A new intelligent prediction model using machine learning linked to grey wolf optimizer algorithm for O-2/N-2 adsorption

To address the deficiency and predict the adsorption performance in different adsorbents, this study proposes a new optimizer linked to the machine learning (ML) model considering the performance of the adsorption process. The main goal is to predict adsorption under different process conditions with different adsorbents and provide a unified framework, leading to the prediction of adsorption phenomena instead of traditional isotherm models. This research focuses on predicting the adsorbed amount of O-2 and N-2 on several carbon-based adsorbents using the ML approach linked to the grey wolf optimizer algorithm (GWO). Experimental isotherm data (dataset 1344) on adsorbent type, temperature, pressure, gas type, and adsorption capacity of the process adsorption were used as input and output datasets. The best algorithm was Broyden-Fletcher-Goldfarb-Shanno (BFGS), a two-layer network from a multi-layer perceptron (MLP) method applying 28 neurons. The new MLP-GWO network would have the best mean square error (MSE) efficiencies of 0.00037, while the R-2 (r-squared) was 0.9934. The new ML-generated model can accurately predict the adsorption process behaviour of different carbon-based adsorbents under various process conditions. The results of this research have the potential to assist a wide range of gas separation industries.

Automated summary

This study proposes a new optimizer linked to the machine learning (ML) model to predict the adsorption performance in different adsorbents. It aims to provide a unified framework for predicting adsorption phenomena under different process conditions. The ML approach linked to the grey wolf optimizer algorithm (GWO) is used to predict the adsorbed amount of O-2 and N-2 on carbon-based adsorbents.