The uses of rice husk are constantly increasing since the last decay. Rice husk is a by-product attained from agricultural activities and has a potential use as a bio-sorbent for the adsorption of heavy metal ions, for example, lead removal from water and other aqueous solutions. The basic objective of this research is to synthesis environment-friendly adsorbent using rice husks. The study also investigated the effects of rice husk treatment on the elimination of Pb(II) ion from aqueous solutions. The results of adsorption capability also been related to the commercial activated carbon (CAC). Rice husks were treated in a few processes which were the pretreatment of raw rice husks, chemical activation, and carbonization process. Subsequently, it was characterized using X-ray diffraction (XRD) and SEM. The structure of the adsorbent using rice husks is found to be amorphous. Based on the treated rice husks characterization, the optimum carbonization temperature is found to be 600 °C. Temperature alteration showed 50–60% weight loss, with up to 3.475% removal. Artificial neural network modeling was applied to predict the experimental data sets using feed forward back-propagation neural network (FFBPNN) and Levenberg–Marquardt (L–M) training algorithm. The customized neural network was applied to emphasize the predicted adsorption capacity and removal/uptake percentage of the investigated bio-sorbents. The outcomes from the artificial neural network model showed high validity of the predicted data compared to the initially examined experimental data sets. The adsorption efficiency increases with the increment of carbon presence ratio in the adsorbent. The adsorption measurements are well presented by the Langmuir isotherm. The kinetic modelling revealed that the adsorption of Pb(II) ions followed the pseudo-second-order models. The reported results in this work can deliberate the rice husks as a potential alternative to commercial adsorbents with lower cost and better environmental aspects. The performance of rice husk adsorption has been investigated on three carbonization temperatures 400, 600, 800 °C and results were analysed Carbonized rice husk at 800 °C gave a higher value of adsorption capacity and percent metal uptake of Pb(II) ions. Our experimental results were correlated with the Langmuir and Freundlich models with satisfactory agreement The neural network and training algorithm to predict the bio-sorption capacity and removal percentage showed high validity and reliability. Adsorption kinetics of Pb(II) ion on carbonized rice husks follows the linear pseudo-second-order rate expression.

中文翻译：

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硝酸处理过的稻壳上 Pb(II) 离子吸附的特征洞察、人工神经网络 (ANN)、平衡和动力学研究

自上次腐烂以来，稻壳的使用量不断增加。稻壳是从农业活动中获得的副产品，具有作为生物吸附剂的潜在用途，用于吸附重金属离子，例如从水和其他水溶液中去除铅。本研究的基本目标是利用稻壳合成环保型吸附剂。该研究还调查了稻壳处理对从水溶液中去除 Pb(II) 离子的影响。吸附能力的结果也与商业活性炭（CAC）有关。稻壳经过生稻壳预处理、化学活化、炭化等几个过程进行处理。随后，使用 X 射线衍射 (XRD) 和 SEM 对其进行表征。发现使用稻壳的吸附剂的结构是无定形的。根据处理过的稻壳特性，发现最佳碳化温度为 600 °C。温度变化显示重量损失 50-60%，去除率高达 3.475%。使用前馈反向传播神经网络 (FFBPNN) 和 Levenberg-Marquardt (L-M) 训练算法，应用人工神经网络建模来预测实验数据集。应用定制的神经网络来强调所研究的生物吸附剂的预测吸附容量和去除/吸收百分比。与最初检查的实验数据集相比，人工神经网络模型的结果表明预测数据的有效性较高。吸附效率随着吸附剂中碳存在率的增加而增加。Langmuir 等温线很好地呈现了吸附测量结果。动力学模型表明 Pb(II) 离子的吸附遵循伪二级模型。这项工作中报告的结果可以考虑将稻壳作为具有较低成本和更好环境方面的商业吸附剂的潜在替代品。稻壳吸附性能已在 400、600、800 °C 三个碳化温度下进行了研究，结果分析表明，800 °C 下的碳化稻壳提供了更高的吸附容量值和 Pb(II) 离子的金属吸收百分比。我们的实验结果与 Langmuir 和 Freundlich 模型相关并具有令人满意的一致性。预测生物吸附能力和去除百分比的神经网络和训练算法显示出较高的有效性和可靠性。Pb(II) 离子在碳化稻壳上的吸附动力学遵循线性准二级速率表达式。