Research groups: models and predictive control for energy plants
Why research groups lack time and people to build physical plant models, predictive control and optimisation for steam, turbines and power-to-X.
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Predictive control of steam production needs models nobody can build in time
What happens. A project aims at predictive control of integrated steam production, with physical models of boilers, turbines and networks. The scientific staff are good at their own field but are few in number, and doctoral positions are slow to fill.
What it costs. Project deadlines slip, models stay unfinished and the results of the control study arrive late for the follow-on application.
Why the usual fix fails. Waiting for the next doctoral student wastes a funding period. Students new to the topic spend months on set-up that has been done elsewhere.
How a fixed-price remote diagnosis checks it. From the project plan, existing models and plant data, I check what model structure and solver set-up the problem needs and which part could be done by an experienced outside engineer. The fixed-price remote diagnosis gives a work plan without changing your team.
Background: Predictive control of process plants
Gas-turbine cycle analysis and optimisation wait for process modellers
What happens. A research centre wants to analyse and improve gas-turbine cycles by process simulation. The positions for people who can build and run these models stay open for months.
What it costs. Studies on efficiency and fuel flexibility are delayed. Teams that depend on the results, for instance in hydrogen operation, have to wait.
Why the usual fix fails. A general simulation tool does not by itself give a validated model of the cycle. Training someone new takes a long time.
How a fixed-price remote diagnosis checks it. From your cycle data, component maps and test results, I check which model depth the question needs and where optimisation would give an answer. The fixed-price remote diagnosis tells you what to build first and what can be done outside the team.
Background: Economic predictive control
Digital twins and optimisation of power-to-X plants need rare skills
What happens. Power-to-X plants make hydrogen or fuels from electricity. They have fast electrical dynamics, slow chemical processes and prices that change by the hour. A twin that spans these time scales, with optimisation on top, is a demanding research task.
What it costs. Plant studies that cannot be completed, results that rest on simplified models and project partners waiting for operating strategies.
Why the usual fix fails. Using one model for all time scales is too slow, and separate models do not talk to each other. Few researchers have worked on both the plant physics and the optimisation.
How a fixed-price remote diagnosis checks it. From plant design data and any measurements, I check how a layered model and optimisation could be built, from fast control to day-ahead scheduling. The fixed-price remote diagnosis tells you what to build first and what data it needs.
Background: Economic predictive control
More problems that concern this industry
These problems are described on the page of the industry where they matter most:
- Helium is scarce and recovery systems are overwhelmed by quenches (Fusion and big-science labs)
- Larger machines hit the limit of heat removal at the lowest temperatures (Quantum computing hardware makers)
Check it on your plant first: a fixed-price remote diagnosis
EUR 4,500 net · 5 working days · remote · availability on request · freelance or through temporary agency work
Within a fixed scope and at a fixed price, I review your data, control structure and measurements remotely and tell you whether the problem on this page is behind your symptoms and what fixing it would take. You get a short summary for decision-makers and a technical report for your engineers.