Abstract
Cycle humidification applied to micro Gas Turbines (mGTs) offers a solution to overcome their limited operational flexibility in terms of variable electrical and thermal power production when used in a Combined Heat and Power (CHP) application. Although the positive impact of this cycle humidification on the performance has already been proven numerically and experimentally, very detailed modeling of the system performance remains challenging, especially the determination of the recuperator effectiveness, which has the highest impact on the final cycle performance. Indeed, the recuperator performance depends strongly on the mass flow rate of the air stream and its humidification level, two parameters that are difficult to measure accurately. Accurate modeling of the recuperator performance under both dry and humidified conditions is thus essential for correct assessment of the potential of humidified mGT cycles in Decentralized Energy Systems (DES).
In this paper, we present a detailed analysis of the recuperator performance under humidified conditions using averaged experimental data, extended with the application of a Support Vector Regression (SVR) on a time series to improve noise-modeling of the output signal, and thus enhance the accuracy of the monitoring process. In a first step, the missing experimental parameters, air mass flow rate and humidity level, were obtained indirectly, using rotational speed, fuel flow rate, exhaust gas composition and pressure level measurements in combination with the compressor map. Despite the low accuracy, some general trends regarding the recuperator performance could be observed based on these experimental data, indicating that the recuperator, despite having an increased total exchanged heat flux, is actually too small to exploit the full potential of the humidification. In a second step, by means of the SVR model, a first attempt was made to improve the accuracy and reduce the scatter on the recuperator performance determination. The predicted results with the SVR indicated indeed a reduced scatter on the determinations of the air mass flow rate and the amount of introduced water, opening a pathway towards online recuperator performance prediction.
A SOFT-SENSING MODEL FOR FEEDWATER FLOW RATE USING FUZZY SUPPORT VECTOR REGRESSION