The injection of carbon dioxide into depleted oil and gas reservoirs has emerged as a promising method for enhanced oil recovery (EOR) and safe carbon storage. This process significantly influences the density of CO2-hydrocarbon mixtures, a critical property that directly affects EOR efficiency and storage integrity. However, accurately predicting mixture density remains challenging due to the complex and nonlinear behavior of hydrocarbons, and many existing empirical models show limited reliability under varying conditions. This study primarily aims to develop robust tools leveraging advanced algorithms, Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Bayesian Neural Network (BNN), Adaptive Boosting (AdaBoost), and Random Forest (RF) to accurately estimate the density of CO2-hydrocarbon mixtures, utilizing a rich and extensive databank (17,081). The model inputs included temperature (T), pressure (P), molecular weight (Mw), pseudo-critical temperature (Tpc), pseudo-critical pressure (Ppc), and the mole fraction of CO2. The results were evaluated against several equations of state (EoSs), including the Cubic-Plus-Association (CPA) model developed in this study, as well as Peng-Robinson (PR), Soave-Redlich-Kwong (SRK), and Redlich-Kwong (RK) models for representative CO2-hydrocarbon mixtures (n-Butane, Propane, and Hexadecane). For these specific mixtures, the CPA and PR models showed the best performance among the examined EoSs, while the XGBoost model achieved the highest overall accuracy, with an R2 of 0.9968 and an average absolute percent relative error (AAPRE) of 0.7647. High R2 values (0.97-0.99) indicate that LightGBM, BNN, AdaBoost, and RF all achieved strong predictive performance. Furthermore, trend analysis confirmed that the XGBoost model can accurately capture density variations in response to changes in pressure variable. Moreover sensitivity analysis indicated that pressure with relevancy factor of (0.2045) has the most significant effect on the output. The leverage technique showed that over 96% of the dataset is statistically reliable. In the final stage, Shapley additive explanations (SHAP) analysis demonstrated that both Tpc and Ppcpositively influence the output parameter. These results demonstrate that the XGBoost model provides a reliable and accurate alternative to experimental methods for predicting CO2-hydrocarbon density across a wide range of operating conditions.
The iron and steel industry is one of the most energy-intensive industrial sectors, accounting for approximately 8 % of global final energy use and nearly 10 % of annual greenhouse gas emissions. Within this value chain, direct reduction iron (DRI) production is among the most energy-demanding stages and a key focus for decarbonization efforts. In this study, the conventional syngas supply for DRI, typically produced via steam methane reforming (SMR), is replaced by a high-temperature solid oxide electrolyzer (SOE)-based system. The required electrical energy and partial thermal energy are provided by solar energy and biomethane, respectively, while waste heat recovery from other steelmaking units is implemented to enhance overall efficiency. The main objectives are to reduce energy consumption, emissions, and production costs while improving exergy efficiency and maintaining compatibility with existing industrial infrastructure. Comprehensive energy, exergy, environmental, economic, and exergo-economic analyses are performed. A multi-objective optimization using a genetic algorithm is then applied to determine optimal operating conditions regarding the four main objectives. The optimized results are benchmarked against conventional DRI routes and literature data. The findings show a 12 % reduction (from 3.06 to 2.72 MWh per ton of DRI) in specific energy consumption and annual energy consumption can be reduced by up to 680 GWh, while CO2 emissions decrease by approximately 1 MtCO2 per year (from 0.6 to 0.2 tonCO2 per ton of DRI). Additionally, syngas production efficiency improves by nearly 30 %, and natural gas consumption is reduced by about 50 %. These results demonstrate the potential of the proposed SOE-based configuration for advancing low-carbon, energy-efficient ironmaking.
Metaheuristic algorithms have become a widely adopted approach for addressing feature selection problems in high-dimensional datasets. Among these methods, Particle Swarm Optimization (PSO) has received attention due to its simple structure, efficient search capability, and adaptability to different optimization scenarios. As a result, numerous PSO-based feature selection methods have been proposed in recent years, each introducing various modifications to improve search performance and subset quality. Despite this rapid development, a structured analysis that highlights the strengths, limitations, and practical implications of these approaches remains necessary. This survey provides a systematic examination of prominent PSO-based feature selection algorithms reported in the literature. The reviewed methods are analyzed and compared with respect to several important aspects, including search behavior, strategies used to balance exploration and exploitation, design of fitness functions for evaluating feature subsets, and commonly used evaluation criteria such as classification accuracy, dimensionality reduction rate, and computational cost. The analysis highlights the main limitations of PSO-based feature selection, including a tendency to premature convergence, sensitivity to parameter settings, and scalability issues in high-dimensional environments. Based on these observations, several open research challenges are identified and potential directions for future work are outlined in order to improve the applicability of PSO-driven feature selection methods.
Combining metal-organic frameworks (MOFs) with active agents is considered an effective procedure to increase the removal efficiency of pristine MOFs. In this work, Cu-MOF/X-Ag nanocomposites with different Ag contents were prepared by a sequential deposition-reduction method. The physicochemical characteristics of the as-prepared samples were evaluated using XRD, FT-IR spectroscopy, FE-SEM, EDS, elemental mapping, TEM, EIS, and PL spectroscopy. Then, the photocatalytic activity of the samples for the removal of tetracycline (TC) as a model pollutant was investigated. Among the nanocomposites, Cu-MOF/12%Ag exhibited the highest photodegradation efficiency under visible light irradiation. 0.5 g L-1 of this photocatalyst degraded 95.5% of 20 mg L-1 TC within 60 min. To study the kinetics of the TC degradation process, the first-order kinetic model was used, and the results exhibited that the rate constant of Cu-MOF/12%Ag was higher than that of Cu-MOF, Cu-MOF/6%Ag, Cu-MOF/8%Ag, Cu-MOF/10%Ag, and Cu-MOF/14%Ag. Furthermore, the Cu-MOF/12%Ag nanocomposite displayed excellent recyclability after six cycles. The radical quenching experiments revealed that (OH)-O-center dot and (center dot)O2- played vital roles in the TC photodegradation process. The antibacterial properties of Cu-MOF and Cu-MOF/12%Ag were evaluated against standard bacterial strains, including Staphylococcus aureus (PTCC 1112), Escherichia coli (PTCC 1330), and methicillin-resistant Staphylococcus aureus (MRSA), using the agar well diffusion method. Results demonstrated that Cu-MOF/12%Ag exhibited remarkable antibacterial efficacy, with the largest inhibition zone (2.6 mm) observed against MRSA.
The management of arsenic-rich industrial residues remains a major environmental challenge due to the high toxicity and mobility of arsenic species. This study investigates the stabilization/solidification (S/S) of arsenic-bearing sulfide tailings generated from the effluent treatment plants (ETP) of the Sarcheshmeh copper complex (Iran). Considering industrial constraints and the availability of construction-grade materials, a cement-based S/S formulation was developed to minimize arsenic leachability while maintaining adequate mechanical performance. The mixture design was initially screened through exploratory tests and subsequently optimized by systematic variation of raw material proportions. TCLP results showed that the optimal formulation-containing 5 wt
