Canadian Journal of Chemical Engineering最新文献_第3页

The combustion mechanism of aluminium in the steam/carbon dioxide mixed atmosphere

IF 1.6 4区工程技术

Canadian Journal of Chemical Engineering Pub Date : 2024-10-09 DOI: 10.1002/cjce.25520

Xiaoyan Qian, Yanwu Ji, Jiquan Wang, Baozhong Zhu, Minggao Xu, Yunlan Sun

{"title":"The combustion mechanism of aluminium in the steam/carbon dioxide mixed atmosphere","authors":"Xiaoyan Qian, Yanwu Ji, Jiquan Wang, Baozhong Zhu, Minggao Xu, Yunlan Sun","doi":"10.1002/cjce.25520","DOIUrl":"https://doi.org/10.1002/cjce.25520","url":null,"abstract":"The ignition and combustion characteristics of aluminium (Al) in the steam/carbon dioxide (H2O/CO2) mixed atmosphere were calculated by using a close homogeneous catch reactor and premixed laminar flame-speed of CHEMKIN-PRO. The effects of initial reaction temperature and H2O/CO2 ratios on the system temperature, products, and ignition delay time were explored. The main reaction paths were analyzed by the temperature sensitivity. Al (l) entered the system with a pronounced phase transition and only one heating stage, but there are two heating stages in the Al (g) system. The reaction of Al in the H2O/CO2 mixed atmosphere has a positive effect on the ignition delay time of the system. The high contents of CO2 in the H2O/CO2 mixed atmosphere can increase the maximum temperature of system and decrease the ignition delay time. Temperature sensitivity analysis shows that AlO, Al2O, and AlOH are important intermediate products, and Al2O2 is the main substance that produces Al2O3. The dominant reaction path is Al → AlOH→AlO → Al2O → Al2O2 → Al2O3. In addition, when the initial temperature was 2300 K, the laminar flame velocity was calculated to reach 35.6 m/s.","PeriodicalId":9400,"journal":{"name":"Canadian Journal of Chemical Engineering","volume":"103 5","pages":"2136-2147"},"PeriodicalIF":1.6,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143835790","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}

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Issue Highlights 发行亮点

IF 1.6 4区工程技术

Canadian Journal of Chemical Engineering Pub Date : 2024-10-06 DOI: 10.1002/cjce.25001

引用次数: 0

ML-driven models for predicting CO2 uptake in metal–organic frameworks (MOFs)

IF 1.6 4区工程技术

Canadian Journal of Chemical Engineering Pub Date : 2024-10-02 DOI: 10.1002/cjce.25509

Sofiene Achour, Zied Hosni

{"title":"ML-driven models for predicting CO2 uptake in metal–organic frameworks (MOFs)","authors":"Sofiene Achour, Zied Hosni","doi":"10.1002/cjce.25509","DOIUrl":"https://doi.org/10.1002/cjce.25509","url":null,"abstract":"This study advances the discourse on the application of machine learning (ML) algorithms for the predictive analysis of CO2 uptake in metal–organic frameworks (MOFs), with a nuanced focus on the CATBoost model's capability to navigate the complexities inherent in MOFs' heterogeneous landscape. Building upon and extending the comparative analysis, our investigation underscores the CATBoost model's remarkable prediction robustness, characterized by a significant reduction in root mean square error (RMSE) and an enhanced R-squared (R2) value, thereby affirming its superior accuracy and reliability in forecasting CO2 adsorption. A pivotal aspect of our research is the integration of SHapley Additive exPlanations (SHAP) values for a detailed assessment of feature importance, which not only corroborated ‘pressure’ and ‘surface area’ as pivotal determinants of CO2 uptake but also illuminated the model's advanced analytical capabilities in handling categorical features and mitigating overfitting, even within a dataset marked by intricate and non-linear patterns. Our quantitative and conceptual analysis, showcasing up to a 15% improvement in RMSE over previous models, reveals the CATBoost model's unparalleled efficiency in discerning the multifaceted interplay of factors influencing CO2 adsorption. This is crucial for the strategic engineering of MOFs with optimized properties. Beyond ‘pressure’ and ‘surface area’, our SHAP analysis highlighted other descriptors with substantial values, elucidating their contributions to CO2 uptake and providing invaluable insights for the MOF design process.","PeriodicalId":9400,"journal":{"name":"Canadian Journal of Chemical Engineering","volume":"103 5","pages":"2161-2173"},"PeriodicalIF":1.6,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cjce.25509","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143835824","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}

引用次数: 0

Moisture content determination along production line of ibuprofen soft gelatin capsule manufacturing by near infrared spectroscopy and ensemble deep neural networks

IF 1.6 4区工程技术

Canadian Journal of Chemical Engineering Pub Date : 2024-10-02 DOI: 10.1002/cjce.25496

Alejandro Romero, Joe Villa-Medina, Jorge Ropero, Néstor Cubillán

{"title":"Moisture content determination along production line of ibuprofen soft gelatin capsule manufacturing by near infrared spectroscopy and ensemble deep neural networks","authors":"Alejandro Romero, Joe Villa-Medina, Jorge Ropero, Néstor Cubillán","doi":"10.1002/cjce.25496","DOIUrl":"https://doi.org/10.1002/cjce.25496","url":null,"abstract":"In the manufacturing process of ibuprofen soft gelatin capsules, controlling moisture content at each production line stage, including in their components—gelatin, fill content, and shell—is vital to ensure quality and stability. This study developed and assessed an analytical method for rapid and non-destructive moisture determination using near-infrared spectroscopy (NIRS) coupled with deep neural networks (DNN) on all stages of the ibuprofen production line. The NIRS-DNN classifier models were able to distinguish between the three components, achieving accuracy scores of up to 99%. The DNN models for moisture quantification also demonstrated high accuracy, with R2 values exceeding 0.997 across all production stages and prediction errors to those of previously reported models. A significant advantage of the NIRS-DNN approach was its ability to maintain accuracy over a wide moisture concentration range, from 7% to 48%. The ensemble model, NIRS-EDNN, seamlessly integrated classification and quantification, revealing its potential for real-time process control in soft gel manufacturing. The comprehensive sampling approach ensured a diverse representation of moisture content, thereby enhancing the understanding of its impact on the final product stability, demonstrating that this methodology is potentially applicable to any soft gelatin capsule tracking worldwide.","PeriodicalId":9400,"journal":{"name":"Canadian Journal of Chemical Engineering","volume":"103 5","pages":"2251-2262"},"PeriodicalIF":1.6,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143835825","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}

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Investigation on the influence of particle size and porosity on solid phase fluidization and heat transfer in carbon fibre porous media

IF 1.6 4区工程技术

Canadian Journal of Chemical Engineering Pub Date : 2024-10-02 DOI: 10.1002/cjce.25511

Licheng Wang, Wenwen Zhang, Zhouzhe Yang

{"title":"Investigation on the influence of particle size and porosity on solid phase fluidization and heat transfer in carbon fibre porous media","authors":"Licheng Wang, Wenwen Zhang, Zhouzhe Yang","doi":"10.1002/cjce.25511","DOIUrl":"https://doi.org/10.1002/cjce.25511","url":null,"abstract":"In this paper, the 3D pore structure was reconstructed and solid phase fluidization in porous media was investigated. Based on the two fluid model, a computational fluid dynamics (CFD) model of gas–solid fluidization was established and the influence of particle size and porosity were investigated. When porosity was constant, during the fluidization process, the particle velocity and solid concentration standard deviation gradually decreased, and the bed height increased. At the same time, the smaller the particle size was, the smaller the solid concentration standard deviation was, the faster the bed height increased, and the more the particle temperature decreased. Based on the fixed value of particle size, when studying the effect of porosity on fluidization, it was found that with the enhancement of solid-phase fluidization, particle velocity, solid concentration standard deviation, and particle temperature decreased, and bed height increased. Moreover, when porosity was large, particle velocity decreased rapidly, and solid concentration standard deviation reached a smaller minimum value, which took a longer time. Within the same time, the particle temperature also decreased less.","PeriodicalId":9400,"journal":{"name":"Canadian Journal of Chemical Engineering","volume":"103 5","pages":"2412-2425"},"PeriodicalIF":1.6,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143835826","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}

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A physics-constrained hybrid residual neural network for the prediction of moisture content in a closed-cycle drying system

IF 1.6 4区工程技术

Canadian Journal of Chemical Engineering Pub Date : 2024-10-02 DOI: 10.1002/cjce.25516

Mengfei Zhou, Ruizhen Wang, Rong Cheng, Qin Sun, Qiqing Yu, Luyue Xia, Xiaofang Sun

{"title":"A physics-constrained hybrid residual neural network for the prediction of moisture content in a closed-cycle drying system","authors":"Mengfei Zhou, Ruizhen Wang, Rong Cheng, Qin Sun, Qiqing Yu, Luyue Xia, Xiaofang Sun","doi":"10.1002/cjce.25516","DOIUrl":"https://doi.org/10.1002/cjce.25516","url":null,"abstract":"Closed-cycle drying technology has the advantages of safety, energy saving, and environmental protection, and has a wide application prospect. Due to the multi-physics and multi-scale nature of heat/mass transfer in the closed-cycle drying process, as well as the characteristics of drying medium circulation and energy integration, it is difficult to obtain the product moisture content prediction model in closed-cycle process based on rigorous closed-cycle drying process mechanism model. However, accurate monitoring of product moisture content in the closed-cycle drying process is the key to improving drying quality and process optimization. Aiming at the challenges in predicting moisture content, this paper proposes a physics-constrained hybrid residual neural network (PC-HRNN) for soft sensor modelling within a designed closed-cycle drying system. The characteristic of this proposed method lies in its effective fusion of physics, knowledge, and data. First, a hybrid residual neural network (HRNN) is constructed based on the integration of a physics model and a data-driven model. The HRNN takes the knowledge from the physics model as its auxiliary features and learns the prediction residuals of the physics model through a neural network model. Then, a new loss function that takes into account physical laws is introduced into HPNN to standardize model training and improve the generalization performance of the model. The experimental results show that the PC-HRNN model improves prediction accuracy and model robustness, reduces the demand for data, and demonstrates stronger extrapolation capabilities under limited data.","PeriodicalId":9400,"journal":{"name":"Canadian Journal of Chemical Engineering","volume":"103 5","pages":"2204-2217"},"PeriodicalIF":1.6,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143835823","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}

引用次数: 0

Evaluating the efficacy of palm waste in adsorption processes for wastewater treatment: A review

IF 1.6 4区工程技术

Canadian Journal of Chemical Engineering Pub Date : 2024-10-02 DOI: 10.1002/cjce.25515

Nicky Rahmana Putra, Muhammad Abbas Ahmad Zaini, Ahmad Syahmi Zaini, Bramantyo Airlangga, Dwila Nur Rizkiyah, Heri Septya Kusuma, Sri Agustini, Syahrial Abdullah, Yustisia Yustisia, Izhar Khairullah, I. Gusti Komang Dana Arsana

{"title":"Evaluating the efficacy of palm waste in adsorption processes for wastewater treatment: A review","authors":"Nicky Rahmana Putra, Muhammad Abbas Ahmad Zaini, Ahmad Syahmi Zaini, Bramantyo Airlangga, Dwila Nur Rizkiyah, Heri Septya Kusuma, Sri Agustini, Syahrial Abdullah, Yustisia Yustisia, Izhar Khairullah, I. Gusti Komang Dana Arsana","doi":"10.1002/cjce.25515","DOIUrl":"https://doi.org/10.1002/cjce.25515","url":null,"abstract":"The growing environmental concerns related to industrial effluents and waste underscore the urgent need for sustainable and cost-effective wastewater treatment solutions. This review investigates the efficacy of palm waste-derived adsorbents for removing heavy metals and organic pollutants from wastewater. It explores various modifications—including physical, chemical, and nanomaterial enhancements—applied to palm waste materials such as palm kernel shells, empty fruit bunches, and palm oil fuel ash, aimed at improving their adsorption capacities. The review reveals that these modified palm waste adsorbents demonstrate high removal efficiencies for contaminants like Cu(II), Pb(II), and organic dyes, often surpassing conventional adsorbents. Nonetheless, challenges remain, such as optimizing adsorbent preparation, understanding adsorption mechanisms in multi-component systems, and enhancing adsorbent reusability. This review highlights the need for continued research to address these issues and advance the application of palm waste-based materials in sustainable wastewater treatment.","PeriodicalId":9400,"journal":{"name":"Canadian Journal of Chemical Engineering","volume":"103 5","pages":"2088-2106"},"PeriodicalIF":1.6,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143835827","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}

引用次数: 0

Identification of sparse nonlinear controlled variables for near-optimal operation of chemical processes

IF 1.6 4区工程技术

Canadian Journal of Chemical Engineering Pub Date : 2024-10-01 DOI: 10.1002/cjce.25514

Xie Ma, Hongwei Guan, Lingjian Ye

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Modelling and parameter identification of penicillin fermentation using physics-informed neural networks

IF 1.6 4区工程技术

Canadian Journal of Chemical Engineering Pub Date : 2024-09-27 DOI: 10.1002/cjce.25510

Siqi Zhao, Zhonggai Zhao, Fei Liu

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Analysis of chemical properties for biodiesel derived from crude palm oil

IF 1.6 4区工程技术

Canadian Journal of Chemical Engineering Pub Date : 2024-09-24 DOI: 10.1002/cjce.25501

Wan Nur Aisyah Wan Osman, Jonathan Khoo Lee Min, Shafirah Samsuri

{"title":"Analysis of chemical properties for biodiesel derived from crude palm oil","authors":"Wan Nur Aisyah Wan Osman, Jonathan Khoo Lee Min, Shafirah Samsuri","doi":"10.1002/cjce.25501","DOIUrl":"https://doi.org/10.1002/cjce.25501","url":null,"abstract":"High biodiesel purity (98.86% to 99.54%) has been achieved using cooking oil, which undergoes various purification stages before being sold. This inherent purity contributes to the high biodiesel purity produced during solvent-aided crystallization (SAC). This study aims to investigate the performance of SAC using unpurified oil as the feedstock for crude biodiesel preparation. Crude palm oil (CPO) was used as the feedstock, and the effects of crystallization temperature (6, 8, 10, 12, and 14°C), crystallization time (20, 25, 30, 35, and 40 min), and shaking speed (32, 43, 61, 71, and 84 rpm) were measured. The highest biodiesel purity achieved was 99.05% at 6°C crystallization temperature, 30 min crystallization time, and medium shaking speed. Additionally, all samples met international standards EN 14214 and ASTM D6751 for chemical properties, including iodine and acid value analysis. These results suggest that SAC is an effective method for producing high-quality biodiesel from less refined feedstock, potentially lowering production costs and expanding the range of viable raw materials for biodiesel production.","PeriodicalId":9400,"journal":{"name":"Canadian Journal of Chemical Engineering","volume":"103 5","pages":"2053-2065"},"PeriodicalIF":1.6,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143836377","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}

引用次数: 0