Control and monitoring system optimalization of combustion in furnace boiler prototype at industrial steam power plant with comparison of Neural Network (NN) and Extreme Learning Machine (ELM) method

A. A. Rahmanda; A. Soeprijanto; A. Muhammad; M. Syaiin; R. Y. Adhitya; B. Herijono; J. Endrasmono; A. Singgih; E. A. Zuliari; S. I. Haryudo; F. Afandi; B. S. Kaloko

doi:10.1109/ISESD.2017.8253317

Control and monitoring system optimalization of combustion in furnace boiler prototype at industrial steam power plant with comparison of Neural Network (NN) and Extreme Learning Machine (ELM) method

A. A. Rahmanda, A. Soeprijanto, A. Muhammad, M. Syaiin, R. Y. Adhitya, B. Herijono, J. Endrasmono, A. Singgih, E. A. Zuliari, S. I. Haryudo, F. Afandi, B. S. Kaloko

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Citations (Scopus)

Abstract

This paper is presenting a design and research studies in industrial steam power plant system called 'control and monitoring system to optimize the combustion process in the furnace boiler prototype with comparison of neural network and extreme learning machine'. Comparison between Neural Network and ELM (Extreme Learning Machine) methods will be used to this combustion control system and will be implemented in a prototype with microcontroller. This prototype is using the value of temperature sensor and value of smoke sensor in the furnace as parameter of heat to control the flow of air and fuel oil. The temperature sensor in this research is type K Thermocouple. The smoke sensor is MQ sensor. This prototype also used fan and pump oil as an actuator. Fans are used to supply the oxygen and pump is used to supply the fuel oil. From the experimental result, this prototype shows the optimization of combustion system using ELM (Extreme Learning Machine) method can work well compared with NN (Neural Network) method. ELM Control System has a very good response and it can work well (RMSE = 6,32456E-05). So, if the system is applied in the industrial steam power plant, it can improve the performance of combustion control systems and able to save the fuel.

Original language	English
Title of host publication	2017 International Symposium on Electronics and Smart Devices, ISESD 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	123-128
Number of pages	6
ISBN (Electronic)	9781538627785
DOIs	https://doi.org/10.1109/ISESD.2017.8253317
Publication status	Published - 1 Jul 2017
Externally published	Yes
Event	2nd International Symposium on Electronics and Smart Devices, ISESD 2017 - Yogyakarta, Indonesia Duration: 17 Oct 2017 → 19 Oct 2017

Publication series

Name	2017 International Symposium on Electronics and Smart Devices, ISESD 2017
Volume	2018-January

Conference

Conference	2nd International Symposium on Electronics and Smart Devices, ISESD 2017
Country/Territory	Indonesia
City	Yogyakarta
Period	17/10/17 → 19/10/17

Keywords

extreme learning machine
furnace boiler
microcontroller
mq7
neural network
type k thermocouple

Access to Document

10.1109/ISESD.2017.8253317

Cite this

Rahmanda, A. A., Soeprijanto, A., Muhammad, A., Syaiin, M., Adhitya, R. Y., Herijono, B., Endrasmono, J., Singgih, A., Zuliari, E. A., Haryudo, S. I., Afandi, F., & Kaloko, B. S. (2017). Control and monitoring system optimalization of combustion in furnace boiler prototype at industrial steam power plant with comparison of Neural Network (NN) and Extreme Learning Machine (ELM) method. In 2017 International Symposium on Electronics and Smart Devices, ISESD 2017 (pp. 123-128). (2017 International Symposium on Electronics and Smart Devices, ISESD 2017; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISESD.2017.8253317

Rahmanda, A. A. ; Soeprijanto, A. ; Muhammad, A. et al. / Control and monitoring system optimalization of combustion in furnace boiler prototype at industrial steam power plant with comparison of Neural Network (NN) and Extreme Learning Machine (ELM) method. 2017 International Symposium on Electronics and Smart Devices, ISESD 2017. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 123-128 (2017 International Symposium on Electronics and Smart Devices, ISESD 2017).

@inproceedings{0765f44b526e4d98998aa0e7e1768496,

title = "Control and monitoring system optimalization of combustion in furnace boiler prototype at industrial steam power plant with comparison of Neural Network (NN) and Extreme Learning Machine (ELM) method",

abstract = "This paper is presenting a design and research studies in industrial steam power plant system called 'control and monitoring system to optimize the combustion process in the furnace boiler prototype with comparison of neural network and extreme learning machine'. Comparison between Neural Network and ELM (Extreme Learning Machine) methods will be used to this combustion control system and will be implemented in a prototype with microcontroller. This prototype is using the value of temperature sensor and value of smoke sensor in the furnace as parameter of heat to control the flow of air and fuel oil. The temperature sensor in this research is type K Thermocouple. The smoke sensor is MQ sensor. This prototype also used fan and pump oil as an actuator. Fans are used to supply the oxygen and pump is used to supply the fuel oil. From the experimental result, this prototype shows the optimization of combustion system using ELM (Extreme Learning Machine) method can work well compared with NN (Neural Network) method. ELM Control System has a very good response and it can work well (RMSE = 6,32456E-05). So, if the system is applied in the industrial steam power plant, it can improve the performance of combustion control systems and able to save the fuel.",

keywords = "extreme learning machine, furnace boiler, microcontroller, mq7, neural network, type k thermocouple",

author = "Rahmanda, {A. A.} and A. Soeprijanto and A. Muhammad and M. Syaiin and Adhitya, {R. Y.} and B. Herijono and J. Endrasmono and A. Singgih and Zuliari, {E. A.} and Haryudo, {S. I.} and F. Afandi and Kaloko, {B. S.}",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2nd International Symposium on Electronics and Smart Devices, ISESD 2017 ; Conference date: 17-10-2017 Through 19-10-2017",

year = "2017",

month = jul,

day = "1",

doi = "10.1109/ISESD.2017.8253317",

language = "English",

series = "2017 International Symposium on Electronics and Smart Devices, ISESD 2017",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "123--128",

booktitle = "2017 International Symposium on Electronics and Smart Devices, ISESD 2017",

address = "United States",

}

Rahmanda, AA, Soeprijanto, A, Muhammad, A, Syaiin, M, Adhitya, RY, Herijono, B, Endrasmono, J, Singgih, A, Zuliari, EA, Haryudo, SI, Afandi, F & Kaloko, BS 2017, Control and monitoring system optimalization of combustion in furnace boiler prototype at industrial steam power plant with comparison of Neural Network (NN) and Extreme Learning Machine (ELM) method. in 2017 International Symposium on Electronics and Smart Devices, ISESD 2017. 2017 International Symposium on Electronics and Smart Devices, ISESD 2017, vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 123-128, 2nd International Symposium on Electronics and Smart Devices, ISESD 2017, Yogyakarta, Indonesia, 17/10/17. https://doi.org/10.1109/ISESD.2017.8253317

Control and monitoring system optimalization of combustion in furnace boiler prototype at industrial steam power plant with comparison of Neural Network (NN) and Extreme Learning Machine (ELM) method. / Rahmanda, A. A.; Soeprijanto, A.; Muhammad, A. et al.
2017 International Symposium on Electronics and Smart Devices, ISESD 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 123-128 (2017 International Symposium on Electronics and Smart Devices, ISESD 2017; Vol. 2018-January).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Control and monitoring system optimalization of combustion in furnace boiler prototype at industrial steam power plant with comparison of Neural Network (NN) and Extreme Learning Machine (ELM) method

AU - Rahmanda, A. A.

AU - Soeprijanto, A.

AU - Muhammad, A.

AU - Syaiin, M.

AU - Adhitya, R. Y.

AU - Herijono, B.

AU - Endrasmono, J.

AU - Singgih, A.

AU - Zuliari, E. A.

AU - Haryudo, S. I.

AU - Afandi, F.

AU - Kaloko, B. S.

PY - 2017/7/1

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N2 - This paper is presenting a design and research studies in industrial steam power plant system called 'control and monitoring system to optimize the combustion process in the furnace boiler prototype with comparison of neural network and extreme learning machine'. Comparison between Neural Network and ELM (Extreme Learning Machine) methods will be used to this combustion control system and will be implemented in a prototype with microcontroller. This prototype is using the value of temperature sensor and value of smoke sensor in the furnace as parameter of heat to control the flow of air and fuel oil. The temperature sensor in this research is type K Thermocouple. The smoke sensor is MQ sensor. This prototype also used fan and pump oil as an actuator. Fans are used to supply the oxygen and pump is used to supply the fuel oil. From the experimental result, this prototype shows the optimization of combustion system using ELM (Extreme Learning Machine) method can work well compared with NN (Neural Network) method. ELM Control System has a very good response and it can work well (RMSE = 6,32456E-05). So, if the system is applied in the industrial steam power plant, it can improve the performance of combustion control systems and able to save the fuel.

AB - This paper is presenting a design and research studies in industrial steam power plant system called 'control and monitoring system to optimize the combustion process in the furnace boiler prototype with comparison of neural network and extreme learning machine'. Comparison between Neural Network and ELM (Extreme Learning Machine) methods will be used to this combustion control system and will be implemented in a prototype with microcontroller. This prototype is using the value of temperature sensor and value of smoke sensor in the furnace as parameter of heat to control the flow of air and fuel oil. The temperature sensor in this research is type K Thermocouple. The smoke sensor is MQ sensor. This prototype also used fan and pump oil as an actuator. Fans are used to supply the oxygen and pump is used to supply the fuel oil. From the experimental result, this prototype shows the optimization of combustion system using ELM (Extreme Learning Machine) method can work well compared with NN (Neural Network) method. ELM Control System has a very good response and it can work well (RMSE = 6,32456E-05). So, if the system is applied in the industrial steam power plant, it can improve the performance of combustion control systems and able to save the fuel.

KW - extreme learning machine

KW - furnace boiler

KW - microcontroller

KW - mq7

KW - neural network

KW - type k thermocouple

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Rahmanda AA, Soeprijanto A, Muhammad A, Syaiin M, Adhitya RY, Herijono B et al. Control and monitoring system optimalization of combustion in furnace boiler prototype at industrial steam power plant with comparison of Neural Network (NN) and Extreme Learning Machine (ELM) method. In 2017 International Symposium on Electronics and Smart Devices, ISESD 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 123-128. (2017 International Symposium on Electronics and Smart Devices, ISESD 2017). doi: 10.1109/ISESD.2017.8253317

Control and monitoring system optimalization of combustion in furnace boiler prototype at industrial steam power plant with comparison of Neural Network (NN) and Extreme Learning Machine (ELM) method

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Keywords

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