Optimization of Control Strategy Based on Soft Computing Approaches and Real Time Implementation in Cement Industries

This paper describes a control strategy for a nonlinear model of cement industry system and real time system designs are analyzed and their implementation in Labview simulation is outlined. Based on the Alopex evolutionary optimization algorithm with constrained T-S model, an intelligent fuzzy predictive controller to solve the control difficulties of industry process with multi-variables is approached. The application on cement rotary kiln control is discussed in detail. The rotary kiln calcinations is the most important part of cement production including complicated physical and chemical reaction processes with large inertia, pure hysteresis, nonlinearity and strong coupling characteristics and multi-variables. The main control system structure includes three control loops as the pressure control loop, the burning zone control loop and the backend of kiln temperature control loop. Parameters of the simulation model were set up based on the actual cement mill characteristics. The performances of the proposed control technique are compared with various conventional control techniques. The results of the proposed control technique indicate that this algorithm can prevent the cement industry effectively compared to the other control technique.