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Biological and social issues rise with faults that occur in waste water treatment plant (WWTP). Nature as well as humans are negatively impacted by the dangerous effects of poorly treated wastewater. This paper combines the fuzzy logic, chaos theory, whale optimization algorithm (WOA) and BAT algorithm (FCW-BAT) to create a novel model for parameter estimation. The WWTP applications are exposed to FCW-BAT algorithm for identifying non-well-structured domain, validating decision rules, cost reduction and estimation of several relevant attributes from the complete dataset. The significant data is retained while reducing the complete feature set using FCW-BAT prior to the classification process. Estimation of data uncertainty and fuzzification is performed with the cost function fast fuzzy c-means. The WOA parameters are estimated and tuned with the help of several chaos sequence maps. Complex real-time datasets consisting of missing values and several uncertainty features are tested and experimented.  Shorter execution time, higher convergence speed, lower error and improved performance are obtained with the sine chaos map embedded in the proposed algorithm. Additionally, the WWTP sensor process faults may also be detected by the proposed model with great levels of accuracy enabling the system operators to make appropriate control decisions.

Fuzzy Chaos Whale Optimization and BAT Integrated Algorithm for Parameter Estimation in Sewage Treatment