Abstract
AbstractAiming at achieving optimum operations of wastewater treatment plant (WWTP) processes, it is essential to develop accurate predictive models for expected influent pollutant loads. To overcome limited timespan data and model complexity, this study is devoted to propose an integration of inputs' sequential search with the adaptive neuro‐fuzzy inference system (ANFIS) for reducing the modelling complexity. The input data included nine influent parameters measured bi‐weekly for 12 years at a WWTP, South Africa (SA). The sequential search process was used for input optimization to select the most representative inputs in modelling four parameters. The obtained results indicated a strong correlation with R2 and NSE above 0.85 for the four targeted influent parameters. After validating the developed models using different criteria, the missing records were predicted throughout the study period. The integration of sequential search input optimization and ANFIS modelling was able to provide a high performance in modelling WWTP datasets.
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