Canadian Journal of Chemical Engineering最新文献

{"title":"Impact of raw material on thermo-physical properties of carbon foam","authors":"Mohammad Rajab Al-Majali,&nbsp;Mingcong Zhang,&nbsp;Yahya T. Al-Majali,&nbsp;Jason P. Trembly","doi":"10.1002/cjce.25448","DOIUrl":"10.1002/cjce.25448","url":null,"abstract":"<p>Carbon foam materials are currently used in several industrial and engineering applications due to their outstanding properties. The properties of carbon foam can be altered through the manufacturing processes applied in specific applications. In this paper, we collected and analyzed four samples manufactured by CFOAM and one sample developed by Ohio University (OU) to understand the behaviour of this material and determine its properties. We utilized advanced techniques to experimentally measure and determine the following properties: pore size and volume, porosity, specific surface area, mass, density, and thermal conductivity. Among the samples, the low-porosity CFOAM (CF35) and the OU sample exhibited higher specific surface areas and densities compared to the others. However, CF35 demonstrated the highest thermal conductivity, while OU displayed the lowest. As a result, CF35 emerges as the optimal choice for applications requiring high-rate heat transfer, while the remaining CFOAM samples are well-suited for lightweight applications. Thus, OU foam proves to be a highly suitable candidate for insulation applications such as building sidewalls.</p>","PeriodicalId":9400,"journal":{"name":"Canadian Journal of Chemical Engineering","volume":"103 3","pages":"1309-1318"},"PeriodicalIF":1.6,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cjce.25448","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142195345","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":"Machine learning and metaheuristics in microfluidic transport characterization and optimization: CFD and experimental study integrated with predictive modelling","authors":"Afshin Kouhkord,&nbsp;Moheb Amirmahani,&nbsp;Faridoddin Hassani,&nbsp;Naser Naserifar","doi":"10.1002/cjce.25430","DOIUrl":"10.1002/cjce.25430","url":null,"abstract":"<p>This study presents a comprehensive numerical and experimental analysis on microfluidic cell lysis through computational fluid dynamics (CFD), data-driven modelling, and multi-objective optimization. The proposed intelligent framework integrates artificial intelligence and CFD for data generation and extraction, alongside machine learning analysis and experimental studies for transport phenomena characterization in the cell lysis process. The framework explores compound effects of various inflow Reynolds numbers and geometrical parameters, including obstacle configurations and microchannel thickness. It shows substantial effects on flow patterns and mixing in varied microfluidic designs. A surrogate model, developed via central composite design, exhibits high accuracy in assessing system functionality (<span></span><math>\u0000 <mrow>\u0000 <msup>\u0000 <mi>R</mi>\u0000 <mn>2</mn>\u0000 </msup>\u0000 <mo>&gt;</mo>\u0000 <mn>0.92</mn>\u0000 </mrow></math>). The height of the implemented baffles from its lower value to the upper bound resulted in more than 42% and 14% increase in the mixing index at low and high Reynolds numbers, respectively, with minimal impact on pressure drop. The framework introduces data-driven modelling coupled with multi-objective optimization by desirability function (DF), non-dominated sorting genetic algorithm (NSGA-II), and differential evolution (DE). In the optimization of microfluidic processes, machine learning algorithms outperform desirability-based methods, and the DE algorithm surpasses the NSGA-II. An optimum micromixing reducing the mixing length by over 50% and mixing index above 97% achieved, fabricated, and experimental investigations conducted to validate numerical process. Through the precise control of microfluidic variables and the exploitation of microtransfer phenomena, it is possible to enhance the efficiency and selectivity of cell lysis. This not only improves the accuracy of diagnostic information but also opens up new avenues for personalized medicine and therapeutic development.</p>","PeriodicalId":9400,"journal":{"name":"Canadian Journal of Chemical Engineering","volume":"103 3","pages":"1400-1418"},"PeriodicalIF":1.6,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141926727","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":"Advanced EOR screening methodology based on LightGBM and random forest: A classification problem with imbalanced data","authors":"Masoud Seyyedattar,&nbsp;Majid Afshar,&nbsp;Sohrab Zendehboudi,&nbsp;Stephen Butt","doi":"10.1002/cjce.25433","DOIUrl":"10.1002/cjce.25433","url":null,"abstract":"&lt;p&gt;In an unstable oil market with volatile prices due to various natural and geopolitical factors, it is crucial for oil-producing companies to enhance the value of their assets by improving the recovery factors of petroleum reservoirs. Primary recovery through natural depletion or artificial lift and secondary recovery using waterflooding and immiscible gas injection typically recover no more than 10%–40% of the available reserves. A significant portion of the hydrocarbons remain unproduced if enhanced oil recovery (EOR) methods are not implemented. EOR projects are extremely costly, complex, and usually have long lead times from the decision-making and design phases to pilot and full-field implementations. Therefore, oil and gas operator companies need reliable insights into the best possible EOR options from the early stages of any field development planning. Since screening potential EOR choices is the first step in deciding future production scenarios, a smart EOR screening tool can add significant value by streamlining the EOR decision-making process. In this study, we developed an EOR screening tool based on two advanced machine learning classification algorithms, random forest and light gradient boosting machine (LightGBM). These tree-based ensemble learning classifiers were trained on an extensive dataset of 1384 worldwide EOR implementations, encompassing various reservoir conditions and reservoir rock and fluid properties as the feature space, to predict the EOR type as the class label. Considering EOR screening as a classification problem, an essential aspect of model development would be addressing the data imbalance of EOR datasets. To tackle this issue, the adaptive synthetic (ADASYN) sampling method was used to reduce classification bias by oversampling the training sets to achieve uniform class distributions. We designed an iterative model development procedure in which the classifiers were trained and tested on various training and test subsets split by stratified random sampling. For each classifier, the classification results at each iteration were used to build the confusion matrix and calculate model evaluation metrics (accuracy, precision, recall, and F1–score), which were then averaged over all independent runs to provide a fair assessment of classification performance. Moreover, binary receiver operating characteristic (ROC) curves were used to evaluate the classifier predictions and improvements obtained by oversampling. The results showed that both random forest and LightGBM classifiers made accurate class predictions, with LightGBM achieving slightly better classification performance in each modelling scenario (with or without oversampling). In both cases, the oversampling of the training dataset resulted in significant improvement of the classifiers, as evidenced by higher values of the evaluation metrics, leading to considerably more accurate EOR type predictions; specifically, oversampling boosted the prediction acc","PeriodicalId":9400,"journal":{"name":"Canadian Journal of Chemical Engineering","volume":"103 2","pages":"846-867"},"PeriodicalIF":1.6,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141928313","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":"Investigation of instability in the dynamic behaviour of a bubble","authors":"Qiang Li,&nbsp;Shaobo Lu,&nbsp;Jialin Liu,&nbsp;Mao Lei,&nbsp;Jiahan Gao,&nbsp;Weiwei Xu","doi":"10.1002/cjce.25444","DOIUrl":"10.1002/cjce.25444","url":null,"abstract":"<p>In order to obtain the laws of the bubble's dynamic behaviours, the interFoam solver in OpenFOAM was used to simulate the bubbles, and the experimental device was built to prove the reliability of the results. The Eötvös number (Eo) and the Galileo number (Ga) were used to classify the bubbles into four regions according to their different dynamic behaviours: straight line without deformation region, slight zigzag without deformation region, zigzag with slight deformation region, and zigzag with strong deformation region. Eo of bubbles in the straight line without deformation region is extremely small and is greatly influenced by surface tension. The bubbles do not deform and rise linearly along the axis of symmetry. Eo of bubbles in the slight zigzag without deformation region is still small and the bubbles do not deform, but the path is curved for a period of time. As the value of Eo increases, the bubble in the zigzag with the slight deformation region is weakened. The path is a regular zigzag, and the axisymmetric structure of the bubbles is destroyed. In the zigzag with the strong deformation region, the values of Eo and Ga are large. The path amplitude increases and the periodic law is broken. The bubble's deformation and vortex shedding interact with each other, both of which are the causes of the bubble's path instability.</p>","PeriodicalId":9400,"journal":{"name":"Canadian Journal of Chemical Engineering","volume":"103 3","pages":"1419-1432"},"PeriodicalIF":1.6,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141931509","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":"Selective adsorption of aromatic sulfonic acid from wastewater using a surface imprinted polymer: H-acid as a representative contaminant","authors":"Jiaqi Yao,&nbsp;Yue Sun,&nbsp;Yan Liu,&nbsp;Yingpeng Gu,&nbsp;Weisheng Zheng","doi":"10.1002/cjce.25432","DOIUrl":"10.1002/cjce.25432","url":null,"abstract":"<p>Aromatic sulfonic acids (ASAs) play a pivotal role as essential intermediates in numerous industrial manufacturing, while a large amount wastewater with various ASAs and high concentration of inorganic salts is subsequently generated. The effective separation and removal of ASAs from wastewater is challenging due to their complex chemical composition and the limited selectivity of common adsorbents. Herein, a novel surface imprinted polymer (H-SIP) with high selectivity and excellent salt resistance was designed with PEI/Cl-PS-DVB as the carrier and 1-amino-8-naphthol-3,6-disulfonic acid (H-acid) as the target pollutant. Compared to non-imprinted polymer (NIP), H-SIP exhibited superior salt resistance in the presence of Na₂SO₄ concentration ranging from 20 to 80 mg/L. The relative selectivity coefficients determined in the binary-solutes experiments proved that H-SIP demonstrated favourable selectivity towards H-acid in binary systems of H-acid/T-acid or H-acid/2-NSA. Moreover, H-SIP could effectively treat the simulated complex wastewater within 24 bed volume (BV) in the column adsorption, and the desorption rate exceeded 90% when eluted by NaOH solution and distilled water, respectively. Therefore, these results confirmed that surface imprinting technique was a promising method for effectively and selectively removal of ASA wastewater in the application.</p>","PeriodicalId":9400,"journal":{"name":"Canadian Journal of Chemical Engineering","volume":"103 2","pages":"834-845"},"PeriodicalIF":1.6,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141931456","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":"Effects of cellulose addition on sodium lignosulfonate pyrolysis: Product distribution and formation pathway","authors":"Lichao Ge,&nbsp;Can Zhao,&nbsp;Yang Wang,&nbsp;Zhifu Qi,&nbsp;Ruikun Wang,&nbsp;Qianqian Yin,&nbsp;Yuli Zhang,&nbsp;Chang Xu","doi":"10.1002/cjce.25450","DOIUrl":"10.1002/cjce.25450","url":null,"abstract":"<p>Copyrolysis of lignin and cellulose can effectively improve pore structure and optimize product distribution. Therefore, the distribution, characteristics, components, and formation mechanism of the copyrolysis products of cellulose and sodium lignosulfonate were studied. The pyrolysis of sodium lignosulfonate was effectively inhibited by cellulose, especially when the amount of doped cellulose was 40 wt.%, and tubes presumed to be carbon nanotubes were prepared under these conditions. For bio-oil, the contents of phenol, 2-methoxy-, and 4-aminopyridine increased with decreasing amounts of doped cellulose. However, cellulose substantially reduced the content of 2-furanmethanol. H₂, CO₂, CO, and CH₄ were the main components of the biogas; among them, H₂ was the most abundant component in the biogas. Considering the characteristics of the three-phase product, a higher C content in the volatiles (especially bio-oil) can promote the formation of carbon nanotubes. Finally, the formation mechanism and interactions of the main components in the volatiles of cellulose and sodium lignosulfonate were proposed.</p>","PeriodicalId":9400,"journal":{"name":"Canadian Journal of Chemical Engineering","volume":"103 3","pages":"1285-1294"},"PeriodicalIF":1.6,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141931507","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":"Minimum spouting velocity of fine particles in fountain confined conical spouted beds using machine learning and least square fitting approaches","authors":"Mohammad Amin Moradkhani,&nbsp;Ali Reza Miroliaei,&nbsp;Nasim Ghasemi,&nbsp;Seyyed Hossein Hosseini,&nbsp;Mikel Tellabide,&nbsp;Martin Olazar","doi":"10.1002/cjce.25429","DOIUrl":"10.1002/cjce.25429","url":null,"abstract":"<p>The present study concerns the development of new models to estimate the minimum spouting velocity (<i>U</i>_ms) in various configurations of fountain-confined conical spouted beds (FC-CSBs) with fine particles. Existing literature correlations were found to be inaccurate for FC-CSBs. Therefore, smart modelling techniques were employed to design more accurate predictive tools. The radial basis function (RBF) approach provided the best predictions for systems without draft tubes as well as those with open-sided draft tubes. Additionally, the Gaussian process regression (GPR) approach yielded the best predictions for systems with nonporous draft tubes. The mean absolute percentage error (MAPE) values for the testing phase were 5.80%, 5.67%, and 5.59%, respectively. These models consider how bed shape and particle properties affect <i>U</i>_ms. The sensitivity analysis was conducted to determine the factors with more importance in controlling <i>U</i>_ms. Finally, simpler correlations were derived for <i>U</i>_ms prediction in different FC-CSB configurations, with accuracy around 12% error.</p>","PeriodicalId":9400,"journal":{"name":"Canadian Journal of Chemical Engineering","volume":"103 2","pages":"880-898"},"PeriodicalIF":1.6,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141931455","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":"Leveraging neural networks to estimate parameters with confidence intervals","authors":"Nigel Mathias,&nbsp;Lauren Weir,&nbsp;Brandon Corbett,&nbsp;Prashant Mhaskar","doi":"10.1002/cjce.25438","DOIUrl":"10.1002/cjce.25438","url":null,"abstract":"<p>This manuscript presents a proof of concept for the estimation of parameters in a bioprocess while providing reliable confidence intervals. Specifically, Bayesian inference is used to estimate the uncertainty in the prediction of a parameter due to the presence of measurement noise in the process. The resultant joint probability distribution is utilized to infer the confidence interval of the resultant estimates. This method is numerically applied using a technique known as nested sampling. This algorithm iteratively samples parameters from a pre-determined range of values to compare model predictions and obtain a probability density function. One challenge typically associated with this algorithm is in the determination of the prediction error, especially when a high-fidelity dynamic model is being utilized. For the motivating example in the present manuscript, where a high-fidelity simulated bioprocess is being considered, the use of the high-fidelity model provided by Sartorius AG as part of the estimation algorithm poses computational challenges. To overcome this challenge, a universal approximator such as a parameterized neural network is used. This neural network is designed to simulate the results of the first principles model (while also capturing the dependence of the model parameters on the output), and once trained can provide near instantaneous results making the use of nested sampling computationally tractable for the application. Simulation results demonstrate the feasibility and capability of the proposed approach.</p>","PeriodicalId":9400,"journal":{"name":"Canadian Journal of Chemical Engineering","volume":"103 2","pages":"666-678"},"PeriodicalIF":1.6,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cjce.25438","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141931454","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":"Hydrodynamics and mass transfer studies on plate-type microchannel reactor for liquid–liquid systems","authors":"Pranita A. Karekar,&nbsp;Vishwanath H. Dalvi,&nbsp;Chandrakanth R. Gadipelly,&nbsp;Ashwin W. Patwardhan","doi":"10.1002/cjce.25441","DOIUrl":"10.1002/cjce.25441","url":null,"abstract":"<p>This work reports hydrodynamic and mass transfer studies on a novel microreactor that can passively break up liquid–liquid slugs using judiciously placed internals. The reactors were fabricated in stainless steel (SS-316 L, hereafter SS) and PMMA (hereafter acrylic). The performance of both is comparable to the current state-of-the-art in microreactor technologies. A separated flow model is proposed to estimate the pressure drop for two-phase flows, with a mean absolute error (MAE) of 15.44% in SS and 19.83% in acrylic, respectively. Pulse tracer experiments were performed for residence time distribution (RTD) studies. They are fitted to a model for the prediction of RTD for single and two-phase flows. The results obtained from mass transfer experiments show that the volumetric mass transfer coefficient (<span></span><math>\u0000 <mrow>\u0000 <msub>\u0000 <mi>k</mi>\u0000 <mi>L</mi>\u0000 </msub>\u0000 <mi>a</mi>\u0000 </mrow></math>) in the case of SS reactor is, on average, 2.4 times higher than acrylic. A correlation is developed for estimating the <span></span><math>\u0000 <mrow>\u0000 <msub>\u0000 <mi>k</mi>\u0000 <mi>L</mi>\u0000 </msub>\u0000 <mi>a</mi>\u0000 </mrow></math> based on total velocity and phase fraction, providing better fits than the models based on energy dissipation. All studies show that wall characteristics significantly impact the hydrodynamics and mass transfer phenomena since the pressure drop and the <span></span><math>\u0000 <mrow>\u0000 <msub>\u0000 <mi>k</mi>\u0000 <mi>L</mi>\u0000 </msub>\u0000 <mi>a</mi>\u0000 </mrow></math> are greater in (the rougher) SS than in acrylic.</p>","PeriodicalId":9400,"journal":{"name":"Canadian Journal of Chemical Engineering","volume":"103 2","pages":"964-980"},"PeriodicalIF":1.6,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141931511","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":"Integrating autoencoder with Koopman operator to design a linear data-driven model predictive controller","authors":"Xiaonian Wang,&nbsp;Sheel Ayachi,&nbsp;Brandon Corbett,&nbsp;Prashant Mhaskar","doi":"10.1002/cjce.25445","DOIUrl":"10.1002/cjce.25445","url":null,"abstract":"<p>Non-linear model predictive control (NMPC) is increasingly seen as a promising tool to tackle the problem of handling process nonlinearity and achieve optimal operation. One roadblock to NMPC implementation, however, is the lack of a good model, whether a first-principles-based or a non-linear data-driven-based model such as artificial neural networks (ANN). This manuscript proposes a data-driven modelling approach that integrates an autoencoder-like network and dynamic mode decomposition (DMD) methods to result in a non-linear modelling technique where the non-linearity in the model stems from the modelling of the measured variables. The proposed method results in a semi-linear state-space model where the mapping between the model state and outputs are non-linear (via the autoencoder-like network) while the model dynamics are linear. In the subsequent model predictive controller (MPC) implementation, the autoencoder translates setpoints and outputs to the states of a state space model. The proposed approach is illustrated using a continuously stirred tank reactor simulation example. For comparison, a linear MPC and non-linear MPC based on a traditional neural network (NN) model, a classic Koopman operator-based MPC, and (to benchmark) a perfect model-based NMPC are implemented and tested on several setpoint tracking tasks. The proposed MPC design outperforms the other data driven MPCs, and has similar performance as the first-principles-based NMPC while requiring less computational time and without requiring first principles knowledge.</p>","PeriodicalId":9400,"journal":{"name":"Canadian Journal of Chemical Engineering","volume":"103 3","pages":"1099-1111"},"PeriodicalIF":1.6,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cjce.25445","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141931508","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}