Understanding monoacylation of symmetrical diamines: A kinetic study of acylation reaction of m-phenylenediamine and benzoic anhydride in microreactor

Xu, Qilin; Fan, Huachun; Yao, Hongmiao; Wang, Duoheng; Yu, Hangwei; Chen, Bingbing; Yu, Zhiqun

doi:10.1016/j.cej.2020.125584

Cited by 23 publications

(15 citation statements)

References 38 publications

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“…Generation of experimental data. Since our group has been developing kinetic models extensively over the last five years, we have accumulated a number of kinetic models of flow chemistry reactions that are based on and validated by a large number of experimental results [34][35][36] . Therefore, in this study, we used four of our models as well as one model from the Jensen group 37 to simulate the experimental results.…”

Section: Single-objective Optimization(soo)mentioning

confidence: 99%

FlowBO: A Flow Chemistry Bayesian Optimization Framework Benchmarked by Kinetic Models

Gui-hua

Yang

et al. 2023

Preprint

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The applications of flow chemistry (continuous flow reactions) in the synthesis of pharmaceuticals and fine chemicals require more advanced optimization algorithms to guide laboratory-scale and industry-scale optimization. Although several Bayesian Optimization (BO) frameworks have been developed, they are rarely equipped with state-of-the-art noise-handling acquisition functions and have not been benchmarked by multiple real-world continuous flow kinetic models. In this study, we developed FlowBO for flow chemistry, equipped with the recently-developed MOO algorithm qNEHVI that can better handle experimental noise and make parallel recommendations. Also, five kinetic models built from experimental results, including four series reactions, were used as benchmarks for FlowBO and two other recognized BO frameworks. The results show that FlowBO outperforms in all four series reaction cases with optimization results >99.9% for conversion and selectivity. At the same time, FlowBO offers a range of optimum advantages with a wide choice of temperature, residence time, and reactant concentration, facilitating process optimization for subsequent steps (i.e. separation).

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Section: Single-objective Optimization(soo)mentioning

confidence: 99%

FlowBO: A Flow Chemistry Bayesian Optimization Framework Benchmarked by Kinetic Models

Gui-hua

Yang

et al. 2023

Preprint

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Section: Coupling Reactionsmentioning

confidence: 99%

“…Through the analysis of the accurate and effective kinetic model, the influence of parameters on the main reaction can be well predicted, so as to obtain the reaction conditions corresponding to the best selectivity and yield. [107][108][109] Non-isothermal models using nondimensional numbers (ratio of relative time scales), such as mixing (Da M ), reaction time (Da RM , Da RS ), and heat transfer (Q), can be used to study the effects of parameters and variables such as temperature, molar ratio, Da RM /Da RS , and activation energy. The variable Da RM /Da RS , the ratio of the time scale of the main and side reactions, clearly reflects the influence of the system on selectivity.…”

Section: Meerwein Approachmentioning

confidence: 99%

Revisiting aromatic diazotization and aryl diazonium salts in continuous flow: highlighted research during 2001–2021

et al. 2022

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“…Although the batch process is widely used in organic synthesis due to its versatility and flexible production planning and scheduling, its application in heterogeneous hydrogenation is limited by the poor gas/liquid/solid multiphase mixing performance and the high flammability of H 2 . Our group has been exploring the protocols to achieve the efficient preparation of fine chemicals through continuous-flow technology [ 30 , 31 , 32 , 33 ]. Continuous-flow technology has unique advantages in terms of the process enhancement, such as safer operating environment, excellent mass and heat transfer, accurate control over the process parameters, low back-mixing, solid catalyst immobilization, etc.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Continuous Flow Synthesis for the Preparation of N-Arylhydroxylamines: Via a DMAP-Mediated Hydrogenation Process

et al. 2023

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The selective hydrogenation of nitroarenes to N-arylhydroxylamines is an important synthetic process in the chemical industry. It is commonly accomplished by using heterogeneous catalytic systems that contain inhibitors, such as DMSO. Herein, DMAP has been identified as a unique additive for increasing hydrogenation activity and product selectivity (up to >99%) under mild conditions in the Pt/C-catalyzed process. Continuous-flow technology has been explored as an efficient approach toward achieving the selective hydrogenation of nitroarenes to N-arylhydroxylamines. The present flow protocol was applied for a vast substrate scope and was found to be compatible with a wide range of functional groups, such as electron-donating groups, carbonyl, and various halogens. Further studies were attempted to show that the improvement in the catalytic activity and selectivity benefited from the dual functions of DMAP; namely, the heterolytic H2 cleavage and competitive adsorption.

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Understanding monoacylation of symmetrical diamines: A kinetic study of acylation reaction of m-phenylenediamine and benzoic anhydride in microreactor

Cited by 23 publications

References 38 publications

FlowBO: A Flow Chemistry Bayesian Optimization Framework Benchmarked by Kinetic Models

FlowBO: A Flow Chemistry Bayesian Optimization Framework Benchmarked by Kinetic Models

Revisiting aromatic diazotization and aryl diazonium salts in continuous flow: highlighted research during 2001–2021

An Efficient Continuous Flow Synthesis for the Preparation of N-Arylhydroxylamines: Via a DMAP-Mediated Hydrogenation Process

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