Modeling of liquid flow control process using improved versions of elephant swarm water search algorithm

• Published in: SN applied sciences Vol. 1; no. 8; p. 886
• Main Authors: Mandal, Sudip, Dutta, Pijush, Kumar, Asok
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.08.2019; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Applied and Technical Physics; Chemistry/Food Science; Computer applications; Computing time; Earth Sciences; Engineering; Engineering: Bio-Inspired Optimization Algorithms; Environment; Flow control; Flow rates; Flow velocity; Fuzzy logic; Intelligence; Investigations; Liquid flow; Materials Science; Modelling; Neural networks; Optimization; Optimization techniques; Process controls; Research Article; Response surface methodology; Search algorithms; Sensors; Statistical analysis; Variables; Variance analysis; Viscosity; Elephant swarm water search algorithm (ESWSA); Analysis of variance (ANOVA); Parametric optimization; Response surface methodology (RSM); Liquid flow control process
• ISSN: 2523-3963; 2523-3971
• DOI: 10.1007/s42452-019-0914-5

In process industry, liquid flow rate is one of the important variables which need to be controlled to obtain the better quality and reduce the cost of production. The liquid flow rate depends upon number of parameters like sensor output voltage, pipe diameter etc. Conventional approach involves manual tuning of these variables so that optimal flow rate can be achieved which is time consuming and costly. However, estimation of an accurate computational model for liquid flow control process can serve as alternative approach. It is nothing but a non-linear optimization problem. In this work, three different improved versions of original elephant swarm water search algorithm (ESWSA) is proposed and tested against the present problem of liquid flow control. Equations for response surface methodology and analysis of variance are being used as non-linear models and these models are optimized using those newly proposed optimization techniques. The statistical analysis of the obtained results shows that the proposed MESWSA has highest overall efficiency (i.e. 45%) and it outperformed the others techniques for the most of the cases of modeling for liquid flow control process. But one of the major disadvantages of MESWSA is its slow convergence speed. On the other hand, ESWSA is better for finding the best fitness and LESWSA has better stability in output. Moreover, LMESWSA is found to be best efficient algorithm with respect to success rate and computational time. However, all algorithms and models can predict the liquid flow rate with satisfactory accuracy.