A fuzzy model based control of well mixed reactor with cooling jacket

Fuzzy logic control uses linguistic rather than crisp numerical rules to control the industrial processes. Fuzzy logic control may be attractive when the process is either difficult to control or to model by conventional methods. Fuzzy control of processes is an alternative when systems cannot be well controlled by classical or modern control techniques which are based on crisp mathematical models and also reply heavily on measurements to indicate variation in process conditions. For many processes, models and measurements are very difficult to obtain correctly and they show nonlinear behavior. Chemical reactors, pH neutralization process and waste water treatment are the examples for these processes. In such a systems, control rules can be developed depending on whether process is to be controllable. If the operators knowledge and experience can be explained in words, then linguistic rules can be written easily. In such cases, fuzzy model refers to the description of the operators input/output control actions using fuzzy implications. In the present work inlet temperature of a well mixed reactor with cooling jacked was controlled by using fuzzy model based control algorithm at desired value. Inlet and outlet temperature of the cooling water of the jacket were measured with two thermocouples which are connected to the on-line computer. Cooling water was pumped to the system at a certain temperature. Heat input from immersed heater was given to the system with definite values of heat input and cooling flow rate system shows steady-state behavior. With some physical properties these system are observed nonlinearities. Heat input was adjusted by Triack Module and it was chosen as an manipulated variable. The reference sets and scaling factors are used for mixing tank temperature and heat input deviation variables from steady state values. Necessary data was generated by applying the pseudo random uniform effects in every 15 minutes. The deviations from steady state of heat input are distributed between ±9000 cal/sec. Six thousand data points were recorded for run time. The identification algorithm was applied and the model relation matrix was evaluated. The developed model is used to predict the output for possible control actions. Nine allowable control changes in heat input are taken as the values of 0.0, ±500, ±1000, ±4000, ±9000 cal/sec. These were added in turn to the current values of heat input and fed to the model together with the current values of tank temperature. The model calculates the expected value of tank temperature at the reset control interval. The decision maker then selects the most favorable action to take the one which results in the smallest error. The selected control action is then applied to the process and the this produce is repeated every control interval. Fuzzy control of the reactor temperature was realized experimentally. Also theoretically work has been done using simulation program. It was observed that theoretical result gave very good agreement with experimental datas.