Canadian Journal of Chemical Engineering最新文献

The thermal decomposition of ethane (C₂H₆) and the steam cracking of fossil fuels are the main sources of ethylene (C₂H₄). However, it usually contains 5%–9% of C₂H₆ residue, which must be reduced to ensure its utilization during polymerization. C₂H₆ and C₂H₄ have comparable kinetic diameters and boiling points (C₂H₆: 4.44, 184.55 K; C₂H₄: 4.16, 169.42 K), which makes the separation process very difficult. This contribution employs a methodology that integrates machine learning (ML) with Monte Carlo simulations to evaluate the ddmof database to develop a predictive model for separating ethane (C₂H₆) and ethylene (C₂H₄). The ML model's input is the metal–organic frameworks (MOFs) chemical and structural descriptors. The grand canonical Monte Carlo (GCMC) simulations in RASPA software were carried out to calculate the equilibrium adsorption of ethane and ethylene. Different ML models such as random forest, decision tree, and deep neural network models have been tested to estimate the selectivity and ethane uptake from the MOF data being generated. Interpretable ML model using SHapley Additive exPlanations (SHAP) is developed for the better understanding of the impact of the parameters on selectivity and ethane uptake. A user-friendly graphical user interface (GUI) is presented, allowing users to predict the ethane uptake and selectivity of MOFs simply by entering the values of chemical and structural descriptors.

Herein, we perform a stability assessment of two conjugated polymers that are conventionally used as electron donor polymers in the active layer of organic photovoltaic (OPV). More specifically, the impact of thermal annealing, a post-treatment commonly applied in the OPV community, is evaluated in terms of device performance and stability. The two polymers are PTB7-Th and QX1, and they are respectively blended with a non-fullerene electron acceptor, herein a derivative of N-annulated perylene diimide, that is, tPDI₂N-EH. These blends are targeted for their relatively high power conversion efficiency in outdoor conditions, but also for their potential as efficient active layer in low-intensity (indoor) conditions—while these blends have been reported, no study on the impact of thermal annealing on their stability has been performed yet. The performance stability of these devices, tracked via the open circuit voltage, the short-circuit current, the fill factor, and the power conversion efficiency metrics, were evaluated each day for 2 weeks and correlated to an evaluation of the microstructure of the active layer, evaluated using atomic force microscopy and UV–visible absorbance spectroscopy. Finally, transistors were prepared using only the two electron donor polymers, PTB7-Th and QX1, to assess if some correlations could be made between the behaviour of the OPV devices and that of the electronic charge mobilities. Results contribute to identify which molecular structures and which post-treatments are ideal to promote the stability of the active layers in the context of OPV devices.

Stacked autoencoders (SAEs) have great potential in developing soft sensors due to their excellent feature extraction capabilities. However, the pre-training stage of SAE is unsupervised and some important information related to target variables may be discarded. Meanwhile, as the depth of the network increases, reconstruction errors continue to accumulate, resulting in incomplete feature representations of the original input. In addition, the dynamic nature of the data affects the predictive results of the model. To address these issues, the stacked dynamic target regularization enhanced autoencoder (SDTR-EAE) method is proposed, which adds the DTR and the original input information layer by layer to enhance the feature extraction. To adapt to the dynamic changes in data and extract target-related features, entropy weight grey relational analysis (EW-GRA) is used as the DTR term to constrain the weight matrix and suppress irrelevant features. To reduce the accumulation of information loss during the reconstruction, an information enhancement layer is introduced, where the original inputs and the information of the hidden layers of previous DTR-EAE units are added to the follow-up DTR-EAE unit. Finally, in the regression process, the DTR term is used again to fully utilize depth features for quality prediction and prevent overfitting. Experimental verifications using the debutanizer column and thermal power plant are conducted to validate the effectiveness of the proposed modelling method.

Wood pellets, which are manufactured from sawmill and forest residues, are sold in bulk for biomass power generation or in bags for residential heating. Wood pellet production involves combustible dust, which presents the risk of fires and explosions. Process safety management (PSM) is a framework for preventing and mitigating process-related incidents. While PSM has historically been integrated within the chemical process industries, there is a need to systematically manage process-related hazards in other sectors, including wood pellet and wood product manufacturing. However, there is a need to identify an approach to PSM implementation that is reasonable and achievable based on the relative complexity of the production process, as well as onsite resources. The scope of this project was to develop an integration tool for wood pellet production to serve as the foundation for a long-term strategy and implementation plan led by industry. This research resulted in a PSM integration tool consisting of a PSM survey for gap analysis, self-assessment worksheets that include numerous PSM best practices, factsheets, and an implementation strategy. Using the CSA Z767 Process Safety Management standard as the basis, the research included the development of a phased approach to integrating PSM elements to help improve feasibility. The selection of PSM element phases was informed by surveys of operations and subject matter experts. This research recognizes that, while PSM is currently mostly voluntary in Canada, some organizations have adopted the CSA Z767 standard into regulations and proactively implementing a PSM framework will position companies well should regulations change.

Only a few of the students who graduated from Chemical & Petroleum Engineering at the University of Calgary found jobs in 1983 because of a severe recession and the National Energy Program artifically deflating the oil price. Despite the slow start to their careers, the graduates have made substantial contributions to industry, government agencies, and academia. They worked on over 60 projects in more than 40 countries, many of which were valued in the billions of dollars (excluding projects in Canada). Because of the volatility in the petroleum industry, the graduates often moved from one company to another: 6 individuals worked for 10 or more companies, while only 2 spent their entire career at a single company. In 1981, we were told that the half-life of an engineering career was 5 years, but while many did take up senior management positions and business roles, most remained very close to the engineering profession throughout their careers. Here, we summarize the career paths in broad terms, like how frequently graduates changed jobs, how much time they averaged in each company, and mention the role of education in their work. Of the 60 students who graduated in 1983, this perspective article excludes seven engineers who passed away prematurely, and another seven who could not be reached.

Oily wastewater poses a significant threat to human health and the environment, especially when it contains emulsified oil. Traditional treatment methods often fail to address this type of wastewater effectively. Therefore, developing advanced treatment methods to make such water suitable for various applications has become a pressing issue. The electrospinning technology has emerged as the most effective method due to its high separation efficiency. This review provides a comprehensive overview of the methodologies employed in nanofibres production across diverse techniques, along with concise insights. It also offers a survey of various methods for fabricating polymer membranes via the electrospinning technique, shedding light on the parameters affecting the electrospinning process. Furthermore, this review elucidates the fundamental concepts of membrane fouling, clarifying the mechanisms and factors contributing to fouling. We addressed advantages and disadvantages of methods used to create polymeric nanofibre membranes via the electrospinning technique. The needleless electrospinning technique eliminates the need for a nozzle to jet the nanofibres, preventing clogging. This method results in higher nanofibres production rates compared to the needle electrospinning technique. However, it does require a more complex setup. On the other hand, the needle electrospinning technique is often successfully employed in laboratory-scale settings due to its more straightforward setup. However, it necessitates using a cleaning device for each needle, which can become impractical for nanofibre production. The main challenges facing electrospun nanofibrous membranes were also presented. The development of eco-friendly nanofibers is outlined in the future perspective of this review.

Renewable and sustainable energy production has gained significant attention to meet sustainable development goals (SDGs). Pine needles, an abundant typical forestry residue, can be used as a renewable biomass source for sustainable energy production. Pyrolysis is a well-established and commercialized technique for the thermochemical valorization of lignocellulosic biomass. The present work focuses on improving the bio-oil yield by introducing SiO₂-Al₂O₃-based catalysts, including different zeolites and SiO₂-Al₂O₃ materials with varying SiO₂-Al₂O₃ ratios, during the pyrolysis. Bio-oil yield increased from 45.2 wt.% to 47.2 wt.% with the introduction of SiO₂-Al₂O₃ catalysts and increased to 51.2 wt.% and 50.6 wt.% with HZSM-5 and Y-zeolite, respectively, and decreased to 40.0 wt.% with β-zeolite catalyst. The pyrolysis experiments of physically mixed biomass and catalyst were carried out in a fixed-bed down-flow reactor. Various process parameters such as temperature, retention time, and catalyst-to-biomass ratio were examined to evaluate their effect on product yield. The catalyst's introduction slightly decreased phenolic compound content, enhancing carbonyl and hydrocarbon production. Maximum improvement in bio-oil yield by 6 wt.% was achieved using an H-ZSM-5 catalyst at 450°C temperature and 30 min residence time with a catalyst-to-biomass ratio of 1:4.

Prior to investigating the guest gas replacement characteristics, the estimation of equilibrium condition for the coexisting hydrate–liquid–vapour (HLV) phases is crucial. For this, there are various studies which have reported the physical thermodynamic model for equilibrium estimation. In this contribution, a data-driven formulation is developed as an alternative approach within the framework of artificial intelligence (AI) to predict the three-phase equilibrium of binary and ternary mixed hydrates associated with guest swapping at diverse geological conditions. For this, we use the experimental data sets related to guest (pure and mixed CO₂) replacement in hydrate structures with and without salts (i.e., single and multiple salts of NaCl, KCl, and CaCl₂). Various training algorithms, namely Levenberg–Marquardt (LM), scaled conjugate gradient (SCG), Broyden–Fletcher–Goldfarb–Shanno (BFGS) quasi-Newton, and Bayesian regularization (BR), are employed to formulate the artificial neural network (ANN) model. Performing a systematic comparison between them, we select the best option suited for the hydrate system. The best performing ANN model is compared with an existing physical thermodynamic model for predicting the equilibrium condition in pure water. It is observed that the ANN (BR) model consistently secures the lower percent absolute average relative deviation (i.e., %AARD <2%) than the latest physical model. Finally, the developed AI model is extended to predict the three-phase HLV equilibrium in presence of salt solutions.

Recent advancements in artificial intelligence (AI) have significantly influenced scientific discovery and analysis, including liquid crystals. This paper reviews the use of AI in predicting the properties of liquid crystals and improving their sensing applications. Typically, liquid crystals are utilized as sensors in biomedical detection and diagnostics, and in the detection of heavy metal ions and gases. Traditional methods of analysis used in these applications are often subjective, expensive, and time-consuming. To surmount these challenges, AI methods such as convolutional neural networks (CNN) and support vector machines (SVM) have been recently utilized to predict liquid crystal properties and improve the resulting performance of the sensing applications. Large amounts of data are, however, required to fully realize the potential of AI methods, which would also need adequate ethical oversight. In addition to experiments, modelling approaches utilizing first principles as well as AI may be employed to supplement and furnish the data. In summary, the review indicates that AI methods hold great promise in the further development of the liquid crystal technology.

Tight sandstone contains a large number of oil and gas resources, but because of its ultra-low porosity, permeability, and strong hydrophilicity, the oil recovery is low. Microfluidic technology, as an emerging research technique, offers advantages in visualizing fluid flow, reducing experimental reagent consumption, and accurately simulating the pore structure of sandstone using microfluidic chips. This study presents an effective research methodology for improving tertiary oil recovery efficiency in sandstone. By analyzing pore slice images of sandstone cores and employing image processing techniques, the study extracted characteristic dimensions of the sandstone and designed a microfluidic chip. A displacement system was constructed using high-speed cameras, constant-pressure pumps, and microscopes to monitor the oil displacement process. A bubble generation device based on ultrafiltration membranes was proposed to introduce generated bubbles into the microfluidic chip with a sandstone structure for oil displacement studies. Real-time monitoring of the displacement process was conducted. Water and foam were used as displacing agents to investigate the displacement process in the microfluidic chip mimicking the sandstone core structure. Additionally, analysis and comparison were performed on foam formulation, surfactant concentration, and foam proportion, quantitatively evaluating the oil displacement efficiency under various experimental conditions. The proposed research is helpful for the understanding of the foam flooding process on a micro-scale and of significant application potential for the enhanced oil recovery of sandstone reservoirs.