فهرست نشریات علمی دسترسی آزاد ایران

BACKGROUND AND OBJECTIVES: Polymeric flocculants derived from the coagulation-flocculation procedure, such as chitosan, effectively remove algal-bacterial biomass from wastewater treatment, demonstrating efficiencies comparable to ferric salts at reduced dosages. The optimal operating condition of coagulation-flocculation in aquaculture wastewater treatment particularly requires defining accurate values of three input parameters: potential of hydrogen, chitosan dose, and settling time. Therefore, the objective of this study is determining the optimal operating condition of coagulation-flocculation through optimizing the values of the three input parameters.METHODS: Artificial intelligence and recent optimzation algorithms are integrated to achieve the purpose of this study work. Initially, an adaptive neuro-fuzzy inference system model of the wastewater treatment process was built based on experimental data. The white shark optimizer is then used to calculate the optimal chitosan dose, potential of hydrogen, and settling time for reducing turbidity and salinity. During the optimization process, these controlling parameters are used as a design variable whereas the objective function required to me be a maximum is the summation of turbidity removal and salinity removal. FINDINGS: The adaptive neuro-fuzzy inference system turbidity removal model has root mean square error values of 3.52e-05 and 1.51 for training and testing data, respectively. The R-squared values for testing and training are 0.76 and 1.0, respectively. In compared to analysis of variance adaptive neuro-fuzzy inference system lowered the root mean square error from 2.8 to 0.836 (a 70 percent decrease). The estimated R-squared value rose by 11.76 percent, from 0.68 with analysis of variance to 0.76 with adaptive neuro-fuzzy inference system. The adaptive neuro-fuzzy inference system approach to salinity removal has root mean square error values of 7.13e-06 and 2.045 for training and testing. The R-squared values for training and testing are 1.0 and 0.82, respectively, and adaptive neuro-fuzzy inference system reduced the root mean square error from 6.8 with analysis of variance to 1.135 with adaptive neuro-fuzzy inference system achieving an 83.5 percent reduction. The R-squared value for prediction rose 7.2 percent from 0.765 via analysis of variance to 0.82 via adaptive neuro-fuzzy inference system.CONCLUSION:  Adaptive neuro-fuzzy inference system succseded to present an accurate model of  wastewater treatment process  and white shark optimizer defined accutrelly the values of chitosan dose, pontential of hydrogen, and settling time to reduce turbidity and salinity. Accordingly, optimizing chitosan-based coagulation-flocculation offers significant environmental benefits by reducing toxic chemical usage and minimizing sludge generation, leading to less pollution.