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.
Highlights
In most of the process control industry, there is a no requirement to calculate the input to a process that will steer its outputs in a craved way and attain some optimum goal
The algorithms are tested against both cases of Response Surface Methodology (RSM) or Analysis of Variance (ANOVA) based model as described earlier section where 117 number of data sets are used to train the data & construct the objective function of liquid Flow rate using ANOVA & RSM .The experimental dataset has been obtained from the laboratory experimentation as mentioned in earlier section
The dataset consists of 17 data points of sensor output voltage (E), pipe diameter (D) and liquid flow rate (F) liquid conductivity(k) & liquid viscosity (n).This experimental dataset has been used a test dataset for the parametric optimization of Genetic Algorithm based model of liquid flow control process
Summary
In most of the process control industry, there is a no requirement to calculate the input to a process that will steer its outputs in a craved way and attain some optimum (desired) goal. OPTIMIZATION OF LIQUID FLOW CONTROL SYSTEM literature survey is conduct on the different types of optimization techniques applied in flow rate control system. A semiconductor based Anemometer type flow meter is designed in [3], and [4] propose to eliminate the non linearity relation between the flow rate & Anemometer flow sensor output. Bera et al.[5] shows a comparative study between the matched pair transistors flow meter and platinum resistance temperature detector. The results showed a linear relationship between the sensor output and flow rate; whereas for the turbulent flow the relationship followed a non linear relation.Santhosh KV and BK Roy [6] proposed a model to make the quantification system adaptive to variations in pipe diameter, liquid density, and liquid temperature using optimized using ANN.Proposed measurement technique attains the objectives quite decently. Estimation of a highly accurate model for describing a liquid flow control process is still an open problem to us
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