Modelling of Liquid Flow control system Using Optimized Genetic Algorithm

Abstract

Estimation of a highly accurate model for liquid flow process industry and control of the liquid flow rate from experimental data is an important task for engineers due to its non linear characteristics. Efficient optimization techniques are essential to accomplish this task.In most of the process control industry flowrate depends upon a multiple number of parameters like sensor output,pipe diameter, liquid conductivity ,liquid viscosity & liquid density etc.In traditional optimization technique its very time consuming for manually control the parameters to obtain the optimial flowrate from the process.Hence the alternative approach , computational optimization process is utilized by using the different computational intelligence technique.In this paper three different selection of Genetic Algorithm is proposed & tested against the present liquid flow process.The proposed algorithm is developed based on the mimic genetic evolution of species that allow the consecutive generations in population to adopt their environment.Equations for Response Surface Methodology (RSM) and Analysis of Variance (ANOVA) are being used as non-linear models and these models are optimized using the proposed different selection of Genetic optimization techniques. It can be observed that the among these three different selection of Genetic Algorithm ,Rank selected GA is better than the other two selection (Tournament & Roulette wheel) in terms of the accuracy of final solutions, success rate, convergence speed, and stability.

An Improved Grey Wolf Optimization Algorithm for Liquid flow Control System

“…Numerous specialists have exhibited a few hybridization variations for heuristic variations. Hybridization can be done by combining the two variants either in low level or elevated level with transfer or co-transformative systems as heterogeneous or homogeneous [41,42,43]. In this, we hybridize Particle Swarm Optimization with Gray Wolf Optimizer calculation utilizing low-level co transformative hybridization.…”

Parametric optimization of Liquid Flow Process by ANOVA Optimized DE, PSO & GA Algorithms

“…The experimental work is carried out with the Flow & level measurement & control set up [14](model no. WFT -20-I) shown in Fig .…”

“…Proposed model utilized in ultrasonic flow sensor based process system. Moreover an empirical model: ANOVA & RSM was used to make a relation between process output & input variables from given train dataset obtained from the experimental set up & finally optimized genetic algorithm used to predict liquid flow rate for a given set of input [14].From the result analysis it was seen that RSM based GA algorithm best fitted the experimental result. An ANN model used to made a non linear relationship between input & output variables of training result in liquid flow model & Genetic algorithm used to optimized the process parameters to make the model best fitted [15].…”

Whale Inspired Optimization Algorithm for Optimal Design of Low Voltage Amplifier

“…Metaheuristic optimization algorithms are recommended in such a case to generate good solutions in a reasonable time because they: 1) rely on quite simple concepts and are easy to implement; 2) can bypass local optima; 3) can be used for a wide range of problems in different disciplines [19]. Several metaheuristics have been suggested for the optimization of analog circuits, namely, particle swarm optimization (PSO) [20], butterfly optimization algorithm (BOA) [12], ant colony optimization (ACO) [21], genetic algorithm (GA) [22], simulated annealing (SA) [23], grey wolf optimization (GWO) [24], etc., These algorithms have proved to be useful and interesting in this field and their variety clearly shows that the debate remains open and that new methods are welcome to enrich this field of investigation. In this paper, we present an adaptation of the WOA algorithm for the analog integrated circuit design.…”