Combined Heat and Power (CHP) - sample application
Cogeneration plants are specified for a nominal operating condition, but real-world conditions (required loads, fuel composition and state etc.) change both seasonally and between day / night.
Using the simevo Process Simulation technology we have developed a sample model for natural-gas-fed Combined Heat and Power (CHP) plants with microturbine/s and a single flue gas heat recovery heat exchanger.
The process model calculates the Key Performance Indicators as a function of inlet and operating conditions.
Problem
In the early stages of the project it is often required to forecast the key performance indicators (turbine inlet and exhaust temperature, net electrical efficiency, cogeneration efficiency) at conditions different from nominal:
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to evaluate the return of investment (RoI)
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to cross-check and validate vendor-supplied data
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to compare the performance of equipment from different vendors
Once the plant is in operation, forecasting can be used for:
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troubleshooting
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diagnosis
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optimization
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and predictive maintenance.
Solution
For all these purposes a steady-state, first-principle process model ("digital twin") is the way to go; just stay away from spreadsheets (because they suck at modeling !) and simulate your process using the simevo process technology .
For illustration purposes we have developed a sample model for natural-gas-fed Combined Heat and Power (CHP) plants with microturbine/s and a single flue gas heat recovery heat exchanger; these are typical where no renewable fuel is available and the heating load is prevalent over the electricity demand.
The process model calculates the Key Performance Indicators (KPIs) as a function of inlet and operating conditions, and can be deployed as:
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a small-footprint, standalone custom process simulator for desktop use
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hosted in the public or private cloud for integration with other services, access via a dedicated web application on saas.simevo.com
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a standalone online application that communicates with the control system or with the Supervisory Control and Data Acquisition (SCADA).
In all cases you can shield the user from implementation details, modeling assumptions and hypotheses. This is useful to make the model robust and simple to use, and / or to protect your know-how.
Related applications
The model can be easily modified to take into account:
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Alternative gaseous and liquid fuels such as vegetable oil, biodiesel, biogas, syngas etc.
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Trigeneration i.e. Combined Cooling, Heat and Power (CCHP)
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Oxy-fuel combustion (using pure oxygen rather than ambient air as oxidant)
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Internal Combustion Engines or Fuel Cells instead of the turbine for electricity production.