# Machine Learning-Assisted Discovery of Flow Reactor Designs

arXiv: https://arxiv.org/abs/2308.08841 · HTML: https://mlsanity.com/paper/2308.08841/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 35 references that are in the map.
- First posted on arXiv 17 Aug 2023.

## Builds on (most cited first, 5 of 5)

- [BOAH: A Tool Suite for Multi-Fidelity Bayesian Optimization & Analysis of Hyperparameters](https://mlsanity.com/paper/1908.06756/) (2019)
- [Combining Multi-Fidelity Modelling and Asynchronous Batch Bayesian Optimization](https://mlsanity.com/paper/2211.06149/) (2022)
- [Multi-Fidelity Data-Driven Design and Analysis of Reactor and Tube Simulations](https://mlsanity.com/paper/2305.00710/) (2023)
- [Practical Bayesian Optimization for Variable Cost Objectives](https://mlsanity.com/paper/1703.04335/) (2017)
- [Deep Gaussian Process-based Multi-fidelity Bayesian Optimization for Simulated Chemical Reactors](https://mlsanity.com/paper/2210.17213/) (2022)

## Built on by (most cited first, 8 of 8)

- [Best Practices for Multi-Fidelity Bayesian Optimization in Materials and Molecular Research](https://mlsanity.com/paper/2410.00544/) (2024)
- [Multi-fidelity batch Bayesian optimization for bioprocess development across scales](https://mlsanity.com/paper/2508.10970/) (2025)
- [Survey and Tutorial of Reinforcement Learning Methods in Process Systems Engineering](https://mlsanity.com/paper/2510.24272/) (2025)
- [Modern, Efficient, and Differentiable Transport Equation Models using JAX: Applications to Population Balance Equations](https://mlsanity.com/paper/2411.00742/) (2024)
- [A Guide to Bayesian Optimization in Bioprocess Engineering](https://mlsanity.com/paper/2508.10642/) (2025)
- [DropMicroFluidAgents (DMFAs): Autonomous Droplet Microfluidic Research Framework Through Large Language Model Agents](https://mlsanity.com/paper/2501.14772/) (2024)
- [Reduced-Space Multi-Fidelity Bayesian Optimization of Process Simulation Models](https://mlsanity.com/paper/2609.17440/) (2026)
- [Atoms to Processes: The Role of Artificial Intelligence and Machine Learning in Chemical Engineering](https://mlsanity.com/paper/2610.02014/) (2026)

## Abstract (arXiv)

Additive manufacturing has enabled the fabrication of advanced reactor geometries, permitting larger, more complex design spaces. Identifying promising configurations within such spaces presents a significant challenge for current approaches. Furthermore, existing parameterisations of reactor geometries are low-dimensional with expensive optimisation limiting more complex solutions. To address this challenge, we establish a machine learning-assisted approach for the design of the next-generation of chemical reactors, combining the application of high-dimensional parameterisations, computational fluid dynamics, and multi-fidelity Bayesian optimisation. We associate the development of mixing-enhancing vortical flow structures in novel coiled reactors with performance, and use our approach to identify key characteristics of optimal designs. By appealing to the principles of flow dynamics, we rationalise the selection of novel design features that lead to experimental plug flow performance improvements of 60% over conventional designs. Our results demonstrate that coupling advanced manufacturing techniques with `augmented-intelligence' approaches can lead to superior design performance and, consequently, emissions-reduction and sustainability.