The accurate prediction of carbon dioxide (CO₂) adsorption in metal–organic frameworks (MOFs) is critical for accelerating the discovery of high-performance materials for post-combustion carbon capture. Experimental screening of MOFs is often costly and time-consuming, creating a strong incentive to develop reliable data-driven models. Despite extensive research, most studies rely on standalone models or generic ensemble strategies that fall short in handling the complex, nonlinear relationships inherent in adsorption data. In this study, a novel ensemble learning framework is developed by integrating five distinct regression algorithms: Random Forest, XGBoost, LightGBM, Support Vector Regression, and Multi-Layer Perceptron. These algorithms are combined into four custom ensemble strategies: equal-weighted voting, performance-weighted voting, stacking, and manual blending. A dataset comprising 1212 experimentally validated MOF entries with input descriptors including BET surface area, pore volume, pressure, temperature, and metal center is used to train and evaluate the models. The stacking ensemble yields the highest performance, with an R² of 0.9833, an RMSE of 1.0016, and an MAE of 0.6630 on the test set. Model reliability is further confirmed through residual diagnostics, prediction intervals, and permutation importance, revealing pressure and temperature to be the most influential features. Ablation analysis highlights the complementary role of all base models, particularly Random Forest and LightGBM, in boosting ensemble performance. This study demonstrates that custom ensemble learning strategies not only improve predictive accuracy but also enhance model interpretability, offering a scalable and cost-effective tool for guiding experimental MOF design.

Enthalpy is a key thermodynamic parameter governing energy extraction efficiency in Enhanced Geothermal Systems (EGS). Although Machine Learning (ML) has been widely applied in geothermal modeling, few studies have systematically integrated Response Surface Methodology (RSM) with ML to develop and compare multiple predictive models for enthalpy production. Using datasets from CMG STARS simulations, we developed predictive models based on RSM and four ML techniques (Random Forest, Decision Tree, XGBoost, and Support Vector Machine). A Central Composite Design (CCD) in CMG CMOST established relationships between operational parameters and enthalpy, while Particle Swarm Optimization (PSO) determined the optimal operating conditions within the investigated design space. The RSM model achieved an \(R^2\) of 0.999404, outperforming Random Forest (0.9929), Decision Tree (0.9882), XGBoost (0.9883), and Support Vector Machine (0.9888). PSO optimization yielded a maximum cumulative enthalpy of \(4.507 \times 10^{16}\) J. These results demonstrate that integrating RSM with ML provides accurate and robust predictive capability with efficient optimization of geothermal reservoir performance. This study offers a structured framework combining statistical and data-driven approaches to improve geothermal energy assessment and decision-making.

Presplit drilling and blasting are frequent excavation methods used in the mining and construction industries, but they can be challenging to implement which can also lead to inconsistent results. This review identifies the key mechanisms behind presplit drilling and blasting, and discusses the significant impact that this technique has on the industry. It also emphasizes the major issues that must be addressed before presplit drilling and blasting can be properly implemented, such as the drilling program.The review then introduces the potential application of Artificial Intelligence (AI) tools in presplitting, and discusses how AI can be used to optimize the future design of presplit blast patterns, predict the performance of presplit blasts, and monitor the progress of presplit blasts in real time. The application of AI tools in presplitting has the potential to improve the safety, efficiency, and cost-effectiveness of blasting operations. The review concludes by discussing the future of drilling and blasting in the mining and construction industries, and emphasizes the role of AI optimization as a future tool in moving this field into the autonomous dimension.