Standard monitoring within a gas-turbine based cogeneration system includes key flow rates, temperatures, pressures and turbine vibration. These standard measurements can be enhanced with continuous emissions monitoring to help pinpoint system problems. A combination of these measurements, a fast prediction model and data reconciliation constitute an improved monitoring and diagnostic tool that can quantitatively predict the existence of turbine problems (for example, damaged combustor nozzles) even when standard turbine monitoring indicates no problems exist.
Data reconciliation is widely used in the chemical process industry to suppress the influence of random errors in process data and help detect gross errors. Data reconciliation is currently seeing increased use in the power industry. Here, we use data from a recently constructed cogeneration system to show the data reconciliation process and the difficulties associated with gross error detection and suspect measurement identification. Problems in gross error detection and suspect measurement identification are often traced to weak variable redundancy, which can be characterized by variable adjustability and threshold value. Proper suspect measurement identification is accomplished using a variable measurement test coupled with the variable adjustability. Cogeneration and power systems provide a unique opportunity to include performance equations in the problem formulation. Gross error detection and suspect measurement identification can be significantly enhanced by increasing variable redundancy through the use of performance equations. Cogeneration system models are nonlinear, but a detailed analysis of gross error detection and suspect measurement identification is based on model linearization. A Monte Carlo study was used to verify results from the linearized models.
This paper explores the modeling and simulation of combustion processes in a gas turbine combustor both with and without water injection for NO x control. The combustor can be modeled as a series and/or parallel combinations of perfectly stirred reactors (PSRs) and plug fl ow reactors (PFRs). Kinetics models require solution of non-linear and stiff ordinary differential equations (ODEs). The CVODE code from Lawrence Livermore National Laboratory can be used for the solution of these ODEs.Here we show that a reduced kinetics set coupled with an Excel callable version of CVODE (as a dynamic link library (dll)) can be used to predict emission trends in combustors. Student use is promoted by using Excel as the pre-and post-processor.
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