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Machine Learning Models to Predict Early Breakthrough of Recalcitrant Organic Micropollutants in Granular Activated Carbon Adsorbers
Environmental Science & Technology ( IF 10.8 ) Pub Date : 2024-09-13 , DOI: 10.1021/acs.est.4c01316 Yoko Koyama 1, 2 , Mohammad A K Fasaee 1 , Emily Z Berglund 1 , Detlef R U Knappe 1
Environmental Science & Technology ( IF 10.8 ) Pub Date : 2024-09-13 , DOI: 10.1021/acs.est.4c01316 Yoko Koyama 1, 2 , Mohammad A K Fasaee 1 , Emily Z Berglund 1 , Detlef R U Knappe 1
Affiliation
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Granular activated carbon (GAC) adsorption is frequently used to remove recalcitrant organic micropollutants (MPs) from water. The overarching aim of this research was to develop machine learning (ML) models to predict GAC performance from adsorbent, adsorbate, and background water matrix properties. For model calibration, MP breakthrough curves were compiled and analyzed to determine the bed volumes of water that can be treated until MP breakthrough reaches ten percent of the influent MP concentration (BV10). Over 400 data points were split into training, validation, and testing sets. Seventeen variables describing MP, background water matrix, and GAC properties were explored in ML models to predict log10-transformed BV10 values. Using the ML models on the testing set, predicted BV10 values exhibited mean absolute errors of ∼0.12 log units and were highly correlated with experimentally determined values (R2 ≥ 0.88). The top three drivers influencing BV10 predictions were the air-hexadecane partition coefficient and hydrogen bond acidity (Abraham parameters L and A) of the MPs and the dissolved organic carbon concentration of the GAC influent water. The model can be used to rapidly estimate the GAC bed life, select effective GAC products for a given treatment scenario, and explore the suitability of GAC treatment for remediating emerging MPs.
中文翻译：
机器学习模型预测颗粒活性炭吸附器中顽固有机微污染物的早期突破
颗粒活性炭（GAC）吸附经常用于去除水中顽固的有机微污染物（MP）。本研究的总体目标是开发机器学习 (ML) 模型，根据吸附剂、吸附质和背景水基质特性预测 GAC 性能。为了进行模型校准，编制并分析了 MP 突破曲线，以确定 MP 突破达到进水 MP 浓度 (BV10) 的 10% 之前可处理的水床体积。超过 400 个数据点被分为训练集、验证集和测试集。在 ML 模型中探索了描述 MP、背景水基质和 GAC 特性的 17 个变量，以预测 log 10转换的 BV10 值。在测试集上使用 ML 模型，预测的 BV10 值的平均绝对误差约为 0.12 个对数单位，并且与实验确定的值高度相关 ( R 2 ≥ 0.88)。影响 BV10 预测的三大驱动因素是 MP 的空气-十六烷分配系数和氢键酸度（亚伯拉罕参数L和A ）以及 GAC 进水的溶解有机碳浓度。该模型可用于快速估计 GAC 床位寿命，针对给定的治疗场景选择有效的 GAC 产品，并探索 GAC 治疗对治疗新发 MP 的适用性。
更新日期：2024-09-13
中文翻译：
机器学习模型预测颗粒活性炭吸附器中顽固有机微污染物的早期突破
颗粒活性炭（GAC）吸附经常用于去除水中顽固的有机微污染物（MP）。本研究的总体目标是开发机器学习 (ML) 模型，根据吸附剂、吸附质和背景水基质特性预测 GAC 性能。为了进行模型校准，编制并分析了 MP 突破曲线，以确定 MP 突破达到进水 MP 浓度 (BV10) 的 10% 之前可处理的水床体积。超过 400 个数据点被分为训练集、验证集和测试集。在 ML 模型中探索了描述 MP、背景水基质和 GAC 特性的 17 个变量，以预测 log 10转换的 BV10 值。在测试集上使用 ML 模型，预测的 BV10 值的平均绝对误差约为 0.12 个对数单位，并且与实验确定的值高度相关 ( R 2 ≥ 0.88)。影响 BV10 预测的三大驱动因素是 MP 的空气-十六烷分配系数和氢键酸度（亚伯拉罕参数L和A ）以及 GAC 进水的溶解有机碳浓度。该模型可用于快速估计 GAC 床位寿命，针对给定的治疗场景选择有效的 GAC 产品，并探索 GAC 治疗对治疗新发 MP 的适用性。
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