Teaching–Learning-Based Optimization for Parameter Identification of an Activated Sludge Process Model

摘要

Abstract This paper proposes a recent optimization method called TLBO (teaching–learning-based optimization) to identify the unknown parameters of an activated sludge process model. This method employs two search phases, namely, the teacher phase and the learner phase. Its performance is evaluated in four-parameter identification problem. The considered model is a complex nonlinear model for an activated sludge process that alternates between two phases (aerobic and anoxic). Simulation results prove the excellent performance of TLBO compared with three well-known meta-heuristic techniques and another classical parameter extraction method suggested in the literature.