The antibiotic-contaminated water treatment is an important step for pollutant reduction and the promotion of water environment quality. Uncertainty in wastewater treatment technology, fluctuations in effluent water quality, and operation costs cause an emerging issue to develop materials effective for the removal of antibiotics. The environment-friendly clay such as vermiculite could be potentially promising candidates for removing 6-APA (6-aminopenicillanic) from pharmaceutical effluent. Antibiotic removal was achieved by using an eco-friendly, time-saving, powerful, and easy applying synthesis method via tetraethoxysilane (Si). Expert systems are widely powerful tools for minimizing the complexities and complications in wastewater treatment. Response surface methodology (RSM) and adaptive neuro-fuzzy inference system (ANFIS) models were used to develop systematically predicting interactions of synthesis conditions on 6-APA adsorption capacity and optimize the best amount of compound. The three parameters of the amount of adsorbent (weight.), initial concentration (mg/mL), and reaction time (min) are selected as input and the adsorption capacity (mg/g) were computed as the output of the models. The effect of process variables investigated by RSM through central composite design matrix and the results compared with ANFIS model. The maximum amount of adsorption capacity predicted by RSM for VMT and VMT-Si were 162.5 and 179.8 mg/g, respectively. The suggested models were successfully validated with the acceptable confidence levels 0.99 and 0.97, for VMT and VMT-Si using RSM and 0.99 and 0.99 by ANFIS. ANFIS model demonstrated higher predictive capability than RSM model based on the good agreement in predictable dataset to experimental data.

抗生素污染水处理是减少污染物和改善水环境质量的重要步骤。废水处理技术的不确定性、出水水质的波动和运营成本导致开发有效去除抗生素的材料成为一个新问题。蛭石等环保粘土可能是从制药废水中去除 6-APA（6-氨基青霉酸）的潜在候选材料。通过使用环保、省时、功能强大且易于应用的四乙氧基硅烷 (Si) 合成方法实现抗生素去除。专家系统是一种广泛强大的工具，可以最大限度地减少废水处理的复杂性和并发症。响应面方法 (RSM) 和自适应神经模糊推理系统 (ANFIS) 模型用于开发系统地预测合成条件对 6-APA 吸附能力的相互作用并优化最佳化合物量。选择吸附剂的量（重量）、初始浓度（mg/mL）和反应时间（min）这三个参数作为输入，并计算吸附容量（mg/g）作为模型的输出。RSM 通过中心复合设计矩阵研究过程变量的影响，并将结果与​​ ANFIS 模型进行比较。RSM 预测的 VMT 和 VMT-Si 的最大吸附容量分别为 162.5 和 179.8 mg/g。对于 VMT 和 VMT-Si，使用 RSM 和 0 成功验证了建议的模型，可接受的置信水平为 0.99 和 0.97。ANFIS 的 99 和 0.99。基于可预测数据集与实验数据的良好一致性，ANFIS 模型表现出比 RSM 模型更高的预测能力。