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Advances in Clean Energy Technologies pp 141–151Cite as

Development of Correlation for Efficiency of Incineration Plants Using Deep Neural Network Model

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Abstract

In the present era, production of municipal solid waste (MSW) has become unrestrained due to rapid growth in population and urbanization. Therefore, people are facing various challenges such as health and environmental safety. But, this huge potential of MSW can be used as a promising source for electricity production to reduce the greenhouse gas (GHG) emissions. Incineration is well-known technique which has been extensively used to produce economically affordable energy from MSW. The purpose of the incineration plant is to get the maximum desirable outputs (heat and power) out of waste and minimize undesirable outputs (emissions and bottom ash). The value of heat or power recovered from waste burning in incineration plant depends on the heating value of the waste. Determining this heating value of each waste sample has been considered as complex and time consuming task due to different moisture, ash, and chemical composition. Under the present study, an attempt has been made to develop a correlation to calculate efficiency of the plant using composition of waste. In order to develop this correlation, concept of deep neural network model from machine learning has been used in this paper. The developed application may be useful for plant design engineer to predict the performance of plant for given range of parameters.

Keywords

  • Municipal solid waste
  • Incineration Plant
  • Efficiency of plant
  • Composition of waste
  • Deep neural networks

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Fig. 11.1
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Fig. 11.4

Abbreviations

f 1NN :

Single layer in the neural network

f DNN :

Combination of all f1NN

k :

No. of layers involved in the neural network

x :

Input values to the network

y :

Output value of the network for that specific x value

\(w\) * :

Output weights

\(\kappa\) :

Loss fraction

W:

List of all parameters

\(\eta\) :

Learning Rate of the model

WD:

Weight decay

\(\kappa_{{{\text{WD}}}}\) :

Loss fraction added by weight decay

\(\lambda\) :

Normalization constant

References

  1. Pollution Index by Country 2019 Mid-Year. https://www.numbeo.com/pollution/rankings_by_country.jsp. Last accessed 2019/12/31

  2. Bioenergy: sustainable renewable energy. https://www.energy.vic.gov.au/renewable-energy/bioenergy/bioenergy-sustainable-renewable-energy. Last accessed 2019/12/17

  3. Urban Development Series—Knowledge Papers. https://siteresources.worldbank.org/INTURBANDEVELOPMENT/Resources/336387-1334852610766/Chap3.pdf. Last accessed 2020/01/10

  4. Countries that Produce the Most Waste. https://www.investopedia.com/articles/markets-economy/090716/5-countries-produce-most-waste.asp. Last accessed 2020/01/25

  5. Waste to energy in India. https://www.eai.in/ref/ae/wte/wte.html. Last accessed 2020/02/25

  6. A beginner’s guide to neural networks and deep learning. https://pathmind.com/wiki/neural-network. Last accessed 2020/01/25

  7. Y. Chen, C. Chen, The privatization effect of MSW incineration services by using data envelopment analysis. Waste Manag. 32(3), 595–602 (2012)

    CrossRef  Google Scholar 

  8. H. Shu, H. Lu, H. Fan, M. Chang, J. Chen, Prediction for energy content of Taiwan municipal solid waste using multilayer perceptron neural networks. J. Air Waste Manag. Assoc. 56(6), 852–858 (2006)

    CrossRef  Google Scholar 

  9. S. Hosseinpour, M. Aghbashlo, M. Tabatabaei, E. Khalife, Exact estimation of biodiesel cetane number (CN) from its fatty acid methyl esters (FAMEs) profile using partial least square (PLS) adapted by artificial neural network (ANN). Energy Convers. Manag. 124, 389–398 (2016)

    CrossRef  Google Scholar 

  10. A. Giantomassi, G. Ippoliti, S. Longhi, I. Bertini, S. Pizzuti, On-line steam production prediction for a municipal solid waste incinerator by fully tuned minimal RBF neural networks. J. Process Control. 21(1), 164–172 (2011)

    CrossRef  Google Scholar 

  11. M. Kraus, S. Feuerriegel, A. Oztekin, Deep learning in business analytics and operations research: models, applications and managerial implications. Eur. J. Oper. Res. 281(3), 628–641 (2019)

    CrossRef  Google Scholar 

  12. J. Brau, M. Morandin, T. Berntsson, Hydrogen for oil refining via biomass indirect steam gasification: energy and environmental targets. Clean Technol. Environ. Policy 15(3), 501–512 (2013)

    CrossRef  Google Scholar 

  13. M.H.M. Yusoff, R. Zakaria, Combustion of municipal solid waste in fixed bed combustor for energy recovery. J. Appl. Sci. 12(11), 1176–1180 (2012)

    Google Scholar 

  14. G. Nordi, R. Palacios-Bereche, A. Gallego, S. Nebra, Electricity production from municipal solid waste in Brazil. Waste Manage. Res. 35(7), 709–720 (2017)

    CrossRef  Google Scholar 

  15. B. Dasgupta, M. Mondal, Bio energy conversion of organic fraction of Varanasi’s municipal solid waste. Energy Procedia 14, 1931–1938 (2012)

    CrossRef  Google Scholar 

  16. N. Khairuddin, L. Abd Manaf, M. Hassan, W. Wan Abdul Karim Ghani, N. Halimoon, Biogas harvesting from organic fraction of municipal solid waste as a renewable energy resource in malaysia: a review. Polish J Environ Stud 24, 1477–1490 (2015)

    Google Scholar 

  17. S. Kerdsuwan, K. Laohalidanond, W. Jangsawang, Sustainable development and eco-friendly waste disposal technology for the local community. Energy Procedia 79, 119–124 (2015)

    CrossRef  Google Scholar 

  18. N. Gnanapragasam, B. Reddy, M. Rosen, Optimum conditions for a natural gas combined cycle power generation system based on available oxygen when using biomass as supplementary fuel. Energy 34(6), 816–826 (2009)

    CrossRef  Google Scholar 

  19. W. Tsai, An Analysis of Operational Efficiencies in the Waste-to-Energy (WTE) Plants of Kaohsiung Municipality (Taiwan). Resources 8(3), 125 (2019)

    CrossRef  Google Scholar 

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Authors and Affiliations

  1. Vellore Institute of Technology, Vellore, 632014, India

    Deepuphanindra Gannamani & Anuj Kumar

Authors
  1. Deepuphanindra Gannamani
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  2. Anuj Kumar
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Corresponding author

Correspondence to Anuj Kumar .

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Editors and Affiliations

  1. Department of Energy, Energy Centre, Maulana Azad National Institute of Technology, Bhopal, Madhya Pradesh, India

    Dr. Prashant V. Baredar

  2. Department of Mechanical Engineering, Dr. B. R. Ambedkar National Institute of Technology Jalandhar, Jalandhar, Punjab, India

    Dr. Srinivas Tangellapalli

  3. Department of Energy Science and Engineering, Indian Institute of Technology Bombay, Mumbai, Maharashtra, India

    Prof. Dr. Chetan Singh Solanki

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Gannamani, D., Kumar, A. (2021). Development of Correlation for Efficiency of Incineration Plants Using Deep Neural Network Model. In: Baredar, P.V., Tangellapalli, S., Solanki, C.S. (eds) Advances in Clean Energy Technologies . Springer Proceedings in Energy. Springer, Singapore. https://doi.org/10.1007/978-981-16-0235-1_11

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  • DOI: https://doi.org/10.1007/978-981-16-0235-1_11

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