Iranian Journal of Chemical Engineering (IJChE)

Abstract Sulfur compounds pose significant challenges for environmental pollution. This issue has led to the application of various methods for sulfur detection and removal, which differ depending on the sulfur content. Among these methods is oxidative desulfurization, which has attracted considerable attention due to its mild operating conditions, ambient pressure, and relatively low temperature. In this study, a combination of chromium and nickel metals supported on ZSM-5 zeolite was used for oxidative desulfurization. Initially, nickel (3–8 wt.%) and chromium (1–8 wt.%) were separately loaded onto the zeolite to investigate the effect of each metal individually. Subsequently, a combination of 5 wt.% nickel and 1–8 wt.% chromium was simultaneously loaded onto the zeolite. The material containing 3 wt.% chromium and 5 wt.% nickel exhibited the highest efficiency within two hours. The synthesized catalysts were thoroughly characterized using FTIR, XRD, FESEM, EDX, and BET techniques. ZSM-5 zeolite with 5 wt.% nickel and 3 wt.% chromium showed 63% sulfur removal in the oxidative desulfurization process over two hours, indicating the favorable performance of this catalyst for the process.

Abstract To enhance the efficiency of oil recovery, a combination of multiple methods or materials can be utilized. This study aimed to investigate a hybrid method of using smart water and polymers to improve water flooding. Moreover, in this paper the influence of sulfate on the performance of polymers for increasing the efficiency of this method is investigated. To this end, we examine mechanisms, including changes in viscosity and interfacial tension (IFT), affecting oil recovery. To assess the impact of the injected fluid on oil recovery, a micro-model consisting of sand packs prepared from carbonate rock grains was employed as a two-dimensional simulated porous medium. The Response Surface Methodology (RSM) and Central Composite Design (CCD) were used as the most precise methods to design the experiments. The design was based on five levels and three variables including three parameters of the concentration of polymers ranging from 1000 to 3000 ppm, the concentration of the ions of smart water ranging from 12210 to 54180 ppm, and the temperature of 25–75°C. Considering the defined conditions, the effects of the prepared compositions on viscosity, IFT, and oil recovery were evaluated. The highest final oil recovery obtained, for the polymer-smart water without the NaCl (Sw-NaCl) solution, was reported about 60.32% of oil originally in place (OOIP), while the lowest one was reported to be about 50.67% of OOIP.

Abstract In this study, a novel strategy was devised to evaluate the mechanical properties of semi-crystalline polymer nanocomposites. To this end, the Halpin-Tsai model was comprehensively improved to also include the impact of dispersion quality, nanoparticle aggregation/agglomeration, the formation of the polymer/particle interphase region and induced/self-crystallinity. Besides enhancing the tensile modulus of the system, a particular fraction of either dispersed or clustered nanoparticle domains was considered to participate in the induced crystallization process (active domains). The content of active domains was estimated using the Gutzow-Dobreva theory or a specifically designed method based on the requirements of the system. On the contrary, the so-called inactive domains were assumed to remain distributed within the polymer matrix and contribute to the system response mechanism. The mechanical features of the interphase region were defined using the De Gennes’s self-similar carpet theory considering the molecular characteristics of the polymer phase. Moreover, the porosity index and bonding nature between aggregated or agglomerated nanoparticles were used to estimate the tensile modulus of the clustered doamins. The model validation process was conducted via benchmarking its predictions against the obtained data from the literature, which showed acceptable agreement and proved the model to be a quite accurate interpretation tool.

Abstract Objective: The aim of this study was to investigate the combined effects of styrene–ethylene–butylene–styrene (SEBS) and polyisobutylene (PIB) on the thermal, mechanical, and morphological properties of high-impact polystyrene (HIPS) and to evaluate their effectiveness in improving the performance of HIPS through binary and ternary blend formulations.
Methods: Binary and ternary HIPS/PIB/SEBS blends containing 1–2 wt% PIB and 1–5 wt% SEBS were prepared using twin-screw extrusion followed by injection molding. The blends were characterized by the melt flow rate (MFR) test, Vicat softening temperature(VST) test, thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), scanning electron microscopy (SEM), Izod impact test, and environmental stress crack resistance (ESCR) test based on elongation-at-break measurements before and after immersion in oil.
Results: PIB acted primarily as a plasticizer, resulting in increased melt flowability, whereas SEBS significantly enhanced the thermal stability and toughness of HIPS.VST dropped from 95.3 to 94.5 and 93.1, then increased to 96.4 and 96.5 in blends No. 1-8 and No. 9-13 respectively. The thermogravimetric analysis demonstrated improved thermal degradation resistance with increasing SEBS content (the highest result achieved at 446.9 C for HI-3), while DSC results indicated good compatibility among the blend components. SEM observations revealed finer elastomer dispersion and a fracture morphology with greater ductility. Furthermore, the simultaneous addition of PIB and SEBS markedly improved the impact strength up to 19.2 j/m and environmental stress crack resistance, with the best overall performance achieved for blends containing 1 wt.% PIB and 2 wt.% SEBS.
Conclusion: The incorporation of SEBS and PIB provides an effective strategy for simultaneously improving the flowability, toughness, thermal stability, and environmental stress crack resistance of HIPS. The proposed ternary blends offer enhanced performance and demonstrate promising potential for engineering applications to improve mechanical and environmental durability.

Abstract Conducting enhanced oil recovery (EOR) operations remains a critical challenge in the petroleum industry, necessitating innovative techniques to increase the efficiency of production from mature and low-permeability oil reservoirs. In this paper, a hybrid approach combining silica (SiO₂) and gamma-alumina (γ-Al₂O₃) nanoparticles with an anionic surfactant was experimentally investigated to assess their combined effects on key solution properties. Experiments were conducted under constant salinity and temperature to evaluate changes in wettability, interfacial tension (IFT), viscosity, density, and pH. A Central Composite Design (CCD) method was employed to optimize the concentration of nanoparticles and analyze their interactions with SDS. Results indicated that the formulated hybrid nanofluid substantially altered the wettability of the oil-wet glass surface, as indicated by the increase in the contact angle. The lowest IFT (1.485 mN/m) was achieved with one formulation, while the formulation selected for micromodel flooding (0.09 wt% SiO₂ + 0.05 wt% γ-Al₂O₃ + 0.1 wt% SDS) was chosen based on its overall performance across the measured properties, particularly wettability alteration and viscosity enhancement. The solutions also exhibited increases in viscosity and density by increasing the concentration of nanoparticles, which may influence fluid mobility and transport behavior. Oil recovery experiments in a quasi two-dimensional glass micromodel confirmed the effectiveness of the selected formulation, achieving an oil recovery of 60.6% of the original oil in place (OOIP) under the investigated conditions, compared with 13% for brine. These findings suggest that the hybrid nanoparticle–surfactant system may improve oil displacement under the investigated laboratory conditions.

Abstract Hydrocarbon reforming processes are important routes for the production of synthesis gas and hydrogen. However, the complex interactions among operating conditions, reaction kinetics, transport phenomena, and catalyst properties make process prediction challenging. This review examines the application of machine learning (ML) and artificial neural networks (ANNs) in the modeling, prediction, and optimization of hydrocarbon reforming processes, with emphasis on methane reforming. Studies employing ANN, Random Forest, Support Vector Machine, Gradient Boosting, XGBoost, and CatBoost were reviewed and compared based on the statistical metrics, validation strategies, size and quality of datasets, overfitting, and generalizability of the model. The results indicate that the performance of the model depends on the reforming process, target output, data structure, and validation strategy, and no single algorithm is universally superior. Although MLP has shown better statistical performance than RBF in some studies on dry methane reforming, independent test datasets may reveal limitations in the generalizability of ANN. Ensemble methods, including Random Forest, CatBoost, and Gradient Boosting, have demonstrated competitive performance, although the best-performing model varies depending on the predicted output. Furthermore, SHAP and Partial Dependence Plots can identify influential factors such as the reaction temperature, feed ratio, and metal loading. Overall, rigorous validation, independent testing, overfitting control, and interpretability are essential for the reliable ML application in reforming processes.