REVIEWS IN CHEMICAL ENGINEERING化学工程评论
REVIEWS IN CHEMICAL ENGINEERING(英文缩写 REV CHEM ENG),ISSN 0167-8299,eISSN 2191-0235,中文译名:化学工程评论 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 | 8.742 | Q1 |
| 2022 | 4.700 | Q2 |
| 2023 | 4.900 | Q1 |
| 2024 | 6.600 | Q1 |
| 2025 | 4.300 | Q2 |
REVIEWS IN CHEMICAL ENGINEERING 最新收录文献
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1. Trends and perspectives in deterministic MINLP optimization for integrated planning, scheduling, control, and design of chemical processes.
PMID:日期:2025-03-26Mixed integer nonlinear programming (MINLP) in chemical engineering originated as a tool for solving optimal process synthesis and design problems. Since then, the application of MINLP has expanded to encompass control and operational decisions that are in line with the arising challenges faced by the industry, e.g., sustainability, competitive markets, and volatile supply chain environments. Nowadays, process plants are transitioning from traditional manufacturing practices to automated solutions able to integrate decision-making within manufacturing enterprises. This paradigm shift aims to increase profits, optimize resource utilization efficiency, promote long-term sustainability, minimize waste, and enhance responsiveness under uncertainties and perturbations. Accordingly, the development of reliable, computationally efficient, and robust MINLP algorithms capable of simultaneously handling process design, planning, scheduling, or control decisions are crucial to achieving Industry 4.0 integration goals. This work explores potential research opportunities and recent advances toward the development of integrated decision-making frameworks, focusing on their underlying state-of-the-art optimization tools. We provide an overview of emerging deterministic MINLP optimization algorithms for simultaneous decision-making problems. Furthermore, we constructively discuss the versatility and limitations of these optimization tools. We also highlight how novel optimization theories, both within and outside the chemical engineering domain, can be incorporated into advanced MINLP frameworks suitable for process integration.
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2. Machine learning applications for thermochemical and kinetic property prediction.
PMID:日期:2024-11-29Detailed kinetic models play a crucial role in comprehending and enhancing chemical processes. A cornerstone of these models is accurate thermodynamic and kinetic properties, ensuring fundamental insights into the processes they describe. The prediction of these thermochemical and kinetic properties presents an opportunity for machine learning, given the challenges associated with their experimental or quantum chemical determination. This study reviews recent advancements in predicting thermochemical and kinetic properties for gas-phase, liquid-phase, and catalytic processes within kinetic modeling. We assess the state-of-the-art of machine learning in property prediction, focusing on three core aspects: data, representation, and model. Moreover, emphasis is placed on machine learning techniques to efficiently utilize available data, thereby enhancing model performance. Finally, we pinpoint the lack of high-quality data as a key obstacle in applying machine learning to detailed kinetic models. Accordingly, the generation of large new datasets and further development of data-efficient machine learning techniques are identified as pivotal steps in advancing machine learning's role in kinetic modeling.
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3. Hollow microgels and their hybrids: classification, synthesis, characterization, properties and applications.
PMID:日期:2023-05-16Hollow microgels and their hybrids have gained much attention in modern-day research because of their fascinating properties and potential applications. This article gives classification, preparation methods, properties and applications of hollow microgels and their hybrids in modern research. Some of the hollow microgels and their hybrids respond to change in specific environmental stimuli like pH, ionic strength and temperature of the medium. They are called smart polymer hollow microgels and smart hybrid microgels, respectively. This peculiar behaviour makes them a suitable candidate for their use in drug delivery, sensing, catalysis and many other fields. This review represents the current advancement along with future perspectives and suggestions for further research in this particular area.
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4. Interpenetrating polymer network hydrogels as bioactive scaffolds for tissue engineering.
4. 互穿聚合物网络水凝胶作为组织工程的生物活性支架PMID:日期:2020-09-14Tissue engineering, after decades of exciting progress and monumental breakthroughs, has yet to make a significant impact on patient health. It has become apparent that a dearth of biomaterial scaffolds that possess the material properties of human tissue while remaining bioactive and cytocompatible has been partly responsible for this lack of clinical translation. Herein, we propose the development of interpenetrating polymer network hydrogels as materials that can provide cells with an adhesive extracellular matrix-like 3D microenvironment while possessing the mechanical integrity to withstand physiological forces. These hydrogels can be synthesized from biologically-derived or synthetic polymers, the former polymer offering preservation of adhesion, degradability, and microstructure and the latter polymer offering tunability and superior mechanical properties. We review critical advances in the enhancement of mechanical strength, substrate-scale stiffness, electrical conductivity, and degradation in IPN hydrogels intended as bioactive scaffolds in the past five years. We also highlight the exciting incorporation of IPN hydrogels into state-of-the-art tissue engineering technologies, such as organ-on-a-chip and bioprinting platforms. These materials will be critical in the engineering of functional tissue for transplant, disease modeling, and drug screening.