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Author: search.filters.author.Bhanot, SurekhaSubject: search.filters.subject.Chemical Engineering

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    Particle Swarm Optimization based Fuzzy Logic Control of pH Neutralization Process
    (IJAER, 2015-06) Mohanta, Hare Krishna; Bhanot, Surekha
    pH control plays an important role in many modern industrial plants due to strict environment regulations. This paper presents fuzzy logic based pH control scheme for neutralization process in which particle swarm algorithm is used to optimize the input and output membership functions of fuzzy inference system. Performance of control scheme has been evaluated for servo and regulatory operations.
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    Genetic Optimization based Adaptive Fuzzy Logic Control of a pH Neutralization Process
    (Sersc, 2014) Mohanta, Hare Krishna; Bhanot, Surekha
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    Optimized and Self-Organized Fuzzy Logic Controller for pH Neutralization Process
    (MCES Press, 2013) Mohanta, Hare Krishna; Bhanot, Surekha
    To conform to strict environmental safety regulations, pH control is used in many industrial applications. For this purpose modern process industries are increasingly relying on intelligent and adaptive control strategies. On one hand intelligent control strategies try to imitate human way of thinking and decision making using artificial intelligence (AI) based techniques such as fuzzy logic whereas on the other hand adaptive mechanism ensures adjusting of the controller parameters. A self-organized fuzzy logic controller (SOFLC) is intelligent in nature and adapts its performance to meet the figure of merit. This paper presents an optimized SOFLC for pH control using performance correction table. The fuzzy adaptation mechanism basically involves a penalty for the output membership functions if the controller performance is poor. The evolutionary genetic algorithm (GA) is used for optimization of input-output scaling factors of the conventional fuzzy logic controller (FLC) as well as elements of the fuzzy performance correction table. The resulting optimized SOFLC is compared with optimized FLC for servo and regulatory control. Comparison indicate superior performance of SOFLC over FLC in terms of much reduced integral of squared error (ISE), maximum overshoot and undershoot, and increased speed of response
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    Neural Control of Neutralization Process using Fuzzy Inference System based Lookup Table
    (IJCA, 2021-10-20) Mohanta, Hare Krishna; Bhanot, Surekha
    Over a number of years, pH control of neutralization process is recognized as a benchmark for modeling and control of nonlinear processes. This paper first describes dynamic modeling of pH neutralization process. Thereafter fuzzy logic based pH control scheme for neutralization process is developed. Further, a two-dimensional (2-D) lookup table is generated based on defuzzification mechanism of fuzzy inference system (FIS). Finally, using this lookup table, a neural network control for pH neutralization process is developed. Performances of fuzzy logic based control and lookup table based neural network control for servo and regulatory operations are compared based on integral square error (ISE) and integral absolute error (IAE) criterions. Results indicate that lookup table based neural network control performs better than fuzzy logic based control.