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

Yangmin Ren, Mingcan Cui, Yongyue Zhou, Shiyu Sun, Fengshi Guo, Junjun Ma, Zhengchang Han, Jooyoung Park, Younggyu Son, Jeehyeong Khim

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


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.

Original languageEnglish
Article number121097
JournalWater Research
Publication statusPublished - 2024 Mar 1

Bibliographical note

Publisher Copyright:
© 2024 Elsevier Ltd


  • Design methodology
  • Evaluation index
  • Permeable reactive barrier
  • SHapley Additive exPlanation
  • Total petroleum hydrocarbon
  • XGBoost

ASJC Scopus subject areas

  • Environmental Engineering
  • Civil and Structural Engineering
  • Ecological Modelling
  • Water Science and Technology
  • Waste Management and Disposal
  • Pollution


Dive into the research topics of 'Utilizing machine learning for reactive material selection and width design in permeable reactive barrier (PRB)'. Together they form a unique fingerprint.

Cite this