ChemEngineering

26 pages, 3995 KB

Open AccessArticle

Energy Recovery from Iron Ore Sinter Using an Iron Oxide Packed Bed

by Sam Reis, Peter J. Holliman, Stuart Cairns, Sajad Kiani and Ciaran Martin

ChemEngineering 2025, 9(6), 118; https://doi.org/10.3390/chemengineering9060118 (registering DOI) - 24 Oct 2025

This study investigated a novel method of recovering energy from iron ore sinter using solid iron oxide heat transfer materials. Traditionally, air is passed through the sinter either in an open conveyor or a sealed vessel to recover energy. The bed materials used were a magnetite concentrate, hematite ore, goethite–hematite ore and sinter fines. A shortwave thermal camera and quartz reactor were used measure infrared radiation from the process. The thermal imaging was combined with image analysis techniques to visualise the transfer of thermal energy through the system. The results showed that energy moved rapidly through the system with peak heating rates of 18 °C/min at a lump sinter temperature of 600 °C. The ratio of heating rate to cooling rate was as high as 8.6:1.0, indicating efficient retention of energy by the bed materials. The bed composition, determined by X-ray fluorescence and X-ray diffraction was used to calculate the heat capacity based on pure material properties. The resultant energy balance determined thermal efficiency to be between 32 and 46% for the sinter fines and hematite–goethite ore, resulting in predicted fuel savings of up to 9.4kg/tonne with similar heat utilisations to the air recovery process. Thermal imaging combined with Brunauer–Emmett–Teller surface area measurements and scanning electron microscopy analysis experimentally replicated mathematical heat transfer model predictions that a smaller total pore volume resulted in less thermally resistive bed. Image analysis illustrated the breaking of the heat front between the less resistive solid and more resistive air in porous beds versus even conduction of heat through a dense bed. The oxide distribution in the bed materials impacted heat transfer, as at a lump temperature of 500 °C was controlled by hydrated oxide content whereas at 600 °C Fe₂O₃ was the more dominant driver. Full article

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34 pages, 25642 KB

Open AccessArticle

Copper Recovery from Copper Sulfide Ore by Combined Method of Collectorless Flotation and Additive Roasting Followed by Acid Leaching

by Bekhzod Gayratov, Bobur Gayratov, Labone L. Godirilwe, Sanghee Jeon, Abduqahhor Saynazarov, Saidalokhon Mutalibkhonov and Atsushi Shibayama

ChemEngineering 2025, 9(6), 117; https://doi.org/10.3390/chemengineering9060117 (registering DOI) - 24 Oct 2025

Abstract

Copper sulfide ores often contain significant amounts of silica and sulfur-bearing gangue minerals, complicating flotation efficiency. However, these challenges can be mitigated through collectorless flotation, which exploits the natural floatability of chalcopyrite and the hydrophilicity of silica minerals. Pyrite, the main sulfur gangue mineral, is also depressed under these conditions, improving concentrate quality by reducing the sulfur and iron content. Air exposure and pulp pre-aeration techniques can enhance chalcopyrite floatability, resulting in high recovery and grade. However, further processing of chalcopyrite concentrate using direct leaching remains challenging due to sulfur passivating layers. To overcome this, additive roasting is used as a pretreatment to improve the leachability of chalcopyrite. This study explored a combined collectorless flotation and additive roasting-leaching method using copper sulfide ore with chalcopyrite, quartz, and pyrite as the main minerals. Collectorless flotation achieved 94.5% recovery and a concentrate of 7.12% Cu from an initial 0.94%. Roasting this concentrate with additives like KCl and NaOH at 600 °C for 1 h, followed by leaching in 0.1 M H₂SO₄ at 25 °C with a hydrogen peroxide (H₂O₂) addition, resulted in copper dissolutions of 97% and 96.5%, respectively, with low iron dissolution. The proposed process achieved an overall copper recovery of 92%, demonstrating the effectiveness of combining collectorless flotation with additive roasting and atmospheric leaching. Full article

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13 pages, 2069 KB

Open AccessArticle

Biodiesel Carbonaceous Nanoparticle-Supported Potassium Carbonate as a Catalyst for Biodiesel Production via Transesterification

by Chuan Li, Tianyu Shi, Yijun Chen, Li Zhang, Zhiquan Yang, Lin Xu, Yong Luo and Xiaoyong Xu

ChemEngineering 2025, 9(6), 116; https://doi.org/10.3390/chemengineering9060116 - 22 Oct 2025

Abstract

This study primarily focuses on the development and optimization of a high-efficiency catalyst for biodiesel production. Potassium carbonate-supported solid catalysts were synthesized using soot as the support material via an equal-volume impregnation method. Transmission electron microscopy (TEM) and X-ray diffraction (XRD) analyses confirmed the successful deposition of potassium carbonate onto the soot surface, resulting in uniformly dispersed spherical nanoparticles on the catalyst. The catalytic performance was evaluated through single-factor experiments, assessing the effects of catalyst loading, alcohol-to-oil molar ratio, reaction temperature, and reaction time on the transesterification reaction. The maximum biodiesel yield obtained from the Single-factor experiments was 95.29% under the optimal conditions of 6 wt% catalyst loading (relative to oil), alcohol-to-oil molar ratio of 14:1, reaction temperature of 60 °C, and reaction time of 3 h. Furthermore, response surface methodology (RSM) using a four-factor, three-level Box–Behnken design (BBD) was employed to systematically analyze the interaction effects of these variables on the biodiesel yield. The optimized conditions identified by RSM were 61.1 °C, 3.3 h, alcohol-to-oil molar ratio of 14.2:1, and 6.1 wt% catalyst dosage, yielding 95.37% biodiesel conversion. These findings demonstrate that the soot-supported potassium carbonate catalyst developed in this study exhibits excellent catalytic activity, offering a novel catalyst system for industrial biodiesel production with significant academic and practical potential. Full article

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47 pages, 2962 KB

Open AccessArticle

Mathematical and Neuro-Fuzzy Modeling of a Hollow Fiber Membrane System for a Petrochemical Process

by Bryand J. Garcia-Sigales, Jose A. Ruz-Hernandez, Jose-Luis Rullan-Lara, Alma Y. Alanis, Mario Antonio Ruz Canul, Juan Carlos Gonzalez Gomez and Francisco J. Romero-Sotelo

ChemEngineering 2025, 9(6), 115; https://doi.org/10.3390/chemengineering9060115 - 22 Oct 2025

Abstract

This work presents a hybrid model that integrates a mechanistic multicomponent transport scheme in hollow-fiber membranes with an Adaptive Neuro-Fuzzy Inference System (ANFIS). The physical model incorporates pressure drops on the feed and permeate sides (Hagen–Poiseuille), non-ideal gas behavior (Peng–Robinson equation of state), and temperature-dependent viscosity; species permeances are treated as constant for model validation. After validation, a post-validation parametric exploration of permeance variability is carried out by perturbing the methane (CH₄) permeance by one decade up and down. From an initial set of 18 variables, 4 key parameters were selected through rigorous statistical analysis (Pearson correlation, variance inflation factor (VIF), and mean absolute error (MAE)); likewise, other physical criteria have been considered: permeance, retentate volume, retentate pressure, and retentate viscosity. Trained with 70% of the simulated data and validated with the remaining 30%, the model achieves a coefficient of determination (R²) close to 0.999 and a root mean square error (RMSE) below 8 × 10⁻⁸ m³/h in predicting the methane volume in the retentate, effectively responding to both steady and dynamic fluctuations. The combination of first-principles modeling and adaptive learning captures both steady-state and dynamic behavior, positioning the approach as a viable tool for real-time analysis and supervisory control in petrochemical membrane operations. Full article

(This article belongs to the Special Issue New Advances in Chemical Engineering)

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ChemEngineering, Volume 9, Issue 6 (December 2025) – 4 articles

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