Canadian Journal of Chemical Engineering最新文献

Corrosion poses a great risk to the integrity of oil and gas pipelines, leading to substantial investments in corrosion control and management. Several studies have been conducted on accurately estimating the maximum pitting depth in oil and gas pipelines using available field data. Some of the frequently employed machine learning techniques include artificial neural networks, random forests, fuzzy logic, Bayesian belief networks, and support vector machines. Despite the ability of machine learning methods to address a variety of problems, traditional machine learning methods have evident limitations, such as overfitting, which can diminish the model's generalization capabilities. Additionally, traditional machine learning models that provide point estimations are incapable of addressing uncertainties. In the current study, a Bayesian neural network is proposed to include uncertainty in estimating the corrosion defect of a pipeline exposed to external pitting corrosion. The results are then incorporated into a Bayesian belief network for evaluating the probability of failure and its corresponding consequences in terms of social impact, thus forming a comprehensive risk assessment framework. The results of the Bayesian neural network are validated using field data and achieved a testing accuracy of 90%. The framework of the study offers a powerful decision-making tool for the integrity management of pipelines against external corrosion.

The use of irregular pore-scale models to study heavy oil reservoirs with high-temperature, high-pressure, and high-stress characteristics is effective. Previous studies have typically focused on regular models and conventional environmental reservoirs, with limited exploration of irregular models and reservoirs in extreme environments. In investigating the process of water displacing heavy oil within reservoirs under high-temperature, high-pressure, and high-stress conditions at the pore scale, the utilization of the four-parameter method creates a micro-scale irregular porous media model. The model systematically considers the variation of physical properties of rocks and heavy oil with temperature. The results indicate that an appropriate increase in water injection rate or a decrease in reservoir contact angle will increase the recovery rate, temperature, and stress of the reservoir. At a displacement time of 0.3 s, with the water injection rate increasing from 0.004 to 0.01 m ∙ s⁻¹, the reservoir's recovery degree experiences an increase of 0.091. Simultaneously, the average temperature and average stress of the reservoir increase by 29.66 K and 1.9464 × 10⁹ N · m⁻², respectively. At a displacement time of 0.3 s and with the contact angle decreasing from 2π/3 to π/3, the reservoir's recovery degree increases by 0.44537, and the average temperature and average stress of the reservoir increase by 2.87 K and 1.86 × 10⁸ N · m⁻², respectively.

Model-based monitoring and control of chemical and biochemical processes rely on state estimators such as extended Kalman filters (EKFs) to ensure accurate online model predictions. Accurate predictions depend on appropriate model parameters and suitable state-estimator tuning factors. Extensions to our previously developed simultaneous parameter estimation and tuning (SPET) method are proposed so that SPET can be used for systems with nonstationary disturbances, time-varying parameters, multi-rate data, and measurement delays. A continuous stirred tank reactor (CSTR) case study with simulated data is used to illustrate and test the proposed method. Superior online model predictions and state-estimator performance are achieved using SPET compared to a traditional approach for parameter estimation and EKF tuning, with improvements in the average sum-of-squared prediction errors ranging from 3% to 52% for the scenarios tested. The SPET approach will also be useful for more-advanced state estimators that require the same tuning information as EKFs.

The paper presents the results of the first research on the synthesis of nano-ZIF-90 from zinc chloride. More specifically, the paper also introduces the results of large-scale pure nano-ZIF-90 synthesis with a high specific surface, uniform cubic crystals, and good thermal strength. ZIF-90 is synthesized from zinc chloride-containing medium capillaries, which have both a weak acid center and a strong base center. The post-synthetic ZIF-90 was also evaluated for heat storage based on its ability to adsorb water, methanol, and ethanol. XRD, FTIR, SEM, TEM, N₂ adsorption and desorption methods, TG mass thermal analysis, and NH₃-TPD/CO₂-TPD were used to study the ZIF-90 crystallization process with modifications of precursors, solvents, additives, and reaction conditions. From this, a large-scale synthesis of nano-ZIF-90 from high-efficiency zinc chloride has been derived. DSC measurements are used to evaluate the enthalpy adsorption of water, methanol, and ethanol on ZIF-90.

In this study, microwave pyrolysis of waste printed circuit boards (WPCBs) was carried out in an inert atmosphere, and the effects of pyrolysis temperature and nitrogen flow rate on the yield and composition of pyrolysis products were investigated. With the increase of pyrolysis temperature, the yield of liquid product increases gradually, and the yield of solid product decreases gradually. At 600°C, the yield of each phase tends to be stable. When the temperature continues to rise, the content of H₂ and CO decreases, and the content of C6 ~ C9 in the liquid product decreases. Microwave heating promotes the pyrolysis of brominated epoxy resin, which helps to improve the recovery rate of valuable substances and reduce the environmental impact of waste treatment. This study demonstrates that the microwave pyrolysis of WPCBs in nitrogen atmosphere has great potential in the green recovery process.

The accurate estimation of the power number for closed clearance impellers holds significant importance in industries such as chemical, biochemical, paper and pulp, as well as paints, pigments, and polymers. Existing state-of-the-art correlations for predicting power numbers, however, are inaccurate for impeller Reynolds number $��\left(�R�e_I�\right)�>�100�$. In this study, we compiled a dataset of 1470 data points from 15 research articles in the open literature, covering five types of impellers: (i) anchor; (ii) gate; (iii) single helical ribbon; (iv) double helical ribbon; and (v) helical ribbon with screw. Six machine learning models, namely artificial neural networks (ANN), CatBoost regressor, extra tree regressor, support vector regressor, random forest, and XGBoost regressor, were developed and compared. The results revealed that ANN emerged as the most efficient model, demonstrating the highest testing R² value of 0.99 and the lowest testing MAPE of 7.3%. Further, we used the ANN model to develop a novel set of process correlations to estimate impeller power numbers for the industrially important anchor and double helical ribbon impellers: which significantly outperform the existing state-of-the-art correlations available in literature.

Lithium-ion batteries offer significant advantages in terms of their high energy and power density and efficiency, but capacity degradation remains a major issue during their usage. Accurately estimating the remaining capacity is crucial for ensuring safe operations, leading to the development of precise capacity estimation models. Data-driven models have emerged as a promising approach for capacity estimation. However, existing models predominantly focus on constant current charging conditions, limiting their applicability in real-world scenarios where fast-charging conditions are commonly employed. The primary objective of this work is to develop a more versatile machine learning model (i.e., support vector regression [SVR]) capable of estimating battery capacity under fast-charging conditions, with broader applicability across various work conditions. Genetic algorithm and cross-validation techniques are employed to simultaneously optimize feature extraction hyperparameters and SVR hyperparameters. A model bagging method is further implemented to address prediction challenges under unknown fast-charging conditions. The effectiveness of the developed model is validated on a cycling dataset of lithium-ion batteries under different two-stage fast-charging conditions.

The experiments in high-pressure pipelines simulate the formation and fluid of hydrate under deep-sea conditions, which has practical significance for the deep-sea landfill of carbon dioxide (CO₂) and the safe running of oil and gas pipelines. In this paper, pure water, white oil, CO₂, and arquad 2C-75 were used to study the formation and flow characteristics of CO₂ hydrate in the oil–water system with the help of a loop device, and the growth morphology of hydrate was observed through the view-window on the loop. The experimental results show that in the low water cut system without anti-agglomerates, hydrate mainly forms on the surface of water droplets and the surface of the free water layer at the bottom of the loop. In the high water cut system, hydrate forms on the pipe wall in the form of hydrate film. Increasing water cuts can shorten the induction period of hydrate formation. Anti-agglomerates in the oil–water system can inhibit the growth of hydrate film on the pipe wall effectively. Anti-agglomerates can shorten the induction time of hydrate formation.

This paper aims to enhance the understanding of hydrogen explosions in hydrogen refuelling stations and evaluate associated risk factors using computational fluid dynamics simulations. The model is first validated against the measured data for hydrogen dispersion and explosion. Different scenarios are then modelled to understand the ignition timing and location. The study estimates acceptable distances to minimize asset damage and human injury from explosion incidents. It has been found that higher wind speeds lead to faster and more extensive dispersion of the hydrogen gas released during a leak. In addition, since strong wind can act as a powerful driving force for the shock wave, the impact of the explosion is found to be less. Interestingly, moving the source of ignition to regions with higher hydrogen concentration has a marginal impact on overpressure and temperature; however, the blockage ratio can significantly amplify the overpressure. It is found that cases with high blockage, including storage room, and cases with large volumes of flammable cloud, including leakage from compressor towards the ground, have the highest hazards. The findings will provide valuable insights into fire and explosion prevention in various areas of hydrogen refuelling stations and contribute to safer hydrogen infrastructure construction.

The escalating incidence of chronic diseases and infections has driven an increase in the use of antibiotics, raising concerns regarding their disposal and presence in water sources. Antibiotic-resistant genes (ARGs) can arise in bacteria and other microorganisms when antibiotics are present in the water. Human, plant, and animal physiological processes may be negatively impacted by extended exposure to these substances. Since MXenes are effective photocatalysts and adsorption agents, they have garnered a lot of attention in the wastewater treatment industry. While employing MXene alone typically yields inadequate results, it is advantageous to combine MXene with other materials to generate derivatives or composites. This comprehensive review meticulously examines MXene composites with various materials to enhance their photocatalytic prowess, unveiling composite systems capable of achieving an exceptional degradation efficiency of up to 99%, as exemplified by the UiO-66/MXene composite and g-C₃N₄/Ti₃C₂ MXene/black phosphorus heterojunction. Additionally, this paper provides critical insights into the intrinsic characteristics, synthesis methodologies, and performance efficiencies for these composites, thereby serving as an invaluable resource for researchers and practitioners in the field.