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Modeling, optimization and understanding of adsorption process for pollutant removal via machine learning: Recent
Wentao Zhang1, Wenguang Huang2, Jie Tan2
1Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, People's Republic of China.
Chemosphere
|November 4, 2022
Summary
Machine learning (ML) offers a novel approach to optimize water pollutant removal via adsorption. This review highlights ML
Area of Science:
- Environmental Science
- Water Treatment Technologies
- Computational Chemistry
Background:
- Reducing water pollutant concentrations is critical for environmental safety.
- Adsorption is a cost-effective technology for pollutant removal.
- Current experimental methods for adsorption studies are inefficient and time-consuming.
Purpose of the Study:
- To review the application of machine learning (ML) in pollutant adsorption.
- To summarize ML workflows and algorithms relevant to adsorption.
- To discuss the potential of ML in optimizing adsorption processes and understanding mechanisms.
Main Methods:
- Literature review of machine learning applications in pollutant adsorption.
- Summarization of common ML algorithms and their workflow.
- Analysis of ML's role in regulating adsorption efficiency, operating conditions, and mechanisms.
Main Results:
- Machine learning provides an innovative paradigm to overcome limitations of traditional adsorption experiments.
- ML can comprehensively regulate adsorption efficiency, operating conditions, and elucidate adsorption mechanisms.
- General guidelines for applying ML in pollutant adsorption are presented.
Conclusions:
- Machine learning is a promising tool to advance adsorption technology for water purification.
- Further research is needed to address existing challenges and enhance ML interpretability in this field.
- This review aims to promote ML adoption and improve understanding in pollutant adsorption research.
Keywords:
Adsorption mechanismArtificial intelligenceMachine learningPollutant adsorptionProcess parameterRemoval efficiencyMore Related Videos
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