Skip to main content
SpringerLink
Log in

Application of artificial neural network model for the development of optimized complex medium for phenol degradation using Pseudomonas pictorum (NICM 2074)

  • Original Paper
  • Published:
Biodegradation Aims and scope Submit manuscript

Abstract

Biodegradation of phenol using Pseudomonas pictorum (NICM 2074) a potential biodegradant of phenol was investigated for its degrading potential under different operating conditions. The neural network input parameter set consisted of the same set of four levels of maltose (0.025, 0.05, 0.075 g/l), phosphate (3, 12.5, 22 g/l), pH (7, 8, 9) and temperature (30°C, 32°C, 34°C) on phenol degradation was investigated and a Artificial Neural Network (ANN) model was developed to predict the extent of degradation. The learning, recall and generalization characteristic of neural networks was studied using phenol degradation system data. The efficiency of the model generated by the ANN, was tested and compared with the results obtained from an established second order polynomial multiple regression analysis (MRA). Further, the two models (ANN and MRA) were used to predict the percentage of degradation of phenol for blind test data. Performance of both the models were validated in the cases of training and test data, ANN was recommended based on the following higher coefficient of determination R 2; lower standard error of residuals and lower mean absolute percentage deviation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Abramowicz DA (1990) Aerobic and an aerobic biodegradation of PCBs a review. Crit Rev Biotechnol 10:241–251

    CAS  Google Scholar 

  • Akay G, Erhan E, Keskinler B (2005) Bioprocess intensification in flow-through monolithic microbioreactors with immobilized bacteria. Biotechnol Bioeng 90:180–190

    Article  CAS  Google Scholar 

  • Alessandro DA, Marika R, Vanessa L, Daniele Q, Enrico M, Maurizio P (2005) Degradation of aromatic hydrocarbons by white-rot fungi in a historically contaminated soil. Biotechnol Bioeng 90:723–731

    Article  CAS  Google Scholar 

  • Alexander M (1999) Biodegradation and Bioremediation. Academic Press, San Diego, California

    Google Scholar 

  • Angela RB, Todd C (2005) Dual substrate biodegradation of a nonionic surfactant and pentachlorophenol by Sphingomonas chlorophenolica RA2. Biotechnol Bioeng 89:680–689

    Article  CAS  Google Scholar 

  • Alvarez PJJ, Anid PJ, Vogel TM (1991) Kinetics of aerobic biodegradation of benzene and toluene in sandy aquifer material. Biodegradation 2:43–51

    Article  CAS  Google Scholar 

  • Andre NP, Bernard A, Grandijean, Jules T (1993) PVT data analysis using neural network models. Ind Eng Chem Res 32:970–975

    Article  Google Scholar 

  • Annadurai G, Raju V, Cellapandian M, Krishnan MRV (1996) Citric acid production. Bioprocess Eng 16:13–15

    Article  CAS  Google Scholar 

  • Arinjay K, Shashi K, Surendra K (2005) Biodegradation kinetics of phenol and catechol using Pseudomonas putida MTCC 1194. Biochem Eng J 22(2):151–159

    Article  CAS  Google Scholar 

  • Baker KH, Herson DS (1994) Bioremediation. McGraw Hill, New York

    Google Scholar 

  • Bhat NV, Peter A, Mindermen JT, Nam SH (1990) Modeling chemical process system via neutral computation. IEEE Control Syst 10:24–30

    Article  Google Scholar 

  • Box GEP, Behnken DW (1960) Some new three level design for the study of quantative variables. Technometrics 2:455–475

    Article  Google Scholar 

  • Bruno G (2005) Compound-specific stable-isotope (13 C) analysis in soil science. J Plant Nutr Soil Sc 168:633–648

    Article  CAS  Google Scholar 

  • Chitra S, Gowri GC (1996) Response of phenol degradation Pseudomonas pictorium to changing loads of phenolic compounds. J Environ Sci Health A31(3):599–619

    Article  CAS  Google Scholar 

  • Clessceri WG, Trossel AF (1989) Standard methods for the determination of water and waste water. American Public Health Allocation, Washington, DC 2005, pp 5.48–5.53

    Google Scholar 

  • Cochran WG, Cox DW (1968) Experimental design. John Wiley and Sons. Inc, New York, pp 611–626

    Google Scholar 

  • Duncan IS, Paul CN (2004) Application of the operation window concept to n remediation option selection. Remed J 14:55–64

    Google Scholar 

  • George AS, Makram DV, Radisav VM, Stephen MV (1993) Competitive adsorption of phenols on GAC 11. Adsorption dynamics under anoxic conductions. J␣Environ Engg Div ASCE 19:1044–1088

    Google Scholar 

  • Giorgia S, Marco DF (2005) Modeling of a vapor-phase fungi bioreactor for the abatement of hexane: fluid dynamics and kinetic aspects. Biotechnol Bioeng 89:319–328

    Article  CAS  Google Scholar 

  • Hardman DJ, McElddowney S., Waite S (1993) Pollution ecology and biotreatment. Longman Scientific and Technical, U.K

    Google Scholar 

  • Hopffield JJ (1992) Neural networks and physical systems with emergent collective computational abilities. Proc Natt Acad Sci USA 79:2554–2558

    Article  Google Scholar 

  • Illaria G, Stephen M, Ibrahim MB (2003) Bacterial biodegradation of phenol and 2,4,-dichlorophenol. J␣Chem Technol Biotechnol 78:959–963

    Article  CAS  Google Scholar 

  • Inniss WE (1976) Interaction of temperature on organophilic microogranisms. Annu Rev Microbiol 29:445–465

    Article  Google Scholar 

  • Jiang Y, Wen J, Li H, Yang S, Hu Z (2005) The biodegradation of phenol at high initial concentration by the yeast Candida tropicalis. Biochem Eng J 24(3):243–247

    Article  CAS  Google Scholar 

  • Kim JW, Armstrong ME (1981) Comprehensive study on the biological treatabilities of phenol and methanol II. The effects of temperature, pH salinity and neutrients. Water Res 15:1238–1247

    Google Scholar 

  • Kuo CC, Jane YW, Wen BY, Szchwun JH (2003) Evaluation of effective diffusion coefficient and intrinsic kinetic parameters on azo dye biodegradation using PVA-immobilized cell beads. Biotechnol Bioeng 83:821–832

    Article  CAS  Google Scholar 

  • Kushner DJ (1978) Micriobial life at low temperature chemical evolution of the giant planets. Academic Inc., New York pp 85–93

    Google Scholar 

  • Mahadeva S, Mall ID, Prasad B, Mishra LM (1997) Removal of phenol by adsorption on coal fly ash and activated carbon. Poll Res 16(3):170–175

    Google Scholar 

  • Marc R, James U (2005) Rhodococcus phenolicus sp. a novel bioprocessor isolated actinomycete with the ability to degrade chlorobenzene, dichlorobenzene and phenol as sole carbon sources. Syst Appl Microbiol 28(8):695–701

    Article  CAS  Google Scholar 

  • Mark TB, Vissanu M, Phillip CW (2002) Kinetic analyisi of high-concentration isopropanol biodegradation by a solvent-tolerant mixed nicrobial culture. Biotechnol Bioeng 78:708–713

    Article  CAS  Google Scholar 

  • Mario Z, Attilio C, Renzo DF (2003) Macrokinetic and quantitative microbial investigation on a benchscale biofilter treating styrene-polluted gaseous streams. Biotechnol Bioeng 83:29–38

    Article  CAS  Google Scholar 

  • Manivasakam N (1984) Industrial effluents origin, characteristics effects analysis and treatment. Sakthi Publications, Coimbatore, India, pp 458–463

    Google Scholar 

  • Mathalai Balan S, Annadurai G, Sheja RY, Srinivasamoorthy VR, Murugesan T (1999) Modeling of phenol degradation system using artificial neural networks. Bioprocess Eng 21:129–134

    Article  Google Scholar 

  • Ming JL, Joi TC (1974) Fluid property predictions with the aid of neural networks. End Eng Chem Res 37:995–997

    Google Scholar 

  • Murty SSN, Ravi V, Reddy PJ, Reddy RC (1997) Artificial neural networks applied to defluoridation. Poll Res 16(1):177–182

    CAS  Google Scholar 

  • Myung KC, Thomas CV, Craig SC (1993) Kinetic of competitive inhibition and cometabolism in the biodegradation of benzene, toluene, and p-xylene by two pseudomonas isolates. Biotechnol Bioeng 41:1057–1065

    Article  Google Scholar 

  • Novak JT (1974) Temperature—substrate interactions in biological treatment. J Water Pollut Control Fed 46:1984–1986

    CAS  Google Scholar 

  • Polland IF, Brouthsond MR, Garrison DE, San KY (1992) Process identification using neural network. Comput Chem Eng 16(4):253–270

    Article  Google Scholar 

  • Pandey G, Jain RK (2002) Bacterial chemotaxis toward environmental pollutants: role in bioremediation. Appl Environ Microbiol 68:5789–5795

    Article  CAS  Google Scholar 

  • Prevost A, Isambert D, Depeyrne C, Dunadille A, Preibbe R (1994) Some practical insights into neural network implementation in the metallurgical industry. Comput Chem Eng 18:1157–1170

    Article  CAS  Google Scholar 

  • Qian N, Senjnowski TJ (1988) Predicting the secondary structure of globular proteins wing neural network models. J Mol Biol 202:865–884

    Article  CAS  Google Scholar 

  • Rakesh Kumar J, Manisha K, Sumeet L, Banwari L, Priyangshu Manab S, Dhruva B, Indu Shekhar T (2005) Microbial diversity: application of microorganisms for the biodegradation of xenobiotics. Curr Sci 89(1):101–112

    Google Scholar 

  • Ravi V, Reddy PJ, Reddy RC (1995) Multiple regression analysis applied to defluoridation. Indian J Environ Prot 16:528–533

    Google Scholar 

  • Rumelhert DE, Hinton E, Williams RJ (1986) Learning internal representations by error propagation paraller distributed processing. In: Rumelhert DE, McCellord JC (eds) Explorations, MIT Press, Cambridge, Mass

  • Safonova KV, Kvitko MI, Lankevitch LF, Surgko IA, Afti WR (2004) Biotreatment of industrial wastewater by selected algal-bacterial consortia. Eng Life Sci 4:347–353

    Article  CAS  Google Scholar 

  • Sejnowski TJ, Rosenberg CR (1987) Parallel networks that learn to prononuce English text. Comp Syst 1:145–168

    Google Scholar 

  • Shingleton JT, Applegate BA, Baker AJ, Bienkowski PR (2001) Quantification of toluene dioxygenase induction and kinetic modeling of TCE cometabolism by pseudomonas putida TVA8. Biotechnol Bioeng 76:341–350

    Article  CAS  Google Scholar 

  • Tawtiki KH, Lepine F, Bisaillon JG, Beaudet R, Haeari J, Guiot SR (2000) Effects of bioaugmentation strategies in UASB reactors with a methanogenic consortium for removal of phenolic compounds. Biotechnol Bioeng 67:100–109

    Google Scholar 

  • Tesauro G, Sejnowski TJ (1989) A parallel network that learns to play backgammon language. Artif Intell 37:357–390

    Article  Google Scholar 

  • Tesema C (2005) Remediation of persisitent organic pollutants (POPs) in two different soils. Remed J 16:117–139

    Article  Google Scholar 

  • Thibault J, Breusegam VV, Chervy A (1990) On line prediction of fermentation variables using neural networks. Biotechnol Bioeng 30(2):1041–1048

    Article  Google Scholar 

  • Truu JL, Karme ET, Heinaru EE, Vedler AH (2003) Phytoremediation of solid oil shale waste form the chemical industry. Acta Biotechnol 23:301–307

    Article  CAS  Google Scholar 

  • Tsuey PC, Pei CW, Ruey SJ (2004) Process development for degradation of phenol by Pseudomonas putida in hollow-fiber membrane bioreactors. Biotechnol Bioeng 87(2):219–227

    Article  CAS  Google Scholar 

  • Vela GR, Rainey JR (1976) Microbial degradation of phenol in the effluent from a water treatment. Tex J Sci 27:197–206

    CAS  Google Scholar 

  • Vela GR, James RR (1978) The effect of temperature on phenol degradation in waste water. Can J Microbiol 24:1366–1370

    Article  CAS  Google Scholar 

  • Venkatasubramanian V, Vaidyanathan R, Yamamoto Y (1990) Process fault detection and diagnosis using neural network—1. Steedy state process. Comput Chem Eng 14:666–712

    Article  Google Scholar 

  • Vera LS, Nadia MH, Danubia TB, Andrea SM, Valter RL (2003) Phenol degradation by a Graphium sp. FIB4 isolated from industrial effluents. J Basic Microbiol 43:238–248

    Article  Google Scholar 

  • Villaverde S, Mirpuri PG, Zbigniew L, Jones WL (1997) Physiological and chemical gradients in a pseudomonas putida 54G biofilm degradating toluene in a plate vapor phase bioreactor. Biotechnol Bioeng 56:361–371

    Article  CAS  Google Scholar 

  • WHO (1989) Environmental health criteria, 93: Chlorophenols other than pentachlorophenols. World Health Organization, Geneva

    Google Scholar 

  • Widrow B, Hoff M (1960). Adaptive switching circuits. IRF WESCON Convnetion Record Part 4:96–104

    Google Scholar 

  • Yamuna Rani K, Ramachandra Rao R (1999) Control of fermentans. A review. Bioprocess Eng 21:77–88

    CAS  Google Scholar 

Download references

Acknowledgements

Support for this work by the National Science Council, ROC, under Grant NSC 94-2811-E-008-010 is highly appreciated.

Author information

Authors and Affiliations

  1. Graduate Institute of Environmental Engineering, National Central University, Chung-Li, 320, Taiwan, ROC

    Gurusamy Annadurai & Jiunn-Fwu Lee

Authors
  1. Gurusamy Annadurai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jiunn-Fwu Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gurusamy Annadurai.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Annadurai, G., Lee, JF. Application of artificial neural network model for the development of optimized complex medium for phenol degradation using Pseudomonas pictorum (NICM 2074). Biodegradation 18, 383–392 (2007). https://doi.org/10.1007/s10532-006-9072-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10532-006-9072-8

Keywords

Search

Navigation