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Related Experiment Video

Updated: Jun 2, 2026

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
10:52

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Published on: April 12, 2019

A Hybrid Data-Driven Intensified Mechanistic Modeling Framework for Accurate and Reliable Wastewater Treatment

Wenlang Xie1, Jiahua Guo1, Hao Li1

  • 1Guangdong Provincial Key Lab of Environmental Pollution Control and Remediation Technology, School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou 510275, China.

Environmental Science & Technology
|June 1, 2026
PubMed
Summary

A new hybrid model combines data-driven and mechanistic approaches for wastewater treatment (WWT). This framework improves prediction accuracy and robustness, enhancing sulfide removal and sulfur recovery in challenging WWT systems.

Keywords:
accuracy and interpretabilitymachine learningmechanistic modeloptimization and controlparameter functionalizationselective sulfide oxidation

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Last Updated: Jun 2, 2026

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
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Mesocosm-Scale Constructed Wetland Design for Wastewater Treatment
08:24

Mesocosm-Scale Constructed Wetland Design for Wastewater Treatment

Published on: May 2, 2025

Area of Science:

  • Environmental Engineering
  • Computational Science
  • Biotechnology

Background:

  • Wastewater treatment (WWT) design relies on computational modeling.
  • Challenges include nonlinearities, influent fluctuations, and microbial interactions, limiting mechanistic model accuracy.
  • Data-driven models offer dynamic capture but can yield unstable predictions.

Purpose of the Study:

  • To introduce a hybrid data-driven intensified mechanistic modeling (HyDIM) framework.
  • To enhance predictive accuracy and robustness in WWT modeling.
  • To improve decision support and control in high-stakes WWT systems.

Main Methods:

  • Developed a HyDIM framework integrating data-driven functionalization of kinetic parameters with a mechanistic core.
  • Validated HyDIM in a sulfide-laden WWT system critical for hydrogen sulfide mitigation and sulfur recovery.
  • Employed a bi-objective optimization strategy for stable long-term performance.

Main Results:

  • HyDIM significantly improved prediction accuracy for sulfide, sulfur, and sulfate (R² increased from ~0.3 to ~0.7).
  • Predictive robustness was enhanced, reducing extreme deviations by 78.2% compared to data-driven models.
  • Achieved 96% sulfide removal and 91% sulfur recovery, a 41% improvement over mechanistic strategies.

Conclusions:

  • The HyDIM framework offers a novel paradigm for reliable WWT modeling.
  • It enhances predictive capabilities and control in dynamic and high-stakes environments.
  • HyDIM facilitates stable long-term performance and optimized resource recovery in WWT.