Modelling of Liquid Flow control system Using Optimized Genetic Algorithm
AbstractEstimation 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.
P. Dutta, S. Mandal, and A. Kumar, Comparative study :FPA based response surface methodlogy and ANOVA for the parameter Optimization in Process Control, Advances in Modelling and analysis C, vol. 73, no. 1, pp. 23–27, 2018.
P. Dutta, S. Mandal and A. Kumar, Application of FPA and ANOVA in the Optimization of Liquid Flow Control Process, Review of Computer Engineering , vol. 5, no. 1, pp. 7–11, 2018.
S. C. Bera, B. Chakraborty, and D. N.Kole, Study of a Modified Anemometer Type Flow Meter, Sensors & Transducers Journal, vol.83, no. 9, pp. 1521–1526, 2007.
S. C. Bera, and M. Samik Study of a simple linearization technique of a p-n junction typeanemometer flow sensor, IEEE Transaction Instrumentation and Measurement, vol. 61, no. 9, pp. 545–552, 2012.
S. C. Bera, and J. K. Roy, An approach to the design and fabrication of a micro processor based flow meter using resistance and semiconductor probe, IETE Technical Review, vol. 18, no. 5, pp. 355–360, 2001.
K. V. Santosh, and K. V. Roy, An Intelligent Flow Measurement Technique using Ultrasonic Flow Meter with Optimized Neural Network, International Journal of Control and Automation, vol. 5, no. 4, pp. 185–196, 2012.
P. Dutta, and A. Kumar, Intelligent calibration technique using optimized fuzzy logic controller forultrasonic flow sensor, Mathematical Modelling of Engineering Problems , vol. 4,no. 2, pp. 91–94, 2017.
P. Dutta, and A. Kumar, Design an intelligent flow measurement technique by optimized fuzzy logic controller, Journal Europen des Systmes Automatiss, vol. 51, pp. 89–107, 2018.
P. Dutta, and A. Kumar, Study of Optimizated NN model for Liquid Flow sensor Based on Different Parameters, International conference on Materials,Applied Physics & Engineering, 2018.
P. Dutta, and A. Kumar, Flow sensor Analogue:Realtime Prediction Analysis using SVM & KNN, 1st International conference on Emerging trends in Engineering & science (ETES) on 23rd-24th March, 2018.
P. Dutta, and A. Kumar, Design an intelligent calibration technique using optimized GA-ANN for liquid flow control system, Journal Europen des Systmes Automatiss, vol. 50, no. 4-6, pp. 449–470, 2017.
P. Dutta, and A. Kumar, Application of an ANFIS model to Optimize the Liquid Flow Rate of a Process Control System, Chemical Engineering Transactions, vol. 71, pp. 991–996, 2018.
P. Dutta, and A. Kumar, Modeling and Optimization of a Liquid Flow Process using an Artificial Neural Network-Based Flower Pollination Algorithm, journal of intelligence system, https://doi.org/10.1515/jisys-2018-0206, 2018.
P. Dutta,S. Mandal, and A. Kumar, Modeling of Liquid Flow Control Process Using Improved Versions of Elephant Swarm Water Search Algorithm, SN Applied Sciences , SN Appl. Sci. (2019) 1: 886. https://doi.org/10.1007/s42452-019-0914-5, 2019.
L. Bianchi, A survey on metaheuristics for stochastic combinatorial optimization, Natural computing: an international Journal, vol. 8, no. 2, pp. 239–287, 2009.
R. C. Eberhart, Y. H. Shi, Comparing inertia weights and constriction factors in particle swarm optimization , Proceeding of IEEE Congress on Evolutionary Computation, pp. 84–88, 2000.
X. S. Yang, A New Metaheuristic Bat-Inspired Algorithm, Nature Inspired Cooperative Strategies for Optimization (NISCO 2010), vol. 284, pp. 65–74, 2010.
X. S. Yang, and S. Deb, Engineering Optimisation by Cuckoo Search, Int. J. Mathematical Modelling and Numerical Optimisation, vol. 1, no. 4, pp. 330–343, 2010.
X. S. Yang, Flower pollination algorithm for global optimization, Proceeding of Unconventional Computation and Natural Computation 2012, Lecture Notes in Computer Science , vol. 7445, pp. 240–249, 2012.
X. S. Yang, Firefly algorithm, stochastic test functions and design optimization, International Journal of Bio-Inspired Computation, vol. 2, pp. 78–84, 2010.
M. Dorigo, V. Maniezzo, and A. Colorni, Ant System: Optimization by a colony of cooperating agents, IEEE Transactions on Systems, Man, and Cybernetics C Part B, vol. 26, no. 1, pp. 29-41, 1996.
D. Karaboga, and B. Basturk, A powerful and efficient algorithm for numerical function optimization:artificial bee colony (ABC)algorithm Journal of Global Optimization, vol. 39, no. 3, pp. 459–471, 2007.
S. Mirjalili, S. M. Mirjalili, and A. Lewis, Grey wolf optimizer, Advances in engineering software, vol. 69, pp. 46–61, 2014.
S. Mirjalili, and Lewis, The whale optimization algorithm, Advances in Engineering Software, vol. 95, pp. 51–67, 2016.
D. H. Wolpert, and W. G. Macready, No Free Lunch Theorems for Optimization, IEEE Transactionson Evolutionary Computation, vol. 1, no. 1, pp. 67–82, 1997.
M. Montazeri, A. Poursamad, and B. Ghalichi, Application of genetic algorithm for optimization of control strategy in parallel hybrid electric vehicles, Journal of the Franklin Institute, Vol. 343, no. 4C5, pp. 420–435, 2006.
M. Loomans, and X. Visser, Application of the genetic algorithm for optimisation of large solar hot water systems, Solar Energy, Vol. 72, no. 5, pp. 427–439, 2002.
M. Lin, Application of Optimized Genetic Algorithm in Building Energy-Saving Optimization Control, Lecture Notes in Real-Time Intelligent Systems, pp. 182-188, 2017.
L. M. Fernandes, I.N. Figueiredo, J. J. Judice, L.A. Costa, and P.N. Oliveira, Application of Genetic Algorithms to plate optimization, Proceedings of the Fourth Congress on Computational Mechanics,Computational mechanics, Barcelona, Spain, 1998.
A. A. Freitas, A survey of Evolutionary Algorithms for Data Mining and Knowledge Discovery, Advances in Evolutionary Computation, Ser. A, vol. 92, pp. 103–118, 2002.
M. Pei, E. D. Goodman, and W. F. Punch, Pattern Discovery from Data Using Genetic Algorithms, Proceeding of 1st Pacific-Asia Conference Knowledge Discovery & Data Mining (PAKDD-97), 1997.
P. L. Schoonover, W. A. Crossley, and W. A. Heister, Application of Genetic Algorithm to the optimization of hybrid Rockets , Journal of Space craft and Rockets, vol. 37, no. 5, pp. 991–996, 2000.
M. A. Bezerra, R. E. santelli, E. P. Oliveira, L. S. Villar, L. S. Escaleria, response surface methodology (RSM) as a Tool for
Optimization in analytical Chemistry, Talanta, vol. 76, pp. 965–977, 2008.
S. A. Glantz, B. K. Slinker, and T. B. Neilands, Primer of applied regression & analysis of variance, Mcgraw-Hill Medical publishing Division, 2016.
H. J. Keselman, C. J. Huberty, L. M. Lix, R. A. cribbie, Statistical practices of educational researches:analysis of their ANOVA, MANOVA, and ANOCOVA analysis, Review in Education & Research, vol. 68, pp. 350–386, 1998.
Z. Michalewicz, Genetic Algorithms + Data Structures = Evolution Programs, 3rd Ed. Springer-Verlag, 1996.
Y. Park, and Song M, genetic algorithm for clustering problems. Genetic Programming 1998, Proceeding of 3rd Annual Conference, pp. 568–575. Morgan Kaufmann, 1998.
A. K. Jain, Zongker D, Feature Selection: Evaluation, Application, and Small Sample Performance, IEEE Transaction on Pattern Analysis and Machine Intelligence, vol. 19, no. 2, pp. 153–158, 1997.
E. Falkenauer, Genetic Algorithms and Grouping Problems, John Wiley & Sons, 1998.
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).