Design an intelligent calibration technique using optimized GA-ANN for liquid flow control system

Dutta, Pijush; Kumar, Asok

doi:10.3166/jesa.50.449-470

Cited by 11 publications

(8 citation statements)

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“…For MESWSA and LMESWSA, the value of p(t) is changed according to the Eq. (12). For ESWSA, MESWSA, LESWSA and LMESWSA, the global search is followed according to Eqs.…”

Section: Lévy Based Modified Elephant Swarm Water Search Algorithm (Lmentioning

confidence: 99%

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

Mandal¹,

Dutta²,

Kumar

2019

SN Appl. Sci.

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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.Keywords Liquid flow control process · Parametric optimization · Elephant swarm water search algorithm (ESWSA) · Response surface methodology (RSM) · Analysis of variance (ANOVA)

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“…For MESWSA and LMESWSA, the value of p(t) is changed according to the Eq. (12). For ESWSA, MESWSA, LESWSA and LMESWSA, the global search is followed according to Eqs.…”

Section: Lévy Based Modified Elephant Swarm Water Search Algorithm (Lmentioning

confidence: 99%

“…Moreover, Dutta et al [12] investigated a hybrid Genetic Algorithm-Neural network (GA-ANN) model. It was employed for the prediction and optimization of liquid parameter, Anemometer sensor output and pipe diameter.…”

Section: Introductionmentioning

confidence: 99%

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

Mandal¹,

Dutta²,

Kumar

2019

SN Appl. Sci.

Self Cite

View full text Add to dashboard Cite

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“…The framework finds the data pattern to foresee future informational collections. A hybrid GA-optimized ANN model has been examined for the liquid flow process [15]. From the numerical outcomes, it was seen that rank choice hybrid GA-ANN model is superior with respect to other choices: Tournament and Roulette wheel & it produced the exactness of 98.42%.…”

Section: Introductionmentioning

confidence: 99%

An Improved Grey Wolf Optimization Algorithm for Liquid flow Control System

Dutta¹,

Majumder²,

Kumar³

2021

IJEM

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Liquid flow in a process industry is one of the significant factors which should be controlled to get the better quality and decrease the expense of generation. Customary methodology includes manual tuning of the input process parameter to obtain the required flow rate is tedious and exorbitant. Notwithstanding, estimation of a precise computational model for fluid stream control procedure can fill in as elective methodology. It is only a non-straight enhancement issue. As a contextual investigation, the WFT -20-I measure control arrangement for flow rate measurement and Control issue is thought of. In this work we proposes a hybrid improved particle swarm optimization (PSO-GWO) used to start the people's position, which can build the decent variety of the wolf pack, balance the global and neighborhood search capacity of the calculation and improve the intermingling pace of the calculation contrast with the Gray wolf enhancement (GWO) and Particle swarm advancement (PSO). Non linear models are improved utilizing those recently proposed streamlining strategies. Additionally all the utilized optimization techniques can anticipate the fluid stream rate with good exactness. The outcomes were investigated by utilizing the root mean square error (RMSE), exactness, and the different measures to evaluate the level of identification performance of the liquid flow contextual analysis model. The trustworthiness of the present models was compared with the past model for similar subsystems utilizing competitive intelligent methodologies. The measurable examination of the acquired outcomes produced the proposed HPSOGWO has most elevated generally speaking proficiency (i.e.99.96%) and it beat the others strategies for the majority of the instances of demonstrating for fluid stream control process. The outcomes of the present model show that the proposed approach gives prevalent demonstrating execution and outflanks its rivals.

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“…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]. Best accuracy obtained by Rank parent selected GA-ANN model about 98.42%.…”

Section: Introductionmentioning

confidence: 99%

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

Dutta¹,

Majumder²,

Kumar³

2021

IJEM

Self Cite

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Design an intelligent calibration technique using optimized GA-ANN for liquid flow control system

Cited by 11 publications

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

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

An Improved Grey Wolf Optimization Algorithm for Liquid flow Control System

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

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