Canadian Journal of Chemical Engineering最新文献

{"title":"Siliceous nanosphere material for CO2 capture: Kinetics, isotherm, and multicyclic performance studies","authors":"Ravisha Goswami,&nbsp;Babuni Prasad,&nbsp;Rajender Gupta,&nbsp;Arunkumar Samanta","doi":"10.1002/cjce.70339","DOIUrl":"https://doi.org/10.1002/cjce.70339","url":null,"abstract":"<p>In this work, novel, fibrous-shaped siliceous nanosphere KAUST Catalysis Center-1 (KCC-1) material is synthesized, characterized, and functionalized using different weight percentages of polyethyleneamine for post-combustion carbon dioxide (CO₂) capture. To study the structural properties, various characterization methods, such as field-emission scanning electron microscopy (FE-SEM), high-resolution transmission electron microscopy (HR-TEM), nitrogen adsorption/desorption isotherms, and powder X-ray diffraction (PXRD), are used. Morphological studies reveal the fibrous structure of the synthesized KCC-1 with an average pore size of 3.50 nm and a high specific surface area of 561.13 m²/g. The CO₂ capture capacity experiments are investigated using the thermogravimetric method. With an optimized amine loading of 60 wt.% and an adsorption temperature of 75°C, the equilibrium CO₂ uptake capacity is 2.90 mmol CO₂/g. From the 27 consecutive temperature swing adsorption/regeneration cycles, it is found that the working CO₂ capacity of the sorbent decreases marginally (only 4.80%) and remains nearly stable at about 2.59 mmol CO₂/g. Finally, the kinetic and isothermal modelling suggest that the kinetics of CO₂ capture is best described by the pseudo-fractional order model with a deviation of less than 3.62% from experimental data when the adsorption temperature is 75°C, while the modified Toth model is found to best fit the isotherm data with a deviation of less than 2.76% from experimental findings.</p>","PeriodicalId":9400,"journal":{"name":"Canadian Journal of Chemical Engineering","volume":"104 9","pages":"4841-4853"},"PeriodicalIF":2.2,"publicationDate":"2026-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148696209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"In-situ asphaltene capture with iron oxide nanoparticles in steam-assisted gravity drainage (SAGD) for improved recovery and partial upgrading of bitumen","authors":"Luis Prada,&nbsp;Saleh S. Baakeem,&nbsp;Mabkhot BinDahbag,&nbsp;M. Daniela Contreras-Mateus,&nbsp;Jesus Botett,&nbsp;Afif Hethnawi,&nbsp;Nashaat N. Nassar","doi":"10.1002/cjce.70307","DOIUrl":"https://doi.org/10.1002/cjce.70307","url":null,"abstract":"<p>Nanoparticles have emerged as promising additives to enhance steam-assisted gravity drainage (SAGD) efficiency by improving heat transfer, reducing viscosity, and promoting asphaltene adsorption and catalytic upgrading. This study integrates iron oxide (Fe₃O₄) nanoparticles into the SAGD process to simultaneously improve oil recovery and partially upgrade bitumen via in-situ asphaltene capture. Four nanoparticle-assisted SAGD experiments were conducted, varying nanoparticle concentrations (1000, 3000, and 5000 mg/kg of porous media) and spatial distribution, and were compared against a baseline SAGD scenario. Fe₃O₄ nanoparticles, synthesized via co-precipitation, were selected for their high adsorption affinity for asphaltenes. Thermal performance was assessed through temperature distribution and thermogravimetric analysis (TGA), while recovery efficiency was evaluated by oil production and the cumulative steam–oil ratio (cSOR). Oil quality was analyzed using asphaltene content, carbon–hydrogen–nitrogen–sulphur (CHNS) elemental analysis, high-resolution proton nuclear magnetic resonance (¹H NMR), matrix-assisted laser desorption/ionization–time of flight (MALDI-TOF), and rheological measurements. Uniformly dispersed 1000 ppm nanoparticles reached the highest recovery (83%), uniform heat flux, and the greatest asphaltene retention, with an asphaltene retention index (IR) of 0.23. Nanoparticle-assisted oil exhibited reduced high-molecular-weight species, partial breakdown of heavy hydrocarbons, lower viscosity, and weakened asphaltene networks. These results showed that Fe₃O₄ nanoparticles enhance heat transfer and selectively adsorb asphaltenes under SAGD conditions. Recovery was improved by enabling uniform spatial distribution, while oil quality benefited from appropriate nanoparticle concentration, highlighting the need to control both placement and dosage for optimal outcomes. Optimizing both the spatial distribution and concentration of Fe₃O₄ nanoparticles maximizes SAGD recovery and upgrades the produced oil via in-situ asphaltene adsorption.</p>","PeriodicalId":9400,"journal":{"name":"Canadian Journal of Chemical Engineering","volume":"104 9","pages":"4787-4808"},"PeriodicalIF":2.2,"publicationDate":"2026-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cjce.70307","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148696577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DeNOx mechanism of iron-manganese ore catalysts at low temperatures","authors":"Qian Zhang,&nbsp;Xianhang Sun,&nbsp;Yun Xu,&nbsp;Fahua Zhu,&nbsp;Jun Liu,&nbsp;Ke Zhuang,&nbsp;Yunlan Sun","doi":"10.1002/cjce.70372","DOIUrl":"https://doi.org/10.1002/cjce.70372","url":null,"abstract":"<p>Nitrogen oxides (NO_x) are primary pollutants that pose threats to both the human health and the environment. Among the various methods for mitigating NO_x emissions, selective catalytic reduction (NH₃-SCR) is as a widespread and effective method. However, traditional catalysts such as vanadium-titanium catalysts face challenges in meeting the requirements to remove NOx at low temperature. In this study, iron-manganese ore that is widely available and inexpensive was used as deNOx catalyst in flue gas at low temperatures, which exhibits outstanding low-temperature SCR deNO_x performance, achieving nearly 100% deNO_x efficiency within the temperature range of 80–180°C. The optimal preparation and operating parameters of the iron-manganese ore were determined and its deNOx mechanism were revealed iron-manganese ore via experiments and density functional theory calculation.</p>","PeriodicalId":9400,"journal":{"name":"Canadian Journal of Chemical Engineering","volume":"104 9","pages":"4688-4701"},"PeriodicalIF":2.2,"publicationDate":"2026-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148696486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Nanostructured carbon-filled carbon fibre reinforced polymer composites: Properties, fabrication methods, applications, and future prospects: A review","authors":"Saurabh Mishra,&nbsp;Ravindra Kumar,&nbsp;Vijay Verma,&nbsp;Vimal Kishore,&nbsp;Kamal K. Kar","doi":"10.1002/cjce.70327","DOIUrl":"https://doi.org/10.1002/cjce.70327","url":null,"abstract":"<p>The persistent demand for high-performance, lightweight, and multifunctional materials has positioned carbon fibre-reinforced polymer composites (CFRPCs) at the forefront of innovation across critical sectors, including aerospace, defence, civil engineering, and energy. This review provides a critical examination of how various micro- and nanostructured carbonaceous fillers—specifically, carbon nanofibres, carbon nanotubes (CNTs), graphene, graphite flakes, nano-diamonds, and human hair-derived porous carbon—are fundamentally altering the mechanical, viscoelastic, and electrical characteristics of CFRPCs. The present study reveals that these fillers, which span one-dimensional (1D), two-dimensional (2D), and three-dimensional (3D) architectures, allow for highly tailored reinforcement and establish electrical percolation thresholds at remarkably low loadings. Notably, CNT-reinforced composites have demonstrated substantial gains, achieving flexural strengths up to 580 MPa. Furthermore, this study explores the significant synergistic effects in hybrid filler systems, which consistently yield superior stiffness, damping capacity, and conductivity compared to their single-filler counterparts, applications, challenges, and future directions. The paper also provides a comparative analysis of advanced fabrication techniques such as vacuum-assisted resin transfer moulding, compression moulding, and 3D printing, highlighting their direct influence on scalability, fibre wet-out quality, and overall structural integrity. Despite these clear advances, persistent challenges remain in achieving uniform filler dispersion, cost-effective processing, and material recyclability.</p>","PeriodicalId":9400,"journal":{"name":"Canadian Journal of Chemical Engineering","volume":"104 9","pages":"4809-4828"},"PeriodicalIF":2.2,"publicationDate":"2026-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148696091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Numerical simulation of the combustion characteristics and NOx generation of a 660 MW pulverized coal boiler","authors":"Yi Ding,&nbsp;Yinqiang Song,&nbsp;Tuo Zhou,&nbsp;Man Zhang,&nbsp;Hairui Yang","doi":"10.1002/cjce.70346","DOIUrl":"https://doi.org/10.1002/cjce.70346","url":null,"abstract":"<p>Coal-fired power plants play a vital role in China's power generation market, and the design, operation, and retrofitting of the boilers depend heavily on the deep insight into the multiphase flow, heat and mass transfer, and combustion. This study investigated the combustion characteristics and NOx generation of a realistic 660 MW four-corner tangential firing boiler by computational fluid dynamics. Specific sub-models were reasonably integrated into the simulation framework to represent the important sub-processes. By comparing with the experimental measurements, the simulation results were rationally validated. In detail, the gas temperature at specific sites (<i>h</i> = 52.4 m) and the NOx concentration at the boiler outlet were analyzed and compared. Subsequently, the distribution of the gas velocity and temperature and also some important species (O₂ and CO) was evaluated to assess the combustion characteristics. Finally, the NOx distribution was presented and its generation was analyzed based on the coal combustion process.</p>","PeriodicalId":9400,"journal":{"name":"Canadian Journal of Chemical Engineering","volume":"104 9","pages":"4719-4729"},"PeriodicalIF":2.2,"publicationDate":"2026-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148696151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comprehensive prediction model for NOx concentration at the SNCR outlet during rapid load fluctuations","authors":"Qi Wang,&nbsp;Xiaoliang Wang,&nbsp;Haitao Zhai,&nbsp;Yunfei Xie,&nbsp;Yao Bai","doi":"10.1002/cjce.70344","DOIUrl":"https://doi.org/10.1002/cjce.70344","url":null,"abstract":"<p>To address the issues of untimely and unstable control during rapid load fluctuations in the selective non-catalytic reduction (SNCR) denitrification systems at thermal power plants, which lead to potential exceedances of pollutant emission limits, this study proposes a comprehensive predictive model to forecast NO_x emission concentrations at the system outlet. Firstly, raw data collected from industrial sites undergoes filtering and noise reduction. The multi-dimensional time series is then dynamically calibrated and reduced in dimension utilizing the maximum information coefficient (MIC). Subsequently, combining the advantages of feature extraction and fusion from cross-attention, differential convolutions, and long short-term memory (LSTM) gating, this study proposes a deep integration architecture and introduces a multi-angle improved crested porcupine optimizer (ICPO) algorithm to optimize network hyperparameters adaptively. This approach enhances the model's expressive capability while minimizing computational complexity. Experimental results based on operational data from a 350 MW supercritical circulating fluidized bed (CFB) boiler in a thermal power plant demonstrate that the proposed model outperforms comparison models under various operating conditions, accurately predicting NO_x emission concentrations at the SNCR outlet.</p>","PeriodicalId":9400,"journal":{"name":"Canadian Journal of Chemical Engineering","volume":"104 9","pages":"4613-4629"},"PeriodicalIF":2.2,"publicationDate":"2026-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148696448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A hybrid autoencoder–transformer-based soft sensor with temporal embeddings for real-time and noise-resistant effluent quality prediction in wastewater treatment plants","authors":"Toqeer Ahmed,&nbsp;Yiqi Liu,&nbsp;Abid Aman,&nbsp;Raheel Aslam,&nbsp;Kanwal Waqar","doi":"10.1002/cjce.70357","DOIUrl":"https://doi.org/10.1002/cjce.70357","url":null,"abstract":"<p>Precise, real-time forecasting of effluent quality variables is imperative for the stable operation of wastewater treatment plants (WWTPs). However, conventional laboratory-based measurements of effluent parameters, such as chemical oxygen demand (CODe), total nitrogen (TNe), and total phosphorus (TPe), are expensive and time-consuming. They, thus, cannot be used for online measurements, feedback control, and optimization. The current models of soft sensors, such as statistical and deep learning approaches, are usually unable to capture nonlinear dynamics, long-range temporal dependencies, and noise in multivariate time series data, leading to limited robustness and scalability. To address these weaknesses, this paper proposes a hybrid soft sensor, the temporal embedding stacked autoencoder transformer (TE_SAEFormer) for effluent quality prediction. The method combines a stacked autoencoder to learn denoising and feature compression, and temporal embeddings to encode periodicities (daily and weekly), and a transformer encoder with ProbSparse attention to learn both short- and long-term dependencies effectively. The model has been rigorously tested on Benchmark Simulation Model No. 2 (BSM2) and the real-world Dongguan WWTP datasets, where it consistently outperformed eight state-of-the-art baselines. TE_SAEFormer had higher predictive accuracy (<i>R</i>² of about 95.5% on BSM2 and approximately 92% on DWWTP), smaller predictive errors, and greater noise resistance. The results indicate that TE_SAEFormer can provide a practical, efficient platform for real-time effluent monitoring, reducing reliance on laboratory results and promoting compliance with environmental standards.</p>","PeriodicalId":9400,"journal":{"name":"Canadian Journal of Chemical Engineering","volume":"104 9","pages":"4656-4671"},"PeriodicalIF":2.2,"publicationDate":"2026-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148696588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Issue Highlights","authors":"","doi":"10.1002/cjce.70263","DOIUrl":"https://doi.org/10.1002/cjce.70263","url":null,"abstract":"","PeriodicalId":9400,"journal":{"name":"Canadian Journal of Chemical Engineering","volume":"104 8","pages":""},"PeriodicalIF":2.2,"publicationDate":"2026-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148395101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data-driven simulation of crude distillation using Aspen HYSYS and comparative machine learning models","authors":"Aldimiro Paixão Domingos,&nbsp;Heleno Bispo,&nbsp;Thiago Vaz da Costa,&nbsp;Sérgio Mauro da Silva Neiro","doi":"10.1002/cjce.70297","DOIUrl":"https://doi.org/10.1002/cjce.70297","url":null,"abstract":"<p>Crude oil refining is a complex process requiring precise modelling to optimize yield, quality, and efficiency. This study integrates Aspen HYSYS® simulations with machine learning techniques to develop predictive models for key refinery variables. Using crude oils from Arabian Light, Arabian Medium, and Gimboa, a dataset of 154 entries was generated to analyze the effects of feed composition and distillation unit operating temperatures. Four supervised learning models, linear regression (LR), random forest regression (RFR), support vector regression (SVR), and artificial neural networks (ANN), were trained and evaluated. ANN outperformed the others, achieving an <i>R</i>² score of 0.9742 and the lowest mean squared error of 0.1367, demonstrating its effectiveness in capturing nonlinear relationships. Results showed that increasing feed temperature enhances kerosene and gasoil yields while reducing the residue fraction. The study highlights the potential of combining data-driven models with process simulations to improve refinery efficiency and decision-making.</p>","PeriodicalId":9400,"journal":{"name":"Canadian Journal of Chemical Engineering","volume":"104 8","pages":"4079-4100"},"PeriodicalIF":2.2,"publicationDate":"2026-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cjce.70297","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148395057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transient thermal modelling of green anode cooling in the aluminium industry","authors":"Mohammadhossein Dabaghi,&nbsp;Duygu Kocaefe,&nbsp;Yasar Kocaefe","doi":"10.1002/cjce.70249","DOIUrl":"https://doi.org/10.1002/cjce.70249","url":null,"abstract":"<p>Anodes, which are used in electrolytic aluminium production, are made by mixing dry aggregate (petroleum coke, rejected anodes, and butts) with coal tar pitch, compacting the resulting paste (green anode), and baking (baked anode). After compaction, the anodes are cooled to maintain their structural integrity before being sent to anode baking furnaces. This study focuses on developing and validating a 3D transient thermal model to simulate the green anode cooling process. The model predicts temperature distribution in the anode during the cooling phase. This will help assess different cooling strategies. Heat transfer by convection (in air and water) and radiation (only in air) was incorporated into the boundary conditions to predict the thermal behaviour of the anodes. Various cooling media, such as air, water, and water spray, were used in free or forced convection modes. The model predictions were validated by comparing them with experimental data obtained from laboratory tests on pilot green anodes of about 10 kg. The results demonstrated a strong correlation between the simulated and experimental temperatures, validating the model. Thus, the model can be used to identify efficient cooling strategies and, consequently, improve anode cooling processes, leading to enhanced product quality.</p>","PeriodicalId":9400,"journal":{"name":"Canadian Journal of Chemical Engineering","volume":"104 8","pages":"4120-4134"},"PeriodicalIF":2.2,"publicationDate":"2026-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cjce.70249","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148395001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}