Using a core-vector machine to correct the steam-separator temperature deviations of a 1000 MW boiler

Abstract

Steam-separator temperature is an important parameter of ultra-supercritical boilers, where temperature deviations result in an increase in feed-water and a fast decline in steam temperature. Optimizing temperature deviations through manual operating-variables adjustments is difficult because of the complex relationships among influencing factors, as well as unacceptable increases in combustion air from opening baffles. Therefore, this research has used a core-vector regression (CVR) algorithm to model steam-separator temperature deviations. CVR is an extremely fast way to model the process and gives more accurate predictions than a support vector machine (SVM). Seventy-seven operating parameters were used as inputs, the objective was set as the temperature deviation factor of all the steam separators, and in total 17,338 experimental cases from the DCS were used in this study. Secondary-air volume adjustments at the C and D levels in #4 corner were carried out at different boiler loads in field tests, and the temperature deviation after each test was compared with the original value. Results showed that steam-temperature deviation was decreased by 29.6% and 36.3% respectively at 700MW and 530MW after secondary-air volume was adjusted to the target value.