Canadian Journal of Chemical Engineering最新文献

The process operating performance assessment (POPA) of process industry plays a key role in the safe, stable, and efficient operation of production process. However, the actual industrial production process often exhibits nonstationary characteristics, leading to frequent anomalies that complicate POPA. Traditional POPA methods are mostly based on limited stable working conditions and single control limits, so it is difficult to fully evaluate the anomaly degree and lack comprehensive assessment indexes. To address these issues, this paper proposes a CVAE-SFA dual-channel multi-index comprehensive POPA method for nonstationary conditions. It introduces four control limits of ‘optimal, good, general, and poor’ based on the optimal reconstruction error from a conditional variational autoencoder (CVAE) and kernel density estimation (KDE). This creates a CVAE optimal grade classification channel for simultaneous qualitative and quantitative evaluations of current operating conditions. Additionally, to monitor dynamic characteristics under nonstationary conditions, a slow feature analysis (SFA) $��S^2�$ statistic anomaly monitoring channel is established. By identifying slow-changing essential features within the system, dynamic anomaly monitoring is achieved. Finally, a comprehensive scoring index (CSI) integrates dual-channel monitoring results to evaluate quality, yield, energy consumption, and stability. The effectiveness of this method is validated through experiments in the cement clinker production process (CCPP).

Cyclic solvent injection (CSI) is an effective enhanced oil recovery (EOR) technique with several economic and environmental benefits. However, the successful implementation of CSI at commercial scales requires reliable scaling criteria. In this study, CSI has been formulated comprehensively by integrating material balance, mass transfer, and pseudo-chemical reaction equations to derive key dimensionless scaling terms based on the Buckingham π theorem. Accordingly, a total of 11 dimensionless terms have been identified, which encompass a wide range of phenomena, including foamy oil mobility and its intricate dynamics, as well as solvent exsolution processes. In addition, to account for pressure propagation delay in larger reservoirs, an effective workflow was established to systematically modify the permeability in lab settings in such a manner that its results can be translatable into larger models. Furthermore, two sandpack models were employed to perform CSI experiments. These models were subsequently scaled up into two synthetic reservoirs, constructed using the CMG software package, by applying the proposed scaling methodology to validate the proposed scaling approach. The findings indicate a reasonable match in terms of recovery factor, cumulative gas production per unit pore volume, and cumulative gas–oil ratio (CGOR) versus dimensionless time between the synthetic reservoirs and the sandpack models. The proposed scaling workflow offers a foundation for future research on scaling methodologies in solvent-based heavy oil recovery processes. Additionally, it can be used to optimize recovery strategies and reservoir management by enabling more accurate predictions of CSI performance at larger scales.

Electrochemical CO₂ reduction to value-added chemicals offers a sustainable solution to environmental challenges. This study investigates nickel-ceria/expanded reduced graphene oxide (ex Ni/Ce-rGO) nanocatalysts, demonstrating enhanced performance with minimal Ni loading (0.2 wt.%). Ni/expanded Ce-rGO achieves a Faradaic efficiency of 28.97% at −0.61 V in 0.1 M Na₂CO₃. Structural and chemical characterization—including X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), high-resolution transmission electron microscopy (HR-TEM), hydrogen temperature-programmed reduction (H₂-TPR), X-ray photoelectron spectroscopy (XPS), and ultraviolet–visible spectroscopy (UV–Vis)—reveals that 3.28 nm Ni nanoparticles on expanded Ce-rGO enhance activity via increased active sites and improved electron transfer, further amplified by Ni-Ce synergy. These results position Ni/Ce-rGO as a promising catalyst for scalable CO₂ utilization.

To understand the behaviour and potential hazards of gaseous premixed flames, particularly in industrial settings, it is crucial to accurately characterize them. This study presents a novel method for the quantitative analysis of flame geometry using infrared (IR) thermographic imaging to improve image analysis and data acquisition. A FLUKE RSE300 thermographic camera was employed to capture high-resolution images of premixed LP gas flames under controlled conditions. The acquired IR images were processed using grayscale conversion, Otsu's binarization, and edge detection techniques to extract key geometrical parameters, including flame length, width, surface area, and volume. A scale factor was introduced to convert pixel measurements into real-world dimensions, ensuring precise quantification. Statistical analysis of multiple trials demonstrated the robustness of the method, as the frequency in the result data appears to be a normal distribution with a modal value around the centre of the histogram, with uncertainties primarily attributed to manual scale factor selection, showing an uncertainty of 7.3% and 8.7% for the volume and superficial area results, respectively.

In solid–liquid–vapour systems, the effect of film pressure ($��{\pi }_e�$) by vapour adsorbate molecules becomes significant when the solid surface energy is similar to or larger than that of the liquid. We extend Young equation applicability to estimate contact angles of pure and mixed liquids on smooth solids by including $��{\pi }_e�$, obtained from a novel dimensionless surface energy parameter. We use the Owens–Wendt–Kaelble interfacial tension model and perturbed-chain polar statistical associating fluid theory (PCP-SAFT) to calculate dispersion and non-dispersion surface energy contributions. The model is developed using diverse solid–liquid systems with 39 liquids and 30 solids. For binary liquid mixtures, conventional PCP-SAFT and molar-average mixing rules are used to obtain surface energies. We correlate $��{\pi }_e�$ to dispersion and non-dispersion contributions to the liquid and solid surface energies and use $��{\pi }_e�$ to improve estimation accuracy. Using a comprehensive dataset, comprising of 107 experimental datapoints, the average absolute deviation (AAD) decreases from 6.5° to 3.6° by including $��{\pi }_e�$. Using the $��{\pi }_e�$ correlation developed for pure systems, we accurately predict contact angles of binary liquid mixtures. Interestingly, the molar-average mixing rule gives more accurate contact angle results despite its simplicity where PCP-SAFT is only executed for the pure components comprising the liquid mixture. Overall, for binary mixtures, the AAD decreases from 9.2° to 4.5° using 105 datapoints and by including $��{\pi }_e�$. Our proposed framework significantly improves the contact angle estimations and enables the estimation of the contact angle of liquid mixtures for diverse systems.

Learning from incidents is a crucial step in preventing and mitigating adverse events. Incident databases offer valuable insights for safety management improvements by cause and contributing factors. However, extracting meaningful information from incident investigation reports poses a significant challenge. This study introduces a data-driven methodology to assess drivers of critical safety (DCS), which is essential for enhancing the safety of the process industries and protecting workers and the environment. Natural language processing (NLP) can offer automated, actionable insights from incident investigation reports. This automation is important in identifying DCS from incident reports to ensure proactive prevention and effective mitigation of risks, thereby protecting assets, workers, and the environment. Based on lagging safety indicators (causes or contributing factors), we aim to develop leading safety improvements to enhance the safety management system. A crucial step involves developing a DCS hierarchy to assess their role within the overall safety management framework. This hierarchy quantifies the driving and dependence power of each driver. The former refers to the number of drivers affected by each driver, while the latter determines the number of drivers impacted by each driver. This hierarchy facilitates resource allocation and determines each driver's effectiveness in safety management. The tool is developed and trained using the publicly available CSB database, a comprehensive source of incident investigation data. To further verify the model's effectiveness, it is tested and verified on an unseen database of 26 release incidents released by CSB in January 2025. The model successfully identifies the DCS responsible for each incident.

Aiming at the problem of high energy consumption in traditional water-carrying agent process for producing ethyl propionate, based on the thermodynamic analysis of residual curve map, a low energy consumption strategy for producing ethyl propionate via reactive distillation was proposed featured with extreme excess of propionic acid. It could realize the complete conversion (>99%) of ethanol via thermodynamic driving from propionic acid excess and facilitate effective separation of the water-ethyl propionate distillate which forms two immiscible liquid phases and enables simple decantation with 98% EP recovery from organic phase through natural phase stratification, leading to significant reduction in energy consumption. A comprehensive investigation of process design and analysis for high-purity ethyl propionate production with extreme excess of propionic acid was carried out by integrating process simulation and experimental validation in this work. Parametric studies of reactive distillation including excessive degree of propionic acid, holdup in bottom kettle reboiler, number of theoretical plates, and reflux ratio on the process performance and energy consumption was investigated. Simulation results of this new process were validated through experiments. Subsequently, the energy-saving designs of further EP and water purification were implemented, including a weak alkaline solution washing to remove trace propionic acid from the distillate and distillation for separation of ethyl propionate and water. Finally, under preferred conditions, the proposed process with molar ratio of propionic acid to ethanol of 30:1, only required about 1700 kJ/kg for producing high-purity ethyl propionate (0.99999), saving 68% energy consumption by comparison with the traditional process.

Drag reducers have become a common method for reducing resistance and increasing transportation efficiency in pipeline transportation of waxy crude oil. The prominent heat transfer weakening effect of drag reducers may impact the deposition of waxy oil. This study uses cold finger experiments to clarify wax deposition characteristics and mechanisms in drag-reduced waxy oil. The wax deposition mass and components are examined under different temperatures, stirring rates, drag reducer dosages, and types. The results show that a low dosage of drag reducer at high stirring rates can simultaneously reduce the total wax deposition and the overall wax content in high waxy oil. 5 mg/kg drag reducer can reduce total wax deposition by up to 15.66% and overall wax content by 7.26%. A systematic investigation reveals that adding drag reducers changes the carbon number distribution in the wax deposition layer. This change is similar to the effect of increasing wall temperature. The heat insulation and transfer weakening effects of drag-reducing fluids are likely the main mechanisms inhibiting the increase in wax deposition mass and wax content. The heat insulation effect reduces the temperature gradient near the pipe wall, while the transfer weakening effect decreases radial heat transfer. However, under low stirring rates and high dosages, the deposition mass of the drag-reduced waxy oil system increases, with the adhesive effect of polymer-type drag reducers becoming more pronounced. These findings provide a foundation for optimizing drag reducer usage in waxy crude oil pipelines, potentially leading to significant energy savings and improved operational efficiency.

A probability-based approach for multi-objective optimization is employed to develop efficient defoamers, addressing the challenges of drilling fluid foaming and the complex task of synergistically optimizing multiple performance indices of existing defoamers. An L₁₆(4³) orthogonal array (comprising 16 experimental runs with 3 factors at 4 levels each) is selected for the optimization process. The evaluation factors include the ratio of polyoxyethylene polyoxypropylene glycerol polyether (GPE) to polypropylene glycol (PPG), reaction temperature, and reaction time. The evaluation indicators encompass the stationary defoaming time and density recovery of freshwater and saline slurries at both room and elevated temperatures. Each performance indicator is classified as either favourable or unfavourable. Partial and total preference probabilities are calculated for each attribute. The optimal preparation plan is identified through screening, yielding a GPE:PPG ratio of 1:1, a reaction temperature of 80°C, and a reaction time of 2 h. Experimental results demonstrate that the defoamer effectively eliminates foam in drilling fluids at dosages ranging from 0.2% to 0.3%, outperforming typical commercially available defoamers. The defoamer achieves a density recovery rate exceeding 81% for both freshwater and saline slurries at room and high temperatures. Characterization via infrared spectroscopy, thermal stability assessments, and fluorescence testing confirms that the defoamer retains excellent performance under the complex working conditions encountered in drilling fluids, showcasing strong adaptability.

The selective separation of copper and lead ions in acidic solutions was successfully achieved utilizing a thiourea-functionalized resin, specifically Puromet MTS 9140, which demonstrated a strong adsorption preference for copper ions. To further elucidate the adsorption mechanism of Cu(II) ions, density functional theory (DFT) calculations using the B3LYP technique and ab initio unrestricted Hartree–Fock (UHF) methods were employed to analyze electronic structure features, such as Mulliken population/charge, frontier orbital energies, and Gibbs free energy (∆G°_f) of three structural units in the resin (i.e., m-thiourea-styrene, o-thiourea-styrene, and p-thiourea-styrene) and their interaction with Cu(II) and Pb(II) ions. The Frontier molecular orbital (FMO) theory analysis using the B3LYP DFT method verified that the resin bearing thiourea group interacts with Cu(II) and Pb(II) ions through S atom bonding. This research enhances the understanding of mechanisms involved in the recovery and removal of metal ions from aqueous systems using selective resins, indicating potential applications in water and wastewater treatment.