Comprehensive Compilation on Esterification Reactions and Predicting Reaction Kinetics and Equilibrium Using PC-SAFT

IF 4.3 Q2 ENGINEERING, CHEMICAL

ACS Engineering Au Pub Date : 2025-04-02 DOI:10.1021/acsengineeringau.5c0000210.1021/acsengineeringau.5c00002

Iman Bahrabadi Jovein, Sindi Baco, Gabriele Sadowski, Ferruccio Doghieri, Marco Giacinti Baschetti, Gangqiang Yu, Sébastien Leveneur, Julien Legros and Christoph Held*,

{"title":"Comprehensive Compilation on Esterification Reactions and Predicting Reaction Kinetics and Equilibrium Using PC-SAFT","authors":"Iman Bahrabadi Jovein, Sindi Baco, Gabriele Sadowski, Ferruccio Doghieri, Marco Giacinti Baschetti, Gangqiang Yu, Sébastien Leveneur, Julien Legros and Christoph Held*, ","doi":"10.1021/acsengineeringau.5c0000210.1021/acsengineeringau.5c00002","DOIUrl":null,"url":null,"abstract":"Knowledge of the equilibrium and kinetics of reactions is critical for optimizing industrial chemical processes. In this study, the equilibrium and kinetics of esterification reactions were systematically investigated for a series of carboxylic acids (acetic acid, propionic acid, formic acid, and levulinic acid) and alcohols (methanol, ethanol, n-propanol, and n-butanol), giving a total set of 16 esterification reactions at different temperatures. First, formation properties of reactants and products were utilized to calculate the reaction equilibrium constants Keq of these reactions. These were compared with Keq values obtained by one equilibrium experiment coupled to PC-SAFT predictions. The comparison yielded outstanding agreement between PC-SAFT-assisted Keq values and the formation-property-derived Keq values. The Keq values were then used in activity-based kinetic expressions, and the predicted reaction kinetics were validated against experimental data to demonstrate the model’s accuracy. The deviations between PC-SAFT and experimental data were AAD% (Keq) = 1.66% for the reaction equilibrium and AAD% (r) = 13.8% for the kinetic curves. The Arrhenius equation and van ’t Hoff equation were applied to depict the temperature dependence of reaction rate constants and of Keq for each esterification reaction in a range of 303.15–423.15 K. Thus, activity-based thermodynamic standard properties are provided in this work, guiding the optimization of esterification reactions in a broad range of conditions.","PeriodicalId":29804,"journal":{"name":"ACS Engineering Au","volume":"5 3","pages":"234–246 234–246"},"PeriodicalIF":4.3000,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acsengineeringau.5c00002","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Engineering Au","FirstCategoryId":"1085","ListUrlMain":"https://pubs.acs.org/doi/10.1021/acsengineeringau.5c00002","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, CHEMICAL","Score":null,"Total":0}

引用次数: 0

Abstract

Knowledge of the equilibrium and kinetics of reactions is critical for optimizing industrial chemical processes. In this study, the equilibrium and kinetics of esterification reactions were systematically investigated for a series of carboxylic acids (acetic acid, propionic acid, formic acid, and levulinic acid) and alcohols (methanol, ethanol, n-propanol, and n-butanol), giving a total set of 16 esterification reactions at different temperatures. First, formation properties of reactants and products were utilized to calculate the reaction equilibrium constants K_eq of these reactions. These were compared with K_eq values obtained by one equilibrium experiment coupled to PC-SAFT predictions. The comparison yielded outstanding agreement between PC-SAFT-assisted K_eq values and the formation-property-derived K_eq values. The K_eq values were then used in activity-based kinetic expressions, and the predicted reaction kinetics were validated against experimental data to demonstrate the model’s accuracy. The deviations between PC-SAFT and experimental data were AAD% (K_eq) = 1.66% for the reaction equilibrium and AAD% (r) = 13.8% for the kinetic curves. The Arrhenius equation and van ’t Hoff equation were applied to depict the temperature dependence of reaction rate constants and of K_eq for each esterification reaction in a range of 303.15–423.15 K. Thus, activity-based thermodynamic standard properties are provided in this work, guiding the optimization of esterification reactions in a broad range of conditions.

查看原文本刊更多论文

用PC-SAFT预测酯化反应动力学和平衡

平衡和反应动力学的知识是优化工业化学过程的关键。本研究系统地研究了一系列羧酸（乙酸、丙酸、甲酸和乙酰丙酸）和醇（甲醇、乙醇、正丙醇和正丁醇）在不同温度下的酯化反应的平衡和动力学，得到了16个酯化反应。首先，利用反应物和生成物的生成性质，计算了这些反应的反应平衡常数Keq。这些结果与一个平衡实验与PC-SAFT预测相结合得到的Keq值进行了比较。通过比较，pc - saft辅助的Keq值与地层属性推导的Keq值非常吻合。然后将Keq值用于基于活度的动力学表达式，并根据实验数据验证预测的反应动力学，以证明模型的准确性。PC-SAFT与实验数据的偏差为：反应平衡曲线的AAD% (Keq) = 1.66%，动力学曲线的AAD% (r) = 13.8%。在303.15 ~ 423.15 K范围内，用Arrhenius方程和van’t Hoff方程描述了各酯化反应的反应速率常数和Keq的温度依赖性。因此，本工作提供了基于活度的热力学标准性质，指导在广泛条件下酯化反应的优化。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

ACS Engineering Au 化学工程技术-

自引率

0.00%

发文量

期刊介绍：）ACS Engineering Au is an open access journal that reports significant advances in chemical engineering applied chemistry and energy covering fundamentals processes and products. The journal's broad scope includes experimental theoretical mathematical computational chemical and physical research from academic and industrial settings. Short letters comprehensive articles reviews and perspectives are welcome on topics that include:Fundamental research in such areas as thermodynamics transport phenomena (flow mixing mass & heat transfer) chemical reaction kinetics and engineering catalysis separations interfacial phenomena and materialsProcess design development and intensification (e.g. process technologies for chemicals and materials synthesis and design methods process intensification multiphase reactors scale-up systems analysis process control data correlation schemes modeling machine learning Artificial Intelligence)Product research and development involving chemical and engineering aspects (e.g. catalysts plastics elastomers fibers adhesives coatings paper membranes lubricants ceramics aerosols fluidic devices intensified process equipment)Energy and fuels (e.g. pre-treatment processing and utilization of renewable energy resources; processing and utilization of fuels; properties and structure or molecular composition of both raw fuels and refined products; fuel cells hydrogen batteries; photochemical fuel and energy production; decarbonization; electrification; microwave; cavitation)Measurement techniques computational models and data on thermo-physical thermodynamic and transport properties of materials and phase equilibrium behaviorNew methods models and tools (e.g. real-time data analytics multi-scale models physics informed machine learning models machine learning enhanced physics-based models soft sensors high-performance computing)