Hybrid Intelligent Control Scheme for Activated Sludge Wastewater Treatment

E. Sánchez, E. A. Hernández, C. Cadet, J. Béteau
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引用次数: 1

Abstract

This paper presents a neural network identification scheme to estimate substrate, biomass and dissolved oxygen concentrations in an activated sludge wastewater treatment. This scheme is based on a discrete-time high order neural network (RHONN) trained on-line with an extended Kalman filter (EKF)-based algorithm. Then, the identification scheme is associated with a fuzzy control to regulate the ratio between substrate and biomass concentrations. Obtained simulation results are very encouraging.
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活性污泥废水处理的混合智能控制方案
本文提出了一种神经网络识别方案,用于估计活性污泥废水处理过程中基质、生物量和溶解氧浓度。该方案基于基于扩展卡尔曼滤波(EKF)算法在线训练的离散时间高阶神经网络(RHONN)。然后,将识别方案与模糊控制相关联,以调节底物浓度与生物量浓度之间的比例。仿真结果令人鼓舞。
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