Iranian Journal of Chemical Engineering (IJChE)

Abstract The rapid growth in global demand for clean and sustainable energy has intensified the need for efficient hydrogen (H2) production technologies. Thermochemical recycling of plastic waste has emerged as a promising approach, offering both environmental benefits and the generation of high-purity, low-carbon H2. This study evaluates the technical and economic feasibility of H2 production from diverse plastic waste streams using Aspen HYSYS and the Aspen Economic Analyzer. The process integrates polymer pyrolysis, chlorine removal, and steam reforming. Feedstocks (200×10³ t/y) include polyethylene (PE), polypropylene (PP), polyvinyl chloride (PVC), polystyrene (PS), polyethylene terephthalate (PET), and mixed industrial and municipal waste from packaging (PKG), medical (MED), automotive (AUT), municipal solid waste (MSW), construction (CON), and textile (TEX) sectors. H2 production yield strongly depends on the feedstock composition, ranging from 0.14 to 0.31 t H₂/t feed. PET with the lowest H/C ratio, exhibits the lowest yield, whereas PE and PP achieve the highest yields, albeit with incomplete carbon-to-CO conversion. Oxygenated polymers, such as PET, generate the highest CO2 emissions (~1.4 t/t feed). Economic analysis indicates that PE and MED are the most cost-effective feedstocks, with gross margins of 62 and 66% and annual net profits of 232×10⁶ and 223×10⁶ USD, respectively.

Abstract The present study numerically investigates the removal of hydrogen sulfide (H₂S) from crude oil using natural gas as a stripping medium in a T-junction microchannel through three-dimensional computational fluid dynamics (CFD) simulations. The microchannel geometry was adapted from a previously reported experimental configuration and further optimized to reduce natural gas consumption and operating temperature. The Volume of Fluid (VOF) model coupled with the SIMPLE algorithm was implemented in ANSYS Fluent to simulate the gas–liquid two-phase flow and evaluate mass transfer characteristics. Simulations were conducted for gas flow rates of 200–1200 mL/min and oil temperatures in the range of 20–40 °C. The results showed that the H₂S removal efficiency increased with crude oil temperature and gas flow rate but decreased with higher oil flow rate. The predicted efficiencies ranged between 65.7% and 77.8%, in close agreement with experimental data (maximum relative error: 5.6%). The cold-stripping configuration achieved high desulfurization performance even at low gas temperatures (about 18 °C) while reducing gas consumption by nearly one-third compared with conventional units. This study proposes validated correlations and optimized operating parameters for efficient desulfurization of sour crude oil using a microchannel-based cold stripping process.

Abstract This study presents a numerical investigation into the influence of the magnet position and its distance from the channel inlet on heat transfer and flow behavior of ferrofluid (FF), including Fe₃O₄/water flowing through a horizontal channel under a constant wall heat flux. Three magnet positions were considered—at the inlet, middle, and outlet of the channel—to identify the best configuration for heat transfer enhancement. Permanent magnets with a remanent magnetic flux density of 0.4 T were modeled. The nanoparticle concentration was 5 Vol.%, and the Reynolds number was 100. The effects of magnet positions on the local magnetic flux density, Kelvin force, streamlines, velocity and temperature distributions, and Nusselt number (Nu) were investigated. The problem was solved by assuming incompressible, laminar, and steady-state flow. The Galerkin weighted residual finite element method was used to solve the governing equations simultaneously. Results revealed that when magnets were positioned at the inlet or outlet, the magnetic field effects were localized and produced minimal impact on the flow and temperature fields. Conversely, when the magnets were located in the middle of the channel, the most substantial magnetic field gradients and Kelvin forces were generated, which created recirculation zones and increased fluid mixing, resulting in a more uniform temperature distribution and a significant enhancement in the local Nu and an average Nu of 5.31. Finally, this study proposes placing the magnet in the middle of the channel as the most effective configuration for enhancing convection heat transfer.

Abstract Conventional plastics have been a significant source of the environmental pollution, prompting considerable research into the development of biodegradable plastics using biological methods. Poly (3-hydroxybutyrate-co-3-hydroxyvalerate) has garnered particular attention due to its unique properties, including high flexibility and resistance to organic solvents. It has been demonstrated that certain microorganisms possess the intracellular capability to synthesize this biopolymer from organic waste. This study investigates bacteria that have been isolated from the municipal landfill of the Kermanshah Waste Recycling and Organic Fertilizer Production Company. Among the isolates, a strain capable of synthesizing biopolymers, exhibiting high similarity to the genus Stenotrophomonas geniculata strain Flmat 1, was identified via 16S rRNA gene sequencing. In order to verify the production of the biopolymer, Fourier-transform infrared spectroscopy (FTIR) and proton nuclear magnetic resonance (1H-NMR) analyses were employed. The results have shown that the isolated bacteria is capable of producing PHBV from unrelated carbon sources from waste food and producing 2.835 g/L biopolymer with the yield of 0.52 g PHBV per gram of CDW using waste food, at the pH of 9, temperature of 33°C, concentrations of 13.25 g/L and 27.71 g/L of nitrogen and glucose respectively.

Abstract Polymer nanocomposites reinforced with multi-walled carbon nanotubes (MWCNTs) offer promising mechanical performance; however, predicting their tensile modulus remains challenging due to the complex interplay of multiple factors such as filler content, functionalization, and interphase quality. In this study, a dataset of 229 samples was compiled from the literature, augmented via cubic spline interpolation to 4,933 training points, and analyzed using six machine learning models, including SVR, Random Forest, Gradient Boosting Regressor, XGBoost, KNN, and Artificial Neural Networks (ANNs). The inclusion of the interphase modulus (Ei), calculated via an extended Ji model, proved critical for improving prediction accuracy. Among all models, Gradient Boosting Regressor and XGBoost achieved the best predictive performance (Test R² = 0.9868 and 0.9837, respectively), while ANN demonstrated competitive accuracy (Test R² = 0.9703) but higher sensitivity under cross-validation (Mean CV R² = 0.7486). Feature importance analysis using SHAP further confirmed the significant contribution of Ei to prediction outcomes. Overall, this work demonstrates that incorporating physically-informed features like interphase modulus, combined with robust machine learning pipelines, can substantially enhance the predictive modeling of nanocomposite mechanical properties, providing a valuable tool for material design and optimization.

Abstract Given the high share of energy consumption in the building sector and the need to enhance thermal performance in cold climates, this study investigates the effect of the paraffin-based phase change material RT22HC on improving the thermal efficiency of a double-skin building facade. This material has a melting temperature in the range of 20–23°C (peak 22°C) and a latent heat storage capacity of about 190 kJ/kg, which enables storing and releasing heat at an approximately constant temperature. The aim of the study is to analyze the impact of removing thermal insulation and replacing it with an air cavity containing PCM on heating and cooling loads during cold periods in the city of Tabriz. Energy modeling was performed using DB software, and the heat transfer analysis was conducted with the Finite Difference algorithm. Three scenarios were examined: a base facade; a double-skin facade with PCM and thermal insulation; and a double-skin facade with PCM and an air cavity. The results showed that in the third case, the melting and solidification mechanism of RT22HC reduced heat flux and increased temperature stability; such that the annual sensible heat load decreased from 27276.61 kWh to 9985.8 kWh (equivalent to 63%). Moreover, indoor temperature fluctuations and mean radiant temperature differences decreased, improving thermal comfort conditions. Overall, the low thermal conductivity (0.2 W/m·K) and high heat capacity of PCM led to proposing this material as an effective substitute for conventional thermal insulations in DSF facades in cold climates.