Augmentation of energy efficiency in the power generation
systems
can aid in decarbonizing the energy sector, which is also recognized
by the International Energy Agency (IEA) as a solution to attain net-zero
from the energy sector. With this reference, this article presents
a framework incorporating artificial intelligence (AI) for improving
the isentropic efficiency of a high-pressure (HP) steam turbine installed
at a supercritical power plant. The data of the operating parameters
taken from a supercritical 660 MW coal-fired power plant is well-distributed
in the input and output spaces of the operating parameters. Based
on hyperparameter tuning, two advanced AI modeling algorithms, i.e.,
artificial neural network (ANN) and support vector machine (SVM),
are trained and, subsequently, validated. ANN, as turned out to be
a better-performing model, is utilized to conduct the Monte Carlo
technique-based sensitivity analysis toward the high-pressure (HP)
turbine efficiency. Subsequently, the ANN model is deployed for evaluating
the impact of individual or combination of operating parameters on
the HP turbine efficiency under three real-power generation capacities
of the power plant. The parametric study and nonlinear programming-based
optimization techniques are applied to optimize the HP turbine efficiency.
It is estimated that the HP turbine efficiency can be improved by
1.43, 5.09, and 3.40% as compared to that of the average values of
input parameters for half-load, mid-load, and full-load power generation
modes, respectively. The annual reduction in CO2measuring
58.3, 123.5, and 70.8 kilo ton/year (kt/y) corresponds to half-load,
mid-load, and full load, respectively, and noticeable mitigation of
SO2, CH4, N2O, and Hg emissions is
estimated for the three power generation modes of the power plant.
The AI-based modeling and optimization analysis is conducted to enhance
the operation excellence of the industrial-scale steam turbine that
promotes higher-energy efficiency and contributes to the net-zero
target from the energy sector.