The Canadian Journal of Chemical Engineering最新文献

{"title":"Production of esters with numerous applications: Kinetics of Dowex 50W catalyzed transesterification of methyl acetate with three‐ and four‐carbon structured alcohols","authors":"Bader H. Albusairi, Abdulwahab S. Almusallam, Sami H. Ali, Sabiha Q. Merchant, Ali Y. Bumajdad","doi":"10.1002/cjce.25203","DOIUrl":"https://doi.org/10.1002/cjce.25203","url":null,"abstract":"In this investigation, a strongly acidic exchange resin was used for the transesterification of methyl acetate with n‐propanol, n‐butanol, and iso‐butanol. Kinetic and equilibrium experiments for the three systems were conducted using a temperature‐controlled batch reactor setup. The effects of the following operating parameters on the transesterification were explored: reaction temperature, catalyst loading, and methyl acetate‐to‐alcohol molar ratio. The conversion of the limiting reactant in the reaction mixture increased with increasing reaction temperature, catalyst loading, and varying reactant proportions from 1:1 to other ratios. It was found that excess methyl acetate would result in higher limiting reactant conversion than using excess alcohol with the same initial molar proportionality between the excess and the limiting reactants. It was found that an increase in the chain length of the alcohol and/or branching suppressed the conversion of the reactants owing to steric hindrance. To mathematically correlate the data, several kinetic models were tested, and the Eley–Rideal model was selected. Accordingly, a reaction mechanism was proposed.","PeriodicalId":501204,"journal":{"name":"The Canadian Journal of Chemical Engineering","volume":"35 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139861173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sustainable process design for heat exchanger network considering inherent safety and process economics","authors":"Muhammad Athar, A. M. Shariff, Muhammad Imran Rashid, Mahboob Ahmed Aadil, Asim Umer, Muhammad Irfan","doi":"10.1002/cjce.25202","DOIUrl":"https://doi.org/10.1002/cjce.25202","url":null,"abstract":"Process lifecycle has several stages, including process design covered in multiple stages. Process economics is a vital factor in finalizing the process design. Besides economics, inherent safety is an important concept contributing to sustainable process design generation. The inherent safety concept has been applied via equipment characteristics for individual equipment. Since a method considering inherent safety with equipment aspects and process economics has not been available, therefore, a new method has been proposed, namely sustainable process design for heat exchanger network (SPDHEN), to integrate inherent safety, equipment aspects, and process economics. SPDHEN uses indexing to identify the critical heat exchanger, which is then examined via hazard analysis for an explosion. For unacceptable hazards, inherent safety principles are engaged to generate design alternatives for which process economics is examined too. The final design would be inherently safer with the best profit margin. The proposed method has been studied for the ammonia synthesis loop. It is concluded that the explosion hazard has been reduced to a tolerable level by using inherent guide words with a marginal compromise on quantity of ammonia produced, that is, 0.32%. This method is straightforward and can be useful for process engineers to generate sustainable process designs for heat exchanger networks considering safety and process economics simultaneously.","PeriodicalId":501204,"journal":{"name":"The Canadian Journal of Chemical Engineering","volume":"319 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139799066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards strengthening resilience of organizations by risk management tools: A scientometric perspective on COVID‐19 experience in a healthcare and industrial setting","authors":"Bruno Fabiano, Mariangela Guastaferro, M. Pettinato, Hans J. Pasman","doi":"10.1002/cjce.25196","DOIUrl":"https://doi.org/10.1002/cjce.25196","url":null,"abstract":"During the COVID‐19 pandemic, the healthcare system and the global supply chain were exposed to an unpredicted event, which increased awareness about the need of more effective strategies to support decision‐making process and to empower safety barriers. In this work, a combined scientometric and systematic review was performed to analyze tools and methodologies able to combine resilience with more traditional risk assessment, learning from the experience posed by the COVID‐19 crisis. Bibliometric and literature content analyses were carried out focusing on resilience management upon the incoming of an unexpected event. The systematic analysis of the methods and models developed on the basis of different pandemic waves provides a natural guide for future research development.","PeriodicalId":501204,"journal":{"name":"The Canadian Journal of Chemical Engineering","volume":"17 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139868973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A machine learning approach for modelling and optimization of complex systems: Application to condensate stabilizer plants","authors":"Mohammed Alkatheri, Farzad Hourfar, Ladan Khoshnevisan, Hedia Fgaier, Ali Almansoori, Ali Elkamel","doi":"10.1002/cjce.25180","DOIUrl":"https://doi.org/10.1002/cjce.25180","url":null,"abstract":"Recent advancements in supervised machine learning tools have demonstrated their ability to achieve accurate and efficient prediction results. In this paper, we leverage these tools as alternative approaches to model a specific application in the gas industry, based on operating data. The chosen application is a natural gas condensate stabilization process, in which light end components are removed to reduce condensate vapour pressure, meeting storage and transportation specifications. Here, we develop and evaluate various supervised machine learning models to predict the performance of two industrial condensate stabilizer units. By utilizing large datasets from these units and encompassing comprehensive operating data of input–output variables, we not only demonstrate the capability of these techniques to offer reliable and accurate predictions but also shed light on their potential impacts and implementations. The impacts of applying selected AI and machine learning algorithms are two-fold. First, our research presents an innovative approach to process modelling and optimization in the gas industry, showing the potential for enhanced operational efficiency, profitability, and safety. Second, we propose a data-driven surrogate-based optimization framework, where the generated machine learning models can replace detailed first-principle models, offering a streamlined method to find optimal values for variables to reduce operational energy consumption. Furthermore, we address the validation requirements of our machine learning models, ensuring their robustness and reliability in real-world applications. By incorporating rigorous validation procedures, we guarantee the quality of our predictions and support their practical implementation. In conclusion, our research not only highlights the capabilities of machine learning in gas industry applications but also emphasizes their potential impacts and contributions to operational excellence. So, the presented approach can pave the way for improved performance, efficiency, and profitability in the gas industry.","PeriodicalId":501204,"journal":{"name":"The Canadian Journal of Chemical Engineering","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139495982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}