ADMADE Project: Dynamic Models for Energy Sector Optimization
ADAPTIVE DYNAMIC MODELS FOR
MAINTENANCE-ON_DEMAND AND
PROCESS OPTIMIZATION OF
COMBINED HEAT AND POWER
PLANTS (ADMADE)
Prof Erik Dahlquist
Malardalen University
erik.dahlquist@mdh.se
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
•
Mälarenergi AB
•
Eskilstuna Energy and Environment
•
ENA Energy
•
Vattenfall
"The ADMADE project, led by Prof. Erik Dahlquist at Malardalen University, focuses on developing adaptive dynamic models for maintenance-on-demand and process optimization of combined heat and power plants. The project aims to create mathematical tools for the future energy sector, emphasizing renewable and intelligent energy systems. By analyzing and utilizing measured process data, the project seeks to optimize performance and fuel properties, integrating physical and statistical models in a hierarchical structure. Partners include Mälarenergi AB, Eskilstuna Energy and Environment, ENA Energy, and Vattenfall."
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Presentation Transcript
ADAPTIVE DYNAMIC MODELS FOR MAINTENANCE-ON_DEMAND AND PROCESS OPTIMIZATION OF COMBINED HEAT AND POWER PLANTS (ADMADE) Prof Erik Dahlquist Malardalen University erik.dahlquist@mdh.se
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 M larenergi AB Eskilstuna Energy and Environment ENA Energy Vattenfall
