Advertisement

Genetic Programming and Evolvable Machines

, Volume 17, Issue 3, pp 231–249 | Cite as

Prediction of the natural gas consumption in chemical processing facilities with genetic programming

  • Miha KovačičEmail author
  • Franjo Dolenc
Article

Abstract

Cinkarna Ltd. is a chemical processing company in Slovenia and the country’s largest manufacturer of titanium oxides (TiO2). Chemical processing and titanium oxide manufacturing in particular requires high natural gas consumption, and it is difficult to accurately pre-order gas from suppliers. In accordance with the Energy Agency of the Republic of Slovenia regulations, each natural gas supplier regulates and determines the charges for the differences between the ordered (predicted) and the actually supplied quantities of natural gas. Yearly charges for these differences total 1.11 % of supplied natural gas costs (average 50,960 EUR per year). This paper presents natural gas consumption prediction and the minimization of associated costs. The data on daily temperature, steam boilers, sulfur acid and TiO2 production was collected from January 2012 until November 2014. Based on the collected data, a linear regression and a genetic programming model were developed. Compared to the specialist’s prediction of natural gas consumption, the linear regression and genetic programming models reduce the charges for the differences between the ordered and the actually supplied quantities by 3.00 and 5.30 times, respectively. Also, from January until November 2014 the same genetic programming model was used in practice. The results show that in a similar gas consumption regime the differences between the ordered and the actually supplied quantities are statistically significant, namely, they are 3.19 times lower (t test, p < 0.05) than in the period in which the specialist responsible for natural gas consumption made the predictions.

Keywords

Natural gas consumption prediction Chemical processing Modeling Genetic programming 

References

  1. 1.
    S. Adebola, M. Shahbaz, Natural gas consumption and economic growth: the role of foreign direct investment, capital formation and trade openness in Malaysia. Renew. Sustain. Energy Rev. 42, 835–845 (2015)CrossRefGoogle Scholar
  2. 2.
    Ö. Dilaver, Z. Dilaver, L.C. Hunt, What drives natural gas consumption in europe? Analysis and projections. J. Nat. Gas Sci. Eng. 19, 125–136 (2014)CrossRefGoogle Scholar
  3. 3.
    F. Kesikoğlu, E. Yıldırım, The causal effect of shifting oil to natural gas consumption on current account balance and economic growth in 11 OECD countries: evidence from Bootstrap-corrected Panel causality test. Procedia Soc. Behav. Sci. 143, 1064–1069 (2014)CrossRefGoogle Scholar
  4. 4.
    D. Romero-Jordán, C. Peñasco, P. del Río, Analysing the determinants of household electricity demand in Spain. An econometric study. Int. J. Electr. Power Energy Syst. 63, 950–961 (2014)CrossRefGoogle Scholar
  5. 5.
    M. Shahbaz, N. Khraief, M.K. Mahalik, K.U. Zaman, Are fluctuations in natural gas consumption per capita transitory? Evidence from time series and panel unit root tests. Energy 78, 183–195 (2014)CrossRefGoogle Scholar
  6. 6.
    B. Soldo, Forecasting natural gas consumption. Appl. Energy 92, 26–37 (2012)CrossRefGoogle Scholar
  7. 7.
    T.B. Andersen, O.B. Nilsen, R. Tveteras, How is demand for natural gas determined across European industrial sectors? Energy Policy 39(9), 5499–5508 (2011)CrossRefGoogle Scholar
  8. 8.
    V. Bianco, F. Scarpa, L.A. Tagliafico, Scenario analysis of nonresidential natural gas consumption in Italy. Appl. Energy 113, 392–403 (2014)CrossRefGoogle Scholar
  9. 9.
    V. Bianco, F. Scarpa, L.A. Tagliafico, Analysis and future outlook of natural gas consumption in the Italian residential sector. Energy Convers. Manag. 87, 754–764 (2014)CrossRefGoogle Scholar
  10. 10.
    E. Fernandes, M.V.A. Fonseca, P.S.R. Alonso, Natural gas in Brazil’s energy matrix: demand for 1995–2010 and usage factors. Energy Policy 33(3), 365–386 (2005)CrossRefGoogle Scholar
  11. 11.
    M. Forouzanfar, A. Doustmohammadi, M.B. Menhaj, S. Hasanzadeh, Modeling and estimation of the natural gas consumption for residential and commercial sectors in Iran. Appl. Energy 87(1), 268–274 (2010)CrossRefGoogle Scholar
  12. 12.
    A.H. Kani, M. Abbasspour, Z. Abedi, Estimation of demand function for natural gas in Iran: evidences based on smooth transition regression models. Econ. Model. 36, 341–347 (2014)CrossRefGoogle Scholar
  13. 13.
    J. Li, X. Dong, J. Shangguan, M. Hook, Forecasting the growth of China’s natural gas consumption. Energy 36(3), 1380–1385 (2011)CrossRefGoogle Scholar
  14. 14.
    M. Melikoglu, Vision 2023: forecasting Turkey’s natural gas demand between 2013 and 2030. Renew. Sustain. Energy Rev. 22, 393–400 (2013)CrossRefGoogle Scholar
  15. 15.
    J. Parikh, P. Purohit, P. Maitra, Demand projections of petroleum products and natural gas in India. Energy 32(10), 1825–1837 (2007)CrossRefGoogle Scholar
  16. 16.
    P. Potočnik, B. Soldo, G. Šimunović, T. Šarić, A. Jeromen, E. Govekar, Comparison of static and adaptive models for short-term residential natural gas forecasting in Croatia. Appl. Energy 129, 94–103 (2014)CrossRefGoogle Scholar
  17. 17.
    N.D. Uri, Natural gas demand by agriculture in the USA. Energy Econ. 11(2), 137–146 (1989)CrossRefGoogle Scholar
  18. 18.
    Y.X. He, T. Xia, Y.Y. Liu, L.F. Zhou, B. Zhou, Residential natural gas price affordability analysis—a case study of Beijing. Renew. Sustain. Energy Rev. 28, 392–399 (2013)CrossRefGoogle Scholar
  19. 19.
    K. Sabo, R. Scitovski, I. Vazler, M. Zekić-Sušac, Mathematical models of natural gas consumption. Energy Convers. Manag. 52(3), 1721–1727 (2011)CrossRefGoogle Scholar
  20. 20.
    S.H. Yoo, H.J. Lim, S.J. Kwak, Estimating the residential demand function for natural gas in Seoul with correction for sample selection bias. Appl. Energy 86(4), 460–465 (2009)CrossRefGoogle Scholar
  21. 21.
    A. Azadeh, S.M. Asadzadeh, A. Ghanbari, An adaptive network-based fuzzy inference system for short-term natural gas demand estimation: uncertain and complex environments. Energy Policy 38(3), 1529–1536 (2010)CrossRefGoogle Scholar
  22. 22.
    M. Fast, T. Palmé, Application of artificial neural networks to the condition monitoring and diagnosis of a combined heat and power plant. Energy 35(2), 1114–1120 (2010)CrossRefGoogle Scholar
  23. 23.
    M. Kirschen, K. Badr, H. Pfeifer, Influence of direct reduced iron on the energy balance of the electric arc furnace in steel industry. Energy 36(10), 6146–6155 (2011)CrossRefGoogle Scholar
  24. 24.
    M. Kirschen, V. Risonarta, H. Pfeifer, Energy efficiency and the influence of gas burners to the energy related carbon dioxide emissions of electric arc furnaces in steel industry. Energy 34(9), 1065–1072 (2009)CrossRefGoogle Scholar
  25. 25.
    M. Kovačič, B. Šarler, Genetic programming prediction of the natural gas consumption in a steel plant. Energy 66, 273–284 (2014)CrossRefGoogle Scholar
  26. 26.
    E.F. Sánchez-Úbeda, A. Berzosa, Modeling and forecasting industrial end-use natural gas consumption. Energy Econ. 29(4), 710–742 (2007)CrossRefGoogle Scholar
  27. 27.
    P.R. Shukla, S. Dhar, D.G. Victor, M. Jackson, Assessment of demand for natural gas from the electricity sector in India. Energy Policy 37(9), 3520–3534 (2009)CrossRefGoogle Scholar
  28. 28.
    J.A. Rodger, A fuzzy nearest neighbor neural network statistical model for predicting demand for natural gas and energy cost savings in public buildings. Expert Syst. Appl. 41(4), 1813–1829 (2014)CrossRefGoogle Scholar
  29. 29.
    J. Vondráček, E. Pelikán, O. Konár, J. Čermáková, K. Eben, M. Malý, M. Brabec, A statistical model for the estimation of natural gas consumption. Appl. Energy 85(5), 362–370 (2008)CrossRefGoogle Scholar
  30. 30.
    J.H. Herbert, L.J. Barber, Regional residential natural gas demand. Resour Energy 10(4), 387–391 (1988)CrossRefGoogle Scholar
  31. 31.
    L. Zhu, M.S. Li, Q.H. Wu, L. Jiang, Short-term natural gas demand prediction based on support vector regression with false neighbours filtered. Energy 80, 428–436 (2015)CrossRefGoogle Scholar
  32. 32.
    A. Azadeh, S.M. Asadzadeh, G.H. Mirseraji, M. Saberi, An emotional learning-neuro-fuzzy inference approach for optimum training and forecasting of gas consumption estimation models with cognitive data. Technol. Forecast. Soc. Change (2014). doi: 10.1016/j.techfore.2014.01.009 Google Scholar
  33. 33.
    M.R.V. Schwob, M. Henriques, A. Szklo, Technical potential for developing natural gas use in the Brazilian red ceramic industry. Appl. Energy 86(9), 1524–1531 (2009)CrossRefGoogle Scholar
  34. 34.
    R.G. Palomino, S.A. Nebra, The potential of natural gas use including cogeneration in large-sized industry and commercial sector in Peru. Energy Policy 50, 192–206 (2012)CrossRefGoogle Scholar
  35. 35.
    J.O. Jaber, Future energy consumption and greenhouse gas emissions in Jordanian industries. Appl. Energy 71(1), 15–30 (2002)CrossRefGoogle Scholar
  36. 36.
    M. Kovačič, B. Šarler, Application of the genetic programming for increasing the soft annealing productivity in steel industry. Mater. Manuf. Process. 24(3), 369–374 (2009)CrossRefGoogle Scholar
  37. 37.
    M. Kovačič, Modeling of total decarburization of spring steel with genetic programming. Mater. Manuf. Process. 30(4), 434–443 (2014)Google Scholar
  38. 38.
    J.R. Koza, F.H. Bennett I, D. Andre, M.A. Keane, Genetic programming III: Darwinian invention and problem solving (1999). http://dl.acm.org/citation.cfm?id=553446

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Štore Steel Ltd.ŠtoreSlovenia
  2. 2.Institute of Metals and TechnologyLjubljanaSlovenia
  3. 3.Cinkarna Ltd.CeljeSlovenia

Personalised recommendations