Canadian Journal of Chemical Engineering最新文献

The hydrophobicity and floatability of fine coal slime are severely diminished by surface coatings of gangue minerals, complicating coal–gangue separation in slurry systems. Traditional pulping methods struggle to efficiently remove fine mud from coal particles, reducing recovery efficiency. To address this, a self-designed impact flow slurry conditioning device was developed to enhance reagent adsorption on coal surfaces. Combining computational fluid dynamics (CFD) simulations, reagent adsorption rate analysis, and contact angle measurements, this study optimized slurry impact velocity to evaluate flow field dynamics and conditioning mechanisms. Flotation experiments revealed that strain rate increased with impact velocity, peaking at 774 s⁻¹ (5 m/s), while the minimum vortex scale reached 1.04 μm at 4 m/s. At 4 m/s, the collector adsorption rate and coal contact angle were maximized, achieving a combustible recovery rate of 98.18%, indicating optimal flotation performance. The impact flow method effectively strips surface gangue coatings, enhances coal-gangue separation, and improves coal hydrophobicity and floatability. The device integrates a disturbing cone and plate to generate localized turbulence and shear fields, significantly boosting reagent adsorption efficiency and overcoming structural limitations of traditional stirring equipment. These innovations provide critical insights into shear-driven adsorption mechanisms and advance coal slurry flotation technology, offering a scalable solution for industrial applications. This research establishes a foundation for developing efficient, high-performance coal processing systems.

The present research provides a thorough investigation to enhance the delignification process in a 2G biorefinery by implementing a dynamic process simulator. Developed using the Julia programming language, this simulator enables the study of crucial variables such as liquid–solid ratio (LSR), hydrogen peroxide concentration (C_HP), and processing time (t) during the alkaline hydrolysis stage. Optimal operating conditions were determined through rigorous simulation and meticulous experimental validation, resulting in an optimal configuration of 4% H₂O₂ concentration, LSR of 15:1 v/w, and 50 h of processing time under standard pressure and temperature conditions. These findings yielded a lignin removal percentage of 93.02%. Furthermore, the experimental validation process facilitated the recalibration of kinetic parameters. Overall, this research underscores the potential of the dynamic simulator in optimizing critical variables across various raw materials.

Aiming at the problems of layer information loss as well as the effectiveness of process data spatio-temporal feature fusion in stacked network-based soft sensor methods, this paper proposes a dual-attention spatio-temporal interaction network (DA-TSINET) method. Firstly, the dual-attention stacked network is constructed to overcome the layer information loss. Self-attention is added to different layers of stacked denoising autoencoder (SDAE) to enhance local denoising features, and global enhanced features are obtained by self-attention fusion. A gated recurrent unit (GRU) and a convolutional neural network (CNN) are used in parallel to further extract spatio-temporal relations, and an interactive gating module is designed for fusion to obtain globally enhanced spatio-temporal features for constructing a soft sensor model. Simulation experiments are carried out by debutanizer column and thermal power plant and compared with stacked autoencoder (SAE), SDAE, stacked isomorphic autoencoder (SIAE), variable-wise weighted SAE (VW-SAE), and gated stacked target-related autoencoder (GSTAE); the results show that the proposed method has high prediction accuracy, which verifies the effectiveness of the proposed method.

The present research provides a thorough investigation to enhance the delignification process in a 2G biorefinery by implementing a dynamic process simulator. Developed using the Julia programming language, this simulator enables the study of crucial variables such as liquid–solid ratio (LSR), hydrogen peroxide concentration (C_HP), and processing time (t) during the alkaline hydrolysis stage. Optimal operating conditions were determined through rigorous simulation and meticulous experimental validation, resulting in an optimal configuration of 4% H₂O₂ concentration, LSR of 15:1 v/w, and 50 h of processing time under standard pressure and temperature conditions. These findings yielded a lignin removal percentage of 93.02%. Furthermore, the experimental validation process facilitated the recalibration of kinetic parameters. Overall, this research underscores the potential of the dynamic simulator in optimizing critical variables across various raw materials.

This study presents a three-dimensional computational analysis of inertial flow behaviour, hydraulic resistance, and thermodynamic performance in asymmetrical converging–diverging microchannels. Three configurations are examined by introducing asymmetries in the lower arm—through variations in tapering angle, throat width, and throat length—while keeping the upper arm unchanged. Flow distribution analysis reveals that the steep-angle design achieves the most balanced flow split, improving symmetry by approximately 55% and 83% compared to the extended-throat and wide-throat designs, respectively. At a representative flow rate of 20 μL/min, the wide-throat configuration exhibits the lowest flow resistance, reducing pressure drop by 46.7% and 28.0% relative to the extended-throat and steep-angle cases, respectively. The steep-angle design also outperforms the extended-throat geometry by 26.0%. In terms of thermodynamic performance, entropy generation is lowest in the steep-angle configuration, showing a 27% reduction compared to the extended-throat case for Re $��\ge �$ 2.82. Overall, the wide-throat design minimizes energy loss and hydraulic resistance, the steep-angle configuration offers a balanced trade-off between flow symmetry and efficiency, and the extended-throat geometry results in the highest pressure and entropy penalties. These findings offer quantitative guidance for the optimized design of energy-efficient microfluidic systems operating under inertial flow regimes.

Advancements in the chemical engineering field, particularly in small-scale processes, have led to an increasing demand for high-performance heat-dissipating microsystems. Thus, in the present study, a MWCNT-Al₂O₃-CuO/(10:90) EG.W ternary hybrid nanoliquid flow in a microtube with viscous dissipation subjected to a constant wall heat flux is investigated numerically. The flow is under a uniform magnetic field with slip velocity at the wall due to wall superhydrophobicity. The set of governing equations and the corresponding boundary conditions were solved by using the finite volume method and the SIMPLER algorithm. The developed code was validated with experimental data from the literature. The main purpose of the present study is to explore the combined effects of Lorentz force and wall superhydrophobicity on the thermohydraulic performance and entropy generation of the ternary hybrid nanoliquid flow. Results showed that increasing the slip parameter enhances the convective heat transfer and decreases the pressure drop with a performance index greater than unity. All entropy production components decrease for high values of slip velocity, while the Bejan number shows opposite behaviour. Higher values of the Hartmann number make convective heat transfer and pressure drop increase with a performance index greater than unity. Magnetic, friction, and global entropy production components increase for high Hartmann values, while thermal entropy production and the Bejan number present opposite behaviour. More details on local and average variations of thermohydraulic performance and irreversibility components are presented and discussed.

In this study, the acid dissolution and subsequent solvent extraction of a Nigerian wolframite ore was examined. The combinations of sulphuric-cum-phosphoric acid solutions were used as the leachants. The effect of process parameters such as reaction temperature, concentration, and particle size were investigated. At optimal leaching conditions (2.0 M H₂SO₄ + 0.15 M H₃PO₄, 75°C, −90 + 75 μm), 93.7% of the ore reacted within 120 min. The Avrami model was proposed and considered based on the experimental data obtained in this study. The derived activation energy of 12.32 kJ/mol from the study indicates the reaction to be of an internal diffusion reaction model with minimal eco-feasible energy. Suggesting that the process is energy saving and eco-friendly. The extraction process by aliquat-336 yielded 95% of pure tungsten at 27 ± 2°C. The optimization of pure tungsten solution through crystallization process gave high-grade AMT of the form (NH₄)₆[H₂W₁₂O₄₀] · 4H₂O: 96-901-3322, melting point 101.4°C is therefore recommended for industrial applications in catalytic operations.

Bioengineering technologies hold significant potential for sustainable development as they play a vital role in waste-to-resource conversion. In this review paper, a thorough examination of the different types of waste which can be transformed into beneficial products using bioengineering technologies is conducted. The lignocellulosic wastes consist of 15.7% of all wastes produced in Europe in 2020. A comprehensive discussion regarding each category of waste and the classifications of bioengineering technologies, like phytoremediation, microbial fermentation, genetic engineering, algal-bio processing, and so forth have been explored. Additionally, it is important to acknowledge the various challenges that accompany these technologies, such as the instability of enzymes in microbial fermentation, the related cost of material and equipment, and the necessity for optimal cultivation condition for algal growth. The latter part of this paper elaborates the critical significance of bioengineering technology in the production of biofuels and bioplastics. This review also incorporates the role of AI and machine learning in optimizing bioengineering processes, real-world integration and industrial implementation possibilities have been discussed. The paper also discusses the advantages, challenges, and limitations of the study, as well as scalability, bio safety, and ethical issues, thereby reflecting on a contemporary theme that provides a future-oriented outlook.

The substantial annual production and low utilization of industrial by-product gypsum (IBPG) have led to significant ecological, environmental, and safety challenges. To address these issues, a cost-effective and environmentally friendly approach to convert IBPG and CO₂-sourced raw material into high-value products, that is, 100% high-purity of calcium carbonate (CaCO₃), ammonium chloride (NH₄Cl), and potassium sulphate (K₂SO₄), is proposed under ambient conditions by using a developed two-step method. By employing a recyclable phase transfer catalyst of ammonium acetate and a mixed solution containing saturated terminal products as a simulated mother liquor, efficient leaching of low-solubility IBPG, impurity separation, and enhanced reaction rates are simultaneously achieved. Some crucial conditions for preparing high-value target products are optimized, and many traditional by-products (e.g., syngenite, koktaite, calcium acetate hydrate, K₂SO₄ · (NH₄)₂SO₄, and KCl · NH₄Cl) are successfully suppressed. The favourable recirculation of the mother liquor demonstrated the prominent advantage in production cost reduction and environmental protection, confirming its industrial feasibility. Therefore, a promising, low-cost, and sustainable solution for IBPG's resource utilization to prepare high-value products is put forward here, providing some significant theoretical and industrial guidelines for solid waste disposal and carbon fixation.