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Show SUMMARY The goal of developing an active control approach that will attain operation of a burner at its optimum performance, and that will maintain peak operation following a change in boundary conditions, has been realized. The approach presented herein has been shown to adjust burner inle't parameters in order to optimize NOx emissions and combustion efficiency. This was accomplished by defining a trade-off between combustion efficiency and NOx concentration in the form of a performance index. The performance index functions as a single search criterion, to which two search techniques were applied in the current work. Further, the simple genetic algorithm has been shown to be a superior search technique, in the case of continuous combustion optimization, as compared to a zero-order direction-set method. The main advantage of the genetic algorithm seems to be its ability to locate a global optimum more reliably than a direction-set technique, without the tendency to settle on a local ridge. While these results are encouraging, care must be taken in applying them to the general problem of practical burner optimization. Certainly, each burner application will have peculiar characteristics that must be accounted for in the development of a control scheme. This research does not indicate that either the direction-set or the genetic algorithm search technique can be applied to the practical control of a burner. Rather, this study should be seen as a first step, providing guidance to future research into the development of a practical application of active control. The achievement of such a goal will benefit from a refmed search mechanism, and an improved emissions and stability sensor. The system developed in the present work continuously searches for the optimum operating condition of a burner, and successfully achieves optimum performance even following a change in load. While the present system optimizes emissions, it does so without any knowledge of that burner's particular emissions character. The only requirement, in the present case, is knowledge of the stability limits. The successful behavior of the control scheme following a large-scale change in boundary conditions (fuel load) implies that the system would respond to smaller-scale changes in boundary conditions as well (fuel composition, equipment degradation, etc.). 14 |
