The electrostatic precipitator (ESP) is an extensively used system in metallurgical industries and the generation of power to decrease the release of dust in the flue gas. In the design of the Electrostatic precipitator unit, gas emission uniform distribution is expected to fulfil its best aggregation performance. Programming Logic Controller (PLC) is a controller for industrial process automation and self-monitoring. A lot of industries utilized PLC to automatically control the entire process with less involvement from the human and to evade errors. In this paper, A mathematical model for Electrostatic precipitator from physical parameters and analysis has been developed. The controller is built depending on this model using the basic principle of a well-known A Proportional Integral Derivative (PID) controller to control the high voltage of the Electrostatic precipitator (ESP) by adjusting the opening of voltage and current by applying analogue signals (4-20 mA) from output cards of the PLC. The simulation results paved the way to build a practical system. building the mathematical model by using the Identification Toolbox of MATLAB® Version 9.6. The system was built using Allen Bradley PLC. The effect of control parameters (PID) in the case of voltage or current has been studied to demonstrate the efficiency of the model for the precipitator and observer in the case of the control system for the Al-Tahady ESP. The PID controller was built and the best values for the Electrostatic Precipitator controller are (KP=2.3904, KI=3.5382, KD=0.3). PID controller reduces steady-state errors.

This paper proposed an optimization algorithm in order to improve path maintaining of swarm of two wheel mobile robots with presence of external disturbance. The three robots forms use the leader-follower strategy, the best path for leader is determined using A* algorithm ,the other two robots follow the leader path. Two PID controller are used in each robot to control the angular and velocity torque of wheel. Each PID controller is tuned using intelligent optimization control method which are Particle swarm optimization ,random occurring distributed time delay particle swarm optimization and hybrid particle swarm optimization and genetic after that the proposed algorithm is used for tuning. The new algorithm is the contribution of this article. It is built by combine the random occurring distributed time delayed and genetic algorithm .The combination of these two algorithms takes the advantage of them by using the historical best global position of particles in random occurring distributed time delayed particle swarm optimization algorithm to update velocity of new population generated by genetic algorithm. The integral absolute error (IAE) is computed for system in each algorithm for comparison between them. The performance of intelligent control systems for controlling the three robots path is tested with presence of external disturbance in environment .Two type of external disturbance is tested, these are constant external disturbance and dynamic external disturbance. The performance of the same optimization algorithm is tested in pure environments. From the obtained result ,the new combination method is the best in both disturbance environments (constant or dynamic) and pure.