Artificial Intelligence Applications in Adsorption Process Optimization

The optimisation of adsorption processes has seen a surge in the application of artificial intelligence (AI) techniques to model complex physicochemical interactions, reduce experimental burden and enhance removal efficiencies of pollutants. By integrating data-driven algorithms with conventional experimental design, researchers have been able to predict equilibrium behaviour and kinetics across a wide range of adsorbent–adsorbate systems. Hybrid workflows commonly employ statistical methods to generate training datasets, followed by machine learning models—such as artificial neural networks (ANNs), adaptive neuro-fuzzy inference systems (ANFIS) and ensemble methods—to capture nonlinearities in process variables. Evolutionary and swarm-based optimisers, including genetic algorithms (GAs) and particle swarm optimisation (PSO), are routinely coupled with these predictive engines to identify optimal operating conditions. Beyond single-component batch systems, recent advances extend to dynamic and multicomponent adsorption, offering robust frameworks for packed-bed design and real-time process control. This convergence of AI and adsorption science underpins sustainable water treatment, resource recovery and pollutant management across diverse industrial and environmental contexts.

Research from Nature Portfolio

Recent studies have demonstrated the effectiveness of hybrid AI approaches in elucidating and optimising heavy-metal removal by nanoscale composites. In foundational work, neural networks integrated with genetic and swarm-based algorithms were used to model copper uptake on reduced graphene oxide-supported zero-valent iron. This joint strategy drastically reduced the number of laboratory runs while achieving high-accuracy predictions of optimum pH, temperature and contact time. The combined ANN-PSO and ANN-GA frameworks delivered superior removal performance and offered mechanistic insights via post-hoc isotherm and kinetic analyses, paving the way for tailored adsorbent design and process intensification.

Artificial Intelligence Applications in Adsorption Process Optimization publication trend

The graph below shows the total number of articles in artificial intelligence applications in adsorption process optimization across all publications each year (not limited to Nature Index journals).

Technical terms

Artificial neural network (ANN): computational model of interconnected nodes inspired by neuronal structures, used to learn nonlinear relationships between inputs and outputs.

Genetic algorithm (GA): optimisation heuristic based on principles of natural selection, utilising crossover and mutation to evolve solutions.

Particle swarm optimisation (PSO): population-based stochastic method inspired by collective animal behaviour, used to search for optimal solutions.

Response surface methodology (RSM): statistical approach for designing experiments and fitting empirical models to explore the effects of multiple variables.

Adsorption isotherm: model that describes the equilibrium relationship between the concentration of a solute in solution and the amount adsorbed at constant temperature.

Adaptive neuro-fuzzy inference system (ANFIS): hybrid framework combining neural network learning with fuzzy-logic reasoning to model complex nonlinear systems.

References

1. Modeling and prediction of copper removal from aqueous solutions by nZVI/rGO magnetic nanocomposites using ANN-GA and ANN-PSO. Scientific Reports (2017).
2. A Generalized Method for Modeling the Adsorption of Heavy Metals with Machine Learning Algorithms. Water (2020).
3. Comparison Study of ANFIS, ANN, and RSM and Mechanistic Modeling for Chromium(VI) Removal Using Modified Cellulose Nanocrystals–Sodium Alginate (CNC–Alg). Arabian Journal for Science and Engineering (2023).
4. Application of neural network in metal adsorption using biomaterials (BMs): a review. Environmental Science Advances (2023).