Abstract This study investigates the estimated adsorption efficiency of artificial Nickel (II) ions with perlite in an aqueous solution using artificial neural networks, based on 140 experimental data sets. Prediction using artificial neural networks is performed by enhancing the adsorption efficiency with the use of Nickel (II) ions, with the initial concentrations ranging from 0.1 mg/L to 10 mg/L, the adsorbent dosage ranging from 0.1 mg to 2 mg, and the varying time of effect ranging from 5 to 30 mins. This study presents an artificial neural network that predicts the adsorption efficiency of Nickel (II) ions with perlite. The best algorithm is determined as a quasi-Newton back-propagation algorithm. The performance of the artificial neural network is determined by coefficient determination (R2), and its architecture is 3-12-1. The prediction shows that there is an outstanding relationship between the experimental data and the predicted values.

中文翻译：

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通过人工神经网络预测含有珍珠岩的水溶液中 Ni (II) 的吸附效率

摘要 本研究基于 140 个实验数据集，使用人工神经网络研究了人工镍 (II) 离子与珍珠岩在水溶液中的估计吸附效率。使用人工神经网络进行预测是通过使用镍 (II) 离子提高吸附效率来进行的，初始浓度范围为 0.1 mg/L 至 10 mg/L，吸附剂剂量范围为 0.1 mg 至 2 mg，以及不同的效果时间从 5 到 30 分钟不等。本研究提出了一种人工神经网络，可预测镍 (II) 离子对珍珠岩的吸附效率。最佳算法确定为拟牛顿反向传播算法。人工神经网络的性能由系数确定（R2）决定，其架构为3-12-1。