Abstract
A methodology for modeling the electrocoagulation of wastewater from the food industres, with high organic loads is proposed. The approach used is a nonlinear model based on Artificial Neural Networks (ANN), which is able to understand the interaction between the variables that define the process, to complement the traditional design of experiments. Where the interaction of variables determines in many cases, a large number of experiments to perform, that involve stages such as planning, organization and execution of experimental activities, also characterization and analysis of wastewater in order to remove chemical oxygen demand (COD) and total dissolved solids (TSS). From this approach it will be possible to find appropriate conditions for these parameters in order to enhance the contaminant removal process with specific routes (experimental conditions).
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Hernández-Ramírez, D.A., Herrera-López, E.J., Rivera, A.L., Del Real-Olvera, J. (2014). Artificial Neural Network Modeling of Slaughterhouse Wastewater Removal of COD and TSS by Electrocoagulation. In: Jamshidi, M., Kreinovich, V., Kacprzyk, J. (eds) Advance Trends in Soft Computing. Studies in Fuzziness and Soft Computing, vol 312. Springer, Cham. https://doi.org/10.1007/978-3-319-03674-8_26
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DOI: https://doi.org/10.1007/978-3-319-03674-8_26
Publisher Name: Springer, Cham
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