https://www.mdu.se/

mdu.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Computationally Inexpensive 1D-CNN for the Prediction of Noisy Data of NOx Emissions From 500 MW Coal-Fired Power Plant
COMSATS Univ Islamabad, Proc & Energy Syst Engn Ctr, Dept Chem Engn, PRESTIGE, Lahore, Pakistan..
COMSATS Univ Islamabad, Proc & Energy Syst Engn Ctr, Dept Chem Engn, PRESTIGE, Lahore, Pakistan..
COMSATS Univ Islamabad, Proc & Energy Syst Engn Ctr, Dept Chem Engn, PRESTIGE, Lahore, Pakistan..
Mälardalen University, School of Business, Society and Engineering, Future Energy Center.ORCID iD: 0000-0003-2661-1961
Show others and affiliations
2022 (English)In: Frontiers in Energy Research, E-ISSN 2296-598X, Vol. 10, article id 945769Article in journal (Refereed) Published
Abstract [en]

Coal-fired power plants have been used to meet the energy requirements in countries where coal reserves are abundant and are the key source of NOx emissions. Owing to the serious environmental and health concerns associated with NOx emissions, much work has been carried out to reduce NOx emissions. Sophisticated artificial intelligence (AI) techniques have been employed during the past few decades, such as least-squares support vector machine (LSSVM), artificial neural networks (ANN), long short-term memory (LSTM), and gated recurrent unit (GRU), to develop the NOx prediction model. Several studies have investigated deep neural networks (DNN) models for accurate NOx emission prediction. However, there is a need to investigate a DNN-based NOx prediction model that is accurate and computationally inexpensive. Recently, a new AI technique, convolutional neural network (CNN), has been introduced and proven superior for image class prediction accuracy. According to the best of the author's knowledge, not much work has been done on the utilization of CNN on NOx emissions from coal-fired power plants. Therefore, this study investigated the prediction performance and computational time of one-dimensional CNN (1D-CNN) on NOx emissions data from a 500 MW coal-fired power plant. The variations of hyperparameters of LSTM, GRU, and 1D-CNN were investigated, and the performance metrics such as RMSE and computational time were recorded to obtain optimal hyperparameters. The obtained optimal values of hyperparameters of LSTM, GRU, and 1D-CNN were then employed for models' development, and consequently, the models were tested on test data. The 1D-CNN NOx emission model improved the training efficiency in terms of RMSE by 70.6% and 60.1% compared to LSTM and GRU, respectively. Furthermore, the testing efficiency for 1D-CNN improved by 10.2% and 15.7% compared to LSTM and GRU, respectively. Moreover, 1D-CNN (26 s) reduced the training time by 83.8% and 50% compared to LSTM (160 s) and GRU (52 s), respectively. Results reveal that 1D-CNN is more accurate, more stable, and computationally inexpensive compared to LSTM and GRU on NOx emission data from the 500 MW power plant.

Place, publisher, year, edition, pages
FRONTIERS MEDIA SA , 2022. Vol. 10, article id 945769
Keywords [en]
NOX prediction, machine learning, 1D-convolutional neural network, LSTM, GRU, coal-fired power plant
National Category
Information Systems
Identifiers
URN: urn:nbn:se:mdh:diva-59890DOI: 10.3389/fenrg.2022.945769ISI: 000847865600001Scopus ID: 2-s2.0-85137032792OAI: oai:DiVA.org:mdh-59890DiVA, id: diva2:1694018
Available from: 2022-09-08 Created: 2022-09-08 Last updated: 2022-09-14Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records

Salman, Chaudhary Awais

Search in DiVA

By author/editor
Salman, Chaudhary Awais
By organisation
Future Energy Center
In the same journal
Frontiers in Energy Research
Information Systems

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 67 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf