# Computer-Aided Design

**Source**: https://ctv.cs.tum.de/en/research/computer-aided-design
**Parent**: https://ctv.cs.tum.de/en/

As we move toward a more circular chemical industry, many processes need to be redesigned or newly developed. To support this shift, we’re creating computer-aided methods for conceptualizing chemical and biotechnological processes. These methods integrate modeling, simulation, and optimization in a tightly linked approach. Advances in machine learning have opened up new opportunities for computer-aided conceptual design.

We’re currently working on the following projects:

## Reinforcement Learning-Based Process Design

In partnership with the [Professorship for Bioinformatics](https://bit.cs.tum.de/en/), we have been at the forefront of using machine learning to create complete chemical processes from the ground up. We have built robust process simulators that can evaluate a wide range of potential process designs. A reinforcement learning agent suggests economically optimal processes within the simulator, trained through self-play using an AlphaZero-based approach. This method allows the agent to generate complex flowsheets, including those with recycle loops and azeotropic distillation sequences. It can even handle multiple chemical systems with a single agent, something not seen before in reinforcement learning-based process design. Beyond process engineering, the developed algorithms are adaptable to any type of planning problem. The project is funded by the DFG under the [SPP 2331 Machine Learning in Chemical Engineering](https://chemengml.org/).