[COLOR=#000000][FONT=Tahoma]Fuzzy logic is a particular area of concentration in the study of artificial intelligence and is based on the value of that information which is neither definitely true nor false. Fuzzy logic seems to be well suited to the nonlinear world of chemical process control. Such techniques allow automated systems to perform analysis and consider behaviors in more human and flexible ways.
The main objective of this project is to study the characteristics of the fuzzy control, and to have an understanding of both theory and practice processes of fuzzy control. At the beginning of this project the concept and history of fuzzy system was studied very carefully. From the literatures it was realized that different structures were used in the fuzzy controller; fuzzification, defuzzification and rule base. Each unit has its own techniques in order to operate the fuzzy control at the desired conditions safely and efficiently.
Following the study of fuzzy concepts and structures, a simulation was carried out using fuzzy control for two different processes; level system and pH_neutralisation in waste water treatment plant, this enable to investigate the results and compare them with results from others traditional controllers.
The simulation study investigates how different types of rule bases fuzzy logic control work on this processes. Fuzzy control system has been implemented based on system model. Mamdani model was used for the modeling of both processes. According to the type of the membership function to be chosen in the simulink, the process operators could enter their personal preferences for the membership curves. The results don’t differ very much from one to another, but a few rules of thumb apply. A term set should be sufficiently wide to allow for noise in the measurement. A certain amount of overlap is desirable; otherwise the controller may run into poorly defined states, where it does not return a well defined output.
The results were very successful and accurate comparing with others conventional controller such as PID, PI. A very fast response and stable operation have been observed. In case of pH process, fuzzy controller shows the most remarkable result of maintaining the output pH to 7±1which is the acceptable pH level for waste water.
As a result from the work in this project, it was realized that if this fuzzy interpretation is correct and if the fuzzy theory works, then one should be able to solve the real-world control problems, because a control system based on fuzzy logic has many advantages as, it is easy to implement since it uses “if-then” logic instead of sophisticated differential equations; also it understands by people who do not have process control backgrounds. The fuzzy modeling techniques, fuzzy logic inference and decision-making and fuzzy control methods should all work for controlling problems – if they are developed correctly and appropriately.