controller yields a superior performance when compared with classical feedback control strategies.     

Observer-based nonlinear control law for a continuous stirred tank reactor with recycle

This paper proposes new results concerning the problem of the control of a continuous stirred tank reactor with recycle. The novelty of the proposed results consists of a new nonlinear observer-based controller which is found by means of recent results of differential geometry for time-delay nonlinear systems, without using linear approximations of the model. Local convergence of the system state to the arbitrarily chosen operating point is theoretically proved. The significance of the proposed control law is shown by many simulations, which show high performances with any initial conditions, even at the start-up, and with critical cases of mismatched parameter values.     

Nonlinear model-based repetitive control of simulated moving bed process

The simulated moving bed (SMB) process, after more than 40 years of successful operation in the petro-chemical industry, has emerged as one of the most important separation processes in the pharmaceutical, fine chemical, and biotechnology fields. However, optimal operation and automatic control of the SMB process is still challenging because of its complex dynamics caused by periodic port switching and inherent nonlinearity. In this research, a novel advanced control technique for the SMB process has been proposed. In the proposed technique, regulation of both extract and raffinate purities measured at the terminal time of each switching period is performed by a nonlinear repetitive controller which utilizes the past period data as feedback information. The repetitive controller was designed on the basis of a fundamental nonlinear model of the SMB process. Through application to a numerical SMB process, it was found that the proposed control technique performs quite satisfactorily against model error as well as set point and disturbance changes.     

Nonlinear model-based dissolved oxygen control in a biological wastewater treatment process

The dissolved oxygen (DO) concentration has been an important process parameter in the biological wastewater treatment process (WWTP). In this paper, we propose a nonlinear control scheme to maintain the dissolved oxygen level of an activated sludge system. Without any linearization or model reduction, it can directly incorporate the nonlinear DO process model with on-line estimation of the respiration rate (R) and the oxygen transfer rate (K_La). Simulation results show that it outperforms a control performance of the PID controller. Since it incorporates the process disturbance and nonlinearity in the controller design, the suggested method can efficiently deal with the operating condition changes that occur frequently in the wastewater treatment process.     

Adaptive pole removal control for a batch polymerization reactor

A systematic method of controller design is introduced to determine the overall charcteristic behaviour developed on process pole removal. The time delay compensation is automatically incorporated in the proposed control law. The implemented tuning parameters in the law are confined to the range between 0 and 1 to guarantee the stability of the overall control system. Subsequently, the fixed and adaptive control strategies are implemented to simulate a batch PVC reaction system. The adaptive control scheme provides good, roubust control of this simulated reactor notwithstanding the wide range of operating conditions and non-linear dynamics of the system. However, the fixed control scheme performs well only for a noise-free system. In addition, two limiting control laws, derived from the proposed method, are also used to simulate the reactor. The results indicate that these laws are not suitable for this non-linear reaction system.     

Model predictive control of a fixed-bed reactor with nonlinear quality inference

A generic model predictive control framework has been proposed for a fixed-bed reactor with exothermic reaction. The proposed framework can conduct nonlinear inferential control of a product concentration together with linear multivariable control of bed temperatures. In addition, the framework can accommodate the multi-rate sampling and analysis delay caused by the product measurement. Performance of the proposed technique has been demonstrated with a non-adiabatic fixed bed reactor model producing maleic anhydride under various operating scenarios.     

Nonlinear control of polymerization reactor

In this work, nonlinear model based control was applied to the free radical solution polymerization of styrene in a jacketted batch reactor and its performance was examined to reach the required monomer conversion and molecular weight. Optimal temperature profiles for the properties of polymer quality were evaluated using the Hamiltonian optimization method. Total simulation program having mass and energy balances of the jacketed polymerization reactor was used to calculate the optimal trajectories. For control purposes, several experimental and theoretical dynamic studies have been made to observe the validity of simulation program. Experimental and theoretical nonlinear model based control have been investigated to track the temperature at the optimal trajectory Two types of parametric and nonparametric models were evaluated to achieve the temperature control. For this purpose, reaction curve was obtained to calculate the system dynamic matrix as a nonparametric model. In all control work, heat input to the reactor was chosen as a manipulated variable. Nonlinear auto regressive moving average exogenous (NARMAX) giving a relation between heat input and reactor temperature was chosen to represent the system dynamic and this model was used to describe the related control system as a parametric model. NARMAX model parameters were determined by using Levenberg Marquard algorithm. A pseudo random binary sequence (P.R.B.S.) signal was employed to disturb the system. Total simulation program was used to calculate the system and control parameters. Several types and orders were used to construct the NARMAX models. The efficiency and the performance of the nonlinear model based control with the NARMAX model and dynamic matrix were tested to calculate the best model. Nonlinear model based control system was used to control the reactor temperature at desired temperature trajectory experimentally and theoretically. Theoretical simulation results were compared with experimental control data. It was concluded that the control simulation program represents the behavior of the controlled reactor temperature well. In addition, nonlinear model based control keeps the reactor temperature of optimal trajectory satisfactorily.     

Optimal model-based aeration control policies in a sequencing batch reactor

We present a non-linear programming formulation for the computation of optimal aeration policies in a sequencing batch reactor for wastewater streams treatment. We assume that organic matter and nitrogen are the main pollutants to be removed to meet water quality targets. The novelty of the work lies in the fact that no binary variables are required to compute the switching time between the aerobic and anoxic stages of the water treatment process leading to a simpler, robust and easier to compute optimization formulation. Moreover, because the control valve, through which air is fed to the reactor, can take either its minimum or maximum bounds as well as any fractional values between such bounds, improved optimal aeration profiles are reported. Such improved profiles mean that shorter processing times are required, compared to previous solutions, leading also to a reduction in the operation cost of the wastewater treatment process. Although the optimal operation policies were computed for a typical home wastewater stream, the optimization formulation can also be extended for the treatment of other polluted streams.     

The bifurcation behavior of a polyurethane continuous stirred tank reactor

In this work the open-loop nonlinear bifurcation analysis of a continuous stirred tank reactor where polyurethane polymerization reactions take place is carried out. The effect of potential manipulated, disturbance and design variables on the reactor nonlinear behavior is addressed. Moreover, the impact of cascade feedback control on the steady-state multiplicity pattern is also discussed. It is shown that cascade control introduces new nonlinearity patterns increasing closed-loop sensitivity.     

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.     

间歇式反应釜温度串级预测函数控制

采用先进预测函数控制(PFC)技术实现实验室规模釜式反应器温度的控制。借助于被控对象的参数模型,对在线不可能测量或很难得到的工艺参数进行了估算,设计并在可编程逻辑控制器(PLC)中实现了温度串级控制。通过修正内嵌参数模型,抵消了模型的不确定性和各种扰动对系统稳定性的影响。结果表明:PFC在控制过程中既能很好地兼顾被控系统中子单元的动态性能,又具有很理想的控制响应品质。     

Hybrid fuzzy model-based predictive control of temperature in a batch reactor

Processes in industry, such as batch reactors, often demonstrate a hybrid and non-linear nature. Model predictive control (MPC) is one of the approaches that can be successfully employed in such cases. However, due to the complexity of these processes, obtaining a suitable model is often a difficult task. In this paper a hybrid fuzzy modelling approach with a compact formulation is introduced. The hybrid system hierarchy is explained and the Takagi–Sugeno fuzzy formulation for the hybrid fuzzy modelling purposes is presented. An efficient method for identifying the hybrid fuzzy model is also proposed.

A MPC algorithm suitable for systems with discrete inputs is treated. The benefits of the MPC algorithm employing the proposed hybrid fuzzy model are verified on a batch-reactor simulation example: a comparison between MPC employing a hybrid linear model and a hybrid fuzzy model was made. We established that the latter approach clearly outperforms the approach where a linear model is used.