Energy Equipment and Systems

Energy Equipment and Systems

Thermal design of fire tube boiler with superheater and estimation of temperature increase in the superheater based on machine learning methods

Document Type : Research Paper

Authors
Ebrahim Pilali 1, 2
Safiye Shafiei 1, 2
Ramin Kouhikamali 3
Mohsen Salimi 4
Majid Amidpour 5
1 Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran
2 Department of Energy System Engineering, Faculty of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran
3 Department of Mechanical Engineering, Isfahan University of Technology, Isfahan, Iran
4 Renewable Energy Research Department, Niroo Research Institute (NRI), Tehran, Iran
5 Department of Energy Systems Engineering, Faculty of Mechanical Engineering, K.N. Toosi University of Technology
10.22059/ees.2024.2030080.1467
Abstract
This research utilizes MATLAB and Python coding to optimize the thermal design of an industrial shell and tube steam boiler with an internal superheater. The paper outlines a systematic approach to steam boiler design, including heat transfer dynamics analysis, superheater configuration optimization, and implementation. They take action to enhance the performance of the third pass. The shell and tube steam boiler specifications, including an internal superheater, have been determined, with a steam capacity of 5 tons/hour and operating at a working pressure of 10 bar. According to the results, the opt substantially impacted 71 tubes in the second pass, each with a diameter of 5 cm, and an additional 82 tubes of identical size in the third pass (which includes revisions). To achieve a desired temperature increase of 15 ℃ in the superheater, incorporating the superheater section into the fire tube resulted in a 23.72% increase in the third pass level compared to the scenario without a superheater. For every 5 ℃ temperature increase in the superheater, the steam velocity in the third pass tubes decreases by approximately 1m/s. Adding the superheater to the end of the third pass  reduces the temperature of this area from 525 ℃ to 500 ℃. Leveraging machine learning algorithms enabled the identification of parameters influencing the rise in superheater temperature. Linear regression emerged as the best predictor of superheater temperature increase among the eight models considered.
Keywords
Heat Transfer
Heat Exchanger Design
Fire Tube Boiler
Superheater
Machine Learning

Subjects

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Energy Equipment and Systems
Volume 12, Issue 4 - Serial Number 4
Autumn 2024
Pages 423-453