Abstract：
Antibiotics, as a class of environmental pollutants, pose a significant challenge due to their persistent nature and resistance to easy degradation. This study delves into modeling and optimizing conventional Fenton degradation of antibiotic sulfamethoxazole (SMX) and total organic carbon (TOC) under varying levels of H2O2, Fe2+ concentration, pH, and temperature using statistical and artificial intelligence techniques including Multiple Regression Analysis (MRA), Support Vector Regression (SVR) and Artificial Neural Network (ANN). In statistical metrics, the ANN model demonstrated superior predictive accuracy compared to its counterparts, with lowest RMSE values of 0.986 and 1.173 for SMX and TOC removal, respectively. Sensitivity showcased H2O2/Fe2+ ratio, time and pH as pivotal for SMX degradation, while in simultaneous SMX and TOC reduction, fine tuning the time, pH, and temperature was essential. Leveraging a Hybrid Genetic Algorithm-Desirability Optimization approach, the trained ANN model revealed an optimal desirability of 0.941 out of 1000 solutions which yielded a 91.18% SMX degradation and 87.90% TOC removal under following specific conditions: treatment time of 48.5 min, Fe2+: 7.05 mg L-1, H2O2: 128.82 mg L-1, pH: 5.1, initial SMX: 97.6 mg L-1, and a temperature: 29.8 °C. LC/MS analysis reveals multiple intermediates with higher m/z (242, 270 and 288) and lower m/z (98, 108, 156 and 173) values identified, however no aliphatic hydrocarbon was isolated, because of the low mineralization performance of Fenton process. Furthermore, some inorganic fragments like NH4+ and NO3- were also determined in solution. This comprehensive research enriches AI modeling for intricate Fenton-based contaminant degradation, advancing sustainable antibiotic removal strategies.
摘要：
抗生素,作为一类环境污染物，由于它们的持久性和抗易降解性，构成了重大挑战。本研究深入研究了不同浓度的H2O2，Fe2浓度下抗生素磺胺甲恶唑（SMX）和总有机碳（TOC）的常规Fenton降解，pH值,和温度去除使用统计和人工智能技术，包括多元回归分析(MRA)，支持向量回归(SVR)和人工神经网络(ANN)。在统计分析中,由于SMX和TOC的最低RMSE值分别为0.986和1.173，因此ANN模型与同行相比具有较高的预测准确性。分别。灵敏度显示H2O2/Fe2+比，时间和pH是SMX降解的关键，同时降低SMX和TOC，微调时间，pH值,温度是必不可少的。利用混合遗传算法-期望度优化方法，经过训练的ANN模型显示出1000个解决方案中的0.941个最佳可取性，在以下特定条件下产生91.18％的SMX降解和87.90％的TOC去除：处理时间为48.5分钟，Fe2+:7.05mgL-1,H2O2:128.82mgL-1,pH:5.1,初始SMX:97.6mgL-1,温度:29.8°C。LC/MS分析显示多个中间体具有较高的m/z（242、270和288）和较低的m/z（98、108、156和173）值，然而，没有分离出脂肪烃，由于Fenton工艺的矿化性能较低。此外，在溶液中还测定了一些无机片段，如NH4和NO3-。这项全面的研究丰富了复杂的基于Fenton的污染物降解的AI建模，推进可持续的抗生素去除策略。
