The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Deep Learning Neural Networks for Online Monitoring of the Combustion Process From Flame Colour in Thermal Power Plants
|
|
Author(s): Sujatha Kesavan (Dr. MGR Educational and Research Institute, India), Sivanand R. (Dr. MGR Educational and Research Institute, India), Rengammal Sankari B. (Dr. MGR Educational and Research Institute, India), Latha B. (Dr. MGR Educational and Research Institute, India), Tamilselvi C. (Dr. MGR Educational and Research Institute, India)and Krishnaveni S. (Dr. MGR Educational and Research Institute, India)
Copyright: 2023
Pages: 21
Source title:
Convergence of Deep Learning and Internet of Things: Computing and Technology
Source Author(s)/Editor(s): T. Kavitha (New Horizon College of Engineering (Autonomous), India & Visvesvaraya Technological University, India), G. Senbagavalli (AMC Engineering College, Visvesvaraya Technological University, India), Deepika Koundal (University of Petroleum and Energy Studies, Dehradun, India), Yanhui Guo (University of Illinois, USA)and Deepak Jain (Chongqing University of Posts and Telecommunications, China)
DOI: 10.4018/978-1-6684-6275-1.ch011
Purchase
|
Abstract
The combustion quality determination in power station boilers is of great importance to avoid air pollution. Complete combustion minimizes the exit of NOx, SOx, CO, and CO2 emissions, also ensuring the consistency in load generation in thermal power plants. This chapter proposes a novel hybrid algorithm, called black widow optimization algorithm with mayfly optimization algorithm (BWO-MA), for solving global optimization problems. In this chapter, an effort is made to develop BWO-MA with artificial neural networks (ANN)-based diagnostic model for onset detection of incomplete combustion. Comparison has been done with existing machine learning methods with the proposed BWO-MA-based ANN architecture to accommodate the greater performance. The comprehensive analysis showed that the proposed achieved splendid state-of-the-art performance.
Related Content
|
Nalini M..
© 2023.
22 pages.
|
|
Balachandar S., Chinnaiyan R..
© 2023.
19 pages.
|
|
V. A. Velvizhi, G. Senbagavalli, S. Malini.
© 2023.
29 pages.
|
|
Amuthan Nallathambi, Kannan Nova.
© 2023.
25 pages.
|
|
Amuthan Nallathambi, Sivakumar N., Velrajkumar P..
© 2023.
17 pages.
|
|
Nayana Hegde, Sunilkumar S. Manvi.
© 2023.
18 pages.
|
|
Udayakumar K., Ramamoorthy S., Poorvadevi R..
© 2023.
26 pages.
|
|
|