Bayesian based reaction optimization for complex continuous gas–liquid–solid reactions

Liang, Runzhe; Duan, Xiaonan; Zhang, Jisong; Yuan, Zhihong

doi:10.1039/d1re00397f

Cited by 19 publications

(15 citation statements)

References 45 publications

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“…For complex continuous gas–liquid–solid reaction systems, Liang et al developed a continuous reaction optimization platform based on Nelder–Mead simplex method and pure BO algorithm. 40 In the hydrogenation reaction of nitrobenzene, 3,4-dichloronitrobenzene, and 5-nitro-isoquinoline, BO outperforms only one variable at a time and obtains higher yields and less experimental cost.…”

Section: Intelligent Algorithms For Self-optimizationmentioning

confidence: 99%

A Review of the Applications of Artificial Intelligence in the Process Analysis and Optimization of Chemical Products

Shen,

2023

Pharmaceutical Fronts

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Continuous flow chemistry is an enabling technology for automated synthesis. Artificial intelligence (AI) is a powerful tool in various areas of automated synthesis in flow chemistry, including process analysis technology and synthesis reaction optimization. The merger of continuous flow chemistry and AI drives chemical production in a more intelligent, automated, and flexible direction. This review discusses the recent application of AI in analyzing and optimizing chemical products produced by continuous flow chemistry with the most innovative equipment and techniques.

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Section: Intelligent Algorithms For Self-optimizationmentioning

confidence: 99%

A Review of the Applications of Artificial Intelligence in the Process Analysis and Optimization of Chemical Products

Shen,

2023

Pharmaceutical Fronts

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“…The same selfoptimization platform was further extended to optimize the complex pharmaceutical reaction processes involving liquid− liquid separation. 329 More recently, Liang et al 321 presented a typical example of how to apply the Bayesian method to optimize reaction conditions for complex continuous gas− liquid−solid flow systems. Comparison with the conventional approach revealed that their developed optimization method can significantly boost both yields and prediction efficiency.…”

Section: Coupling Ml/dl With Experimentsmentioning

confidence: 99%

“…Here, the optimization process does not involve other physical models, and the data sets are collected from experiments. So far, ML/DL has been applied as an optimizer for different reaction systems, and notably growing attention has been paid to flow chemistry systems − and renewable energy systems such as hydrothermal carbonization and hydrothermal liquefaction of algae, , etc. These studies may be further divided into two categories: with and without physical knowledge.…”

Section: Current Status and Challengesmentioning

confidence: 99%

Review of Machine Learning for Hydrodynamics, Transport, and Reactions in Multiphase Flows and Reactors

Zhu

Chen

Ouyang

et al. 2022

Ind. Eng. Chem. Res.

120

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Artificial intelligence (AI), machine learning (ML), and data science are leading to a promising transformative paradigm. ML, especially deep learning and physics-informed ML, is a valuable toolkit that complements incomplete domain-specific knowledge in conventional experimental and computational methods. ML can provide flexible techniques to facilitate the conceptual development of new robust predictive models for multiphase flows and reactors by finding hidden pattern/information/mechanism in a data set. Due to such emergence, we thereby comprehensively survey, explore, analyze, and discuss key advancements of recent ML applications to hydrodynamics, heat and mass transfer, and reactions in single-phase and multiphase flow systems from different aspects: (1) development of multiphase closure models of drag force, turbulence stresses and heat/mass transfer to improve the accuracy and efficiency of typical CFD simulations; (2) image reconstruction, regime identification, key parameter predictions, and optimization of multiphase flow and transport fields; (3) reaction kinetics modeling (e.g., predictions of reaction networks, kinetic parameters, and species production) and reaction condition optimization. These sections also discuss and analyze the key advantages and weakness of ML for solving the problems in the domain of multiphase flows and reactors. Finally, we summarize the under-solving challenges and opportunities in order to identify future directions that would be useful for the research community. Future development and study of multiphase flows and reactors are envisaged to be accelerated by ML and data science.

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“…Then, the next candidate to be evaluated is proposed via an acquisition function. BO has been applied to various problems in chemistry, [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34] including reaction condition optimization in organic synthesis. [35,36] On the other hand, Gibbs energy barriers computed by quantum chemical calculations have been widely used to rationalize [37][38][39][40][41][42][43][44][45][46][47] or even predict [48][49][50][51][52] the reactivity and selectivity of organic reactions.…”

Section: Introductionmentioning

confidence: 99%

On accelerating reaction optimization using computational Gibbs energy barriers: A numerical consideration utilizing a computational dataset

Okada

Maeda

2022

Preprint

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Reaction optimization is a time- and resource-consuming step in organic synthesis. Recent advances in chemo- and materials-informatics provide systematic and efficient procedures utilizing tools like Bayesian optimization (BO). This study explores the possibility of reducing the required experiments further utilizing computational Gibbs energy barriers. To thoroughly validate the impact of using computational Gibbs energy barriers in BO-assisted reaction optimization, this study employs a computational Gibbs energy barrier dataset in the literature and performs an extensive numerical investigation virtually regarding the Gibbs energy barriers as experimental reactivity and those with systematic and random noises as computational reactivity. The present numerical investigation shows that even the computational reactivity affected by noises as much as 20 kJ/mol helps reduce the number of required experiments.

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Bayesian based reaction optimization for complex continuous gas–liquid–solid reactions

Abstract: In recent years, self-optimization strageties have been gradually utilized for the determination of optimal reaction conditions owing to their high convenience and independence of researchers’ experiences. However, most self-optimization algorithms...

Cited by 19 publications

References 45 publications

A Review of the Applications of Artificial Intelligence in the Process Analysis and Optimization of Chemical Products

A Review of the Applications of Artificial Intelligence in the Process Analysis and Optimization of Chemical Products

Review of Machine Learning for Hydrodynamics, Transport, and Reactions in Multiphase Flows and Reactors

On accelerating reaction optimization using computational Gibbs energy barriers: A numerical consideration utilizing a computational dataset

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