Solid particle sedimentation is assumed as a complex procedure in both water and wastewater treatment plants. There is a great deal of interest in applying and developing different simulation and optimization methods to design a primary sedimentation tanks (PSTs). In traditional techniques, mechanical and physical parameters are set by sequential error loops. To eliminate the disadvantage of existing techniques, this study proposes a hybrid method based on the response surface methodology, efficient metaheuristics and scenario building methods using different experimental methods. This novel framework creates a robust and sustainable design for the PSTs. First of all, the parameters of the considered PST based on the economic, improve process and tank efficiency scenarios are tuned and optimized by the Central Composition Design (CCD) and Response Surface Methodology (RSM). To forecast an efficient response value for these scenarios, different metaheuristic algorithms including the Genetic Algorithm (GA), Pattern Search Algorithm (PSA) and Simulate Annealing Algorithm (SAA) are applied. Results demonstrated that PSA, GA and PSA with 0.02, 0.032 and 0.063 in comparison with experimental practices have the best calibration for prediction of response in economic, improve process and tank efficiency scenarios, correspondingly. Finally, experimental tests have proven that the optimum Retention Time (RT) is equal to 2 hours based on the biological oxygen demand and the total suspended solids eliminations in the lab-scale setup.

No abstract

No abstract