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Sökning: WFRF:(Muhammad Khan Asad) > (2022) > Computationally Ine...

  • Saif-Ul-Allah, Muhammad WaqasCOMSATS Univ Islamabad, Proc & Energy Syst Engn Ctr, Dept Chem Engn, PRESTIGE, Lahore, Pakistan. (författare)

Computationally Inexpensive 1D-CNN for the Prediction of Noisy Data of NOx Emissions From 500 MW Coal-Fired Power Plant

  • Artikel/kapitelEngelska2022

Förlag, utgivningsår, omfång ...

  • 2022-08-16
  • FRONTIERS MEDIA SA,2022
  • printrdacarrier

Nummerbeteckningar

  • LIBRIS-ID:oai:DiVA.org:mdh-59890
  • https://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-59890URI
  • https://doi.org/10.3389/fenrg.2022.945769DOI

Kompletterande språkuppgifter

  • Språk:engelska
  • Sammanfattning på:engelska

Ingår i deldatabas

Klassifikation

  • Ämneskategori:ref swepub-contenttype
  • Ämneskategori:art swepub-publicationtype

Anmärkningar

  • 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.

Ämnesord och genrebeteckningar

Biuppslag (personer, institutioner, konferenser, titlar ...)

  • Khan, JavedCOMSATS Univ Islamabad, Proc & Energy Syst Engn Ctr, Dept Chem Engn, PRESTIGE, Lahore, Pakistan. (författare)
  • Ahmed, FaisalCOMSATS Univ Islamabad, Proc & Energy Syst Engn Ctr, Dept Chem Engn, PRESTIGE, Lahore, Pakistan. (författare)
  • Salman, Chaudhary Awais,1986-Mälardalens universitet,Framtidens energi(Swepub:mdh)csn07 (författare)
  • Gillani, ZeeshanCOMSATS Univ Islamabad, Dept Comp Sci, Lahore, Pakistan. (författare)
  • Hussain, ArifCOMSATS Univ Islamabad, Proc & Energy Syst Engn Ctr, Dept Chem Engn, PRESTIGE, Lahore, Pakistan. (författare)
  • Yasin, MuhammadCOMSATS Univ Islamabad, Dept Chem Engn, Lahore, Pakistan. (författare)
  • Ul-Haq, NoamanCOMSATS Univ Islamabad, Dept Chem Engn, Lahore, Pakistan. (författare)
  • Khan, Asad UllahCOMSATS Univ Islamabad, Dept Chem Engn, Lahore, Pakistan.;Natl Univ Sci & Technol, Dept Chem Engn, SCME, Islamabad, Pakistan. (författare)
  • Bazmi, Aqeel AhmedCOMSATS Univ Islamabad, Proc & Energy Syst Engn Ctr, Dept Chem Engn, PRESTIGE, Lahore, Pakistan. (författare)
  • Ahmad, ZubairYeungnam Univ, Sch Chem Engn, Gyongsan, South Korea. (författare)
  • Hasan, MudassirKing Khalid Univ, Coll Engn, Dept Chem Engn, Abha, Saudi Arabia. (författare)
  • COMSATS Univ Islamabad, Proc & Energy Syst Engn Ctr, Dept Chem Engn, PRESTIGE, Lahore, Pakistan.Framtidens energi (creator_code:org_t)

Sammanhörande titlar

  • Ingår i:Frontiers in Energy Research: FRONTIERS MEDIA SA102296-598X

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