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

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.

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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

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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

  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

  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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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)

  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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

  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

    Article  Google 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

    Article  Google 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

  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

    Article  Google Scholar 

  36. 36.

    Atanassov, K.: Type-1 Fuzzy sets and Intuitionistic Fuzzy sets. Algorithms. 10, 106 (2017). https://doi.org/10.3390/a10030106

    MathSciNet  Article  MATH  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google Scholar 

  49. 49.

    Combined effect of fuel injection timing and nozzle opening pressure of a biogas-biodiesel fuelled diesel engine

  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

    Article  Google 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

    Article  Google Scholar 

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Khayum, N., Rout, A., Deepak, B.B.V.L. et al. Application of Fuzzy Regression Analysis in Predicting the Performance of the Anaerobic Reactor Co-digesting Spent Tea Waste with Cow Manure. Waste Biomass Valor 11, 5665–5678 (2020). https://doi.org/10.1007/s12649-019-00874-9

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Keywords

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