Optimization of gasoline engine to maximize brake power and minimize brake specific fuel consumption with artificial neural network and killer whale algorithm

Totok R. Biyanto; Muhammad F. Auliya; Ronny D. Noriyati; Hendra Cordova; Martadji; Hafisyah R. Putra; Titania N. Bethiana; Mohammad O. Mabruri

doi:10.1063/1.5095360

Optimization of gasoline engine to maximize brake power and minimize brake specific fuel consumption with artificial neural network and killer whale algorithm

Totok R. Biyanto^*, Muhammad F. Auliya, Ronny D. Noriyati, Hendra Cordova, Martadji, Hafisyah R. Putra, Titania N. Bethiana, Mohammad O. Mabruri

^*Corresponding author for this work

Institut Teknologi Sepuluh Nopember

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

Abstract

In Formula Society of Automotive Engineering (SAE) competition, the design of efficient and powerful combustion engine is required. This paper discussed optimization of gasoline engine using Killer Whale algorithm. The modelling of gasoline engine was built using Multi-Layer Perceptron - Artificial Neural Network (MLP-ANN). A gasoline engine was simulated using Ricardo Wave commercial software to acquire data for training and testing the proposed ANN. The ANN weights were determined by utilizing Levenberg-Marquardt algorithm. The objective function in this paper is to maximize power, minimize the Brake Specific Fuel Consumption (BSFC) and minimize the operational cost. The optimized variables are engine speed (rpm), Air Fuel Ratio (AFR), Mass Fuel Flow (MFF), Intake Pressure (IP), Intake Air Temperature (IAT), Combustion Start (CS) and Throttle Angle (TA). Root Mean Square Error (RMSE) of ANN modelling is 0.021 kW for power and 0.00032 kg/kW.hr for BSFC. The optimization results show that the power increases to 13%, BSFC decreases to 11% and the cost operation decreases to 23% compare with existing design variables.

Original language	English
Title of host publication	Advanced Industrial Technology in Engineering Physics
Editors	Agus Muhamad Hatta, Katherin Indriawati, Gunawan Nugroho, Totok Ruki Biyanto, Dhany Arifianto, Doty Dewi Risanti, Sonny Irawan
Publisher	American Institute of Physics Inc.
ISBN (Electronic)	9780735418189
DOIs	https://doi.org/10.1063/1.5095360
Publication status	Published - 29 Mar 2019
Event	2nd Engineering Physics International Conference 2018, EPIC 2018 - Surabaya, Indonesia Duration: 31 Oct 2018 → 2 Nov 2018

Publication series

Name	AIP Conference Proceedings
Volume	2088
ISSN (Print)	0094-243X
ISSN (Electronic)	1551-7616

Conference

Conference	2nd Engineering Physics International Conference 2018, EPIC 2018
Country/Territory	Indonesia
City	Surabaya
Period	31/10/18 → 2/11/18

Access to Document

10.1063/1.5095360

Cite this

Biyanto, T. R., Auliya, M. F., Noriyati, R. D., Cordova, H., Martadji, Putra, H. R., Bethiana, T. N., & Mabruri, M. O. (2019). Optimization of gasoline engine to maximize brake power and minimize brake specific fuel consumption with artificial neural network and killer whale algorithm. In A. M. Hatta, K. Indriawati, G. Nugroho, T. R. Biyanto, D. Arifianto, D. D. Risanti, & S. Irawan (Eds.), Advanced Industrial Technology in Engineering Physics Article 060012 (AIP Conference Proceedings; Vol. 2088). American Institute of Physics Inc.. https://doi.org/10.1063/1.5095360

Biyanto, Totok R. ; Auliya, Muhammad F. ; Noriyati, Ronny D. et al. / Optimization of gasoline engine to maximize brake power and minimize brake specific fuel consumption with artificial neural network and killer whale algorithm. Advanced Industrial Technology in Engineering Physics. editor / Agus Muhamad Hatta ; Katherin Indriawati ; Gunawan Nugroho ; Totok Ruki Biyanto ; Dhany Arifianto ; Doty Dewi Risanti ; Sonny Irawan. American Institute of Physics Inc., 2019. (AIP Conference Proceedings).

@inproceedings{309b4810e19f4e00a76d016cbe9220ca,

title = "Optimization of gasoline engine to maximize brake power and minimize brake specific fuel consumption with artificial neural network and killer whale algorithm",

abstract = "In Formula Society of Automotive Engineering (SAE) competition, the design of efficient and powerful combustion engine is required. This paper discussed optimization of gasoline engine using Killer Whale algorithm. The modelling of gasoline engine was built using Multi-Layer Perceptron - Artificial Neural Network (MLP-ANN). A gasoline engine was simulated using Ricardo Wave commercial software to acquire data for training and testing the proposed ANN. The ANN weights were determined by utilizing Levenberg-Marquardt algorithm. The objective function in this paper is to maximize power, minimize the Brake Specific Fuel Consumption (BSFC) and minimize the operational cost. The optimized variables are engine speed (rpm), Air Fuel Ratio (AFR), Mass Fuel Flow (MFF), Intake Pressure (IP), Intake Air Temperature (IAT), Combustion Start (CS) and Throttle Angle (TA). Root Mean Square Error (RMSE) of ANN modelling is 0.021 kW for power and 0.00032 kg/kW.hr for BSFC. The optimization results show that the power increases to 13%, BSFC decreases to 11% and the cost operation decreases to 23% compare with existing design variables.",

author = "Biyanto, {Totok R.} and Auliya, {Muhammad F.} and Noriyati, {Ronny D.} and Hendra Cordova and Martadji and Putra, {Hafisyah R.} and Bethiana, {Titania N.} and Mabruri, {Mohammad O.}",

note = "Publisher Copyright: {\textcopyright} 2019 American Institute of Physics Inc. All rights reserved.; 2nd Engineering Physics International Conference 2018, EPIC 2018 ; Conference date: 31-10-2018 Through 02-11-2018",

year = "2019",

month = mar,

day = "29",

doi = "10.1063/1.5095360",

language = "English",

series = "AIP Conference Proceedings",

publisher = "American Institute of Physics Inc.",

editor = "Hatta, {Agus Muhamad} and Katherin Indriawati and Gunawan Nugroho and Biyanto, {Totok Ruki} and Dhany Arifianto and Risanti, {Doty Dewi} and Sonny Irawan",

booktitle = "Advanced Industrial Technology in Engineering Physics",

}

Biyanto, TR, Auliya, MF, Noriyati, RD, Cordova, H, Martadji, Putra, HR, Bethiana, TN & Mabruri, MO 2019, Optimization of gasoline engine to maximize brake power and minimize brake specific fuel consumption with artificial neural network and killer whale algorithm. in AM Hatta, K Indriawati, G Nugroho, TR Biyanto, D Arifianto, DD Risanti & S Irawan (eds), Advanced Industrial Technology in Engineering Physics., 060012, AIP Conference Proceedings, vol. 2088, American Institute of Physics Inc., 2nd Engineering Physics International Conference 2018, EPIC 2018, Surabaya, Indonesia, 31/10/18. https://doi.org/10.1063/1.5095360

Optimization of gasoline engine to maximize brake power and minimize brake specific fuel consumption with artificial neural network and killer whale algorithm. / Biyanto, Totok R.; Auliya, Muhammad F.; Noriyati, Ronny D. et al.
Advanced Industrial Technology in Engineering Physics. ed. / Agus Muhamad Hatta; Katherin Indriawati; Gunawan Nugroho; Totok Ruki Biyanto; Dhany Arifianto; Doty Dewi Risanti; Sonny Irawan. American Institute of Physics Inc., 2019. 060012 (AIP Conference Proceedings; Vol. 2088).

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

TY - GEN

T1 - Optimization of gasoline engine to maximize brake power and minimize brake specific fuel consumption with artificial neural network and killer whale algorithm

AU - Biyanto, Totok R.

AU - Auliya, Muhammad F.

AU - Noriyati, Ronny D.

AU - Cordova, Hendra

AU - Martadji,

AU - Putra, Hafisyah R.

AU - Bethiana, Titania N.

AU - Mabruri, Mohammad O.

PY - 2019/3/29

Y1 - 2019/3/29

N2 - In Formula Society of Automotive Engineering (SAE) competition, the design of efficient and powerful combustion engine is required. This paper discussed optimization of gasoline engine using Killer Whale algorithm. The modelling of gasoline engine was built using Multi-Layer Perceptron - Artificial Neural Network (MLP-ANN). A gasoline engine was simulated using Ricardo Wave commercial software to acquire data for training and testing the proposed ANN. The ANN weights were determined by utilizing Levenberg-Marquardt algorithm. The objective function in this paper is to maximize power, minimize the Brake Specific Fuel Consumption (BSFC) and minimize the operational cost. The optimized variables are engine speed (rpm), Air Fuel Ratio (AFR), Mass Fuel Flow (MFF), Intake Pressure (IP), Intake Air Temperature (IAT), Combustion Start (CS) and Throttle Angle (TA). Root Mean Square Error (RMSE) of ANN modelling is 0.021 kW for power and 0.00032 kg/kW.hr for BSFC. The optimization results show that the power increases to 13%, BSFC decreases to 11% and the cost operation decreases to 23% compare with existing design variables.

AB - In Formula Society of Automotive Engineering (SAE) competition, the design of efficient and powerful combustion engine is required. This paper discussed optimization of gasoline engine using Killer Whale algorithm. The modelling of gasoline engine was built using Multi-Layer Perceptron - Artificial Neural Network (MLP-ANN). A gasoline engine was simulated using Ricardo Wave commercial software to acquire data for training and testing the proposed ANN. The ANN weights were determined by utilizing Levenberg-Marquardt algorithm. The objective function in this paper is to maximize power, minimize the Brake Specific Fuel Consumption (BSFC) and minimize the operational cost. The optimized variables are engine speed (rpm), Air Fuel Ratio (AFR), Mass Fuel Flow (MFF), Intake Pressure (IP), Intake Air Temperature (IAT), Combustion Start (CS) and Throttle Angle (TA). Root Mean Square Error (RMSE) of ANN modelling is 0.021 kW for power and 0.00032 kg/kW.hr for BSFC. The optimization results show that the power increases to 13%, BSFC decreases to 11% and the cost operation decreases to 23% compare with existing design variables.

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U2 - 10.1063/1.5095360

DO - 10.1063/1.5095360

M3 - Conference contribution

AN - SCOPUS:85064379621

T3 - AIP Conference Proceedings

BT - Advanced Industrial Technology in Engineering Physics

A2 - Hatta, Agus Muhamad

A2 - Indriawati, Katherin

A2 - Nugroho, Gunawan

A2 - Biyanto, Totok Ruki

A2 - Arifianto, Dhany

A2 - Risanti, Doty Dewi

A2 - Irawan, Sonny

PB - American Institute of Physics Inc.

T2 - 2nd Engineering Physics International Conference 2018, EPIC 2018

Y2 - 31 October 2018 through 2 November 2018

ER -

Biyanto TR, Auliya MF, Noriyati RD, Cordova H, Martadji, Putra HR et al. Optimization of gasoline engine to maximize brake power and minimize brake specific fuel consumption with artificial neural network and killer whale algorithm. In Hatta AM, Indriawati K, Nugroho G, Biyanto TR, Arifianto D, Risanti DD, Irawan S, editors, Advanced Industrial Technology in Engineering Physics. American Institute of Physics Inc. 2019. 060012. (AIP Conference Proceedings). doi: 10.1063/1.5095360

Optimization of gasoline engine to maximize brake power and minimize brake specific fuel consumption with artificial neural network and killer whale algorithm

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