A new combination approach for optimal design of sedimentation tanks based on hydrodynamic simulation model and machine learning algorithms. (October 2022) - Ferdowsi, Ahmad; Valikhan-Anaraki, Mahdi; Farzin, Saeed; Mousavi, Sayed-Farhad
Abstract: The current study aims to propose a novel approach to design rectangular primary sedimentation tanks using computational fluid dynamics (CFD) and artificial intelligence. Hybrid adaptive neuro-fuzzy inference system and particle swarm optimization algorithm (ANFIS-PSO) is trained using CFD model's results. ANFIS-PSO is coupled with two multi-objective algorithms to optimize two objectives: Mean velocity and baffles' configurations. For the single-baffle tank, the optimal baffle position regarding flow velocity was approximately at 0.13 of the basin length, with the height ratio (ratio of baffle height to weir height) of 0.21, but the most cost-effective design was obtained when the single baffle was placed at about 0.06 of total length, with the height ratio of 0.17. Using two baffles could enhance the tank's hydraulic performance, especially when the first and second baffles were at 0.11 and 0.25 of the tank's length, respectively, with the height ratio of 0.33 and 0.22, respectively. Graphical abstract: Image 1 Highlights: Introducing a simulation–optimization approach for optimal design of sedimentation tanks. Combining CFD, hybrid ANFIS, single and multi-objective metaheuristics. Determining the best scenario based on highest performance and least cost. Various tradeoffs were proposed for single and double-baffle tanks.