Coal-fired thermal power plant performance optimization using Markov and CFD analysis

Case Study Coal‑fired thermal power plant performance optimization using Markov and CFD analysis Pardeep Kumar1 · Ansar Ali1 · Sandeep Kumar1 Received: 6 August 2019 / Accepted: 8 January 2020 / Published online: 18 January 2020 © Springer Nature Switzerland AG 2020 Abstract In the present stimulated business environment, power sector is playing a major role in the economic growth of India. During the last 20 years, the country had been facing a poor supply of energy and this supply–demand gap is increasing continuously. Therefore, it is important for power plants to improve its power generation capacity drastically by reducing the failure rate. In the present paper, to analyze the causes of poor availability, thermal power plant has divided into six different systems and a system comprising waste gases heating system has been considered. With the help of transition diagram, mathematical equations have been used to find out the availability. After analyzing, it was found that the value of availability is very low and boiler tube failure is one of the most critical factors for this low availability of system. The power plants have low availability causing serious concern and need to identify the responsible factors for this low availability. Boiler tube failure is identified as a responsible factor for low availability, and economizer is the zone where maximum failure occurred. From the maintenance history sheet, it was identified that economizer is a critical zone where maximum tube failures occur and the main cause of economizer failure is due to high-velocity flue gas particle, i.e., erosion. To increase the availability and minimize the failures, erosion process must be reduced in economizer tubes which is mainly responsible for economizer failure and then CFD analysis has been done for this purpose. This results in a decrease in the shutdown period of the plant and an increase in the system availability as well as the power of the system. Keywords Performance analysis · Thermal power plant · Availability · Mathematical modeling · Markov birth–death process 1 Introduction In today’s competitive world, it becomes necessary that thermal power plant will be available for long run without any failure. In India, total installed capacity of electricity generation is 330,354 MW while total thermal installed capacity is 220,456 MW, i.e., 66.8% of the total installed capacity (refer Table 1). The major contribution almost 59% in thermal installed capacity is coal-fired thermal power plant. For continuous power production, boiler becomes the backbone of a thermal power plant. Boiler tube failure is one of the critical problems which are facing the thermal power plant and influence the rate of power generation. This loss of generation increases the operating cost of plant, and a significant amount of water is being waste. Availability analysis gives the necessary information about various parameters of the system. A brief literature review about reliability, availability and maintainability is as follows: Cherry et al. [1] explained the analysis of reliability by measuring long-run cycle availability of a chemical industry. Dai et al. [2] performed both reliability and availability analyses for some different complex systems. Gupta et al. [3, 4] discussed performance modeling using probabilistic approach and developed Markov model for performance evaluation of a system of coal handling of a thermal power * Pardeep Kumar, | 1Meerut Institute of Engineering and Technology, Meerut, Uttar Pradesh, India. SN Applied Sciences (2020) 2:227 | https://doi.org/10.1007/s42452-020-2003-1 Vol.:(0123456789) Case Study SN Applied Sciences (2020) 2:227 | https://doi.org/10.1007/s42452-020-2003-1 Table 1  Installed capacity for different sources of fuel Fuel used Installed capacity (MW) % of total Total thermal Coal Gas Oil Hydro Nuclear Renewable energy sources Total 220,456 194,433 25,185 838 44,614 6780 58,303 330,354 66.8 58.9 7.6 0.3 13.5 2.1 17.7 [9] applied Six Sigma approach to reduce the causes and improve the performance of a coal-fired thermal power plant. Kumar [10, 11] suggested maintenance policy for a different system of a power plant. Sabouhi et al. [12] discussed the reliability modeling and availability analysis of combined cycle power plants (CCPP). Yadav [13] derived equations with the help of Markov model. 2 Availability analysis of waste gases system of a thermal power plant plant. Gupta et al. [5] examined soap production system and flexible powder polymer production system in a soap plant. Gupta et al. [4] discussed Mathematical formulation for reliability analysis in terms of availability of a critical ash handling system. Kumar [6] have done availability analysis using Markov approach of air circulation system. Khanduja [7] framed out a mathematical model with the help of mathematical analysis, and the value of steadystate availability was derived for analysis of system availability of bleaching unit in a paper plant. Lai [8] obtained the availability of steady state for the system of distributed software/hardware with the help of Markov model. Kumar The flow diagram of thermal power plant consisting of waste gases system (refer Fig. 1) shows that flue or waste gases from furnace flow upward and this waste heat is utilized in superheater, economizer and air preheater to raise the temperature of some extent of steam, feed water and air. To find the availability, this system is divided further in four different subsystems. Subsystem A It consists of furnace, superheater, economizer and air preheater and arranged in series to establish a single subsystem. Subsystem B It consists of two electrostatic precipitators (ESP) which make a single subsystem. Fig. 1  Flow diagram of thermal power plant To chimeny Flue Coal Storage Air Preheate Flue Coal Handling 3– Phase Economise Ash Storage Ash Handling Super heater Boiler Stea Main valve Turbin Feed Flue gases Exhaust High pressure Boiler feed Condensate extracon Condenser Low pressure Circulaon water Cooling Vol:.(1234567890) Generato SN Applied Sciences (2020) 2:227 | https://doi.org/10.1007/s42452-020-2003-1 Subsystem C Two forced draft fans working in parallel consist of a subsystem. Subsystem D Three induced draft fans (ID fan) arranged in parallel create one subsystem. P0� (t) + 4 ∑ Case Study 𝜆i P0 (t) = 𝛽1 P12 (t) + 𝛽2 P6 (t) + 𝛽3 P3 (t) + 𝛽4 P1 (t) i=1 (1) 4 Table 2 shows some notations which are used to construct the transition diagram as shown in Fig. 2. P1� (t) + ∑ (𝜆i + 𝛽4 )P1 (t) i=1 = 𝛽1 P11 (t) + 𝛽2 P7 (t) + 𝛽3 P4 (t) + 𝛽4 P2 (t) + 𝛽4 P0 (t) (2) 2.1 Performance modeling of waste gases system The mathematical equations are derived using Chapman–Kolmogorov equation with the help of transition diagram. Table 2  Notation used P2� (t) + 4 ∑ (𝜆i + 𝛽4 )P2 (t) i=1 = 𝛽1 P10 (t) + 𝛽2 P8 (t) + 𝛽3 P5 (t) + 𝛽4 P9 (t) + 𝛽4 P1 (t) Full-capacity states (withou (...truncated)