Abstract
This communication presents the main aim, contextual and development framework of the PhD that is being conducted by the first author. In this PhD, main aim is the application of data driven methods to industrial processes in order to improve and support industrial operations. In this case, Wastewater Treatment Plants (WWTPs) are adopted as the industry where data driven methods will be applied. WWTPs are industries devoted to managing and process residual water coming from urban and industrial areas. Those type of industries apply highly-complex and nonlinear processes to reduce the contamination of water. Therefore, among the different data driven methods, in this PhD we will focus on the application of Artificial Neural Networks (ANNs) in order to improve and support the operations performed in this type of industries. ANNs are considered due to their ability in the modeling of highly-complex and nonlinear processes such as the WWTPs processes.
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Acknowledgments
This work has received the support from the Spanish Ministry of Economy and Competitiveness program under MINECO/FEDER grant DPI2016-77271-R and also from La Secretaria d’Universitats i Recerca del Departament d’Empresa i Coneixement de la Generalitat de Catalunya i del Fons Social Europeu under FI grant. Authors belong to the recognized research groups SGR 1202 and SGR 1670 by the Catalan Government.
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Pisa, I., Vilanova, R., Santín, I., Vicario, J.L., Morell, A. (2019). Artificial Neural Networks Application to Support Plant Operation in the Wastewater Industry. In: Camarinha-Matos, L., Almeida, R., Oliveira, J. (eds) Technological Innovation for Industry and Service Systems. DoCEIS 2019. IFIP Advances in Information and Communication Technology, vol 553. Springer, Cham. https://doi.org/10.1007/978-3-030-17771-3_22
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