AIChE Journal最新文献

A universal machine learning framework is proposed to predict and classify membrane performance efficiently and accurately, achieved by combining classical density functional theory and string method. Through application of this framework, we conducted high-throughput computations under industrial conditions, utilizing an extensive database containing nearly 70,000 covalent organic framework (COF) structures for CH₄/H₂ separation. The best-performing COF identified surpasses the materials reported in the previously documented MOF and COF databases, exhibiting an impressive adsorption selectivity for CH₄/H₂ exceeding 82 and a membrane selectivity reaching as high as 248. More impressively, some of the best candidates identified from this framework have been verified through previous experimental works. Furthermore, the automated machine learning framework and its corresponding scoring system not only enable rapid identification of promising membrane materials from a vast material space but also contribute to a comprehensive understanding of the governing mechanisms that determine separation performance.

Developing the sustainable and cost-effective heterogeneous catalytic system for controlling chemoselectivity holds substantial importance in fine organic chemicals. Herein we construct a unique Zr(OH)₄ + CuO physically hybrid system for selective oxidation of anilines. Zr(OH)₄ alone leads to azoxybenzene formation, and Zr(OH)₄ + CuO shifts the reaction favorably toward nitrosobenzene. The proximity study indicates Zr(OH)₄ + CuO outperforms its counterparts synthesized through methods like ball-milling, loading, and coprecipitation, because the closer proximity exhibits stronger chemical interaction, restricting the activity of Zr-OH hydroxyl sites. Through mechanistic experiments, in situ DRIFT-IR and DFT calculations, a new Ph-$��\dot{N}�$OH intermediate mechanism is firstly proposed. Two Ph-$��\dot{N}�$OH self-condensate to form azoxybenzene for only Zr(OH)₄, whereas Zr(OH)₄ + CuO could promote rapid transformation of Ph-$��\dot{N}�$OH to nitrosobenzene on CuO through a hydrogen transfer process. Moreover, Zr(OH)₄ + CuO displays good recyclability and robust scalability. This is the first report demonstrating the utilization of a physically hybrid catalyst to adjust the selectivity of the aniline oxidation reaction.

In response to the increasing demand for gold in electronic manufacturing, this study addresses the urgent need for gold recycling from electronic waste (E-waste) to ensure the environment and energy safety. This study focuses on the preparation of the tubular carbon nanofibers (TCNFs) integrating NH₄⁺ and SO₃⁻ groups into TCNF-SO₃⁻-NH₄⁺, which exhibited remarkable gold adsorption capacity (2003.25 ± 0.23 mg/g) and selectivity (K_d; 41.7 × 10³ mL/g) at lower pH. TCNF-SO₃⁻-NH₄⁺ achieved a 99.77% gold adsorption efficiency under lower pH conditions (pH = 1) from E-waste. The adsorption capacity reached 3.54 ± 0.11 mg/g at an initial concentration of 0.85 mg/L. The results highlight the efficacy of TCNF-SO₃⁻-NH₄⁺ for extracting gold and other precious metal ions from E-waste, presenting a promising solution for clean energy and environmental protection.

Ethylene is one of the most important chemicals, and scheduling optimization is crucial for the profitability of ethylene cracking furnace systems. With the diversification of feedstocks and the high variability in prices, supply chain fluctuations pose significant challenges to the scheduling decisions. Dynamically responding to these fluctuations has become crucial. Traditional mixed integer nonlinear programming (MINLP) models lack the capability of supply chain response, while receding horizon optimization (RHO) models require parameter prediction and repeated optimization solving. To address this challenge, we propose a deep reinforcement learning-based framework that includes an ethylene dynamic scheduling environment and a decision agent based on deep Q-network. Across three test cases, compared to the MINLP and RHO models, this framework significantly minimizes losses caused by supply chain fluctuations, thereby increasing daily average net profits by 9%–27%, demonstrating its significant potential for application in responsive scheduling in the presence of supply chain fluctuations.

This article presents an experimental application of fault detection, isolation, and estimation in a chemical reactor system, introducing a functional observer-based approach without the need for linear approximation. The residual signal generators, functioning as disturbance-decoupled functional observers, provide fault size estimates and enable fault isolation through multiple generators operating independently. The experimental study focuses on the 3-Picoline oxidation process, deriving a discrete-time model, and constructing specific residual generators for coolant inlet temperature and feed concentration faults. Fault diagnosis employs Fast Fourier Transform (FFT) filtering and Generalized Likelihood Ratio (GLR), facilitating on-the-fly detection during the experiment. The effectiveness of fault detection, disturbance decoupling, and estimation is experimentally validated.

Electrocatalytic semi-hydrogenation of alkynols holds tremendous advantages over conventional thermocatalysis process. However, the selectivity-activity seesaw effect is a principal obstacle to its further development. Inspired by interfacial self-assembled monolayers, alkanethiols with different alkyl chain lengths are employed to modify the Cu surface for controllably modulating the activity and selectivity in the semi-hydrogenation of alkynols. 1-dodecanethiol-modified Cu nanowires (Cu NWs) exhibit the optimal electrosynthesis of 2-methyl-3-buten-2-ol with excellent specific selectivity (above 93%) in the semi-hydrogenation of 2-methyl-3-butyn-2-ol. Mechanistic studies reveal that the proportion of liquid-like water increases while the proportion of isolated water reduces at the hydrophobic interface. Moreover, we assemble a larger 3 × 100 cm² electrolyzer stack, which can deliver a single-pass alkynol conversion rate of 95% and an excellent alkenol selectivity of 94% at a 15 A stack current. Eventually, the Cu NWs catalyst with thiol treatment is also applicable to the semi-hydrogenation of various unsaturated alkynols.

Chloroprocaine is favored in anesthesia for its effectiveness and safety but is produced through complex, environmentally harmful processes. There is a growing interest in developing greener, more cost-effective methods for producing chloroprocaine. Here, we establish a green and cost-effective photoelectrochemical (PEC) approach for synthesizing chloroprocaine from procaine using a WO₃ photoanode with oxygen vacancies. Achieving a high yield of 18.8 μmol cm⁻² h⁻¹ with selectivity of 92% and FE of 99%, this method leverages a free radical chain mechanism initiated by Cl· radicals formed over the WO₃. The incorporation of oxygen vacancies enhances light absorption and charge separation, facilitating Cl· and Cl₂ generation critical for its synthesis. We successfully produced 37.4 mg of chloroprocaine and 5.4 mL of hydrogen using a self-powered PEC device, demonstrating the method effectiveness and environmental friendliness for chloroprocaine synthesis and hydrogen production.

Mixing can vary based on the scale at which the system is observed, and a mixing index that can capture the features at different length scales is desirable. In this article, we analyze the scale dependence of the mixing indices developed for Eulerian multiphase models. Relevant length scales are distinguished by filtering solid fraction fields. The scale-dependence study is first done on manufactured fields of solid fraction to assess the performance of the mixing indices. The study is extended to a two-dimensional CFD simulation of the segregation of a bidisperse gas–solid mixture. The local mixing index performs well in capturing the spatial variation of mixing at different scales. The scale dependence of two global mixing indices is considered in the study, where the state of mixing is defined based on statistical measures. We demonstrate that the choice of measures influences the sensitivity of mixing indices to mixing at different scales.

Although annular centrifugal contactors (ACCs) have been widely applied, their complex operation and its dependence on operational variables are still not fully quantified. That hinders their acceptance and precludes revealing their potential. Intuitive techniques to informedly operate ACCs while maximizing their gain are thus demanded. This work introduces a procedure to define the operation window of an extraction task on ACCs, using CINC V 02 variant, that demonstrates the feasible ranges of the operational variables and the maximum possible throughput. The overlapping impacts of the variables are also visualized, thus understood and, to enhance practicality, the procedure is converted into user-interface software. The concept is intended to adapt to any task on ACCs and it is based on merging a hydraulic part focusing on the rotor and a statistical one modeling the annulus mass transfer. Additional insights on ACC's hydraulics and mass transfer dynamics are presented, including the time needed to establish the steady state.

Accurate detection and analysis of bubble size and shape in bubbly flow are critical to understanding mass and heat transfer processes. Convolutional neural networks have limitations in different bubble images due to their dependence on large amounts of labeled data. A new foundational Segment Anything Model (SAM) recently attracts lots of attention for its zero-shot segmentation performance. Herein, we developed a novel image processing method named bubSAM, which achieves efficient and accurate bubble segmentation and shape reconstruction based on SAM. The segmentation performance of bubSAM is 30% higher than that of SAM, and its accuracy reaches 90% under different bubbly flow conditions. The accuracy of bubble shape reconstruction (BSR) algorithm in bubSAM is about 30% higher than that of typical ellipse fitting method, thus better restoring the geometric shape of bubbles. BubSAM can provide great support for understanding gas–liquid multiphase flow and design of industrial multiphase reactors.