Modeling and control of boiler in thermal power plant using model reference adaptive control
Downloads
Published
DOI:
https://doi.org/10.58414/SCIENTIFICTEMPER.2024.15.1.27Keywords:
Boiler, Model Reference Adaptive Control, Modeling, Multi input Multi Output, SimulationDimensions Badge
Issue
Section
License
Copyright (c) 2024 The Scientific Temper

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
The boiler is a multi-variable system, which is very difficult to control due to its nonlinear behavior, uncertainties, interactions between variables, and unmeasured and frequent disturbances. Instead of conventional control techniques, modern control techniques are being implemented in most boilers by industries. Mathematical modeling is a useful tool to analyze a complex system’s performance and design a controller for the same. The mathematical model is derived from the open-loop data obtained from the process station. The mathematical equation is then derived using the decoupling technique in terms of transfer function. An adaptive controller is designed and implemented for the model and the simulation study for the same is carried out using MATLAB. The proposed method discussed in the paper can adjust the controller parameters in response to changes in plant and disturbance in real-time by referring to the reference model that specifies the properties of the desired control system.Abstract
How to Cite
Downloads
Similar Articles
- R. Kalaiselvi, P. Meenakshi Sundaram, Machine learning-based ERA model for detecting Sybil attacks on mobile ad hoc networks , The Scientific Temper: Vol. 15 No. 04 (2024): The Scientific Temper
- Sivasankar G. A, Study of hybrid fuel injectors for aircraft engines , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- Sharanagouda N. Patil, Ramesh M. Kagalkar, Analysis of substrate materials for flexible and wearable MIMO antenna for wireless communication , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- Santosh Kumar Sahu, B. R. Senthil kumar, Y. Aboobucker parvez, Ashish Verma, Assessment of noise levels by using noise prediction modeling , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- Y. Mohammed Iqbal, M. Mohamed Surputheen, S. Peerbasha, Swarm intelligence-driven HC2NN model for optimized COVID-19 detection using lung imaging , The Scientific Temper: Vol. 16 No. 03 (2025): The Scientific Temper
- Sirajum Munira Priety, Farhan Bin Manjur, AI Driven Approach in Smart Manufacturing in Bangladesh , The Scientific Temper: Vol. 16 No. 10 (2025): The Scientific Temper
- Arvind K Shukla, Balaji V, Dharani R, M Ananthi, R Padmavathy, Romala V. Srinivas, Precision agriculture predictive modeling and sensor analysis for enhanced crop monitoring , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- Amanda Quist Okronipa, Lucy Ewuresi Eghan, A theoretical investigation of students’ adoption of artificial intelligence chatbots using social cognitive theory and uses and gratification theory , The Scientific Temper: Vol. 16 No. 02 (2025): The Scientific Temper
- K. Kalaiselvi, M. Kasthuri, Tuning VGG19 hyperparameters for improved pneumonia classification , The Scientific Temper: Vol. 15 No. 02 (2024): The Scientific Temper
- G. Chitra, Hari Ganesh S., Cultural algorithm based principal component analysis (CA-PCA) approach for handling high dimensional data , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
<< < 9 10 11 12 13 14 15 16 17 18 > >>
You may also start an advanced similarity search for this article.

