Publication detail

Optimization of Palm Oil Mill Effluent (POME) Solubilization Using Linguistic Fuzzy Logic and Machine Learning Techniques

JANKOVÁ, Z. DOSTÁL, P. JANA, D. BHATTACHARJEE, S. BARNALI, B. DEY, P. ROY, S.

Original Title

Optimization of Palm Oil Mill Effluent (POME) Solubilization Using Linguistic Fuzzy Logic and Machine Learning Techniques

Type

conference paper

Language

English

Original Abstract

The continuous hike in the price of the edible vegetable oil has directly impacted upon the price growth of the palm oil. In this palm oil production, Indonesia and Malaysia are in top leading position at present. It is found that an increase in palm oil mill setup is radically increasing the produce of effluent discharge which is a severe threat to our environment. Hence to maintain the balance of our already affected ecosystem, the proper treatment of this residual product is becoming the dire need of the hour. The conventional method used for solubilization of palm oil mill effluent (POME) is thermal alkaline pre-treatment. In this paper, the linguistic fuzzy logic (LFL) and machine learning (ML) techniques have been used to analyze the data, and type-2 fuzzy logic controller (T2FLC) has been used to optimize the solubilization of POME. The effect of reaction time, NaOH concentration, and temperature on the solubilization has been evaluated. From the investigation of the surface plot, which developed in T2FLC environment, it has been observed that NaOH concentration has a significant effect on the solubilization of POME. The prediction efficiency of T2FLC then has been compared with T1FLC and RSM. By evaluating some statistical analyses, the sensitivity and validity of the proposed model have finally been measured.

Keywords

palm oil mill effluent; type-2 fuzzy logic; solubilization; thermo-alkaline pre-treatment; statistical analysis

Authors

JANKOVÁ, Z.; DOSTÁL, P.; JANA, D.; BHATTACHARJEE, S.; BARNALI, B.; DEY, P.; ROY, S.

Released

24. 5. 2023

Publisher

Springer

Location

Singapore

ISBN

978-981-19-8193-7

Book

Springer Proceedings in Mathematics & Statistics

Edition

413

Edition number

1

Pages from

225

Pages to

242

Pages count

18

URL

https://link.springer.com/chapter/10.1007/978-981-19-8194-4_18

BibTex

@inproceedings{BUT183570,
  author="Zuzana {Janková} and Petr {Dostál} and Dipak Kumar {Jana} and Samyabrata {Bhattacharjee} and Bej {Barnali} and Priyanka {Dey} and Sudipta {Roy}",
  title="Optimization of Palm Oil Mill Effluent (POME) Solubilization Using Linguistic Fuzzy Logic and Machine Learning Techniques",
  booktitle="Springer Proceedings in Mathematics & Statistics",
  year="2023",
  series="413",
  number="1",
  pages="225--242",
  publisher="Springer",
  address="Singapore",
  doi="10.1007/978-981-19-8194-4\{_}18",
  isbn="978-981-19-8193-7",
  url="https://link.springer.com/chapter/10.1007/978-981-19-8194-4_18"
}