Canadian Journal of Chemical Engineering最新文献

The current paper deals with viscous dissipation effects in a permeable (or porous) channel filled with non-Newtonian Casson fluid by considering the local thermal non-equilibrium (LTNE) model. The dependency of the effective thermal conductivities of the solid and fluid phases on the respective temperatures has been studied along with the spatially varying Biot number. The Brinkman number $��\left(\mathrm{Br}\right)�,�$ Casson fluid parameter $��\left(\gamma \right)�$, thermal conductivity variation parameter $��\left(\delta \right)�$, porosity $��\left(ϵ\right)�,�$ Darcy number $��\left(\mathrm{Da}\right)�$, and the ratio of fluid and solid phase thermal conductivities $��\left(�k_r�=�\frac{k_f}{k_s}�\right)�$ are the main governing parameters. The Darcy–Brinkman model is employed to govern the fluid flow in permeable media and the velocity profile has been obtained analytically. Moreover, the energy equations for both phases along with suitable boundary conditions are derived and solved with the fourth order boundary value solver. The findings of the current study depict that the Nusselt number increases with the increment in Casson fluid parameter and decreases with the increment in Brinkman number and thermal conductivity variation parameter. Overall, the heat transmission between the solid and fluid phases increases with the decrement in Brinkman number and thermal conductivity variation parameter. On the other hand, the heat transmission between both the phases magnifies by increasing the value of Casson fluid parameter.

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.

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 ($��R^2�>�0.92�$). 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.

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.

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.

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.

The present study concerns the development of new models to estimate the minimum spouting velocity (U_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 U_ms. The sensitivity analysis was conducted to determine the factors with more importance in controlling U_ms. Finally, simpler correlations were derived for U_ms prediction in different FC-CSB configurations, with accuracy around 12% error.

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.

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 ($��k_L�a�$) in the case of SS reactor is, on average, 2.4 times higher than acrylic. A correlation is developed for estimating the $��k_L�a�$ 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 $��k_L�a�$ are greater in (the rougher) SS than in acrylic.