Integration of machine learning to increase steam turbine condenser vacuum and efficiency through gasket resealing and higher heat extraction into the atmosphere

Abstract

In large thermal power facilities, thermal machines mainly fitted with steam turbines are used in thermodynamic process of converting thermal energy into mechanical work. In order to improve the efficiency of heat energy conversion into mechanical work, the increase of vacuum level in the condenser of a steam turbine was analyzed using the machine learning method. The vacuum in the steam turbine condenser increases after replacing the labyrinth gaskets with honeycomb ones, resealing the vacuum system and increasing the amount of cooling water through the condenser. The latter is controlled by algorithm, which is integrated into the steam turbine distribution control system. As a result of the increased vacuum in the steam turbine condenser, the enthalpy difference of the steam turbine low-pressure cylinder increases, and the steam turbine performs more mechanical work resulting in higher conversion efficiency. The drawback of the increased vacuum in the turbine condenser is that the flow of cooling water through the steam turbine condenser increases as well as heat dissipation from the condenser into the environment to be replaced by regenerative condensate heating. The results of the analysis show that the overall balance of the vacuum increase in the steam turbine condenser is positive, the steam turbine generates more power, and consequently the process efficiency increases by as much as 2.48 %.