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.
摘要：
养猪等生产活动是墨西哥经济的基本组成部分。不幸的是,因为这个活动,产生大量的废水，对环境产生负面影响。这项工作展示了一种基于高级氧化工艺（Fenton和太阳能照片Fenton，SPF）在以前的作品中已成功探测。在第一阶段,使用TaguchiL9型实验设计在实验室规模上进行Fenton和SPF。从本次设计的统计分析来看,操作参数：pH，时间,过氧化氢浓度[H2O2]，和铁亚铁浓度[Fe2+]，使响应变量最大化：化学需氧量(COD)，总有机碳(TOC)选择了颜色。从这些,实施级联前向神经网络以建立从变量到要测量的物理化学参数的数据之间的相关性,即获得具有0.99的相关系数的数据的很好的拟合,这允许优化污染物降解并预测在中试规模的去除效率,但对未来工业规模的预测。一个相关的结果，发现最大程度地去除物理化学参数的最佳值为pH=3，时间=60分钟，H2O2/COD=1.5mgL-1,H2O2/Fe2+=2.5mgL-1。在这些条件下，降解百分比为91.44%，47.14%，COD为97.89%，TOC,颜色是从芬顿工艺中获得的，而对于SPF，降解百分比适度增加。从ANN分析来看,已经实现了建立一个智能系统的可能性，该系统允许从操作条件中预测多个结果。
