Machine Learning

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.

Selective hydrogenation units remove trace methylacetylene and propadiene (MAPD) from a propylene stream. The more selective way to dose hydrogen is to ratio it against the measured inlet MAPD concentration. That strategy requires a continuous and trustworthy MAPD signal at every control interval, and the online gas chromatograph (GC) that provides it is intermittent and occasionally unreliable. A soft sensor intended for closed-loop use cannot be judged as a standalone predictor. Low aggregate error and near-nominal interval coverage can still hide local reliability loss during operating-state transitions, which is exactly when a controller can least afford a bad measurement. The soft sensor is therefore treated as part of a reliability-aware measurement layer that decides, at each step, whether to trust the analyzer, rely on the model, or route the controller to a conservative fallback. ...

The Core Problem: Optimization is Hard Plants are Dynamic: Chemical processes rarely sit perfectly still. Raw materials change, equipment ages, customer demand shifts, and unexpected disturbances occur. Trying to find the single “best” steady operating point isn’t always effective because the plant is always changing. Traditional Models are Complex: For decades, engineers have used mathematical models to understand and optimize these processes. Methods like Real-Time Optimization (RTO) try to calculate the best settings based on these models. However, creating accurate dynamic models (models that capture how things change over time) for complex, interconnected plants is incredibly difficult, time-consuming, and expensive. Keeping these models up-to-date is also a major challenge. Resistance to Change: Implementing complex new optimization systems based on these models faces resistance due to cost, complexity, and uncertainty about whether they’ll actually work reliably in the real world. The Proposed Solution: Learn Directly from Experience (Data) ...