AIChE Journal最新文献

The erosion of bed materials and coal ash on water-cooled walls presents a persistent technical challenge in circulating fluidized bed boiler systems, manifesting as increased frequency of unplanned shutdowns and elevated maintenance requirements. Traditional metal anti-wear devices, due to inherent structural limitations, struggle to achieve optimal coordination between velocity and pressure gradient fields, making them prone to erosion and limiting their overall wear resistance. This study proposes bionic anti-wear devices inspired by squid fin and shark dorsal fin. The experimental and simulation results show that bionic devices can optimize the coordination between the velocity and pressure gradient fields. Compared to the traditional right-angle triangular device, the shark dorsal fin-inspired device reduces the windward surface area by 3.25%, maximum pressure coefficient by 50%–60%, and the erosion rate by 93.55%. This study provides an innovative approach for developing next-generation anti-wear devices with enhanced wear resistance.

The standard Dynamic Mode Decomposition (DMD), when used in complex fluid flow modeling, often suffers from situations like noisy data and translational motion, leading to high errors and non-physical results. Meanwhile, purely physics-based numerical methods offer high accuracy but are computationally intensive. To bridge this gap, this paper proposes a Physics-Constrained Dynamic Mode Decomposition (PCDMD) framework, which integrates governing physical laws into the DMD to constrain predicted results by using Kalman correction. This hybrid approach retains the speed of DMD while improving accuracy by ensuring that predictions obey the underlying physics. We systematically evaluated the PCDMD on flow problems with increasing complexity, including lid-driven cavity flow, flow around a cylinder with concentration transport, and a rising bubble system. In each case, PCDMD significantly improves both the predictive accuracy and physical consistency. By balancing between the data-driven modeling and physical correction, the PCDMD remains robust under imperfect data and physical equations.

Flow pattern prediction in multiphase systems is essential for characterizing hydrodynamic properties and optimizing mass/heat transfer efficiency. Herein, we propose a generative artificial intelligence (GenAI) flow pattern prediction framework for rapidly processing and analyzing large–scale flow pattern image data, with the first application to predictive modeling of gas–liquid Taylor flow in microchannels. The forecasting results of this GenAI–based prediction framework do not consist of discrete flow pattern classification labels but rather intuitive, spatially resolved high-fidelity visualization results comparable to experimental observations under steady–state operating conditions (e.g., high–resolution flow pattern images captured by high–speed cameras). Notably, the proposed prediction framework overcomes the limitations of conventional methods that only provide category information of flow patterns. More importantly, the model evaluation results demonstrate that this framework can effectively model the correlation between operating conditions and corresponding flow characteristics within microchannels, thereby validating the great potential of this GenAI technology for multiphase flow research.

We address the identification of the actual stoichiometric network in reacting systems using composition measurements, extending our previous work (Fromer et al. I&ECR 2023). We generalize this algorithm for scenarios where not all species are measured and back-calculate the missing concentrations through the reaction extents of the candidate network. In addition to the prior global accuracy comparison among candidate reaction networks, we introduce a species-by-species F-test accuracy comparison between the most accurate reaction networks from the global assessment. We examine two case studies involving 7 and 11 species participate in 4 or 8 reactions, respectively. In the second case study, the 8 reactions are linearly dependent, presenting an additional challenge. The enhanced algorithm successfully identifies the actual reaction network as the most accurate, even with 4 of the 11 species not measured.

The development of advanced absorbents for effectively capturing carbon dioxide is crucial in mitigating greenhouse gas emissions. This study introduced a series of deep eutectic solvents (DESs) for CO₂ capture and identified the most promising DESs with the stepwise screening method based on their absorption capacity, absorption rate, thermal stability, desorption efficiency, and apparent activation energy. Consequently, compared to the monoethanolamine (MEA), in the 30 wt% aqueous solutions, [1,2,3-Triazolium chloride][diethylenetriamine] ([TrizCl][DETA]) and [Piperazinium chloride][diethylenetriamine] ([PzCl][DETA]) improved the CO₂ absorption capacities by 31% and 34%, absorption rates by 12% and 30%, and the amounts of CO₂ desorbed by 42% and 23%, as well as reduced the apparent activation energies by 9% and 28%, respectively. Meanwhile, their thermal stabilities (degradation onset temperatures, T_onset) were enhanced by 101% and 32%, respectively. The FTIR and NMR analyses were conducted to provide deeper insights into the chemical absorption mechanism of CO₂ by the DESs.