On-line economic optimization of a chemical reactor

An economic optimizing control system for a continuous flow stirred tank chemical reactor is designed, simulated, and installed on a pilot scale reactor. The control scheme utilizes a reaction and reactor model to predict on-line the economic optimum of a reactant concentration. In this manner the control system manipulates the reactant flow rate to maintain the optimum concentration during changes in reaction-reactor parameters with time. Typical parameter changes include the decay of reaction catalyst activity. The optimizing controller will function in conjunction with various operating policies including control to maintain a specified production rate --Abstract, page ii

Model Predictive Control Strategy for Industrial Process

Model Predictive control (MPC) is shown to be particularly effective for the self-tuning control of industrial processes. It makes use of a truncated step response of the process and provides a simple explicit solution in the absence of constraints. Here we use Dynamic Matrix Control (DMC). DMC uses a set of basis functions to form the future control sequence. The industrial success of DMC has mainly come from its application to high dimension multivariable system without constraints. Here main objective of DMC controller is to drive the output as close to the set point as possible in a least square sense with the possibility of the inclusion of a penalty term on the input moves. Therefore, the manipulated variables are selected to minimize a quadratic objective that can consider the minimization of future error. Implementation of the internal model control is also shown here. The control strategy is to determine the best model for the current operating condition and activate the corresponding controller. Internal model control (IMC) continues to be a powerful strategy in complex, industrial processes control application. This structure provides a practical tool to influence dynamic performance and robustness to modeling error transparently in the design. It is particularly appropriate for the design and implementation of controllers for linear open loop stable system. A simulated example of the control of nonlinear chemical process is shown. The nonlinear chemical process study in this work is the exothermic stirred tank reactors system with the first order reaction. The reaction is assumed to be perfectly mixed and no heat loss occurs within the system. Using internal model control and dynamic matrix control has simulated control of the total process in CSTR. Simulation example provided to show the effectiveness of the proposed control strategy

Modelling For Temperature Non-Isothermal Continuous Stirred Tank Reactor Using Fuzzy Logic

As we study chemical engineering and in process engineering, we come across the importance of a Continuous Stirred Tank Reactor, CSTR. Many types of controllers have been applied on the CSTR process to control the reactor temperature. In this research paper, an analysis of the response of the conventional Proportional-Integral-Derivative, PID controller and multi types of Fuzzy Logic controller for temperature control of CSTR and to design a control system of a non-isothermal temperature control of a CSTR in order to produce the most theoretically stable response curve. A mathematical model of a CSTR using the most general operating condition was developed through a set of differential equations which were later then converted into S-function using MATLAB (Matrix Laboratory) editor. After developing the S-function of the defined CSTR model was used in SIMULINK (a graphical programming environment from MATLAB), model by using a path called User-Defined functions which allows us to apply a M.file system that was created prior to the simulation as for this project we used a system block of the S-function or S-function builder

Optimal Control of Unknown Nonlinear System From Inputoutput Data

Optimal control designers usually require a plant model to design a controller. The problem is the controller\u27s performance heavily depends on the accuracy of the plant model. However, in many situations, it is very time-consuming to implement the system identification procedure and an accurate structure of a plant model is very difficult to obtain. On the other hand, neuro-fuzzy models with product inference engine, singleton fuzzifier, center average defuzzifier, and Gaussian membership functions can be easily trained by many well-established learning algorithms based on given input-output data pairs. Therefore, this kind of model is used in the current optimal controller design. Two approaches of designing optimal controllers of unknown nonlinear systems based on neuro-fuzzy models are presented in the thesis. The first approach first utilizes neuro-fuzzy models to approximate the unknown nonlinear systems, and then the feasible-direction algorithm is used to achieve the numerical solution of the Euler-Lagrange equations of the formulated optimal control problem. This algorithm uses the steepest descent to find the search direction and then apply a one-dimensional search routine to find the best step length. Finally several nonlinear optimal control problems are simulated and the results show that the performance of the proposed approach is quite similar to that of optimal control to the system represented by an explicit mathematical model. However, due to the limitation of the feasible-direction algorithm, this method cannot be applied to highly nonlinear and dimensional plants. Therefore, another approach that can overcome these drawbacks is proposed. This method utilizes Takagi-Sugeno (TS) fuzzy models to design the optimal controller. TS fuzzy models are first derived from the direct linearization of the neuro-fuzzy models, which is close to the local linearization of the nonlinear dynamic systems. The operating points are chosen so that the TS fuzzy model is a good approximation of the neuro-fuzzy model. Based on the TS fuzzy model, the optimal control is implemented for a nonlinear two-link flexible robot and a rigid asymmetric spacecraft, thus providing the possibility of implementing the well-established optimal control method on unknown nonlinear dynamic systems

Stabilisation of Time Delay Systems with Nonlinear Disturbances Using Sliding Mode Control

This paper focuses on a class of control systems with delayed states and nonlinear disturbances using sliding mode techniques. Both matched and mismatched uncertainties are considered which are assumed to be bounded by known nonlinear functions. The bounds are used in the control design and analysis to reduce conservatism. A sliding function is designed and a set of sufficient conditions is derived to guarantee the asymptotic stability of the corresponding sliding motion by using the Lyapunov-Razumikhin approach which allows large time varying delay with fast changing rate. A delay dependent sliding mode control is synthesised to drive the system to the sliding surface in finite time and maintain a sliding motion thereafter. Effectiveness of the proposed method is demonstrated via a case study on a continuous stirred tank reactor system

Integrated design of chemical waste water treatment systems.

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Flat systems, equivalence and trajectory generation

Flat systems, an important subclass of nonlinear control systems introduced via differential-algebraic methods, are defined in a differential geometric framework. We utilize the infinite dimensional geometry developed by Vinogradov and coworkers: a control system is a diffiety, or more precisely, an ordinary diffiety, i.e. a smooth infinite-dimensional manifold equipped with a privileged vector field. After recalling the definition of a Lie-Backlund mapping, we say that two systems are equivalent if they are related by a Lie-Backlund isomorphism. Flat systems are those systems which are equivalent to a controllable linear one. The interest of such an abstract setting relies mainly on the fact that the above system equivalence is interpreted in terms of endogenous dynamic feedback. The presentation is as elementary as possible and illustrated by the VTOL aircraft