In this study, multilayer perceptron (MLP) artificial neural network was used to predict the adsorption rate of ammonium on zeolite. pH, inlet ammonium concentration, contact time, temperature, dosage of adsorbent, agitation speed, and particle size in the batch experiments were used as independent variables while flow rate and particle size in column mode were investigated. In MLP application, different architecture structures were tried and the architecture structures with the highest predictive performance were determined. To comparatively evaluate the predictive capabilities of MLP based prediction tool, Response Surface Methodology (RSM) was utilized. When the results were evaluated with Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) values (<1%) for almost all experiments, it was seen that MLP-based prediction tool produces better predictions than RSM. The scatter plots showed that predictions and actual values were quite compatible. Both regression and determination coefficients were interpreted by creating a regression of the predictions against the actual values and these coefficients were obtained as pretty close to 1. The outstanding performance of MLP in out-of-sample data sets without the need for additional experiment demonstrate that MLP can be effectively and reliably used in cases where experimental setups are difficult or costly.

在这项研究中，使用多层感知器（MLP）人工神经网络来预测铵在沸石上的吸附速率。在分批实验中，pH，入口铵浓度，接触时间，温度，吸附剂的剂量，搅拌速度和粒径被用作自变量，而在柱模式下研究了流速和粒径。在MLP应用程序中，尝试了不同的体系结构，并确定了具有最高预测性能的体系结构。为了比较评估基于MLP的预测工具的预测能力，使用了响应面方法（RSM）。在几乎所有实验中，均方根误差（RMSE）和绝对绝对百分比误差（MAPE）值（<1％）均用于评估结果时，可以看出，基于MLP的预测工具比RSM产生更好的预测。散点图显示预测和实际值完全兼容。通过对实际值进行预测回归来解释回归系数和确定系数，并且获得的这些系数非常接近1。MLP在样本外数据集中的出色性能无需进行其他实验即可证明：在实验设置困难或昂贵的情况下，可以有效且可靠地使用MLP。