Enhancing Li+ recovery in brine mining: integrating next-gen emotional AI and explainable ML to predict adsorption energy in crown ether-based hierarchical nanomaterials

Sani I Abba; Jamilu Usman; Ismail Abdulazeez; Lukka Thuyavan Yogarathinam; A G Usman; Dahiru Lawal; Billel Salhi; Nadeem Baig; Isam H Aljundi

doi:10.1039/d4ra02385d

Enhancing Li⁺ recovery in brine mining: integrating next-gen emotional AI and explainable ML to predict adsorption energy in crown ether-based hierarchical nanomaterials

RSC Adv. 2024 May 8;14(21):15129-15142. doi: 10.1039/d4ra02385d. eCollection 2024 May 2.

Authors

Sani I Abba¹, Jamilu Usman¹, Ismail Abdulazeez¹, Lukka Thuyavan Yogarathinam¹, A G Usman^{2

3}, Dahiru Lawal^{1

4}, Billel Salhi¹, Nadeem Baig¹, Isam H Aljundi^{1

5}

Affiliations

¹ Interdisciplinary Research Centre for Membranes and Water Security, King Fahd University of Petroleum and Minerals Dhahran 31261 Saudi Arabia [email protected] [email protected].
² Operational Research Centre in Healthcare, Near East University TRNC Mersin 10 99138 Nicosia Turkey.
³ Department of Analytical Chemistry, Faculty of Pharmacy, Near East University TRNC, Mersin 10 99138 Nicosia Turkey.
⁴ Mechanical Engineering Department, King Fahd University of Petroleum and Minerals Dhahran 31261 Saudi Arabia.
⁵ Chemical Engineering Department, King Fahd University of Petroleum and Minerals Dhahran 31261 Saudi Arabia.

Abstract

Artificial intelligence (AI) is being employed in brine mining to enhance the extraction of lithium, vital for the manufacturing of lithium-ion batteries, through improved recovery efficiencies and the reduction of energy consumption. An innovative approach was proposed combining Emotional Neural Networks (ENN) and Random Forest (RF) algorithms to elucidate the adsorption energy (AE) (kcal mol^-1) of Li⁺ ions by utilizing crown ether (CE)-incorporated honeycomb 2D nanomaterials. The screening and feature engineering analysis of honeycomb-patterned 2D materials and individual CE were conducted through Density Functional Theory (DFT) and Gaussian 16 simulations. The selected honeycomb-patterned 2D materials encompass graphene, silicene, and hexagonal boron nitride, while the specific CEs evaluated are 15-crown-5 and 18-crown-6. The crown-passivated 2D surfaces held a significant adsorption site through van der Waals forces for efficient recovery of Li⁺ ions. ENN predicted the targeted adsorption sites with high precision and minimal deviation. The eTAI (XAI) based Shapley Additive exPlanations (SHAP) was also explored for insight into the feature importance of CE embedded 2D nanomaterials for the recovery of Li⁺ ions. The extreme gradient boosting algorithm (XGBoost) model demonstrated a RT-2-MAPE = 0.4618% and ENN-2-MAPE = 0.4839% for the feature engineering analysis. This research would be an insight into the AI-driven nanotechnology that presents a viable and sustainable approach for the extraction of natural resources through the application of brine mining.