Parametric optimization of liquid flow control process using evolutionary algorithms

The liquid flow control process is the most interesting domain in the process control industry. Generally, process parameters of the liquid flow system are varied frequently during the operation. So, the selection of the level of process parameters or input variables seems to be important for achieving the optimum flow rate. This chapter focuses on the identification of the proper combination of the input parameters in a liquid flow rate process. Flow sensor output, pipe diameter, liquid conductivity, and viscosity have been taken as input parameters; flow rate obtained from the test is taken as a response parameter. Initially, ANOVA has been used as a mathematical model for the process. In the next stage, several recently proposed metaheuristics such as GA, PSO, and DE are applied for parametric optimization of the liquid flow process to maximize the response parameter. From the simulated results, tests and our proposed methodologies are discussed.