A novel interpretable machine learning and metaheuristic-based protocol to predict and optimize ciprofloxacin antibiotic adsorption with nano-adsorbent

被引：17

作者：

Ahmed, Yunus ^{[1
]}

Maya, Akser Alam Siddiqua ^{[1
]}

Akhtar, Parul ^{[1
]}

Alam, Md Shafiul ^{[2
]}

Almohamadi, Hamad ^{[3
]}

Islam, Md Nurul ^{[4
]}

Alharbi, Obaid A. ^{[5
]}

Rahman, Syed Masiur ^{[6
]}

机构：

[1] Chittagong Univ Engn & Technol, Dept Chem, Chittagong 4349, Bangladesh

[2] Univ Asia Pacific, Dept Elect & Elect Engn, Dhaka 1205, Bangladesh

[3] Islamic Univ Madinah, Fac Engn, Dept Chem Engn, Madinah 42351, Saudi Arabia

[4] Univ Hafr Al Batin, Dept Elect Engn, Hafar Al Batin 31991, Saudi Arabia

[5] King Abdulaziz City Sci & Technol KACST, Water Management & Treatment Technol Inst, Sustainabil & Environm Sect, Riyadh 11442, Saudi Arabia

[6] King Fahd Univ Petr & Minerals KFUPM, Appl Res Ctr Environm & Marine Studies, Dhahran 31261, Saudi Arabia

来源：

JOURNAL OF ENVIRONMENTAL MANAGEMENT | 2024年 / 370卷

关键词：

Machine learning; Nano adsorbent; Gradient Boosting; Bayesian optimization; Ciprofloxacin adsorption; DIFFERENTIAL EVOLUTION; DEGRADATION; SURFACE; ACID;

D O I：

10.1016/j.jenvman.2024.122614

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

The existence of antibiotics in water sources poses substantial hazards to both the environment and public health. To effectively monitor and combat this problem, accurate predictive models are essential. This research focused on employing machine learning (ML) techniques to construct some models for analyzing the adsorption capacity of ciprofloxacin (CIP) antibiotic from contaminated water. The robustness of ten machine learning algorithms was evaluated using performance metrics such as the Coefficient of determination (R2), Mean Square Error (MSE), Median Absolute Error (MedAE), Mean Absolute Error (MAE), Correlation coefficient (R), Nash-Sutcliffe Efficiency (NSE), Kling-Gupta Efficiency (KGE), and Root Mean Square Error (RMSE). The hyperparameters of the ML models were fine-tuned using the Bayesian optimization algorithm. The optimized models were comprehensively evaluated using feature importance analysis to quantify the relative significance of operational variables accurately. After a thorough assessment and comparison of various machine learning models, it was evident that the HistGradientBoosting (HGB) model outperformed others in terms of CIP adsorption performance. This was supported by their low MAE value of 0.1865 and high R2 value of 0.9999. The modeling projected the highest antibiotic adsorption (99.28%) under optimized conditions, including 10 mg/L of CIP, 357 mg/L of CuWO4@TiO2 adsorbent, a contact time of 60 min at room temperature, and near neutral pH (7.5). The combination of advanced ML algorithms and nano adsorbents has great potential for addressing the problem of antibiotic pollution in water sources.

引用

页数：15