Fluid catalytic cracking (FCC) has been a significant process in the refining industry for decades, enhancing refining margins by converting heavy hydrocarbon feedstocks into high-value, light hydrocarbon fractions such as light olefins and gasoline. As the supply of relatively light raw materials decreases, there is an increasing demand to use heavy crude oil as feed. Therefore, maximizing refining margins by using heavy feedstocks in the FCC process is attracting significant research attention. Residue fluid catalytic cracking (RFCC) utilizes less-refined atmospheric residue instead of conventional FCC feedstocks. However, this introduces unique challenges to the refining operations and catalyst design. The heavier hydrocarbons, metal contaminants, and high concentrations of nitrogen and sulfur compounds in atmospheric residue can cause reduced product quality, catalyst deactivation, and increased air pollutants emission, respectively. Consequently, addressing these challenges effectively requires both RFCC catalysts with superior performance and robust mitigation plans for contaminants. This review focuses on the design requirements and recent research trends in the development of RFCC catalysts for converting atmospheric residue into high-value products.

Biomass pyrolysis is a promising thermochemical pathway for converting organic waste into high-value products. As a result, it offers practical solutions for sustainable energy generation and waste management. However, the design and optimization of biomass pyrolysis reactors remain technically challenging due to the complex, multiphase behavior of the system. Computational fluid dynamics (CFD)-based simulation has emerged as a powerful tool for physically and precisely analyzing such complex multiphase flow systems. By virtually reproducing internal phenomena that are difficult to observe experimentally, CFD-based simulation enables effective system analysis and improvement. This paper reviews recent research trends focusing on the methods and applications of CFD for the precise design of biomass pyrolysis reactors. It covers the flow characteristics of fixed-bed and fluidized-bed reactors, multiphase flow modeling techniques, and methods for analyzing fluid and heat transfer under various operating conditions and structural configurations. In addition, it covers surrogate models based on artificial neural networks (ANNs), physics-informed neural networks (PINNs) for physical quantity estimation, and strategies for expanding simulations to industrial scales by using high-performance computing (HPC). Ultimately, this review comprehensively summarizes the integration potential of CFD-based analysis and machine learning techniques and discusses possible future directions for achieving precise designs and optimizations for biomass pyrolysis reactors.

MXene is a two-dimensional transition metal-based material with high electrical conductivity, large specific surface area, and diverse surface functional groups. It has attracted attention in applications such as energy storage, catalysis, and electromagnetic interference (EMI) shielding. However, issues such as interlayer restacking, oxidation, and limited mechanical flexibility restrict its performance when used alone. To address these limitations, composite design strategies combining MXene with multidimensional structures have been actively studied. Zero-dimensional (0D) nanoparticles enhance interfacial bonding and structural stability by dispersing between MXene layers, improving EMI absorption and interfacial reactivity. One-dimensional (1D) structures provide charge transport pathways and maintain interlayer spacing, enhancing flexibility and electrochemical performance. Two-dimensional (2D) materials prevent restacking and support efficient ion and electron transport, improving both rate capability and capacity. Three-dimensional (3D) porous structures contribute to structural stability and provide EMI shielding and thermal management functions. This review summarizes recent research trends on MXene-based multidimensional carbon composites, focusing on structural characteristics and application potential. Strategies for composite design and future directions are also discussed.

Accurate wind power forecasting is crucial for grid stability and renewable energy management. While large-scale, data-driven AI models demonstrate high forecasting capabilities, their real-world application is often constrained by significant computational resource limitations. This paper presents a spatially-aware clustering framework for improving the accuracy and computational efficiency of wind power forecasting. The approach leverages turbine-level SCADA data and applies KMeans clustering based on geographical and operational features. For each resulting cluster, individual forecasting models are constructed using the XGBoost algorithm. Experimental results demonstrate that the proposed framework achieves a forecasting accuracy within a 0.9% RMSE difference compared to the global baseline model (RMSE of 467.06 vs. 464.60) for the challenging 48-hour forecast, and reduces the inference time by approximately 86% (from 0.27 s to 0.04 s on average). Additional evaluations using hierarchical agglomerative clustering (HAC) and DBSCAN confirm the robustness of the method. To enhance the interpretability, cluster-level analyses are performed, including comparisons of the average wind speed, output variability, and feature importance. The results indicate that clusters exhibiting more stable environmental conditions tend to yield higher forecasting accuracy. Overall, the proposed approach both enables localized modeling for faster inference and maintains reliable forecasting performance, making it suitable for practical deployment in real-time energy management systems.

Ionic liquids (ILs) have being gaining more attention recently in the chemical industry and among scientists and chemical engineers because of their many fascinating properties as solvents. ILs can be used as additives for azeotropic distillation in cases where the ionic liquids interact more strongly with a certain component in an azeotropic mixture, thus causing a shift in the azeotropic point. This study demonstrates the influence of ILs on shifting the azeotrope of the system, {methyl tert-butyl ether (MTBE) + methanol} using trihexyltetradecylphosphonium chloride ([P666,14][Cl]) and trihexyltetradecylphosphonium bis(2,2,4- trimethylpentyl)phosphinate ([P666,14][TMPP]). The isothermal vapor-liquid equilibrium (VLE) data at 313.15 K for the system {MTBE + methanol} with different concentrations of [P666,14][Cl] and [P666,14][TMPP] were measured using headspace gas chromatography. The mole fraction of the ILs was varied from 0.05 to 0.10. The experimental ternary VLE data were correlated using the Wilson model.

Developing a controllable drug delivery system is critical for reducing side effects and improving drug therapeutic efficacy. The utilization of metal-organic-frameworks (MOFs) in the drug delivery field has attracted immense attention over the past few years due to their unique structural and chemical features. In this study, the potential role of MIL-100(Fe) (MIL stands for Material Institute Lavoisier) as a drug carrier was confirmed through controlled release studies. Using an eco-friendly approach, the synthesized MIL-100(Fe) aided in loading indomethacin (IND) and then the controlled release effects were examined. The estimated drug loading capacity was around 9.6% and was confirmed by ultra-violet visible (UV-vis) spectroscopy and thermogravimetric analysis (TGA). Moreover, the physico-chemical properties of MIL-100(Fe) and MIL-100(Fe)-IND were confirmed and supported by various analysis techniques. Furthermore, the release profile of IND was investigated under two different pH conditions. The obtained release profiles showed diffusion-controlled release indicating that a MIL-100(Fe) MOF could be used as a potential drug carrier.

Rice is a staple food for 40% of the world, so rice production is high to match its demand. As a result, rice husks account for a significant amount of agricultural waste. Rice husk is known to contain a large amount of silica (92 ~ 94 wt%), which if extracted at a high purity, could be used in the semiconductor and battery industries. This study attempted to extract high-purity silica from rice husk based on the environmental, social, governance (ESG) perspective that has recently emerged as an issue. The basic extraction procedure involved preparing the rice husk ash containing nanostructured silica through a hydrochloric acid pretreatment and calcination, mixing the rice husk ash in a NaOH solution to use the sol-gel method, and then appropriate it using sulfuric acid, and finally performing calcination to obtain amorphous nanostructured silica from the silica gel. Cross-experiments were conducted with 0.2 M and 0.5 M NaOH solutions and 600 o C and 850 o C final calcination temperatures as factors affecting the yield and purity of silica, respectively. An optimal purity of 98.5% and yield of 97.2% were obtained with 0.5 M NaOH and 850 o C. A typical process flow diagram (PFD) was then presented and an economic analysis was followed to recommend a potential country where the proposed process could be applied.

In this study, distillation was simulated to recover 2.59 kg/h of high-purity methanol remaining on the spent catalysts discharged from the petrochemical process. The optimal operating conditions were determined by varying key parameters such as the theoretical number of stages, the feed stage of raw material, the reflux ratio, and the distillate to feed ratio (D․F). The column theoretical plate number should be designed in 70 stages to satisfy the target methanol purity and flow rate. As a result, a total of 72 stages were designed taking into consideration the condenser and the reboiler to be installed at the upper and lower parts of the system. The optimal feed stage for the raw material was found to be between the 8th and 63rd stages, where the reboiler heat duty was 27,223 kcal/h. The effects of the reflux ratio on the methanol purity and recovery flow rate were also analyzed. As the reflux ratio increased, the methanol purity improved and the recovery flow rate remained relatively constant. In this study, the reflux ratio was fixed at 40.5 in order to consider the internal diameter of the column and secure the methanol purity and flow rate. A D․F of 0.72 was selected to achieve the target flow rate of 2.59 kg/h.

Flammable substances are used in laboratories and industrial process. The flash point (FP) is one of the most important physical properties for determining the potential of a liquid of being a fire or explosive hazard. The FP data at 101.3 kPa were measured for the binary systems {toluene + m-xylene}, {toluene + n-decane} and {m-xylene + n-decane}. The FP measurements were obtained according to the standard test method (ASTM D3278) using a Stanhope-Seta closed cup flash point tester. The measured flash points were compared with the predicted values calculated using Raoult’s Law and also following two GE models, Wilson and UNIversal QUAsiChemical (UNIQUAC). The average absolute deviation between the predicted and measured FP was less than 1.39 K.

Shoes are difficult to recycle and decompose because they are made of composite plastics. As a result, they are considered one of the major contributors to solid waste problems. To address these challenges, this study proposes and evaluates a novel hydrogen production process for waste shoes using a pyrolysis reactor in a CO2 condition and water gas shift reactors. The syngas produced through pyrolysis mainly consists of CO and H2. The CO is converted into hydrogen through a high-temperature shift reaction, a low-temperature shift reaction, and a combined reaction in which both are connected in series. As a result, the proposed process can produce over 3,000 kg of hydrogen annually at a cost of less than 2.52 $ kg–1. Utilizing waste shoes made from composite plastics as a feedstock contributes to waste reduction and resource circulation. This proposed process presents a sustainable and environmentally friendly alternative to conventional fossil fuel-based hydrogen production methods.