Comparative efficacy of ANN and ANFIS models in estimating biosurfactant production produced by Klebseilla sp. FKOD36

Zulfiqar Ahmad*, Muhammad Arshad, David Crowley, Benyamin Khoshnevisan, Marziye Yousefi, Muhammad Imran, Sabir Hussain

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review


Optimization of environmental and medium parameters is an important step for bioprocess engineering. In the present study, the efficacies of Artificial Neural Networks (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were compared for their utility in estimating biosurfactant production, surface tension reduction, and emulsification under different environmental and medium parameters. Biosurfactant was collected from the bacterial isolate FKOD36. In these models, temperature, pH, incubation period, carbon, nitrogen, and hydrocarbon sources were used as input variables, whereas surface tension reduction, emulsification index, and biosurfactant production were the dependent output variables. Models were trained for six inputs and three ANFIS sub-networks were developed for each output. Each of three ANFIS models was then used to predict one of the three outputs. The performance indices of both ANN and ANFIS illustrate that proposed ANFIS network produced better results with coefficient of determination (R2) values ranging from 0.96 to 0.99 for the training dataset and 0.90–0.99 for the validation dataset as compared to ANN which had R2 values of 0.95–0.99 for the training set and 0.89–0.98 testing set. Based on the results, the multilayer ANFIS model with its fuzzy application rules proved to give better prediction results than the ANN model.

Original languageEnglish
JournalStochastic Environmental Research and Risk Assessment
Issue number1
Pages (from-to)353-363
Number of pages11
Publication statusPublished - 1. Jan 2016

Bibliographical note

Funding Information:
The authors gratefully acknowledge support from the Higher Education Commission of Pakistan for providing an IRSIP Fellowship for the lead author and financial support for the project. The authors also thank Tehran University for its support of this project.

Publisher Copyright:
© 2015, Springer-Verlag Berlin Heidelberg.


  • Adaptive neuro-fuzzy inference system
  • Artificial neural network
  • Biosurfactant production


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