Adaptive recurrent neural network control of biological wastewater treatment

Abstract

Three adaptive neural network control structures to regulate a biological wastewater treatment process are introduced: indirect, inverse model, and direct adaptive neural control. The objective is to keep the concentration of the recycled biomass proportional to the influent flow rate in the presence of periodically acting disturbances, process parameter variations, and measurement noise. This is achieved by the so-called Jordan Canonical Recurrent Trainable Neural Network, which is a completely parallel and parametric neural structure, permitting the use of the obtained parameters, during the learning phase, directly for control system design. Comparative simulation results confirmed the applicability of the proposed control schemes.

The use of artificial neural network (ANN) for modeling of COD removal from antibiotic aqueous solution by the Fenton process

“…Application of ANN to solve environmental engineering problems has been reported in many articles. ANNs were applied in biological wastewater treatment [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26] and physicochemical wastewater treatment [27][28][29][30]. However, few studies on applications of ANN in advanced oxidation processes (AOPs) have been reported [31][32][33].…”

Recurrent Neural Network-Based Control for Wastewater Treatment Process

“…The first level: integral of the absolute error (IAE), integral of the square error (ISE) and maximal deviation from setpoint ( max i Dev ) are taken into account. Index formulas in details are showed in (10). This paper puts the emphasis on the first level of assessment for focusing on the effects of the control.…”

“…ENN is a globally dynamic local feedback RNN [10] first proposed by Elman in 1990, which consists of four layers. Apart from ordinary input, output and hidden layer, there is a special layer which called the context layer.…”

“…The result indicated that the method could accurately identify the servo actuator fault. Baruch, et al [10] used diagonal RNN structure in modeling and adaptive control process of WWTP, and achieved the desired results. Derrick et al [11] developed a probabilistic approach to recursive second-order training of RNNs for improved time-series modeling.…”

Control en Estaciones Depuradoras de Aguas Residuales: Estado actual y perspectivas

“…En las plantas de tratamiento de aguas residuales, por ejemplo, en Zhao y Chai (2005) se utiliza una estructura de red neuronal híbrida con valores retardados 1 combinada con un análisis de componentes principales, como sensor software para predecir el valor de BOD en el efluente. A su vez, las redes neuronales también se han utilizado para el control de los procesos de coagulación, floculación y sedimentación en Lingireddy y Brion (2005) y para mantener la concentración del fango recirculado proporcional al flujo del afluente de manera robusta ante las diferentes perturbaciones, variaciones de parámetros y ruido de medida en Baruch et al (2004). También, tal como se muestra en Olsson et al (2005), las redes neuronales presentan un gran 1 TDNN: hybrid time delay neural networks potencial en el control de procesos de digestión anaerobia.…”