Current Opinion in Chemical Engineering最新文献

英文中文

Toward consistent thermodynamic modeling of CO2 adsorption on Lewatit VPOC 1065 under dry conditions: isotherm variability, data gaps, and model fitting 干燥条件下lewait VPOC 1065上CO2吸附的一致热力学模型：等温线变率，数据缺口和模型拟合

IF 6.8 2区工程技术 Q1 BIOTECHNOLOGY & APPLIED MICROBIOLOGY

Current Opinion in Chemical Engineering

Pub Date : 2026-03-01 Epub Date: 2025-12-03 DOI: 10.1016/j.coche.2025.101201

Mattia Galanti, Rens Teunissen, Ivo Roghair, Martin van Sint Annaland

Accurate thermodynamic characterization of CO₂ adsorption on solid amine-functionalized sorbents is essential for modeling and optimizing direct air capture (DAC) processes. This study presents a systematic compilation and comprehensive analysis of available adsorption isotherm data for Lewatit® VPOC 1065, one of the most studied benchmark sorbents for DAC applications. Six independent literature datasets were critically evaluated, revealing significant discrepancies in reported adsorption capacities and inconsistencies across the temperature and partial pressure ranges, particularly within the low-pressure regime relevant for DAC. Global fitting of a temperature-dependent Toth model was performed to investigate the capability of this widely used single-mechanism approach to capture experimental trends across the entire dataset. The Toth model demonstrated substantial limitations, particularly at low partial pressures, highlighting inadequacies in representing the complex adsorption behavior of the sorbent. Moreover, comparative analyses indicated that these limitations stem partially from inter-author variability, experimental uncertainties at ultra-low pressures, and potential unknown adsorption mechanisms, for example, a physisorption — chemisorption dual site mode. Based on these insights, future research directions were identified.

准确的CO2吸附在固体胺功能化吸附剂上的热力学表征对于模拟和优化直接空气捕获（DAC）过程至关重要。本研究对lewait®VPOC 1065 （DAC应用中研究最多的基准吸附剂之一）的可用吸附等温线数据进行了系统的汇编和综合分析。对六个独立的文献数据集进行了严格评估，揭示了在不同温度和分压范围内，特别是在与DAC相关的低压范围内，报告的吸附能力存在显著差异和不一致性。对温度相关Toth模型进行了全局拟合，以研究这种广泛使用的单机制方法在整个数据集中捕捉实验趋势的能力。Toth模型显示出很大的局限性，特别是在低分压下，突出了在表示吸附剂的复杂吸附行为方面的不足。此外，对比分析表明，这些限制部分源于作者之间的差异、超低压力下实验的不确定性以及潜在的未知吸附机制，例如物理吸附-化学吸附双位点模式。在此基础上，确定了未来的研究方向。

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引用次数: 0

Modeling of industrial multiphase reactors 工业多相反应器的建模

IF 6.8 2区工程技术 Q1 BIOTECHNOLOGY & APPLIED MICROBIOLOGY

Current Opinion in Chemical Engineering

Pub Date : 2026-03-01 Epub Date: 2026-01-13 DOI: 10.1016/j.coche.2025.101223

Jia Wei Chew , Madhava Syamlal , Ronnie Andersson , Ray Cocco

Industrial multiphase reactors remain among the most challenging systems to model due to their complexity, multiscale coupling, and persistent uncertainties in turbulence, interphase transport, and constitutive closures. While traditional approaches combining first-principles physics, empirical correlations, and numerical pragmatism have enabled substantial progress, fundamental limitations persist. This perspective outlines how advances in artificial intelligence (AI), high-performance computing, and, eventually, quantum computing (QC) can steer multiphase modeling toward industry-ready predictive capability with an accuracy unthinkable today.

AI enables more generalizable, physics-constrained closures, while graphics processing units (GPUs) and exascale platforms already enable industry-scale simulations at unprecedented fidelity. Although QC is a longer-term prospect, hybrid quantum–classical approaches offer pathways to address complexities beyond classical limits. These developments promise to transform modeling workflows and engineering practice, with direct implications for scale-up, reliability, sustainability, and cost reduction. We highlight key research priorities, including multiphase-aware turbulence models, AI-assisted closures, hybrid solvers, computing architectures, and rigorous verification, validation, and uncertainty quantification.

工业多相反应器由于其复杂性、多尺度耦合、湍流、相间输运和本构闭等方面的持续不确定性，仍然是最具挑战性的系统建模之一。虽然结合第一性原理物理学、经验相关性和数值实用主义的传统方法取得了实质性进展，但基本的局限性仍然存在。这一观点概述了人工智能（AI）、高性能计算以及最终量子计算（QC）的进步如何将多相建模引导到行业就绪的预测能力，其准确性在今天是不可想象的。人工智能支持更通用的、物理约束的闭包，而图形处理单元（gpu）和百亿亿级平台已经能够以前所未有的保真度实现工业规模的模拟。虽然量子质量控制是一个长期的前景，但混合量子经典方法提供了解决超越经典限制的复杂性的途径。这些发展有望改变建模工作流程和工程实践，并直接影响到规模扩大、可靠性、可持续性和成本降低。我们强调了关键的研究重点，包括多相感知湍流模型、人工智能辅助闭包、混合求解器、计算架构，以及严格的验证、验证和不确定性量化。

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引用次数: 0

Exploring the effect of temperature on the stability and scalability of CO2 electrolysis systems with copper electrodes 探讨温度对铜电极CO2电解系统稳定性和可扩展性的影响

IF 6.8 2区工程技术 Q1 BIOTECHNOLOGY & APPLIED MICROBIOLOGY

Current Opinion in Chemical Engineering

Pub Date : 2026-03-01 Epub Date: 2026-02-05 DOI: 10.1016/j.coche.2026.101231

Kevin Fernández-Caso, Jurriaan Peeters, Ruud Kortlever

The electrochemical reduction of CO₂ using copper-based catalysts represents a promising pathway for producing multi-carbon products from renewable energy. Temperature is a key parameter that not only determines reaction pathways and product selectivity but also strongly affects catalyst stability, electrolyte composition, and membrane integrity. Despite its importance, most studies have primarily focused on catalytic selectivity, often overlooking the thermal and stability aspects recently emphasized in the literature. This perspective underscores the central role of temperature in governing both catalytic performance and the physical and chemical resilience of electrolyzer components under low-temperature (20–80 °C) conditions. These factors become even more critical during scale-up, where heat management and transfer directly influence efficiency and long-term durability, similar to challenges in hydrogen production systems. A comprehensive understanding of thermal effects on both catalytic and non-catalytic elements is therefore essential for optimizing system performance. This work proposes experimental methodologies to evaluate the thermal and chemical stability of catalysts, electrolytes, and membranes, and outlines future research directions aimed at enabling the practical, efficient, and scalable deployment of CO₂ electrolysis through improved thermal design and integrated heat management.

利用铜基催化剂对CO2进行电化学还原，为可再生能源生产多碳产品提供了一条很有前途的途径。温度不仅是决定反应途径和产物选择性的关键参数，而且对催化剂稳定性、电解质组成和膜完整性也有很大影响。尽管它很重要，但大多数研究主要集中在催化选择性上，往往忽略了文献中最近强调的热稳定性方面。这一观点强调了温度在控制低温（20-80°C）条件下电解槽组件的催化性能和物理和化学弹性方面的核心作用。这些因素在大规模生产过程中变得更加重要，热管理和转移直接影响效率和长期耐用性，类似于氢气生产系统的挑战。因此，全面了解催化和非催化元件的热效应对于优化系统性能至关重要。这项工作提出了实验方法来评估催化剂、电解质和膜的热稳定性和化学稳定性，并概述了未来的研究方向，旨在通过改进热设计和集成热管理，实现二氧化碳电解的实用、高效和可扩展部署。

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引用次数: 0

Ionomers impact the performance of gas diffusion electrodes for electrochemical CO2 reduction 离子对电化学CO2还原气体扩散电极性能的影响

IF 6.8 2区工程技术 Q1 BIOTECHNOLOGY & APPLIED MICROBIOLOGY

Current Opinion in Chemical Engineering

Pub Date : 2026-03-01 Epub Date: 2026-02-14 DOI: 10.1016/j.coche.2026.101233

Lydia Weseler , Mohamed A Allam , Laia C Ibañez , Christina Roth , Thomas Turek

Ionomers modulate the microenvironment in gas diffusion electrodes (GDEs) for electrochemical CO₂ reduction and can play a decisive role in the development of active and stable electrodes required for industrial implementation. This is achieved by different mechanisms influencing the adsorption and transport parameters, local values of pH and reactant concentrations as well as the wettability of the pore system. We analyze the existing literature describing the impact of ionomers on GDEs for CO₂ reduction on silver catalysts, yielding carbon monoxide, and on copper catalysts promoting the formation of C₂₊ products. Despite remarkable progress in recent years, it is still challenging to attribute the changes in the electrode performance to the distinct roles of ionomers due to the multitude of processes occurring and the complexity of the electrodes. We propose to complement advanced experimental techniques for operando studies of working electrodes with simulations obtained from model systems with simplified architectures.

离聚体调节气体扩散电极（GDEs）中的微环境，用于电化学CO2还原，并且可以在工业实施所需的活性和稳定电极的开发中发挥决定性作用。这是通过影响吸附和运输参数、局部pH值和反应物浓度以及孔隙系统润湿性的不同机制实现的。我们分析了现有文献中描述的离子对gde的影响，这些gde用于CO2还原，对银催化剂产生一氧化碳，对铜催化剂促进C2+产物的形成。尽管近年来取得了显著的进展，但由于发生的众多过程和电极的复杂性，将电极性能的变化归因于离聚体的不同作用仍然具有挑战性。我们建议补充先进的实验技术，以工作电极的operando研究与模拟得到的模型系统具有简化的架构。

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引用次数: 0

Artificial intelligence and machine learning for process and policy design in the transition towards circular economy systems: advancements and opportunities 在向循环经济系统过渡的过程和政策设计中的人工智能和机器学习：进步和机遇

IF 6.8 2区工程技术 Q1 BIOTECHNOLOGY & APPLIED MICROBIOLOGY

Current Opinion in Chemical Engineering

Pub Date : 2026-03-01 Epub Date: 2025-11-29 DOI: 10.1016/j.coche.2025.101200

Edgar Martín-Hernández , Borja Hernández , Aurora del Carmen Munguia-Lopez , Sidney Omelon

Novel computational techniques raised under the concept of artificial intelligence have vast applications in science and engineering. In this work, we review the most relevant frameworks and applications of artificial intelligence and machine learning oriented to the development of sustainable production and consumption systems using a bottom-up multi-scale approach. Firstly, we address frameworks for molecular and processing unit design and flowsheet design. Secondly, we assess methods proposed for the environmental and social assessment of superstructures. Finally, we also discuss the contributions and applications of artificial intelligence in the development of policies that support the shift of paradigm to the circular economy.

在人工智能概念下提出的新型计算技术在科学和工程中有着广泛的应用。在这项工作中，我们回顾了使用自下而上的多尺度方法开发可持续生产和消费系统的人工智能和机器学习的最相关框架和应用。首先，我们讨论了分子和处理单元设计以及流程设计的框架。其次，我们评估了为上层建筑的环境和社会评估提出的方法。最后，我们还讨论了人工智能在支持范式向循环经济转变的政策制定中的贡献和应用。

引用次数: 0

Anion exchange membrane electrolysis beyond the lab scale: a review on research and industry stacks 实验室规模以外的阴离子交换膜电解：研究和工业进展综述

IF 6.8 2区工程技术 Q1 BIOTECHNOLOGY & APPLIED MICROBIOLOGY

Current Opinion in Chemical Engineering

Pub Date : 2026-03-01 Epub Date: 2026-01-02 DOI: 10.1016/j.coche.2025.101218

Verónica Rodríguez, Celia Gómez-Sacedón, Paula Sánchez, Antonio de Lucas-Consuegra

This review provides a comprehensive overview of the current state-of-the-art in anion exchange membrane (AEM) water electrolysis stacks, bridging advances from both academic research and main supplier companies. On the one hand, academic studies address the key components and operating conditions of AEM stacks, including electrocatalysts for the hydrogen evolution reaction and oxygen evolution reaction, with particular emphasis on catalysts synthesis optimization, scalability, and the transition toward noble-metal-free alternatives. The selection and development of durable AEMs and porous transport layers, as well as parameters such as temperature, electrolyte composition, and concentration, are also examined as key factors that govern stack performance and stability. On the other hand, the principal AEM stacks that are commercially available were also reviewed to compare and evaluate industrial-scale AEM stack operation, thereby contextualizing academic advances within real-world performance.

本文综述了目前阴离子交换膜（AEM）电解技术的最新进展，并结合了学术研究和主要供应商公司的进展。一方面，学术研究涉及AEM堆的关键部件和操作条件，包括析氢反应和析氧反应的电催化剂，特别强调催化剂的合成优化、可扩展性以及向无贵金属替代品的过渡。耐用AEMs和多孔输运层的选择和开发，以及温度、电解质成分和浓度等参数，也被视为影响堆叠性能和稳定性的关键因素。另一方面，还回顾了主要的商用AEM堆栈，以比较和评估工业规模的AEM堆栈操作，从而将学术进展与现实世界的性能相结合。

引用次数: 0

Toward predictive multiscale models for HiGee devices 面向高功率器件的多尺度预测模型

IF 6.8 2区工程技术 Q1 BIOTECHNOLOGY & APPLIED MICROBIOLOGY

Current Opinion in Chemical Engineering

Pub Date : 2026-03-01 Epub Date: 2026-02-05 DOI: 10.1016/j.coche.2026.101230

Bing Wang , Siyuan Chen , Hamed Hoorijani , Qingang Xiong , Kevin M Van Geem , Yi Ouyang

High gravity (HiGee) devices exploit centrifugal fields and compact volumes to intensify transport and reactions. Multiscale modeling advances show how HiGee thins boundary layers, enlarges interfacial areas, and accelerates mixing, heat, and mass transfer, increasing rates in fast reaction systems. A workflow links mechanistic descriptions, computational fluid dynamics reactor simulations, and process-level simulations while identifying gaps at interfaces where turbulence, mass transfer, and kinetics dominate. Priorities include rotation–aware turbulence closures, turbulence–chemistry interaction models for gas–liquid flows, and subgrid or molecular descriptions for reduced-order and process models. Advances drawing on combustion and aerothermodynamics will enable predictive design and scale-up of HiGee devices.

高重力（HiGee）装置利用离心场和紧凑的体积来加强运输和反应。多尺度模拟的进步显示了high如何使边界层变薄，扩大界面面积，加速混合、热和传质，增加快速反应系统的速率。工作流程将机械描述、计算流体动力学反应堆模拟和过程级模拟联系起来，同时确定湍流、传质和动力学占主导地位的界面间隙。优先事项包括旋转感知湍流闭包，湍流-化学相互作用模型的气体-液体流动，和亚网格或分子描述的降阶和过程模型。燃烧和空气热力学的进步将使highee设备的预测设计和规模扩大成为可能。

引用次数: 0

Artificial intelligence at scale in the chemical industry: from legacy to leadership 化学工业的大规模人工智能：从遗产到领导力

IF 6.8 2区工程技术 Q1 BIOTECHNOLOGY & APPLIED MICROBIOLOGY

Current Opinion in Chemical Engineering

Pub Date : 2026-03-01 Epub Date: 2026-02-05 DOI: 10.1016/j.coche.2026.101229

Leo Chiang , Dan Christiansen , Matthew R Malloure , Luis Briceno-Mena , Sun Hye Kim

Artificial intelligence (AI) is rapidly transforming the chemical industry, offering solutions to longstanding challenges in optimization, process monitoring and control, and product development. This article provides new insights by explicitly connecting recent technical advances in AI with organizational strategies, offering an integrated perspective on how these elements collectively drive transformation in the chemical industry. While the article discusses the potential of large language models, it places greater emphasis on the critical role of data availability, policies, and broader AI adoption challenges. The article concludes by listing possible improvements achievable through AI and emphasizing the importance of leadership and collaborative initiatives between industry, academia, and government.

人工智能（AI）正在迅速改变化工行业，为优化、过程监测和控制以及产品开发方面的长期挑战提供解决方案。本文通过明确地将人工智能的最新技术进步与组织战略联系起来，提供了新的见解，为这些要素如何共同推动化工行业的转型提供了一个综合的视角。虽然本文讨论了大型语言模型的潜力，但它更加强调了数据可用性、策略和更广泛的人工智能采用挑战的关键作用。文章最后列举了通过人工智能可能实现的改进，并强调了行业、学术界和政府之间领导力和协作倡议的重要性。

引用次数: 0

Process systems engineering: a key enabler of adsorption-based direct air capture 过程系统工程：基于吸附的直接空气捕获的关键推动者

IF 6.8 2区工程技术 Q1 BIOTECHNOLOGY & APPLIED MICROBIOLOGY

Current Opinion in Chemical Engineering

Pub Date : 2026-03-01 Epub Date: 2025-12-08 DOI: 10.1016/j.coche.2025.101202

Hannah E Holmes , Jinsu Kim , Matthew J Realff

Direct air capture (DAC) is a promising technology for removing carbon dioxide from the atmosphere. However, its widespread deployment is challenged by high energy requirements, water management, sorbent degradation, integration with variable renewable energy sources, and fluctuating climatic conditions. The design, operation, and control of solid adsorption-DAC systems is a complex problem that requires holistic engineering of the adsorbent material, adsorption system, DAC process, and upstream and downstream operations. In this review, we show how Process Systems Engineering (PSE) can address this multiscale system design challenge by highlighting recent PSE advancements in three areas: (i) process-informed sorbent selection, (ii) heat integration and water management, and (iii) technological viability assessments. We summarize the progress that PSE has made in connecting sorbent properties to system design and optimization, outlining the key metrics and workflow needed to advance from sorbent to comprehensive system evaluation. We highlight effective energy and resource management strategies, such as DAC integration with heat and power generation, the use of renewable electricity or underutilized sources from existing infrastructure, and combined heat and water integration. For viability assessments, we emphasize comprehensive approaches that integrate technoeconomic and life cycle assessments with sorbent degradation, geospatial analysis, and scaling predictions. We conclude with future PSE directions that will be important for scaling adsorption-DAC, including process strategies for variable energy and climate conditions, predictive sorbent degradation models, and optimized scheduling to balance energy and capital.

直接空气捕获（DAC）是一种很有前途的从大气中去除二氧化碳的技术。然而，它的广泛应用受到高能量需求、水管理、吸附剂降解、与可变可再生能源的结合以及波动的气候条件的挑战。固体吸附-DAC系统的设计、操作和控制是一个复杂的问题，需要对吸附剂材料、吸附系统、DAC过程以及上下游操作进行整体工程设计。在这篇综述中，我们通过强调过程系统工程（PSE）在三个领域的最新进展，展示了过程系统工程（PSE）如何解决这一多尺度系统设计挑战：(i)过程信息吸附剂选择，（ii）热集成和水管理，以及（iii）技术可行性评估。我们总结了PSE在将吸附剂性能与系统设计和优化联系起来方面所取得的进展，概述了从吸附剂到综合系统评估所需的关键指标和工作流程。我们强调有效的能源和资源管理战略，如DAC与热能和发电相结合，利用可再生电力或现有基础设施中未充分利用的资源，以及热水结合利用。对于可行性评估，我们强调综合方法，将技术经济和生命周期评估与吸附剂降解、地理空间分析和尺度预测相结合。最后，我们总结了未来的PSE方向，这些方向对扩大吸附- dac至关重要，包括可变能源和气候条件下的工艺策略，预测吸附剂降解模型，以及平衡能源和资本的优化调度。

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引用次数: 0

Role of artificial intelligence in the chemical industry transition to a sustainable, circular, and net-zero future 人工智能在化学工业向可持续、循环和净零未来转型中的作用

IF 6.8 2区工程技术 Q1 BIOTECHNOLOGY & APPLIED MICROBIOLOGY

Current Opinion in Chemical Engineering

Pub Date : 2026-03-01 Epub Date: 2026-02-19 DOI: 10.1016/j.coche.2026.101234

Avan Kumar , Sunghoon Kim , Bhavik R Bakshi

This review focuses on the role of artificial intelligence (AI) in the transformation of the chemical industry toward a sustainable, circular, and net-zero emissions future. Key areas of contribution include sustainability assessment tools, materials and molecular design, development of circular reaction networks, and modeling of spatial and intermittent systems. We also identify three overarching priorities for improving AI’s role: developing trustworthy AI models for stakeholders, bridging the gap between academic research and industrial practice, and creating open-access platforms for sustainability-related data and trained models. The fundamental requirements of sustainable systems, preventing burden shifting and respecting nature’s limits, must also be satisfied.

本文重点介绍了人工智能（AI）在化学工业向可持续、循环和净零排放未来转型中的作用。主要贡献领域包括可持续性评估工具、材料和分子设计、循环反应网络的发展以及空间和间歇性系统的建模。我们还确定了提高人工智能作用的三个首要优先事项：为利益相关者开发值得信赖的人工智能模型，弥合学术研究与工业实践之间的差距，以及为与可持续性相关的数据和训练模型创建开放获取平台。还必须满足可持续系统的基本要求，即防止负担转移和尊重自然的极限。

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