Related Experiment Video
Updated: Jul 6, 2025
09:43
Study of Short Peptide Adsorption on Solution Dispersed Inorganic Nanoparticles Using Depletion Method
Published on: April 11, 2020
6.6K
AI-enhanced adsorption modeling: Challenges, applications, and bibliographic analysis.
Sheetal Kumari1, Jyoti Chowdhry2, Manoj Chandra Garg1
1Amity Institute of Environmental Science (AIES), Amity University Uttar Pradesh, Sector-125, Noida, 201313, Gautam Budh Nagar, India.
Journal of Environmental Management
|January 3, 2024
Summary
Artificial intelligence (AI) offers solutions for water pollution by optimizing treatment processes and adsorbent selection for contaminant removal. Challenges in data, reproducibility, and real-world application need addressing for AI
Area of Science:
- Environmental Science
- Water Treatment Technologies
- Artificial Intelligence Applications
Background:
- Water pollution from inorganic and organic contaminants (fertilizers, heavy metals, dyes) is a significant global issue.
- Artificial intelligence (AI) presents promising capabilities for addressing complex challenges across various scientific and industrial fields.
- AI techniques are increasingly recognized for their potential to optimize water treatment and desalination processes, tackling pollution and scarcity.
Purpose of the Study:
- To provide an overview of diverse AI techniques applicable to contaminant adsorption in water treatment.
- To analyze the current landscape of AI in water treatment through bibliometric methods.
- To identify key challenges and future directions for AI implementation in real-world water treatment scenarios.
Main Methods:
- Bibliometric analysis of reviewed publications, examining journal type, publication year, and research context.
- Citation network analysis using tools like VOSviewer to identify research clusters.
- Review and synthesis of AI techniques for contaminant adsorption in wastewater.
Main Results:
- AI demonstrates potential in optimizing water treatment, reducing operational costs, and improving chemical utilization.
- AI models accurately predict the efficacy of adsorbents for removing various wastewater contaminants.
- Analysis revealed research trends and identified key areas for AI application in water treatment.
Conclusions:
- AI offers significant potential for enhancing water treatment efficiency and addressing pollution.
- Further research is needed to overcome challenges related to data availability, reproducibility, and practical implementation of AI in water treatment.
- Addressing these challenges is crucial for the successful integration of AI technologies in the water sector.
More Related Videos
Related Concept Videos
01:09Analyte Adsorption and Distribution
664
In certain chromatographic separations, solutes transfer between the mobile phase and the stationary phase via sorption, which typically refers to the process of adsorption. For many chromatographic systems, the sorption process often depends on the polarity of the compounds—an expression of the overall dipole moment within the molecule. During the separation process, there is competition between the solute and solvent for adsorption to the stationary phase. Highly polar compounds and...
664
01:16Adrenergic Agonists: Chemistry and Structure-Activity Relationship
3.0K
Adrenergic agonists' structure-activity relationship (SAR) determines their selectivity and efficacy. These agonists comprise a phenylethylamine moiety with an aromatic ring and an ethylamine side chain.
Aromatic ring substitutions: Substituting the aromatic ring with –OH groups at positions 3 and 4 yields catecholamines (e.g., epinephrine), which have a high affinity for adrenoceptors. Hydrogen bonding between –OH groups and receptors enhances adrenergic activity.
Separation of...
Aromatic ring substitutions: Substituting the aromatic ring with –OH groups at positions 3 and 4 yields catecholamines (e.g., epinephrine), which have a high affinity for adrenoceptors. Hydrogen bonding between –OH groups and receptors enhances adrenergic activity.
Separation of...
3.0K
01:13Physiological Pharmacokinetic Models: Assumption with Protein Binding
45
Physiological models with protein binding in pharmacokinetics offer a sophisticated approach to understanding drug disposition. These models consider drug-protein interactions, enabling them to effectively predict drug concentrations in different organs and tissues. This precision aids in accurate drug dosing, providing a significant advantage over conventional models. A key process within these models is equilibration, which ensures that drug concentrations achieve a steady state within the...
45
01:26Pharmacokinetic Models: Comparison and Selection Criterion
74
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
74