CHEMICAL ENGINEERING SCIENCE化学工程科学
CHEMICAL ENGINEERING SCIENCE(英文缩写 CHEM ENG SCI),ISSN 0009-2509,eISSN 1873-4405,中文译名:化学工程科学 是一本学术期刊。本页汇总该期刊的最新影响因子、分区信息以及最新收录于 PubMed 的文献,帮助您快速了解期刊全貌。
发文量统计区间：2025-11-01 至 2026-10-31，按本站收录文献的发表日期统计。
期刊介绍
历年影响因子趋势
| JCR 数据年份 | 影响因子 | JCR 分区 |
|---|---|---|
| 2021 | 4.889 | Q2 |
| 2022 | 4.700 | Q2 |
| 2023 | 4.100 | Q2 |
| 2024 | 4.300 | Q2 |
| 2025 | 5.100 | Q2 |
CHEMICAL ENGINEERING SCIENCE 最新收录文献
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1. FMEnets: Flow, material, and energy networks for non-ideal plug flow reactor design.
1. FMEnets：面向非理想平推流反应器设计的流动、物料与能量网络PMID:日期:2025-08-29[中文摘要] 我们提出了FMEnets，一种用于非理想平推流反应器设计与分析的物理信息机器学习框架。FMEnets将基本控制方程（流体流动的Navier-Stokes方程、反应组分传输的物料平衡方程以及温度分布的能量平衡方程）集成到一个统一的多尺度网络模型中。该框架由三个相互关联的子网络组成，各子网络配备独立的优化器，能够同时支持正向和逆向问题求解。在正向模式下，FMEnets仅利用入口和出口信息即可预测速度、压力、组分浓度和温度分布。在逆向模式下，FMEnets利用稀疏的多停留时间测量数据同时推断未知动力学参数和状态。FMEnets可以以FME-PINNs形式实现（采用传统多层感知机），也可以以FME-KANs形式实现（基于Kolmogorov-Arnold网络）。全面的消融研究凸显了FMEnets架构在实现准确预测中的关键作用。具体而言，FME-KANs对噪声的鲁棒性强于FME-PINNs，尽管两种表示在无噪声条件下的精度和速度相当。所提出的框架应用于三组不同的反应场景，并与有限元模拟进行了比较。FMEnets有效捕捉了复杂的相互作用，未知动力学参数的相对误差小于2.5%。该新型网络框架不仅为反应器设计与优化提供了一种计算高效的替代方案，还为整合经验关联式、有限且含噪的实验数据以及基本物理方程以指导反应器设计开辟了新途径。
[英文摘要] We propose FMEnets, a physics-informed machine learning framework for the design and analysis of non-ideal plug flow reactors. FMEnets integrates the fundamental governing equations (Navier-Stokes for fluid flow, material balance for reactive species transport, and energy balance for temperature distribution) into a unified multi-scale network model. The framework is composed of three interconnected sub-networks with independent optimizers that enable both forward and inverse problem-solving. In the forward mode, FMEnets predicts velocity, pressure, species concentrations, and temperature profiles using only inlet and outlet information. In the inverse mode, FMEnets utilizes sparse multi-residence-time measurements to simultaneously infer unknown kinetic parameters and states. FMEnets can be implemented either as FME-PINNs, which employ conventional multilayer perceptrons, or as FME-KANs, based on Kolmogorov-Arnold Networks. Comprehensive ablation studies highlight the critical role of the FMEnets architecture in achieving accurate predictions. Specifically, FME-KANs are more robust to noise than FME-PINNs, although both representations are comparable in accuracy and speed in noise-free conditions. The proposed framework is applied to three different sets of reaction scenarios and is compared with finite element simulations. FMEnets effectively captures the complex interactions, achieving relative errors less than 2.5 % for the unknown kinetic parameters. The new network framework not only provides a computationally efficient alternative for reactor design and optimization, but also opens new avenues for integrating empirical correlations, limited and noisy experimental data, and fundamental physical equations to guide reactor design.
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2. Digital design of an integrated purification system for continuous pharmaceutical manufacturing.
PMID:日期:2023-12-09In this work dynamic models of the continuous crystallization, filtration, deliquoring, washing, and drying steps are introduced, which are developed in the open-source pharmaceutical modeling tool PharmaPy. These models enable the simulation and digital design of an integrated continuous two-stage crystallization and filtration-drying carousel system. The carousel offers an intensified process that can manufacture products with tailored properties through optimal design and control. Results show that improved crystallization design enhances overall process efficiency by improving critical material attributes of the crystal slurry for downstream filtration and drying operations. The digital design of the integrated process achieves enhanced productivity while satisfying multiple design and product quality constraints. Additionally, the impact of model uncertainty on the optimal operating conditions is investigated. The findings demonstrate the systematic process development potential of PharmaPy, providing improved process understanding, design space identification, and optimized robust operation.
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3. Machine Learning Methods for Endocrine Disrupting Potential Identification Based on Single-Cell Data.
PMID:日期:2023-07-18Humans are continuously exposed to a variety of toxicants and chemicals which is exacerbated during and after environmental catastrophes such as floods, earthquakes, and hurricanes. The hazardous chemical mixtures generated during these events threaten the health and safety of humans and other living organisms. This necessitates the development of rapid decision-making tools to facilitate mitigating the adverse effects of exposure on the key modulators of the endocrine system, such as the estrogen receptor alpha (ERα), for example. The mechanistic stages of the estrogenic transcriptional activity can be measured with high content/high throughput microscopy-based biosensor assays at the single-cell level, which generates millions of object-based minable data points. By combining computational modeling and experimental analysis, we built a highly accurate data-driven classification framework to assess the endocrine disrupting potential of environmental compounds. The effects of these compounds on the ERα pathway are predicted as being receptor agonists or antagonists using the principal component analysis (PCA) projections of high throughput, high content image analysis descriptors. The framework also combines rigorous preprocessing steps and nonlinear machine learning algorithms, such as the Support Vector Machines and Random Forest classifiers, to develop highly accurate mathematical representations of the separation between ERα agonists and antagonists. The results show that Support Vector Machines classify the unseen chemicals correctly with more than 96% accuracy using the proposed framework, where the preprocessing and the PCA steps play a key role in suppressing experimental noise and unraveling hidden patterns in the dataset.
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4. Reaction kinetics determination and uncertainty analysis for the synthesis of the cancer drug lomustine.
4. 抗癌药物洛莫司汀合成的反应动力学测定及不确定性分析PMID:日期:2023-07-06Fast and reliable model development frameworks are required to support current trends in modernization of pharmaceutical processing, promoting the use of digital platforms to assist process design and operation. In this work, we use a parameter estimation framework built into the PharmaPy library to determine rate parameters and uncertainty regions of different mechanistic and semi-empirical kinetic expressions for the synthesis of the drug lomustine. The parameter estimation procedure was complemented by identifiability analysis, resulting in simplified reaction mechanisms. Comparison of parameters and their uncertainty in process design was demonstrated through design space analysis, showing important differences in model prediction and the extent of their corresponding design spaces. The results of this work can serve to analyze lomustine manufacturing processes that include separation and isolation steps, where parametric sensitivity is expected to propagate along the manufacturing line and impact process feasible operation, and attainment of critical quality attributes of the product.
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5. Optimization of loop-mediated isothermal amplification (LAMP) assay for robust visualization in SARS-CoV-2 and emerging variants diagnosis.
5. 环介导等温扩增（LAMP）检测方法的优化，以实现严重急性呼吸综合征冠状病毒2型及新出现变异株诊断中的稳健可视化检测PMID:日期:2022-01-13Loop-mediated isothermal amplification (LAMP) is widely used in detection of pathogenic microorganisms including SARS-CoV-2. However, the performance of LAMP assay needs further exploration in the emerging SARS-CoV-2 variants test. Here, we design serials of primers and select an optimal set for LAMP-based on SARS-CoV-2 gene for a robust and visual assay in SARS-CoV-2 diagnosis. The limit of detectable template reaches 10 copies of gene per 25 μL reaction at isothermal 58℃ within 40 min. Importantly, the primers for LAMP assay locate at 12 to 213 nt of gene, a highly conservative region, which serves as a compatible test in emerging SARS-CoV-2 variants. Comparison to a commercial qPCR assay, this LAMP assay exerts the high viability in diagnosis of 41 clinical samples. Our study optimizes an advantageous LAMP assay for colorimetric detection of SARS-CoV-2 and emerging variants, which is hopeful to be a promising test in COVID-19 surveillance.
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6. Mathematical modeling and digital design of an intensified filtration-washing-drying unit for pharmaceutical continuous manufacturing.
6. 用于制药连续生产的强化过滤-洗涤-干燥装置数学建模与数字化设计PMID:日期:2021-11-23This paper introduces a comprehensive mathematical model of a novel integrated filter-dryer carousel system, designed for continuously filtering, washing and drying a slurry stream into a crystals cake. The digital twin includes models for dead-end filtration, cake washing and convective cake drying, based on dynamic multi-component mass, energy and momentum balances. For set of feed conditions and control inputs, the model allows tracking the solvents and impurities content in the cake (critical quality attributes, CQAs) throughout the whole process. The model parameters were identified for the isolation of paracetamol from a multi-component slurry, containing a non-volatile impurity. The calibrated model was used for identifying the probabilistic design space and maximum throughput for the process, expressing the combinations of the carousel feed conditions and control inputs for which the probability of meeting the target CQAs is acceptable.
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7. Synthesis of a novel anti-fog and high-transparent coating with high wear resistance inspired by dry rice fields.
PMID:日期:2021-05-08During the outbreak of COVID-19, the fogging of goggles was a fatal problem for doctors. At present, there are many ways to prevent fogging by adjusting surface wettability. However, the mechanical properties of most super-hydrophilic antifogging coatings are poor, easy to lose their antifogging properties when encountering fingers or cloth friction. To address this issue, the Konjac Glucomannan was cross-linked with water-soluble silicone fluid to form a binder, then being combined with the modified Ecokimera to prepare an eco-friendly super-hydrophilic coating that possessed excellent super-hydrophilicity, and the water contact angle (WCA) was 2.51 ± 1°. In addition, the WCA is still about 5° after 180 times of antifogging tests. The friction resistance of the coating was as high as 24 m. Moreover, the light transmittance was only reduced by 3%. Besides, they also had the excellent self-cleaning property. After being stored in the laboratory environment for 90 days, it can still maintain the hydrophilic property (WCA is about 5°). In general, the method proposed in this study is low-cost and eco-friendly, and can be widely used in the preparation of antifogging coatings.
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8. A comprehensive spatial-temporal infection model.
PMID:日期:2021-01-06Motivated by analogies between the spread of infections and of chemical processes, we develop a model that accounts for infection and transport where infected populations correspond to chemical species. Areal densities emerge as the key variables, thus capturing the effect of spatial density. We derive expressions for the kinetics of the infection rates, and for the important parameter , that include areal density and its spatial distribution. We present results for a batch reactor, the chemical process equivalent of the SIR model, where we examine how the dependence of on process extent, the initial density of infected individuals, and fluctuations in population densities effect the progression of the disease. We then consider spatially distributed systems. Diffusion generates traveling waves that propagate at a constant speed, proportional to the square root of the diffusivity and . Preliminary analysis shows a similar behavior for the effect of stochastic advection.
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9. COVID-19: Mechanistic model calibration subject to active and varying non-pharmaceutical interventions.
PMID:日期:2020-11-26Mathematical models are useful in epidemiology to understand COVID-19 contagion dynamics. We aim to demonstrate the effectiveness of parameter regression methods to calibrate an established epidemiological model describing infection rates subject to active, varying non-pharmaceutical interventions (NPIs). We assess the potential of established chemical engineering modelling principles and practice applied to epidemiological systems. We exploit the sophisticated parameter regression functionality of a commercial chemical engineering simulator with piecewise continuous integration, event and discontinuity management. We develop a strategy for calibrating and validating a model. Our results using historic data from 4 countries provide insights into on-going disease suppression measures, while visualisation of reported data provides up-to-date condition monitoring of the pandemic status. The effective reproduction number response to NPIs is non-linear with variable response rate, magnitude and direction. Our purpose is developing a methodology without presenting a fully optimised model, or attempting to predict future course of the COVID-19 pandemic.
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10. Use of semibatch reactor technology for the investigation of reaction mechanism and kinetics: Heterogeneously catalyzed epoxidation of fatty acid esters.
PMID:日期:2020-10-14Heterogeneously catalyzed epoxidation of vegetable oils by hydrogen peroxide represents a greener route for the production of epoxides and a thermally safer reaction route compared to the classical Prileschajew epoxidation approach. The epoxidation kinetics of the heterogeneous system formed by aluminium oxide catalyst, hydrogen peroxide and methyl oleate as a model compound was studied with semibatch experiments in laboratory scale. It was found that semibatch operation improved the performance significantly compared to classical batch operation, a low and constant volumetric flowrate of hydrogen peroxide increased the final oxirane yield considerably. A semibatch reactor model and a kinetic model were developed, featuring the reaction temperature, the reactant molar ratio, the catalyst loading and the mass flow rate as the most significant experimental parameters. The mathematical model was able to well describe the experimental data. The approach can be applied to other liquid-solid catalyst systems in future in order to optimize the semibatch operation policy for complex reaction systems.