Machine learning vs. statistical model for prediction modeling and experimental validation: Application in groundwater permeable reactive barrier width design.

Permeable reactive barrier (PRB) is an effective in-situ technology for groundwater remediation. The important factors in PRB design are the width and reactive material. In this study, the beaded coal mine drainage sludge (BCMDS) was employed as the filling material to adsorb arsenic pollutants in groundwater, aiming to design the width of PRB. The design methods involving traditional continue column experiments and empirical formulas, as well as machine learning (ML) predictions and statistical methods, which are compared with each other. Traditional methods are determined based on breakthrough curves under several conditions. ML method has advantages in predicting the width of mass transfer zone (WMTZ), which simultaneously consider the characteristics of material, pollutant, and environmental conditions, with data collected from articles. After data preprocessing and model optimizing, selected the XGBoost algorithm based on the high accuracy, which shows good prediction for WMTZ (R2 = 0.97, RMSE = 0.15). The experimentally derived WMTZ values were also used to validate the predictions, demonstrating the ML low error rate of 7.04 % and the feasibility. Subsequent statistical analysis of multiple linear regression (MLR) showed the error rate of 39.43 %, interpret superiority of ML due to the complexity of influencing factors and the insufficient precision of math regression. Compared to traditional width design methods, ML can improve design efficiency and save experimental time and manpower. Further expansion of the dataset and optimization of algorithms could enhance the accuracy of ML, overcoming existing limitations and gaining broader applications.

Utilizing machine learning for reactive material selection and width design in permeable reactive barrier (PRB).

Permeable reactive barrier (PRB) is an important groundwater treatment technology. However, selecting the optimal reactive material and estimating the width remain critical and challenging problems in PRB design. Machine learning (ML) has advantages in predicting evolution and tracing contaminants in temporal and spatial distribution. In this study, ML was developed to design PRB, and its feasibility was validated through experiments and a case study. ML algorithm showed a good prediction about the Freundlich equilibrium parameter (R2 0.94 for KF, R2 0.96 for n). After SHapley Additive exPlanation (SHAP) analysis, redefining the range of the significant impact factors (initial concentration and pH) can further improve the prediction accuracy (R2 0.99 for KF, R2 0.99 for n). To mitigate model bias and ensure comprehensiveness, evaluation index with expert opinions was used to determine the optimal material from candidate materials. Meanwhile, the ML algorithm was also applied to predict the width of the mass transport zone in the adsorption column. This procedure showed excellent accuracy with R2 and root-mean-square-error (RMSE) of 0.98 and 1.2, respectively. Compared with the traditional width design methodology, ML can enhance design efficiency and save experiment time. The novel approach is based on traditional design principles, and the limitations and challenges are highlighted. After further expanding the data set and optimizing the algorithm, the accuracy of ML can make up for the existing limitations and obtain wider applications.

Comparison of effects of multiple oxidants with an ultrasonic system under unified system conditions for bisphenol A degradation.

Bisphenol A (BPA) as a trace contaminant has been reported, due to widespread use in the plastics industry. This study applied the 35 kHz ultrasound (US) to activate four different common oxidants (H2O2, HSO5-, S2O82-, and IO4-) for BPA degradation. With increasing initial concentration of oxidants, the degradation rate of BPA increased. The synergy index confirmed that a synergistic relationship between US and oxidants. This study also examined the impact of pH and temperature. The results showed that the kinetic constants of US, US-H2O2, US-HSO5- and US-IO4-decreased when the pH increased from 6 to 11. The optimal pH for US-S2O82- was 8. Notably, increasing temperature decreased the performance of US, US-H2O2, and US-IO4- systems, while it could increase the degradation of BPA in US-S2O82- and US-HSO5-. The activation energy for BPA decomposition using the US-IO4- system was the lowest, at 0.453nullkJnullmol-1, and the synergy index was the highest at 2.22. Additionally, the ΔG# value was found to be 2.11 + 0.29T when the temperature ranged from 25 °C to 45 °C. The main oxidation contribution is achieved by hydroxyl radicals in scavenger test. The mechanism of activation of US-oxidant is heat and electron transfer. In the case of the US-IO4- system, the economic analysis yielded 271 kwh m-3, which was approximately 2.4 times lower than that of the US process.