Machine Learning Applications in Adsorption Processes

Machine learning has emerged as a powerful tool for understanding and optimising adsorption processes across a range of environmental and industrial applications. By leveraging data‐driven models, researchers can predict adsorption capacities, identify key material and operational parameters, and elucidate underlying mechanisms without exhaustive experimental campaigns. Common approaches include supervised algorithms such as random forest, support vector regression, gradient boosting and artificial neural networks, as well as deep learning architectures for complex, high‐dimensional datasets. These methods have been applied to optimise the synthesis of novel adsorbents, forecast removal efficiencies for organic micropollutants and heavy metals, and refine process conditions for continuous or batch systems. The integration of feature‐selection techniques further enhances model interpretability by pinpointing the most influential adsorbent properties—such as surface area, pore volume, chemical functionality and pH—thereby guiding rational design. Globally, machine‐learning‐enabled adsorption research accelerates the development of efficient water‐ and air‐treatment strategies, supports circular‐economy valorisation of biomass byproducts and underpins the scalable deployment of low‐cost materials for contaminant mitigation.

Research from Nature Portfolio

No recent Nature Portfolio content available.

Research from all publishers

Recent work has demonstrated the versatility of machine learning in both adsorbent synthesis and performance modelling. One study systematically applied ensemble algorithms to optimise biochar production and forecast pollutant uptake, integrating synthesis parameters and cost metrics to propose economically viable process conditions. The resulting models achieved high accuracy in predicting yield, surface properties and adsorption capacity, offering design guidelines for biochar‐based water treatment.

A deep learning neural network approach has been developed to predict sorption parameters for ionisable and polar organic contaminants onto diverse carbonaceous materials. By training on widely available physicochemical descriptors, the model delivered excellent correlation coefficients for Freundlich isotherm parameters, enabling rapid selection of suitable sorbents for emerging pollutants without labour‐intensive sorption experiments.

In a further advance, researchers employed random forest, decision trees and gradient boosting to model dye adsorption onto activated carbon derived from agricultural waste. Through comprehensive feature‐selection analysis, key variables—primarily pore volume, surface area and feedstock pH—were identified as major contributors to removal efficiency. The optimised random forest model achieved predictive accuracy exceeding 90 %, substantially reducing the need for trial‐and‐error screening in wastewater dye removal applications.

Machine Learning Applications in Adsorption Processes publication trend

The graph below shows the total number of articles in machine learning applications in adsorption processes across all publications each year (not limited to Nature Index journals).

Technical terms

Adsorption Isotherm: Relationship between solute concentration in the fluid phase and amount adsorbed at equilibrium under constant temperature conditions.

Artificial Neural Network (ANN): Computational framework inspired by biological neurons, comprising interconnected layers that learn complex nonlinear mappings from input features to outputs.

Random Forest: Ensemble learning method that aggregates predictions from multiple decision trees, each built on random subsets of data and features, to enhance accuracy and robustness.

Gradient Boosting: Sequential ensemble technique that successively fits new models to the residual errors of prior models, improving predictive performance through iterative refinement.

Deep Learning Neural Network: Advanced neural architecture with multiple hidden layers capable of hierarchical feature extraction and representation learning from large or unstructured datasets.

References

1. Synthesis optimization and adsorption modeling of biochar for pollutant removal via machine learning. Biochar (2023).
2. Deep Learning Neural Network Approach for Predicting the Sorption of Ionizable and Polar Organic Pollutants to a Wide Range of Carbonaceous Materials. Environmental Science and Technology (2020).
3. A Study on Machine Learning Methods’ Application for Dye Adsorption Prediction onto Agricultural Waste Activated Carbon. Nanomaterials (2021).