The contamination of water by arsenic (As) poses a substantial environmental challenge with far-reaching influence on human health. Accurately predicting adsorption capacities of arsenite (As(III)) and arsenate (As(V)) on different materials is crucial for the remediation and reuse of contaminated water. Nonetheless, predicting the optimal As adsorption on various materials while considering process energy consumption continues to pose a persistent challenge. Literature data regarding the As adsorption on diverse materials were collected and employed to train machine learning models (ML), such as CatBoost, XGBoost, and LGBoost. These models were utilized to predict both As(III) and As(V) adsorption on a variety of materials using their reaction parameters, structural properties, and composition. The CatBoost model exhibited superior accuracy, achieving a coefficient of determination (R²) of 0.99 and a root mean square error (RMSE) of 1.24 for As(III), and an R² of 0.99 and RMSE of 5.50 for As(V). The initial As(III) and As(V) concentrations were proved to be the primary factors influencing adsorption, accounting for 27.9% and 26.6% of the variance for As(III) and As(V) individually. The genetic optimization led optimisation process, considering the low energy consumption, determined maximum adsorption capacities of 291.66 mg/g for As(III) and 271.56 mg/g for As(V), using C-Layered Double Hydroxide with reduced graphene oxide and chitosan combined with rice straw biochar, respectively. To further facilitate the process design for different real-life applications, the trained ML models are embedded into a web-app that the user can use to estimate the As(III) and As(V) adsorption under different design conditions. The utilization of ML for the energy-efficient As(III) and As(V) adsorption is deemed essential for advancing the treatment of inorganic As in aquatic settings. This approach facilitates the identification of optimal adsorption conditions for As in various material-amended waters, while also enabling the timely detection of As-contaminated water.

中文翻译：

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通过机器学习优化导致节能的亚砷酸盐和砷酸盐吸附在各种材料上


砷 （As） 对水的污染构成了重大的环境挑战，对人类健康产生了深远的影响。准确预测亚砷酸盐 （As（III）） 和砷酸盐 （As（V）） 在不同材料上的吸附能力对于污染水的修复和再利用至关重要。尽管如此，在考虑工艺能耗的同时预测各种材料上的最佳 As 吸附仍然是一个持续的挑战。收集有关不同材料上 As 吸附的文献数据，并将其用于训练机器学习模型 （ML），例如 CatBoost、XGBoost 和 LGBoost。这些模型用于利用其反应参数、结构特性和成分来预测 As（III） 和 As（V） 在各种材料上的吸附。CatBoost 模型表现出卓越的准确性，As（III） 的决定系数 （R²） 为 0.99，均方根误差 （RMSE） 为 1.24，As（V） 的 R² 为 0.99，RMSE 为 5.50。初始 As（III） 和 As（V） 浓度被证明是影响吸附的主要因素，分别占 As（III） 和 As（V） 方差的 27.9% 和 26.6%。遗传优化主导优化过程，考虑到低能耗，使用还原氧化石墨烯的 C 层双氢氧化物和壳聚糖结合水稻秸秆生物炭，分别确定了 As（III） 和 As（V） 的最大吸附容量为 291.66 mg/g，As（V） 的最大吸附容量为 271.56 mg/g。为了进一步促进不同实际应用的工艺设计，训练后的 ML 模型被嵌入到一个 Web 应用程序中，用户可以使用它来估计不同设计条件下的 As（III） 和 As（V） 吸附。 将 ML 用于节能的 As（III） 和 As（V） 吸附被认为对于推进水生环境中无机 As 的处理至关重要。这种方法有助于确定各种材料改性水中 As 的最佳吸附条件，同时还能够及时检测受 As 污染的水。