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Predicting the Calorific Value of Municipal Solid Waste of Ghaziabad City, Uttar Pradesh, India, Using Artificial Neural Network Approach

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Soft Computing: Theories and Applications

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 584))

Abstract

The municipal solid waste (MSW) can be used as renewable energy resource. The amount of energy generated depends upon calorific value of the MSW. The aim of present study is to discuss about the chemical composition and corresponding calorific value of 39 samples of municipal solid waste of Ghaziabad City, Uttar Pradesh (India). In this study, artificial neural network (ANN) technique has been applied to predict the calorific value of MSW of the city. The elements such as carbon, hydrogen, oxygen, nitrogen, sulfur, phosphorus, potassium, and ash obtained from chemical analysis have been used as input data points to predict the calorific value of MSW in ANN model. The developed ANN model has been validated with the help of minimum value of mean squared error [0.003703 (training), 0.03760 (validation), and 0.2269 (testing operations)] and optimized value of coefficient of correlation (0.9088) between observed and predicted calorific values of MSW samples. The proposed ANN model has shown better predictive results.

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References

  1. Dong, C., Jin, B., Li, D.: Predicting the heating value of MSW with a feed forward neural network. Waste Mgmt. 23, 103–106 (2003)

    Article  Google Scholar 

  2. Shu, H.Y., Lu, H.C., Fan, H.J., Chang, M.C., Chen, J.C.: Prediction for energy content of Taiwan municipal solid waste using multilayer perception neural networks. J. Air Waste Mgmt. Assoc. 56, 852–858 (2006)

    Article  Google Scholar 

  3. Abu-Qudais, M., Abu-Qdais, H.A.: Energy content of municipal solid waste in Jordan and its potential utilization. Energy Convers. Mgmt. 41, 983–991 (2000)

    Article  Google Scholar 

  4. Swingler, K.: Applying neural networks: a practical guide, pp. 21–39. Academic Press, London, UK (1996)

    Google Scholar 

  5. Wilson, D.L.: Prediction of heat of combustion of solid wastes from ultimate analysis. Environ. Sci. Tech. 6, 1119–1121 (1972)

    Article  Google Scholar 

  6. Khan, A.M.Z., Abu Ghararah, Z.H.: New approach for estimating energy content of municipal solid waste. J. Environ. Eng. 117, 376–380 (1991)

    Article  Google Scholar 

  7. Liu, J.I., Paod, R.D., Holsen, T.M.: Modeling the energy content of municipal solid waste using multiple regression analyses. J. Air Waste Mgmt. Assoc. 46, 650–656 (1996)

    Article  Google Scholar 

  8. Cooper, C.D., Kim, B., MacDonald, J.: Estimating the lower heating values of hazardous and solid wastes. J. Air Waste Mgmt. Assoc. 49, 471–476 (1999)

    Article  Google Scholar 

  9. Abu-Qudais, M., Abu-Qdais, H.A.: Energy content of municipal solid waste in Jordan and its potential utilization. Energy Convers. Mgmt. 41, 983–991 (2000)

    Article  Google Scholar 

  10. Chang, Y.F., Lin, C.J., Chyan, J.M., Chen, I.M., Chang, J.E.: Multiple regression models for the lower heating value of municipal solid waste in Taiwan. J. Environ. Mgmt. 85, 891–899 (2007)

    Article  Google Scholar 

  11. Meraz, L., Domı́nguez, A., Kornhauser, I., Rojas, F.: A thermochemical concept-based equation to estimate waste combustion enthalpy from elemental composition. Fuel 82, 1499–1507 (2003)

    Article  Google Scholar 

  12. Ogwueleka, TCh., Ogwueleka, F.N.: Modeling energy content of municipal solid waste using artificial neural network. Iran. J. Environ. Health. Sci. Eng. 7(3), 259–266 (2010)

    Google Scholar 

  13. Akkaya, E., Demir, A.: Predicting the heating value of municipal solid waste-based materials: an artificial neural network model. Energy Sour. 32, 1777–1783 (2010)

    Article  Google Scholar 

  14. Jahandideh, S., Jahandideh, S., Asadabadi, E.B., Askarian, M., Movahedi, M.M., Hosseini, S., Jahandideh, M.: The use of artificial neural networks and multiple linear regression to predict rate of medical waste generation. Waste Manage. 29, 2874–2879 (2009)

    Article  Google Scholar 

  15. Coulibaly, P., Baldin, C.K.: Non-stationary hydrological time series forecasting using non-linear dynamic methods. J. Hydrol. 307, 164–174 (2005)

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Vocational Studies & Applied Sciences, Gautam Buddha University, Gautam Budh Nagar, Uttar Pradesh, India

    Dipti Singh

  2. Department of Applied Science & Humanities, Inderprastha Engineering College, Ghaziabad, Uttar Pradesh, India

    Ajay Satija

  3. Department of Civil Engineering, Ch. B.P. Govt. Engineering College, Jaffarpur, New Delhi, India

    Athar Hussain

Authors
  1. Dipti Singh
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  2. Ajay Satija
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  3. Athar Hussain
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Corresponding author

Correspondence to Ajay Satija .

Editor information

Editors and Affiliations

  1. Department of Applied Science and Engineering, IIT Roorkee, Saharanpur, India

    Millie Pant

  2. Department of Physics, Amity School of Applied Sciences, Amity University Rajasthan, Jaipur, Rajasthan, India

    Kanad Ray

  3. Department of Computer Science and Engineering, Amity School of Engineering and Technology, Amity University Rajasthan, Jaipur, Rajasthan, India

    Tarun K. Sharma

  4. Department of Electronics and Communication Engineering, SEEC, Manipal University Jaipur, Jaipur, Rajasthan, India

    Sanyog Rawat

  5. Surface Characterization Group, NIMS, Nano Characterization Unit, Advanced Key Technologies Division, Tsukuba, Ibaraki, Japan

    Anirban Bandyopadhyay

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Singh, D., Satija, A., Hussain, A. (2018). Predicting the Calorific Value of Municipal Solid Waste of Ghaziabad City, Uttar Pradesh, India, Using Artificial Neural Network Approach. In: Pant, M., Ray, K., Sharma, T., Rawat, S., Bandyopadhyay, A. (eds) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol 584. Springer, Singapore. https://doi.org/10.1007/978-981-10-5699-4_46

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  • DOI: https://doi.org/10.1007/978-981-10-5699-4_46

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-5698-7

  • Online ISBN: 978-981-10-5699-4

  • eBook Packages: EngineeringEngineering (R0)

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