Prototype control and monitoring system safety device from leakage ammonia at marine loading arm with comparison of Neural Network (NN) and Extreme Learning Machine (ELM) method

P. Oktavian Hanggara; Mat Syai'In; P. Fahrizal Paradisa; M. Zainal Arifin; Sryang T. Sarena; M. Syaiin; R. Y. Adhitya; Aliy Haydlaar; R. A. Atmoko; P. Asri; A. Soeprijanto

doi:10.1109/ISESD.2017.8253310

Prototype control and monitoring system safety device from leakage ammonia at marine loading arm with comparison of Neural Network (NN) and Extreme Learning Machine (ELM) method

P. Oktavian Hanggara, Mat Syai'In, P. Fahrizal Paradisa, M. Zainal Arifin, Sryang T. Sarena, M. Syaiin, R. Y. Adhitya, Aliy Haydlaar, R. A. Atmoko, P. Asri, A. Soeprijanto

Shipbuilding Institute of Polytechnic Surabaya

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

8 Citations (Scopus)

Abstract

This paper presents design and research studies in marine loading arm plant system. Artificial Neural Network (NN) and ELM (Extreme Learning Machine) methods are used and compared in this valve control system by implement it in a prototype using microcontroller. This prototype use value of temperature sensor and value of ammonia gas sensor in the furnace as parameter of heat to control the flow of air and valve of safety device. The temperature sensor used in this research is the type of DHT11. The ammonia gas sensor is MQ sensor. This prototype also uses fan and servo as the actuator. Fans are used to supply the oxygen and servo is used to control the valve of ammonia. From the experimental result, the data shows that the optimization of safety device system using ELM method works better compared with NN. The control system has a very good response and it can work well (percentage of error is less than 0.4%). Hence, if the system is applied in the marine loading arm plant, it could improve the performance of safety device control systems and save the leakage of ammonia gas.

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	85-89
Number of pages	5
ISBN (Electronic)	9781538627785
DOIs	https://doi.org/10.1109/ISESD.2017.8253310
Publication status	Published - 1 Jul 2017
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

dht11
extreme learning machine
furnace ammonia
microcontroller
mq7
neural network

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/ISESD.2017.8253310

Cite this

Hanggara, P. O., Syai'In, M., Paradisa, P. F., Arifin, M. Z., Sarena, S. T., Syaiin, M., Adhitya, R. Y., Haydlaar, A., Atmoko, R. A., Asri, P., & Soeprijanto, A. (2017). Prototype control and monitoring system safety device from leakage ammonia at marine loading arm with comparison of Neural Network (NN) and Extreme Learning Machine (ELM) method. In 2017 International Symposium on Electronics and Smart Devices, ISESD 2017 (pp. 85-89). (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.2017.8253310

Hanggara, P. Oktavian ; Syai'In, Mat ; Paradisa, P. Fahrizal et al. / Prototype control and monitoring system safety device from leakage ammonia at marine loading arm 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. 85-89 (2017 International Symposium on Electronics and Smart Devices, ISESD 2017).

@inproceedings{84e00aa4b89d467eadee7cc314ddd1c3,

title = "Prototype control and monitoring system safety device from leakage ammonia at marine loading arm with comparison of Neural Network (NN) and Extreme Learning Machine (ELM) method",

abstract = "This paper presents design and research studies in marine loading arm plant system. Artificial Neural Network (NN) and ELM (Extreme Learning Machine) methods are used and compared in this valve control system by implement it in a prototype using microcontroller. This prototype use value of temperature sensor and value of ammonia gas sensor in the furnace as parameter of heat to control the flow of air and valve of safety device. The temperature sensor used in this research is the type of DHT11. The ammonia gas sensor is MQ sensor. This prototype also uses fan and servo as the actuator. Fans are used to supply the oxygen and servo is used to control the valve of ammonia. From the experimental result, the data shows that the optimization of safety device system using ELM method works better compared with NN. The control system has a very good response and it can work well (percentage of error is less than 0.4%). Hence, if the system is applied in the marine loading arm plant, it could improve the performance of safety device control systems and save the leakage of ammonia gas.",

keywords = "dht11, extreme learning machine, furnace ammonia, microcontroller, mq7, neural network",

author = "Hanggara, {P. Oktavian} and Mat Syai'In and Paradisa, {P. Fahrizal} and Arifin, {M. Zainal} and Sarena, {Sryang T.} and M. Syaiin and Adhitya, {R. Y.} and Aliy Haydlaar and Atmoko, {R. A.} and P. Asri and A. Soeprijanto",

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",

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doi = "10.1109/ISESD.2017.8253310",

language = "English",

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Hanggara, PO, Syai'In, M, Paradisa, PF, Arifin, MZ, Sarena, ST, Syaiin, M, Adhitya, RY, Haydlaar, A, Atmoko, RA, Asri, P & Soeprijanto, A 2017, Prototype control and monitoring system safety device from leakage ammonia at marine loading arm 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. 85-89, 2nd International Symposium on Electronics and Smart Devices, ISESD 2017, Yogyakarta, Indonesia, 17/10/17. https://doi.org/10.1109/ISESD.2017.8253310

Prototype control and monitoring system safety device from leakage ammonia at marine loading arm with comparison of Neural Network (NN) and Extreme Learning Machine (ELM) method. / Hanggara, P. Oktavian; Syai'In, Mat; Paradisa, P. Fahrizal et al.
2017 International Symposium on Electronics and Smart Devices, ISESD 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 85-89 (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 - Prototype control and monitoring system safety device from leakage ammonia at marine loading arm with comparison of Neural Network (NN) and Extreme Learning Machine (ELM) method

AU - Hanggara, P. Oktavian

AU - Syai'In, Mat

AU - Paradisa, P. Fahrizal

AU - Arifin, M. Zainal

AU - Sarena, Sryang T.

AU - Syaiin, M.

AU - Adhitya, R. Y.

AU - Haydlaar, Aliy

AU - Atmoko, R. A.

AU - Asri, P.

AU - Soeprijanto, A.

PY - 2017/7/1

Y1 - 2017/7/1

N2 - This paper presents design and research studies in marine loading arm plant system. Artificial Neural Network (NN) and ELM (Extreme Learning Machine) methods are used and compared in this valve control system by implement it in a prototype using microcontroller. This prototype use value of temperature sensor and value of ammonia gas sensor in the furnace as parameter of heat to control the flow of air and valve of safety device. The temperature sensor used in this research is the type of DHT11. The ammonia gas sensor is MQ sensor. This prototype also uses fan and servo as the actuator. Fans are used to supply the oxygen and servo is used to control the valve of ammonia. From the experimental result, the data shows that the optimization of safety device system using ELM method works better compared with NN. The control system has a very good response and it can work well (percentage of error is less than 0.4%). Hence, if the system is applied in the marine loading arm plant, it could improve the performance of safety device control systems and save the leakage of ammonia gas.

AB - This paper presents design and research studies in marine loading arm plant system. Artificial Neural Network (NN) and ELM (Extreme Learning Machine) methods are used and compared in this valve control system by implement it in a prototype using microcontroller. This prototype use value of temperature sensor and value of ammonia gas sensor in the furnace as parameter of heat to control the flow of air and valve of safety device. The temperature sensor used in this research is the type of DHT11. The ammonia gas sensor is MQ sensor. This prototype also uses fan and servo as the actuator. Fans are used to supply the oxygen and servo is used to control the valve of ammonia. From the experimental result, the data shows that the optimization of safety device system using ELM method works better compared with NN. The control system has a very good response and it can work well (percentage of error is less than 0.4%). Hence, if the system is applied in the marine loading arm plant, it could improve the performance of safety device control systems and save the leakage of ammonia gas.

KW - dht11

KW - extreme learning machine

KW - furnace ammonia

KW - microcontroller

KW - mq7

KW - neural network

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U2 - 10.1109/ISESD.2017.8253310

DO - 10.1109/ISESD.2017.8253310

M3 - Conference contribution

AN - SCOPUS:85047454274

T3 - 2017 International Symposium on Electronics and Smart Devices, ISESD 2017

SP - 85

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BT - 2017 International Symposium on Electronics and Smart Devices, ISESD 2017

PB - Institute of Electrical and Electronics Engineers Inc.

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Y2 - 17 October 2017 through 19 October 2017

ER -

Hanggara PO, Syai'In M, Paradisa PF, Arifin MZ, Sarena ST, Syaiin M et al. Prototype control and monitoring system safety device from leakage ammonia at marine loading arm 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. 85-89. (2017 International Symposium on Electronics and Smart Devices, ISESD 2017). doi: 10.1109/ISESD.2017.8253310

Prototype control and monitoring system safety device from leakage ammonia at marine loading arm with comparison of Neural Network (NN) and Extreme Learning Machine (ELM) method

Abstract

Publication series

Conference

Keywords

UN SDGs

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