scholarly journals Application of machine learning to predict the thermal power plant process condition

2022 ◽  
Vol 2150 (1) ◽  
pp. 012029
Author(s):  
M M Sultanov ◽  
I A Boldyrev ◽  
K V Evseev
Keyword(s):  
Machine Learning ◽  
Power Plant ◽  
Thermal Power Plant ◽  
Input Data ◽  
Data Cleaning ◽  
Thermal Power ◽  
Process Condition ◽  
Process Variables ◽  
Machine Learning Model ◽  
Plant Process

Abstract This paper deals with the development of an algorithm for predicting thermal power plant process variables. The input data are described, and the data cleaning algorithm is presented along with the Python frameworks used. The employed machine learning model is discussed, and the results are presented.

A digital solution for risk-oriented management of thermal power plant process equipment condition

2020 ◽  
Vol 5 ◽  
pp. 42-54
Author(s):  
Evgueny Boiko ◽  
Igor Polikarpov ◽  
Aleksey Bobrov ◽  
Sergey Sizintsov ◽  
Valeriy Volnev ◽  
...  
Keyword(s):  
Power Plant ◽  
Thermal Power Plant ◽  
Power Plants ◽  
Business Processes ◽  
Thermal Power ◽  
Facility Management ◽  
Process Equipment ◽  
Thermal Power Plants ◽  
Power Facility ◽  
Plant Process

According to digital engineering, an intelligent digital infrastructure is intended to optimize performance of thermal power plants. This paper presents an intelligent digital approach to power facility management. As an example, Siberian Generating Company thermal power plants were considered. The authors have developed specialized software able to control and predict thermal power plant process equipment conditions comparing monitoring data and failure probabilities with appropriate mathematical models. Based on a life-cycle monitoring model, a management methodology was created to be applied to technical and business processes of a power facility to improve its maintenance strategy.

scholarly journals Performance Evaluation of a Novel Thermal Power Plant Process with Low-Temperature Selective Catalytic Reduction

Energies ◽  
2020 ◽  
Vol 13 (21) ◽  
pp. 5558
Author(s):  
Seongil Kim ◽  
Taeyoung Chae ◽  
Yongwoon Lee ◽  
Won Yang ◽  
Sungho Hong
Keyword(s):  
Power Plant ◽  
Low Temperature ◽  
Selective Catalytic Reduction ◽  
Thermal Power Plant ◽  
Process Model ◽  
Catalytic Reduction ◽  
Thermal Power ◽  
Gas Cleaning ◽  
New Process ◽  
Plant Process

We present the concept of a novel thermal power plant process in conjunction with low-temperature selective catalytic reduction (SCR). This process can be employed to achieve modern standards for NOx emissions and solve problems related to post-gas cleaning processes, such as thermal fatigue, catalyst damage, and an increase in differential pressure in the boiler. Therefore, this study is aimed at evaluating the performance of a novel flue-gas cleaning process for use in a thermal power plant, where a low-temperature SCR is implemented, along with the existing SCR. We developed a process model for a large-scale power plant, in which the thermal power plant was divided into a series of heat exchanger block models. The mass and energy balances were solved by considering the heat transfer interaction between the hot and cold sides to obtain the properties of each material flow. Using the process model, we performed a simulation of the new process. New optimal operating conditions were derived, and the effects that the new facilities have on the existing process were evaluated. The results show that the new process is feasible in terms of operating stability and cost, as well as showing an increase in the boiler thermal efficiency of up to 1.3%.

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