ADAPTIVE DYNAMIC MODELS FOR
MAINTENANCE-ON_DEMAND AND
PROCESS OPTIMIZATION OF
COMBINED HEAT AND POWER
PLANTS (ADMADE)
Prof Erik Dahlquist
Malardalen University
[email protected]
Objectives
• The aim of this application is to build a
foundation of mathematical tools for application
in the future energy sector, including renewable
energy as well as intelligent energy.
• Secondly we need more information on moisture
and heating value of different fuels, to optimize
the performance.
• Measured process data will be analysed and
utilised for process optimization, and not only be
collected and stored as is often the case today.
Project
• In the project we will develop the mathematical modeling foundation for
doing these type of diagnostics and optimizations for later implementation
in different power plant and process industries generally.
• - Physical models will be combined with statistical models in a systematic
way to make it possible to adapt the models as conditions change, and to
follow effect of new fuels.
• - A hierarchical structure will be introduced for
• 1) measurement of fuel properties using NIR and RF together with
statistical models like PLS,
• 2) process diagnostics comparing simulations to measurements in the
process combined with Bayesian Nets and
• 3) production planning including when maintenance has to be done.
• 4) on-line control and optimization using model based, multivariable
control. This includes both the production and district heating system.
Partners
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Mälarenergi AB
Eskilstuna Energy and Environment
ENA Energy
Vattenfall