KSOM and MLP neural networks for on-line estimating the efficiency of an activated sludge process

Abstract : This work is devoted to the study of the Saint Cyprien (south of France) activated sludge WWTP process and to the on-line estimation of chemical parameters (influent and effluent chemical oxygen demand, ammonia and suspended solids) not easily measurable on-line. Their knowledge makes it possible to estimate the process efficiency and to provide reliable information for the plant monitoring. A tool including Kohonen's self-organizing maps and a multi level perceptron is used. The Kohonen's self organizing maps neural network is applied to analyze the multi-dimensional Saint Cyprien process data and to diagnose the interrelationship of the process variables in an activated sludge WWTP. The multi level perceptron is used as estimation tool. The obtained results are satisfactory. The information provided by the estimation procedure is sufficiently reliable and precise to be exploitable by operators in charge of the plant monitoring and maintenance. It allows understanding how the system is evolving. The whole procedure (Kohonen's self-organizing maps and multi level perceptron) uses tools which proved to be efficient and complementary.
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Stéphane Grieu, Frédérik Thiéry, Adama Traoré, Tien Phong Nguyen, Mathieu Barreau, et al.. KSOM and MLP neural networks for on-line estimating the efficiency of an activated sludge process. Chemical Engineering Journal, Elsevier, 2006, 116 (1), pp.1-11. 〈http://www.sciencedirect.com/science/article/pii/S1385894705003669〉. 〈10.1016/j.cej.2005.10.004〉. 〈hal-01273185〉

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