# Integrating process design and control using reinforcement learning

arXiv: https://arxiv.org/abs/2108.05242 · HTML: https://mlsanity.com/paper/2108.05242/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Aug 2021.

## Built on by (most cited first, 6 of 6)

- [Flowsheet synthesis through hierarchical reinforcement learning and graph neural networks](https://mlsanity.com/paper/2207.12051/) (2022)
- [Deep reinforcement learning for process design: Review and perspective](https://mlsanity.com/paper/2308.07822/) (2023)
- [Generative AI and Process Systems Engineering: The Next Frontier](https://mlsanity.com/paper/2402.10977/) (2024)
- [Reinforcement Learning for Efficient Design and Control Co-optimisation of Energy Systems](https://mlsanity.com/paper/2406.19825/) (2024)
- [Data-driven Bi-level Optimization of Thermal Power Systems with embedded Artificial Neural Networks](https://mlsanity.com/paper/2602.13746/) (2026)
- [Enhanced Reinforcement Learning-based Process Synthesis via Quantum Computing](https://mlsanity.com/paper/2605.21213/) (2026)

## Abstract (arXiv)

To create efficient-high performing processes, one must find an optimal design with its corresponding controller that ensures optimal operation in the presence of uncertainty. When comparing different process designs, for the comparison to be meaningful, each design must involve its optimal operation. Therefore, to optimize a process' design, one must address design and control simultaneously. For this, one can formulate a bilevel optimization problem, with the design as the outer problem in the form of a mixed-integer nonlinear program (MINLP) and a stochastic optimal control as the inner problem. This is intractable by most approaches. In this paper we propose to compute the optimal control using reinforcement learning, and then embed this controller into the design problem. This allows to decouple the solution procedure, while having the same optimal result as if solving the bilevel problem. The approach is tested in two case studies and the performance of the controller is evaluated. The case studies indicate that the proposed approach outperforms current state-of-the-art simultaneous design and control strategies. This opens a new avenue to address simultaneous design and control of engineering systems.