The treatment of wastewater from heavy metal ions such as hexavalent chromium Cr(VI) is considered as an important issue in recent years, which is harmful to human health and environment. Since, in engineering, performing the experiments to solve problems is time-consuming and costly. In this study, adaptive neuro-fuzzy inference system (ANFIS) was coupled with particle swarm optimization (PSO) algorithm to develop a predictive model for modeling of Cr(VI) removal percent on NiO nanoparticle. To this end, the trace of four initial parameters containing contact time, Cr(VI) initial concentration, NiO adsorbent dosage, and pH on removing Cr(VI) was investigated. The performance of the developed algorithm was evaluated by statistical parameters such as mean absolute relative deviation mean squared error (MSE) maximum absolute error and, R² and graphic methods. The ANFIS-PSO shows high-performance modeling of Cr(VI) removal with R² = 0.998, MSE = 0.0014, and AARD = 0.0011 compare to the established model in previous works.

中文翻译：

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开发基于粒子群优化模型的自适应神经模糊推理系统，用于预测 NiO 纳米粒子对 Cr(VI) 的去除

六价铬Cr(VI)等重金属离子废水的处理被认为是近年来的一个重要问题，对人类健康和环境有害。因为，在工程中，通过实验来解决问题既费时又费钱。在这项研究中，自适应神经模糊推理系统 (ANFIS) 与粒子群优化 (PSO) 算法相结合，开发了一个预测模型，用于对 NiO 纳米颗粒上的 Cr(VI) 去除百分比进行建模。为此，研究了四个初始参数的轨迹，包括接触时间、Cr(VI) 初始浓度、NiO 吸附剂用量和去除 Cr(VI) 的 pH 值。所开发算法的性能通过统计参数进行评估，例如平均绝对相对偏差均方误差 (MSE) 最大绝对误差和，R ²和图形方法。与 之前工作中建立的模型相比，ANFIS-PSO 显示了高性能的 Cr(VI) 去除建模，R ² = 0.998、MSE = 0.0014 和 AARD = 0.0011。