Investigation of Supercritical Power Plant Boiler Combustion Process Optimization through CFD and Genetic Algorithm Methods

Gavirineni Naveen Kumar and Edison Gundabattini ()
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Gavirineni Naveen Kumar: School of Mechanical Engineering, Vellore Institute of Technology (VIT), Vellore 632 014, India
Edison Gundabattini: Department of Thermal and Energy Engineering, School of Mechanical Engineering, Vellore Institute of Technology (VIT), Vellore 632 014, India

Energies, 2022, vol. 15, issue 23, 1-28

Abstract: One of the main energy sources utilized to produce power is coal. Due to the lack of combustion enhancement, the main issue with coal-based power plants is that they produce significant amount of pollutants. The major problem of slagging formation within the boiler; it sticks to the water tube walls, superheater, and reheater. Slagging might decrease the heat transferred from the combustion area to the water or steam inside the tubes, increasing the amount of coal and extra air. The abrupt fall of slag on the tube surface into the water-filled seal-trough at the bottom of the furnace might occasionally cause boiler explosions. In order to maximize heat transmission to the water and steam tubes by reducing or eliminating slag formation on the tube surface, the work presented here proposes an appropriate computational fluid dynamics (CFD) technique with a genetic algorithm (GA) integrated with conventional supercritical power plant operation. Coal usage and surplus air demand are both decreased concurrently. By controlling the velocity and temperatures of primary air and secondary air, the devised technique could optimize the flue gas temperature within the furnace to prevent ash from melting and clinging to the water and steam tube surfaces. Heat transmission in the furnace increased from 5945.876 W/m 2 to 87,513.9 W/m 2 as a result of the regulated slag accumulation. In addition to reducing CO 2 emissions by 8.55 tonnes per hour and saving close to nine tonnes of coal per hour, the boiler’s efficiency increased from 82.397% to 85.104%.

Keywords: coal consumption; emission generation; boiler efficiency; computational fluid dynamics; genetic algorithm; excess air (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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