Performance prediction of a stirling heat engine using artificial neural network model

M. G.K. Machesa; L. K. Tartibu; F. K. Tekweme; M. O. Okwu; D. E. Ighravwe

doi:10.1109/icABCD49160.2020.9183890

Performance prediction of a stirling heat engine using artificial neural network model

M. G.K. Machesa
, L. K. Tartibu
, F. K. Tekweme
, M. O. Okwu
, D. E. Ighravwe

Mechanical and Industrial Engineering Technology

University of Johannesburg

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

3 Citations (Scopus)

Abstract

Global energy use has increased significantly over the past few years. This increase is as a result of several factors which include growth in population, improved living standards and the development of the trade and commercial industry. With the world's increased reliance on fossil fuels, various environmental issues have surfaced. Several scholars in energy-related research have recommended the adoption of renewable energy as an alternative energy source. However, Stirling engines are among the devices developed by engineers to counter some of the environmental and social implications of fossil fuels. In this study, artificial neural network (ANN) model has been implemented to predict a Stirling heat engine system power and torque. The ANN model used a sigmoid activation transfer function to obtain the optimum architecture for this prediction problem. Python is used to build and train the ANN model and the performance of the algorithm was adjudged using the root mean square error and the coefficient of determination R¹. Based on the analysis, it was observed that a 3-10-1 ANN model gave a good prediction of the engine's torque and power.

Original language	English
Title of host publication	2020 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems, icABCD 2020 - Proceedings
Editors	Sameerchand Pudaruth, Upasana Singh
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728167701
DOIs	https://doi.org/10.1109/icABCD49160.2020.9183890
Publication status	Published - Aug 2020
Event	2020 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems, icABCD 2020 - Durban, KwaZulu Natal, South Africa Duration: 6 Aug 2020 → 7 Aug 2020

Publication series

Name	2020 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems, icABCD 2020 - Proceedings

Conference

Conference	2020 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems, icABCD 2020
Country/Territory	South Africa
City	Durban, KwaZulu Natal
Period	6/08/20 → 7/08/20

UN SDGs

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

SDG 7 Affordable and Clean Energy

Keywords

Artificial neural network
Neural Network
Power
Stirling Engine
Torque

ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications
Computer Science Applications
Computer Vision and Pattern Recognition
Hardware and Architecture
Information Systems and Management

Access to Document

10.1109/icABCD49160.2020.9183890

Cite this

Machesa, M. G. K., Tartibu, L. K., Tekweme, F. K., Okwu, M. O., & Ighravwe, D. E. (2020). Performance prediction of a stirling heat engine using artificial neural network model. In S. Pudaruth, & U. Singh (Eds.), 2020 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems, icABCD 2020 - Proceedings Article 9183890 (2020 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems, icABCD 2020 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/icABCD49160.2020.9183890

Machesa, M. G.K. ; Tartibu, L. K. ; Tekweme, F. K. et al. / Performance prediction of a stirling heat engine using artificial neural network model. 2020 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems, icABCD 2020 - Proceedings. editor / Sameerchand Pudaruth ; Upasana Singh. Institute of Electrical and Electronics Engineers Inc., 2020. (2020 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems, icABCD 2020 - Proceedings).

@inproceedings{cae4e5462a6e4a3f8ddf3801e0cdfd40,

title = "Performance prediction of a stirling heat engine using artificial neural network model",

abstract = "Global energy use has increased significantly over the past few years. This increase is as a result of several factors which include growth in population, improved living standards and the development of the trade and commercial industry. With the world's increased reliance on fossil fuels, various environmental issues have surfaced. Several scholars in energy-related research have recommended the adoption of renewable energy as an alternative energy source. However, Stirling engines are among the devices developed by engineers to counter some of the environmental and social implications of fossil fuels. In this study, artificial neural network (ANN) model has been implemented to predict a Stirling heat engine system power and torque. The ANN model used a sigmoid activation transfer function to obtain the optimum architecture for this prediction problem. Python is used to build and train the ANN model and the performance of the algorithm was adjudged using the root mean square error and the coefficient of determination R1. Based on the analysis, it was observed that a 3-10-1 ANN model gave a good prediction of the engine's torque and power.",

keywords = "Artificial neural network, Neural Network, Power, Stirling Engine, Torque",

author = "Machesa, \{M. G.K.\} and Tartibu, \{L. K.\} and Tekweme, \{F. K.\} and Okwu, \{M. O.\} and Ighravwe, \{D. E.\}",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems, icABCD 2020 ; Conference date: 06-08-2020 Through 07-08-2020",

year = "2020",

month = aug,

doi = "10.1109/icABCD49160.2020.9183890",

language = "English",

series = "2020 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems, icABCD 2020 - Proceedings",

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

editor = "Sameerchand Pudaruth and Upasana Singh",

booktitle = "2020 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems, icABCD 2020 - Proceedings",

address = "United States",

}

Machesa, MGK , Tartibu, LK , Tekweme, FK, Okwu, MO & Ighravwe, DE 2020, Performance prediction of a stirling heat engine using artificial neural network model. in S Pudaruth & U Singh (eds), 2020 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems, icABCD 2020 - Proceedings., 9183890, 2020 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems, icABCD 2020 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 2020 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems, icABCD 2020, Durban, KwaZulu Natal, South Africa, 6/08/20. https://doi.org/10.1109/icABCD49160.2020.9183890

Performance prediction of a stirling heat engine using artificial neural network model. / Machesa, M. G.K.; Tartibu, L. K.; Tekweme, F. K. et al.
2020 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems, icABCD 2020 - Proceedings. ed. / Sameerchand Pudaruth; Upasana Singh. Institute of Electrical and Electronics Engineers Inc., 2020. 9183890 (2020 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems, icABCD 2020 - Proceedings).

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

TY - GEN

T1 - Performance prediction of a stirling heat engine using artificial neural network model

AU - Machesa, M. G.K.

AU - Tartibu, L. K.

AU - Tekweme, F. K.

AU - Okwu, M. O.

AU - Ighravwe, D. E.

PY - 2020/8

Y1 - 2020/8

N2 - Global energy use has increased significantly over the past few years. This increase is as a result of several factors which include growth in population, improved living standards and the development of the trade and commercial industry. With the world's increased reliance on fossil fuels, various environmental issues have surfaced. Several scholars in energy-related research have recommended the adoption of renewable energy as an alternative energy source. However, Stirling engines are among the devices developed by engineers to counter some of the environmental and social implications of fossil fuels. In this study, artificial neural network (ANN) model has been implemented to predict a Stirling heat engine system power and torque. The ANN model used a sigmoid activation transfer function to obtain the optimum architecture for this prediction problem. Python is used to build and train the ANN model and the performance of the algorithm was adjudged using the root mean square error and the coefficient of determination R1. Based on the analysis, it was observed that a 3-10-1 ANN model gave a good prediction of the engine's torque and power.

AB - Global energy use has increased significantly over the past few years. This increase is as a result of several factors which include growth in population, improved living standards and the development of the trade and commercial industry. With the world's increased reliance on fossil fuels, various environmental issues have surfaced. Several scholars in energy-related research have recommended the adoption of renewable energy as an alternative energy source. However, Stirling engines are among the devices developed by engineers to counter some of the environmental and social implications of fossil fuels. In this study, artificial neural network (ANN) model has been implemented to predict a Stirling heat engine system power and torque. The ANN model used a sigmoid activation transfer function to obtain the optimum architecture for this prediction problem. Python is used to build and train the ANN model and the performance of the algorithm was adjudged using the root mean square error and the coefficient of determination R1. Based on the analysis, it was observed that a 3-10-1 ANN model gave a good prediction of the engine's torque and power.

KW - Artificial neural network

KW - Neural Network

KW - Power

KW - Stirling Engine

KW - Torque

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U2 - 10.1109/icABCD49160.2020.9183890

DO - 10.1109/icABCD49160.2020.9183890

M3 - Conference contribution

AN - SCOPUS:85092023518

T3 - 2020 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems, icABCD 2020 - Proceedings

BT - 2020 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems, icABCD 2020 - Proceedings

A2 - Pudaruth, Sameerchand

A2 - Singh, Upasana

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2020 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems, icABCD 2020

Y2 - 6 August 2020 through 7 August 2020

ER -

Machesa MGK , Tartibu LK , Tekweme FK, Okwu MO, Ighravwe DE. Performance prediction of a stirling heat engine using artificial neural network model. In Pudaruth S, Singh U, editors, 2020 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems, icABCD 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2020. 9183890. (2020 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems, icABCD 2020 - Proceedings). doi: 10.1109/icABCD49160.2020.9183890

Performance prediction of a stirling heat engine using artificial neural network model

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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