Canadian Journal of Chemical Engineering最新文献

In today's world, wastewater treatment has become a critical challenge for environmental sustainability and public health, particularly due to the increasing presence of toxic metals and non-biodegradable contaminants. Traditional methods such as adsorption, precipitation, ion exchange, membrane separation, and filtration categorized under chemical, physical, or biological approaches, are often limited by high costs, low efficiency, or negative environmental impacts. The selection of these techniques depends on effluent characteristics, operational conditions, and wastewater volume. Membrane-based technologies have emerged as promising alternatives, offering higher efficiency, selectivity, and adaptability compared to conventional processes. Among these, geopolymer membranes represent a novel class of inorganic materials, synthesized through an eco-friendly and versatile geopolymerization process. These membranes are typically fabricated from aluminosilicate precursors sourced from industrial byproducts like fly ash, rice husk ash, and phosphate tailings, thereby promoting waste valorization and sustainability. What distinguishes geopolymer membranes is their excellent thermal stability, robust chemical resistance, and highly tunable pore structure and surface properties. These characteristics enable them to function effectively under harsh conditions and selectively remove a broad spectrum of contaminants, potentially outperforming traditional polymeric and ceramic membranes. Their modular design also allows integration into customized advanced treatment systems tailored to specific pollutants. This review presents a comprehensive overview of the geopolymerization mechanism, key factors influencing membrane performance, and diverse applications in wastewater treatment. Special emphasis is placed on addressing current challenges such as scalability, fouling resistance, and long-term durability, highlighting how geopolymer membranes can offer innovative solutions for sustainable water management and pollution control.

To explore hydrogen's cooling performance and mechanism as a coolant in heat exchangers, the RNG k-ε turbulence model is employed to simulate the heat transfer characteristics of supercritical hydrogen within U-shaped tubes. The influence of factors such as the heat-to-mass ratio, hydraulic radius, and roughness on the flow and heat transfer characteristics is analyzed. Emphasis is placed on exploring the mechanism by which the introduction of roughness enhances heat transfer. A new heat transfer correlation equation is established. The results show that for vertical U-shaped tubes under cooling conditions, increasing surface roughness or reducing the heat-to-mass ratio and hydraulic radius significantly enhances heat transfer. Compared to increasing the mass flow rate, reducing the heat flux and consequently lowering the heat-to-mass ratio can increase the heat transfer coefficient by approximately three times, with less heat transfer decay at the outlet section. Dimensionless number analysis reveals that buoyancy and flow acceleration effects have negligible impacts on heat transfer during hydrogen flow. However, centrifugal forces alter the circumferential thermal property gradients, effectively enhancing heat transfer in the curved sections. Finally, the proposed heat transfer correlation based on surface roughness predicts an accuracy deviation of ±20%, making it suitable for predicting the heat transfer of supercritical hydrogen.

Numerical simulations were employed to investigate the gas–liquid two-phase flow within a recycling cyclone and the impact of key structural parameters on its separation performance. Using a coupled Reynolds stress model (RSM) for the gas phase and a discrete phase model (DPM) with a discrete random walk (DRW) for liquid droplets, this study analyzed the effects of the recycle line, gap width, baffle plate size, and entrance geometry. Results show that the recycle line significantly enhances separation efficiency, especially at lower inlet velocities. Optimal gap width and baffle plate size are crucial for balancing separation efficiency and operational reliability. While rectangular entrances offer slightly higher separation efficiency than circular ones, they also increase pressure drop. These findings offer valuable guidance for optimizing recycling cyclone design to improve particle separation in industrial settings.

This study investigated the usefulness of measurements from an agitator torque sensor in monitoring the dynamics of suspension polymerization. The main focus was to estimate the viscosity of the reaction mass during polymerization using the agitator torque as a secondary variable. Viscosity is a crucial parameter that plays a vital role in determining the efficiency of the process and the quality of the final product. Accurate viscosity monitoring is essential as it provides valuable insights into the progression of the polymerization process and its dynamic behaviour. This study developed a combined Kalman filter (KF) and fuzzy logic (FL) model to estimate viscosity in real time, addressing the challenges of noise in torque measurements. Experimental validation showed that the KF-fuzzy model improved the accuracy and stability of viscosity predictions, particularly during the critical stages of polymerization. This approach enables better monitoring of reaction dynamics, thereby supporting process optimization and control.

In the realm of industrial production, where the scale is continuously expanding, chemical process variables often exhibit complex characteristics such as nonlinearity, multi-modality, and dynamic behaviour. Traditional fault diagnosis methods based on multivariate statistics, like principal component analysis (PCA), generally operate under the assumption that current values are independent of historical statistical values. Additionally, most of these fault diagnosis algorithms focus on feature extraction, which, despite reducing the number of features, often results in a loss of the original data's characteristics. To address this issue, the fault diagnosis and monitoring algorithm introduced in this study integrates genetic algorithm (GA)-based feature selection with dynamic global–local preserving projection (DGLPP). This approach not only accounts for the dynamic nature of multivariate data but also reduces dimensions while retaining the original features of the data. The effectiveness of this methodology is demonstrated through comparative experiments using the Tennessee Eastman process dataset. This paper compares the proposed model with four existing models: dynamic principal component analysis (DPCA), global–local preserving projection (GLPP), DGLPP, and GA-DPCA and establishes a significant enhancement in performance with the proposed method.

The present work reports on the use of artificial neural networks to predict the adsorption of 5G blue reactive dye (5GBRD) on yellow passion fruit pomace in a fixed-bed process and the % dye removal optimization. The samples were characterized using a thermogravimetric analyzer and scanning electron microscopy. Batch adsorption experiments were conducted to analyze the impact of the initial concentration of 5GBRD, contact time, and solution pH and temperature. For the fixed-bed adsorption experiments, the processing time (0–55 h), inlet flow rate (1–4 mL min⁻¹), initial dye concentration (35–70 mg L⁻¹), and bed height (15–23 cm) were evaluated. The predictive model was built using a multilayer perceptron machine learning (artificial neural network [ANN]) model, and the process optimization used the dividing rectangles (DIRECT) algorithm. The best ANN model architecture was 4–4–1 and the accuracy of testing data were as follows: coefficient of determination ~0.97, mean squared error ~0.004, mean average error ~0.04, and root mean square error ~0.06. The DIRECT optimization algorithm indicated that the maximum % dye removal is achieved at 43.8 h, 3.7 mL min⁻¹, 66 mg L⁻¹, and 19.3 cm. The ANN model and DIRECT optimization algorithm are valuable tools for practical applications in adsorption process modelling and optimization.

This work focused on investigating the effect of oxidants and their interaction with the monomer (m-phenylenediamine) (mPD) on performance of the resulting composite membrane. The synthesized poly(m-phenylenediamine) (PmPD) and poly(vinyl alcohol) (PVA) were deposited onto flat ceramic support made from pozzolan and micronized phosphate. The difference between the two composite membranes is the oxidant used for the chemical polymerization of mPD monomer. The PmPD used to develop the first membrane in this work was synthesized using ammonium persulphate (APS) oxidant. The second membrane was developed in a previous study using ferric chloride (FeCl₃) as oxidant. Although PmPD-based membranes have been explored, few studies have systematically compared the influence of different oxidants on membrane performance, especially for dye removal. This study addresses that gap by evaluating how APS and FeCl₃ affect membrane characteristics and dye rejection efficiency. The effect of oxidants on membrane properties such as microstructure, wettability, permeability, and filtration performances was investigated. The composite membranes were characterized by Fourier transform infrared spectroscopy, scanning electron microscopy, energy dispersive X-ray analysis, and X-ray diffraction technique. The morphology analysis shows that using APS leads to the formation of uniform microparticles compared to FeCl₃ oxidant. It was proven that the use of APS in the polymerization of the mPD enhances the rejection of the membrane accompanied by with a decrease in permeate flux. It removed up to 99.7% of Congo red under optimal conditions (ΔP = 3 bar, C = 600, and pH = 4).

The heavy reliance of modern industries on chemical processes to facilitate the mass production of cosmetics, beverages, food products, and pharmaceuticals has in turn contributed to the heightened significance of pH value regulation that supports product quality assurance. However, the process of pH control is difficult due to its highly sensitive, dynamic, and nonlinear nature. The conventional control approaches like proportional integral derivative (PID) and proportional integral (PI) controller are inept at handling the complex process of pH control. Thereby, in this work adaptive neuro-fuzzy inference system (ANFIS), which combines the accuracy of fuzzy inference system (FIS) and learning capability of adaptive neural network (ANN) is applied for pH process regulation. Moreover, the controller operation is improved further with the application of chicken swarm optimization (CSO) for tuning its input parameters. The primary goal is to accomplish effective load regulation and appropriate set-point tracking using smoother control signal. According to the derived simulation outcomes, it is observed that both the industrial and standard structure of the proposed chicken swarm (CS)-ANFIS controller outperforms other existing control techniques with better disturbance rejection, set-point tracking and excellent sensitivity to change in model parameters.

Bonding polymerization is a straightforward and efficient approach for enhancing the quality of adsorbents and improving the properties of polymers and their bonding chains. The objective of this research was to enhance the ability of polypropylene hollow fibres to adsorb CO₂. The surface of the adsorbent was modified using gamma irradiation in combination with glycidyl methacrylate and different amines, such as ethanolamine, triethylamine, and diethylamine. The efficacy of the modification process was evaluated by altering the graft variables, such as monomer concentration and gamma dose rate to determine the grafting degree (GD, %). Similarly, the amination yield (DA, %) was controlled through changes in the amine parameters, including amine type and concentration. Scanning electron microscope (SEM) techniques were used to examine the morphology of the modified hollow fibre membrane, while Fourier transform infrared spectroscopy (FTIR) was utilized to analyze the chemical structures of the fibres. Subsequently, the impact of gas flow intensity at the CO₂ inlet, with flow rates of 50, 100, and 150 cm³/min, and concentrations of 5%, 10%, and 15%, was investigated. The adsorption rate decreased significantly with an increase in gas flow rate at the inlet due to the short contact time and quick saturation. Additionally, the adsorption rate decreases notably with the increment of CO₂ concentration. The findings of this study indicate that the utilization of radiation resulted in the creation of a unique adsorbent with exceptional adsorption capabilities. Furthermore, this adsorbent was effectively recognized during the process of carbon dioxide adsorption.

This study introduces two novel strategies for regulating the chaotic dynamics of the Belousov–Zhabotinsky (BZ) reaction: a smoothed sliding mode controller (SMC-Proposed), designed to reduce chattering while preserving robustness, and an adaptive fuzzy sliding mode controller (SMC-Fuzzy), applied to the BZ system for the first time. These approaches are compared against a classical sign-based sliding mode controller (SMC-sign) in terms of tracking accuracy, convergence speed, and chattering suppression. Simulation results show that while SMC-sign achieves the lowest tracking error (RMSE = 0.00001), it produces severe chattering (973.4 Hz). In contrast, the SMC-Fuzzy controller reduces chattering to 79.2 Hz, with good accuracy (RMSE = 0.00107) and faster stabilization. The SMC-Proposed model offers a balanced trade-off, achieving moderate accuracy while significantly reducing high-frequency energy without relying on fuzzy logic. Frequency-domain analysis using power spectral density (PSD) confirms the chattering suppression capability of both proposed methods. These findings highlight the practical advantages of the SMC-Fuzzy and smoothed SMC controllers for robust and efficient control of chaotic chemical systems.