Stochastic evolutionary-based optimization for rapid diagnosis and energy-saving in pilot- and full-scale Carrousel oxidation ditches

Alistair Borthwick, Li Li, Li Lei, Maosheng Zheng, Jinren Ni

Research output: Contribution to journalArticlepeer-review

Abstract

Accurate modeling of Carrousel oxidation ditches (ODs) is of great importance in optimizing their operating conditions to reduce energy consumption, ensure qualified effluent, and achieve even deposits of sludge. This paper presents a hybrid model composed of a three-dimensional (3D) three-phase computational fluid dynamics (CFD) model, a multi-site artificial neural network (ANN) model with back propagation, and an accelerating genetic algorithm (AGA) model to achieve real-time simulation and synchronized system optimization in the Carrousel OD. The 3D three-phase CFD model provided comprehensive, precise simulation of OD systems by treating activated sludge as a pseudo-solid phase and considering water-sludge-gas interactions. By coupling the 3D three-phase CFD model with a multi-site ANN model, the resulting hybrid model provided computationally fast, accurate predictions of liquid flow field, sludge sedimentation, dissolved oxygen distribution, and water quality parameters in the OD. An evolution theory-based AGA model, which incorporated the ANN model to compute fitness values, was used to perform global optimization of the operating conditions in the OD, and so identify optimum conditions that reduced energy consumption, prevented uneven deposits of sludge, and satisfied effluent standards. The proposed hybrid model was successfully applied to pilot-scale and full-scale ODs. The results showed the potential of the hybrid model to realize rapid prediction of phase motions and interactions in the OD, and to achieve real-time OD process optimization.
Original languageEnglish
JournalJournal of environmental informatics
Early online date31 Oct 2017
DOIs
Publication statusPublished - Mar 2020

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