Development of an artificial neural network (ANN) for the prediction of a pilot scale mobile wastewater treatment plant performance.

ABSTRACT

Productive activities such as pig farming are a fundamental part of the economy in Mexico. Unfortunately, because of this activity, large quantities of wastewater are generated that have a negative impact in the environment. This work shows an alternative for treating piggery wastewater based on advanced oxidation processes (Fenton and solar photo Fenton, SPF) that have been probed successfully in previous works. In the first stage, Fenton and SPF were carried out on a laboratory scale using a Taguchi L9-type experimental design. From the statistical analysis of this design, the operating parameters pH, time, hydrogen peroxide concentration [H2O2], and iron ferrous concentration [Fe2+] that maximize the response variables Chemical Oxygen Demand (COD), Total Organic Carbon (TOC), and color were chosen. From these, a cascade forward neural network was implemented to establish a correlation between data from the variables to the physicochemical parameters to be measure being that a great fit of the data was obtained having a correlation coefficient of 0.99 which permits to optimize the pollutant degradation and predict the removal efficiencies at pilot scale but with a projection to a future industrial scale. A relevant result, it was found that the optimal values for maximizing the removal of physicochemical parameters were pH = 3, time = 60 min, H2O2/COD = 1.5 mg L-1, and H2O2/Fe2+ = 2.5 mg L-1. With these conditions degradation percentages of 91.44%, 47.14%, and 97.89% for COD, TOC, and color were obtained from the Fenton process, while for SPF the degradation percentage increased moderately. From the ANN analysis, the possibility to establish an intelligent system that permits to predict multiple results from operational conditions has been achieved.