Using the artificial neural network to control the steam turbine heating process

Nowak, Grzegorz; Rusin, Andrzej

doi:10.1016/j.applthermaleng.2016.07.129

Cited by 33 publications

(13 citation statements)

References 12 publications

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“…Instead of using the temperature of steam flowing through the element, the method uses the direct temperature of the metal, which can be determined by means of the finite element or the finite difference method. Paper [13] presents an alternative method using neural networks to determine stresses in turbine elements.…”

Section: Agorithms Based On Green's Functionsmentioning

confidence: 99%

On-Line Control of Stresses in the Power Unit Pressure Elements Taking Account of Variable Heat Transfer Conditions

et al. 2021

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Coal-fired power units, now balancing power shortages in the power system, must be characterised by increasingly higher flexibility of operation. This means faster start-ups and the capacity for frequent decreases and increases in the power output. These processes cause large temperature gradients in elements of the power unit and the turbine and lead to an increase in the stress level. At such an operating regime it is impossible to ensure safety based on start-up characteristics only—it becomes necessary to constantly monitor stress levels in critical areas of machinery and equipment elements. The stress level in turbine elements can be monitored on-line using algorithms based on Green’s functions and Duhamel’s integral. This paper presents examples of modifications of stress calculations in turbine valves and casings during start-ups. By modifying basic algorithms, it is possible to take into account the impact of the variability of heat transfer coefficients on the thermal stress level. Additionally, individual Green’s functions and correction factors were determined for specific stages of start-ups. Due to modifications, it is possible to obtain satisfactory agreement with the results obtained from FEM-based calculations for the entire heating process. Equations are also given that enable estimation of values of the heat transfer coefficient in turbine valves. The proposed modification of the algorithm will substantially improve the accuracy of stress modelling in transient states of the turbine operation. On-line stress monitoring will enable an increase in the flexibility of the power unit operation and facilitate operational control, ensuring safety of individual elements at the same time. The stress values calculated in the on-line mode can also be used to estimate fatigue life consumption and forecast the residual lifetime of individual components.

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Section: Agorithms Based On Green's Functionsmentioning

confidence: 99%

On-Line Control of Stresses in the Power Unit Pressure Elements Taking Account of Variable Heat Transfer Conditions

et al. 2021

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“…However, the method simplifies the rotor as a cylinder, which not only ignores the influence of the geometry on the stress concentration, but also directly treats the heat transfer conditions on the rotor surface and the physical characteristics of the metal as constants, which affects the calculation accuracy. With the rapid development of computer technology, the numerical analysis technology represented by the finite element analysis has been highly valued and widely used in the subject of Turbine Damage Assessment and life prediction [9].…”

Section: Research Background and Research Statusmentioning

confidence: 99%

“…At the same time, it will make the metal material deform, mainly in the form of expansion deformation [14]. Once the thermal stress exceeds the yield limit of the rotor material, the high-temperature components, mainly the turbine rotor, will produce certain damage, which will eventually bring some security risks [15].…”

Section: Research Background and Research Statusmentioning

confidence: 99%

Damage model of turbine rotor based on DPLS

Dong¹,

Qin²,

Sun³

2021

Ann Syst Biol

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In today's society, the development of electric power industry is one of the important conditions for the economic construction of a country. Therefore, it is of great theoretical significance and value to carry out the research on the damage assessment of turbine rotor. The damage of the turbine rotor is directly related to the process quality, material performance, operation conditions of the equipment itself, and also closely related to the performance of the controller. According to the existing data and starting conditions, a dynamic partial least square prediction model is established and analyzed in this paper, so as to effectively evaluate the stress and damage of the turbine rotor. Before the establishment of dynamic partial least squares model, people study the change law of stress value and damage value mainly through complex mathematical formulas and inconvenient fi nite element analysis software. After the establishment of dynamic partial least squares model, the staff only need to input the input data (speed, temperature, back pressure) to the model to get the stress. The function is also realized in this paper.

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“…The outputs were the ultimate tensile strength (UTS), yield strength (YS), and elongation. The details of the neural network methodology and comprehensive treatments regarding ANN can be found elsewhere [24,25,30,31]. In the current study, a number of neural networks with different numbers of neurons in the hidden layer and different transfer functions were trained and tested to optimize the architecture.…”

Section: Experimental and Setup Ann Modelmentioning

confidence: 99%

Quantitative Relationship Analysis of Mechanical Properties with Mg Content and Heat Treatment Parameters in Al–7Si Alloys Using Artificial Neural Network

Zhang

Haiyang

et al. 2019

Materials

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In this paper, an artificial neural network (ANN) model with high accuracy and good generalization ability was developed to predict and optimize the mechanical properties of Al–7Si alloys. The quantitative correlation formulas of the mechanical properties with Mg content and heat treatment parameters were established based on the transfer function and weight values. The relative importance of the input variables, Mg content and heat treatment parameters, on the mechanical properties of Al–7Si alloys were identified through sensitivity analysis. The results indicated that the mechanical properties of Al–7Si alloys were sensitive to Mg content and aging temperature. Then the individual and the combined influences of these input variables on the properties of Al–7Si alloys were simulated and the process parameters were optimized using the artificial neural network model. Finally, the proposed model was validated to be a robust tool in predicting the mechanical properties of the Al–7Si alloy by conducting experiments.

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Using the artificial neural network to control the steam turbine heating process

Cited by 33 publications

References 12 publications

On-Line Control of Stresses in the Power Unit Pressure Elements Taking Account of Variable Heat Transfer Conditions

On-Line Control of Stresses in the Power Unit Pressure Elements Taking Account of Variable Heat Transfer Conditions

Damage model of turbine rotor based on DPLS

Quantitative Relationship Analysis of Mechanical Properties with Mg Content and Heat Treatment Parameters in Al–7Si Alloys Using Artificial Neural Network

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