Energy consumption is a primary issue needed to be considered for wastewater treatment targeting qualified effluent. In this paper, a hybrid model is proposed for rapid diagnosis of operational conditions meeting requirements of discharge standards and energy saving in Carrousel Oxidation Ditches (ODs). Based on a three-dimensional (3D) three-phase computational fluid dynamics (CFD) model, we developed an artificial neural network (ANN) model with back propagation algorithm and an accelerating genetic algorithm (AGA) model to achieve real-time simulation and system optimization in the Carrousel ODs. By incorporating the 3D-CFD and multi-site ANN models, the hybrid model provided reasonable predictions of liquid flow, sludge sedimentation and water quality in the Carrousel ODs. With help of the AGA model based on evolution theory, system optimization could be reached to meet multiple purposes such as energy saving, water-quality improving and normal sludge distribution, which was successfully demonstrated in both pilot- and full-scale Carrousel ODs.

中文翻译：

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基于随机进化的中试和全面卡鲁塞尔氧化沟快速诊断和节能优化

能源消耗是针对合格出水的废水处理需要考虑的主要问题。在本文中，提出了一种混合模型，用于快速诊断卡鲁塞尔氧化沟（OD）中满足排放标准和节能要求的运行条件。基于三维 (3D) 三相计算流体动力学 (CFD) 模型，我们开发了带有反向传播算法的人工神经网络 (ANN) 模型和加速遗传算法 (AGA) 模型，以实现实时仿真和Carrousel OD 中的系统优化。通过结合 3D-CFD 和多站点 ANN 模型，混合模型对 Carrousel OD 中的液体流动、污泥沉降和水质进行了合理的预测。借助基于进化论的 AGA 模型，