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Application of Fuzzy Regression Analysis in Predicting the Performance of the Anaerobic Reactor Co-digesting Spent Tea Waste with Cow Manure

  • Naseem KhayumEmail author
  • Amruta Rout
  • B. B. V. L. Deepak
  • S. Anbarasu
  • S. Murugan
Original Paper
  • 65 Downloads

Abstract

Modelling and optimization of production of different renewable energy sources are receiving great interest by researchers; as they are used to provide gross information on the possibilities of harnessing energy from variety of resources. Spent tea waste (STW) is one of the potential organic wastes remarkably available in India. In this research work, possibility of producing biogas by co-digesting STW with cow manure (CM) was predicted through a novel fuzzy regression approach. Triangular membership functions with five levels were considered for the fuzzy subsets and a Mamdani-type of fuzzy approach was used to implement a total of 125 rules in the IF–THEN format. The digestion time, carbon to nitrogen (C/N) ratio and pH were considered as input parameters, while the biogas yield was considered as an output. Experimental data obtained from the lab scale reactors were used to predict the biogas yield using fuzzy logic methodology. The obtained results were validated with the experimental results by carrying out a regression analysis. The results indicated that a good agreement found between experimental and predicted data with a coefficient of determination R2 = 0.994.

Graphic Abstract

Keywords

Spent tea waste Cow manure Fuzzy logic Biogas Mamdani approach Regression analysis 

Abbreviations

Ai

Actual values

AARE

Absolute average relative error

ANFIS

Adaptive neuro-fuzzy inference system

ANN

Artificial neural network

AR

Anaerobic reactor

ARE

Average relative error

CM

Cow manure

C/N

Carbon to nitrogen ratio

FA

Firely algorithm

FIS

Fuzzy inference system

GA

Genetic algorithm

kg

Kilogram

ml

Millilitre

MSE

Mean squared normalised error

NH3

Ammonia

Pi

Predicted values

RF

Random forest

RMSE

Root mean squared error

RPM

Revolutions per minute

RSM

Response surface methodology

SD

Standard deviation

STW

Spent tea waste

TS

Total solid

Notes

References

  1. 1.
    Khayum, N., Anbarasu, S., Murugan, S.: Biogas potential from spent tea waste: A laboratory scale investigation of co-digestion with cow manure. Energy. 165, 760–768 (2018).  https://doi.org/10.1016/J.ENERGY.2018.09.163 CrossRefGoogle Scholar
  2. 2.
    Sun, C., Cao, W., Banks, C.J., Heaven, S., Liu, R.: Biogas production from undiluted chicken manure and maize silage: a study of ammonia inhibition in high solids anaerobic digestion. Bioresour. Technol. 218, 1215–1223 (2016).  https://doi.org/10.1016/J.BIORTECH.2016.07.082 CrossRefGoogle Scholar
  3. 3.
    Glanpracha, N., Annachhatre, A.P.: Anaerobic co-digestion of cyanide containing cassava pulp with pig manure. Bioresour. Technol. 214, 112–121 (2016).  https://doi.org/10.1016/J.BIORTECH.2016.04.079 CrossRefGoogle Scholar
  4. 4.
    Raheman, H., Mondal, S.: Biogas production potential of jatropha seed cake. Biomass Bioenergy 37, 25–30 (2012).  https://doi.org/10.1016/J.BIOMBIOE.2011.12.042 CrossRefGoogle Scholar
  5. 5.
    Dai, X., Li, X., Zhang, D., Chen, Y., Dai, L.: Simultaneous enhancement of methane production and methane content in biogas from waste activated sludge and perennial ryegrass anaerobic co-digestion: The effects of pH and C/N ratio. Bioresour. Technol. 216, 323–330 (2016).  https://doi.org/10.1016/J.BIORTECH.2016.05.100 CrossRefGoogle Scholar
  6. 6.
    Okeh, O.C., Onwosi, C.O., Odibo, F.J.C.: Biogas production from rice husks generated from various rice mills in Ebonyi State. Nigeria. Renew. Energy. 62, 204–208 (2014).  https://doi.org/10.1016/J.RENENE.2013.07.006 CrossRefGoogle Scholar
  7. 7.
    Haider, M.R., Zeshan, Yousaf, S., Malik, R.N., Visvanathan, C.: Effect of mixing ratio of food waste and rice husk co-digestion and substrate to inoculum ratio on biogas production. Bioresour. Technol. 190, 451–457 (2015).  https://doi.org/10.1016/j.biortech.2015.02.105 CrossRefGoogle Scholar
  8. 8.
    Uçkun Kiran, E., Stamatelatou, K., Antonopoulou, G., Lyberatos, G.: Production of biogas via anaerobic digestion. Handb. Biofuels Prod. 259–301 (2016).  https://doi.org/10.1016/b978-0-08-100455-5.00010-2 Google Scholar
  9. 9.
    Arumugam, T., Parthiban, L., Rangasamy, P.: Two-phase anaerobic digestion model of a tannery solid waste: experimental investigation and modeling with ANFIS. Arab. J. Sci. Eng. 40, 279–288 (2015).  https://doi.org/10.1007/s13369-014-1408-9 CrossRefGoogle Scholar
  10. 10.
    Ruan, J., Chen, X., Huang, M., Zhang, T.: Application of fuzzy neural networks for modeling of biodegradation and biogas production in a full-scale internal circulation anaerobic reactor. J. Environ. Sci. Health., Part A 52, 7–14 (2017).  https://doi.org/10.1080/10934529.2016.1221216 CrossRefGoogle Scholar
  11. 11.
    Waewsak, C., Nopharatana, A., Chaiprasert, P.: Neural-fuzzy control system application for monitoring process response and control of anaerobic hybrid reactor in wastewater treatment and biogas production. J. Environ. Sci. 22, 1883–1890 (2010).  https://doi.org/10.1016/S1001-0742(09)60334-X CrossRefGoogle Scholar
  12. 12.
    Flores-Asis, R., Méndez-Contreras, J.M., Alvarado-Lassman, A., Fernández-Lambert, G., Villanueva-Vásquez, D., Aguilar-Lasserre, A.A.: Analysis of the behavior for operation parameters in the anaerobic digestion process with thermal pretreatment, using fuzzy logic. J. Environ. Sci. Health., Part A 54, 592–602 (2019).  https://doi.org/10.1080/10934529.2019.1593010 CrossRefGoogle Scholar
  13. 13.
    Ghaedi, M., Hosaininia, R., Ghaedi, A.M., Vafaei, A., Taghizadeh, F.: Adaptive neuro-fuzzy inference system model for adsorption of 1,3,4-thiadiazole-2,5-dithiol onto gold nanoparticales-activated carbon. Spectrochim. Acta, Part A 131, 606–614 (2014).  https://doi.org/10.1016/J.SAA.2014.03.055 CrossRefGoogle Scholar
  14. 14.
    Asl, S.H., Ahmadi, M., Ghiasvand, M., Tardast, A., Katal, R.: Artificial neural network (ANN) approach for modeling of Cr(VI) adsorption from aqueous solution by zeolite prepared from raw fly ash (ZFA). J. Ind. Eng. Chem. 19, 1044–1055 (2013).  https://doi.org/10.1016/J.JIEC.2012.12.001 CrossRefGoogle Scholar
  15. 15.
    Turkdogan-Aydınol, F.I., Yetilmezsoy, K.: A fuzzy-logic-based model to predict biogas and methane production rates in a pilot-scale mesophilic UASB reactor treating molasses wastewater. J. Hazard. Mater. 182, 460–471 (2010).  https://doi.org/10.1016/J.JHAZMAT.2010.06.054 CrossRefGoogle Scholar
  16. 16.
    Jha, S.K., Ahmad, Z., Crowley, D.E.: Fuzzy inference for soil microbial dynamics modeling in fluctuating ecological situations. J. Intell. Fuzzy Syst. 35, 1399–1406 (2018).  https://doi.org/10.3233/JIFS-169682 CrossRefGoogle Scholar
  17. 17.
    Jha, S.K., Ahmad, Z., Crowley, D.E.: Fuzzy-genetic approaches for estimation of microbial rock phosphate solubilization in sandy clay loam textured soil. Comput. Electron. Agric. 150, 125–133 (2018).  https://doi.org/10.1016/J.COMPAG.2018.04.014 CrossRefGoogle Scholar
  18. 18.
    Zareei, S., Khodaei, J.: Modeling and optimization of biogas production from cow manure and maize straw using an adaptive neuro-fuzzy inference system. Renew. Energy. 114, 423–427 (2017).  https://doi.org/10.1016/J.RENENE.2017.07.050 CrossRefGoogle Scholar
  19. 19.
    Hajati, S., Ghaedi, M., Mazaheri, H.: Removal of methylene blue from aqueous solution by walnut carbon: optimization using response surface methodology. Desalin. Water Treat. 57, 3179–3193 (2016).  https://doi.org/10.1080/19443994.2014.981217 CrossRefGoogle Scholar
  20. 20.
    Beltramo, T., Ranzan, C., Hinrichs, J., Hitzmann, B.: Artificial neural network prediction of the biogas flow rate optimised with an ant colony algorithm. Biosyst. Eng. 143, 68–78 (2016).  https://doi.org/10.1016/J.BIOSYSTEMSENG.2016.01.006 CrossRefGoogle Scholar
  21. 21.
    Santoso, H., Tani, H., Wang, X.: Random Forest classification model of basal stem rot disease caused by Ganoderma boninense in oil palm plantations. Int. J. Remote Sens. 38, 4683–4699 (2017).  https://doi.org/10.1080/01431161.2017.1331474 CrossRefGoogle Scholar
  22. 22.
    Oloko-Oba, M.I., Taiwo, A.E., Ajala, S.O., Solomon, B.O., Betiku, E.: Performance evaluation of three different-shaped bio-digesters for biogas production and optimization by artificial neural network integrated with genetic algorithm. Sustain. Energy Technol. Assess. 26, 116–124 (2018).  https://doi.org/10.1016/J.SETA.2017.10.006 CrossRefGoogle Scholar
  23. 23.
    Bendu, H., Deepak, B.B.V.L., Murugan, S.: Application of GRNN for the prediction of performance and exhaust emissions in HCCI engine using ethanol. Energy Convers. Manag. 122, 165–173 (2016).  https://doi.org/10.1016/J.ENCONMAN.2016.05.061 CrossRefGoogle Scholar
  24. 24.
    Rao, B.B., Raju, V.R., Deepak, B.B.V.L.: Estimation and optimization of heat transfer and overall pressure drop for a shell and tube heat exchanger. J. Mech. Sci. Technol. 31, 375–383 (2017).  https://doi.org/10.1007/s12206-016-1239-6 CrossRefGoogle Scholar
  25. 25.
    Varne, A.L., Macwan, J.E.M.: Journal of environmental research and development. Global Earth Society for Environmental Energy and Development (2012)Google Scholar
  26. 26.
    Saha, M., Eskicioglu, C., Sadiq, R.: A fuzzy rule-based approach for modelling effects of bench-scale microwave pre-treatment on solubilisation and anaerobic digestion of secondary sludge. Int. J. Environ. Eng. 6, 183 (2014).  https://doi.org/10.1504/IJEE.2014.062156 CrossRefGoogle Scholar
  27. 27.
    Robles, A., Latrille, E., Ruano, M.V., Steyer, J.P.: A fuzzy-logic-based controller for methane production in anaerobic fixed-film reactors. Environ. Technol. 38, 42–52 (2017).  https://doi.org/10.1080/09593330.2016.1184321 CrossRefGoogle Scholar
  28. 28.
    Metternicht, G., Gonzalez, S.: FUERO: foundations of a fuzzy exploratory model for soil erosion hazard prediction. Environ. Model. Softw. 20, 715–728 (2005).  https://doi.org/10.1016/J.ENVSOFT.2004.03.015 CrossRefGoogle Scholar
  29. 29.
    Gharibi, H., Mahvi, A.H., Nabizadeh, R., Arabalibeik, H., Yunesian, M., Sowlat, M.H.: A novel approach in water quality assessment based on fuzzy logic. J. Environ. Manag. 112, 87–95 (2012).  https://doi.org/10.1016/J.JENVMAN.2012.07.007 CrossRefGoogle Scholar
  30. 30.
    FAO in India|Food and Agriculture Organization of the United Nations, http://www.fao.org/india/en/
  31. 31.
    Zadeh, L.A.: Outline of a new approach to the analysis of complex systems and decision processes. IEEE Trans. Syst. Man. Cybern. SMC-3, 28–44 (1973).  https://doi.org/10.1109/tsmc.1973.5408575 MathSciNetCrossRefGoogle Scholar
  32. 32.
    Topçu, İ.B., Sarıdemir, M.: Prediction of rubberized concrete properties using artificial neural network and fuzzy logic. Constr. Build. Mater. 22, 532–540 (2008).  https://doi.org/10.1016/J.CONBUILDMAT.2006.11.007 CrossRefGoogle Scholar
  33. 33.
    Javadian, H., Ghasemi, M., Ruiz, M., Sastre, A.M., Asl, S.M.H., Masomi, M.: Fuzzy logic modeling of Pb(II) sorption onto mesoporous NiO/ZnCl2-Rosa Canina-L seeds activated carbon nanocomposite prepared by ultrasound-assisted co-precipitation technique. Ultrason. Sonochem. 40, 748–762 (2018).  https://doi.org/10.1016/J.ULTSONCH.2017.08.022 CrossRefGoogle Scholar
  34. 34.
    Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. Syst. Man. Cybern. SMC-15, 116–132 (1985).  https://doi.org/10.1109/tsmc.1985.6313399 CrossRefGoogle Scholar
  35. 35.
    Acaroglu, O., Ozdemir, L., Asbury, B.: A fuzzy logic model to predict specific energy requirement for TBM performance prediction. Tunn. Undergr. Sp. Technol. 23, 600–608 (2008).  https://doi.org/10.1016/J.TUST.2007.11.003 CrossRefGoogle Scholar
  36. 36.
    Atanassov, K.: Type-1 Fuzzy sets and Intuitionistic Fuzzy sets. Algorithms. 10, 106 (2017).  https://doi.org/10.3390/a10030106 MathSciNetCrossRefzbMATHGoogle Scholar
  37. 37.
    Alvarez Grima, M., Babuška, R.: Fuzzy model for the prediction of unconfined compressive strength of rock samples. Int. J. Rock Mech. Min. Sci. 36, 339–349 (1999).  https://doi.org/10.1016/S0148-9062(99)00007-8 CrossRefGoogle Scholar
  38. 38.
    Keshwani, D.R., Jones, D.D., Meyer, G.E., Brand, R.M.: Rule-based Mamdani-type fuzzy modeling of skin permeability. Appl. Soft Comput. 8, 285–294 (2008).  https://doi.org/10.1016/J.ASOC.2007.01.007 CrossRefGoogle Scholar
  39. 39.
    Barik, D., Murugan, S.: An artificial neural network and genetic algorithm optimized model for biogas production from co-digestion of seed cake of karanja and cattle dung. Waste Biomass Valorization 6, 1015–1027 (2015).  https://doi.org/10.1007/s12649-015-9392-1 CrossRefGoogle Scholar
  40. 40.
    Chandra, R., Vijay, V.K., Subbarao, P.M.V., Khura, T.K.: Production of methane from anaerobic digestion of jatropha and pongamia oil cakes. Appl. Energy 93, 148–159 (2012).  https://doi.org/10.1016/J.APENERGY.2010.10.049 CrossRefGoogle Scholar
  41. 41.
    Mshandete, A., Kivaisi, A., Rubindamayugi, M., Mattiasson, B.: Anaerobic batch co-digestion of sisal pulp and fish wastes. Bioresour. Technol. 95, 19–24 (2004).  https://doi.org/10.1016/J.BIORTECH.2004.01.011 CrossRefGoogle Scholar
  42. 42.
    Bhatnagar, N., Ryan, D., Murphy, R., Enright, A.M.: Effect of co-digestion ratio and enzyme treatment on biogas production from grass silage and chicken litter. Waste Biomass Valorization (2018).  https://doi.org/10.1007/s12649-018-0377-8 CrossRefGoogle Scholar
  43. 43.
    Zahedi, S., Romero-Güiza, M., Icaran, P., Yuan, Z., Pijuan, M.: Optimization of free nitrous acid pre-treatment on waste activated sludge. Bioresour. Technol. 252, 216–220 (2018).  https://doi.org/10.1016/J.BIORTECH.2017.12.090 CrossRefGoogle Scholar
  44. 44.
    Barik, D., Murugan, S.: Assessment of sustainable biogas production from de-oiled seed cake of karanja-an organic industrial waste from biodiesel industries. Fuel 148, 25–31 (2015).  https://doi.org/10.1016/J.FUEL.2015.01.072 CrossRefGoogle Scholar
  45. 45.
    Akkaya, E., Demir, A., Varank, G.: Estimation of Biogas Generation from a Uasb Reactor via Multiple Regression Model. Int. J. Green Energy 12, 185–189 (2015).  https://doi.org/10.1080/15435075.2011.651754 CrossRefGoogle Scholar
  46. 46.
    Antwi, P., Li, J., Boadi, P.O., Meng, J., Shi, E., Deng, K., Bondinuba, F.K.: Estimation of biogas and methane yields in an UASB treating potato starch processing wastewater with backpropagation artificial neural network. Bioresour. Technol. 228, 106–115 (2017).  https://doi.org/10.1016/J.BIORTECH.2016.12.045 CrossRefGoogle Scholar
  47. 47.
    Dandikas, V., Heuwinkel, H., Lichti, F., Drewes, J.E., Koch, K.: Predicting methane yield by linear regression models: a validation study for grassland biomass. Bioresour. Technol. 265, 372–379 (2018).  https://doi.org/10.1016/J.BIORTECH.2018.06.030 CrossRefGoogle Scholar
  48. 48.
    Jena, S.P., Mishra, S., Acharya, S.K., Mishra, S.K.: An experimental approach to produce biogas from semi dried banana leaves. Sustain. Energy Technol. Assess. 19, 173–178 (2017).  https://doi.org/10.1016/J.SETA.2017.01.001 CrossRefGoogle Scholar
  49. 49.
    Combined effect of fuel injection timing and nozzle opening pressure of a biogas-biodiesel fuelled diesel engineGoogle Scholar
  50. 50.
    Rasi, S., Veijanen, A., Rintala, J.: Trace compounds of biogas from different biogas production plants. Energy 32, 1375–1380 (2007).  https://doi.org/10.1016/j.energy.2006.10.018 CrossRefGoogle Scholar
  51. 51.
    Guo, W., Li, Y., Zhao, K., Xu, Q., Jiang, H., Zhou, H.: Performance and microbial community analysis of anaerobic digestion of vinegar residue with adding of acetylene black or hydrochar. Waste Biomass Valorization (2019).  https://doi.org/10.1007/s12649-019-00664-3 CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  • Naseem Khayum
    • 1
    Email author
  • Amruta Rout
    • 2
  • B. B. V. L. Deepak
    • 2
  • S. Anbarasu
    • 1
  • S. Murugan
    • 1
  1. 1.Department of Mechanical EngineeringNational Institute of Technology RourkelaRourkelaIndia
  2. 2.Department of Industrial DesignNational Institute of Technology RourkelaRourkelaIndia

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