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Paper Title
OPTIMIZED PH CONTROL IN SUGARCANE JUICE CLARIFICATION USING FUZZY NEURAL NETWORKS AND EVOLUTIONARY ALGORITHMS
Authors
Rafiya Begum, Zakeer Husain, S.V. Halse
Keywords
Fuzzy Neural Networks, pH Control, Sugarcane Juice Clarification, Genetic Algorithms, Particle Swarm Optimization, Sugar Refineries, Intelligent Control, and Process Optimization.
Abstract
The clarification process in sugarcane juice refining is crucial for ensuring high-quality sugar production. This study explores the implementation of Fuzzy Neural Networks (FNN) combined with Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) to regulate the pH value during sugarcane juice clarification. Traditional pH control methods in sugar refineries often struggle with nonlinearities and process variations, leading to inefficiencies. By integrating intelligent control techniques, this research proposes a robust and adaptive model that optimizes pH regulation. Experimental results demonstrate that FNN, in conjunction with GA and PSO, enhances system stability and efficiency. The findings indicate improved response time, reduced pH deviation, and enhanced sugar quality.
IJCRT's Publication Details
Unique Identification Number - IJCRT1135963
Paper ID - 278565
Page Number(s) - 735-739
Pubished in - Volume 3 | Issue 2 | June 2015
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Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882
E-ISSN Number - 2320-2882
Cite this article
Rafiya Begum, Zakeer Husain, S.V. Halse, "OPTIMIZED PH CONTROL IN SUGARCANE JUICE CLARIFICATION USING FUZZY NEURAL NETWORKS AND EVOLUTIONARY ALGORITHMS", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.3, Issue 2, pp.735-739, June 2015, Available at :http://www.ijcrt.org/papers/IJCRT1135963.pdf
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ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
ISSN and 7.97 Impact Factor Details
ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
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