Simulation

TEP Studio is a new implementation of the Tennessee Eastman challenge problem. The Tennessee Eastman process has been a standard testbed for process control and fault diagnosis for three decades because it models a real industrial plant, with its full nonlinearity, coupling, and disturbance structure. TEP Studio brings that model into a form that ML and reasoning systems can use directly. Three properties distinguish it: It is a self-contained Python implementation, and it is able to simulate one process-hour in 8 ms — more than five times faster than the Fortran reference and about two orders of magnitude faster than an existing Python implementation. It allows for systematic LLM access. The process description is exposed through a Model Context Protocol (MCP) server. It is built from the bottom up to serve ML and data-intensive applications. It is also a Gymnasium environment, so reinforcement learning and control methods can train on it directly. Code is on GitHub, and you can try the simulator live in the TEP Studio demo. I’ve also written up the design principles behind it in more detail on the projects page.

Dynamic process models are increasingly consumed by automated agents rather than by control engineers reading source code. These agents include data pipelines that assemble training sets, reinforcement-learning algorithms, and language models. Each depends on process meaning that a numerical interface does not expose, such as what each variable represents, which signals are available online, and which limits trigger a shutdown. Today that meaning lives in source code and informal convention, so recovering it is error-prone and does not transfer across a model’s many independent implementations. ...