TUNING OF A PROPORTIONAL–INTEGRAL–DERIVATIVE CONTROLLER USING A MULTIOBJECTIVE GENETIC ALGORITHM NONDOMINATED SORTING GENETIC ALGORITHM-II APPLIED TO A pH PROCESS
Most control engineering problems are characterized by several, often contradicting, objectives, which have to be satisfied simultaneously. Two widely used methods for finding the optimal solution to such problems are aggregating to a single criterion, and using Pareto-optimal solutions. Here we propose the nondominated sorting genetic algorithm (NSGA-II) to find the constant proportional–integral–derivative (PID) control gains for a chemical neutralization plant. Known to be highly nonlinear and with varying time delay, this plant provides a challenging test bed for nonlinear control strategies. The results confirm that a multiobjective, Pareto-based GA search gives a better performance than a single objective GA. The former method was also used to design a gain-scheduled PID controller. PRACTICAL APPLICATIONS
The control of the pH neutralization process plays a very important role in chemical industries, such as wastewater treatments, polymerization reactions, fatty acid production, biochemical processes, pharmaceuticals, biotechnology and so on. The pH neutralization process shows a strong nonlinear behavior and time-varying nonlinear characteristics resulted from the variation of the feed components or total ion concentrations. In the case of fermentation by the microorganisms, the growth of microorganisms has a pH optimum similar to most biochemical processes. A pH value drifting away from this optimum often inhibits growth of the essential microorganisms, alters the bacterial population and inhibits the desirable enzymatic activities. The result is a delay in the fermentation process, which in turn results in financial loss.
Document Type: Research Article
Publication date: February 1, 2010