Synthetic dyes used in the textile and paper industries pose a major threat to the environment. In the present research work, the adsorption efficiency of the natural adsorbent Strychnos potatorum Linn (Fam: Loganiaceae) seeds were examined against the reactive orange-M2R dye from aqueous solution by varying the process conditions such as contact time, pH, adsorbent dosage, and initial dye concentration on adsorption of anionic azo dye. This study compares different types of artificial neural networks which are feedforward artificial neural network (FANN) and nonlinear autoregressive exogenous (NARX) model to predict the efficiency of a cost-effective natural adsorbent Strychnos potatorum Linn seeds on removing reactive orange-M2R dye from aqueous solution. Twelve training algorithms of neural network were compared, and the prediction on the adsorption performance of anionic azo dye from aqueous solution using Strychnos potatonum Linn seeds was evaluated by using the root mean squared error (RMSE), mean absolute error (MAE), coefficient of determination (R²), and accuracy. For FANN model, Levenberg–Marquardt (LM) backpropagation with 19 hidden neurons was selected as the optimum FANN model, with R² of 0.994 and accuracy of 87.20%, 98.21%, and 66.60% for training, testing, and validation datasets, respectively. For NARX model, LM with 8 hidden neurons was selected as the most suitable training algorithm, with R² value of more than 0.99 and accuracy of 88.00%, 90.91%, and 75.00% for training, testing, and validation datasets, respectively. NARX model accurately predicted the adsorption of anionic azo dye from aqueous solution using Strychnos potatonum Linn seeds with better performance than FANN model.

纺织和造纸工业中使用的合成染料对环境构成重大威胁。在本研究工作中，天然吸附剂的吸附效率马钱子potatorum林恩（FAM：马钱科）种子对来自水溶液的反应性橙M2R染料通过改变工艺条件，如接触时间，pH值，吸附剂用量检查，并阴离子偶氮染料吸附的初始染料浓度。本研究比较了不同类型的人工神经网络，即前馈人工神经网络 (FANN) 和非线性自回归外源 (NARX) 模型，以预测具有成本效益的天然吸附剂马铃薯的效率从水溶液中去除活性橙-M2R 染料的 Linn 种子。神经网络的十二个训练算法进行比较，并从水溶液中使用阴离子偶氮染料的吸附性能的预测马钱子potatonum属种子通过使用均方根误差（RMSE），平均绝对误差（MAE），系数的评价确定 ( R ² ) 和准确性。对于 FANN 模型，选择具有 19 个隐藏神经元的 Levenberg-Marquardt (LM) 反向传播作为最佳 FANN 模型，R ²为 0.994，训练、测试和验证数据集的准确率分别为 87.20%、98.21% 和 66.60% . 对于 NARX 模型，具有 8 个隐藏神经元的 LM 被选为最合适的训练算法，其中训练、测试和验证数据集的R ²值超过 0.99，准确率分别为 88.00%、90.91% 和 75.00%。NARX 模型准确预测了使用马铃薯Linn 种子从水溶液中吸附阴离子偶氮染料，性能优于 FANN 模型。