Optimisation study on solid–gas sorption based thermal energy storage using artificial intelligence approaches

• Optimisation of energy storage based on solid–gas sorption using AI approaches. • Data-driven framework based on ANNs for system modelling and performance prediction. • Intelligent framework based on MGA to enhance the system economic viability. • A storage system, using activated carbon and CO 2 , was developed and tested. • A critical enthalpy threshold identified to determine the system profitability path. Thermal energy storage based on solid–gas sorption is a promising solution for efficient renewable energy utilisation. However, the complex nonlinear dynamics of system operation and interdependent economic factors make performance prediction and profit exploration challenging. To address this, an optimisation platform based on Artificial Intelligence was developed. The platform consisted of a data-driven framework based on Artificial Neural Networks (ANNs) for predicting and monitoring system performance, and an intelligent framework based on Multi-population Genetic Algorithm (MGA) to enhance the system economic viability. An experimental storage system was developed and tested, and experimental data collected under varying adsorbate input conditions were used to train and validate the ANN models and develop economic models for optimisation. By accounting for temporal dependencies and delayed physical responses, the developed ANN models achieved high predictive accuracy, with absolute relative errors below 1 %. By establishing logic algorithms that demonstrated interdependence between investment cost and profit gain, economic optimisation was conducted using the MGA for wind energy utilisation across the UK and Ireland. Results showed that the optimal adsorbate input pressure to maximise net profits ranged from 5.2 bar to 8.7 bar. Further optimisation was carried out to raise the net profits by increasing the reaction enthalpy. The results revealed that a critical enthalpy threshold existed which determined the profitability path of the storage system. This study presents an intelligent tool that supports deployment of solid–gas sorption energy storage into net-zero pathways, enabling more efficient and economically viable utilisation of renewable energy sources.