Optimization of Steam Distribution Mode for Turbine Units Based on Governing Valve Characteristic Modeling

Zhang, Lei; Qiao, Zongliang; Hei, Bingsen; Tang, Youfei; Li, Shasha

doi:10.3390/en15239139

Cited by 2 publications

(2 citation statements)

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“…Zhou et al deployed an extreme learning model to monitor the performance degradation of the steam turbine regenerative system for ensuring the safety and economy of the coal-fired power plant operation. In another study, the mode of steam distribution for power generation under different scenarios was optimized based on governing valve characteristic modeling . Zhu et al constructed a mixed-integer nonlinear programming model to optimize the operation of various power generation capacity steam turbine networks for the petrochemical complex.…”

Section: Introductionmentioning

confidence: 99%

“…In another study, the mode of steam distribution for power generation under different scenarios was optimized based on governing valve characteristic modeling. 39 Zhu et al 40 constructed a mixed-integer nonlinear programming model to optimize the operation of various power generation capacity steam turbine networks for the petrochemical complex. In another study, a data-driven robust optimization analysis was performed to optimize the steam power system of a chemical plant under multiscenario demand uncertainty.…”

Section: Introductionmentioning

confidence: 99%

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Artificial Intelligence Modeling-Based Optimization of an Industrial-Scale Steam Turbine for Moving toward Net-Zero in the Energy Sector

et al. 2023

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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 CO2 measuring 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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%