Universiti Teknologi Malaysia Institutional Repository

Adaptive neuro fuzzy inference system (ANFIS) modelling for quality estimation in palm oil refining process

Ali, N. S. and Yusof, K. M. and Othman, M. F. and Latip, R. A. and Ismail, M. S. N. (2019) Adaptive neuro fuzzy inference system (ANFIS) modelling for quality estimation in palm oil refining process. Journal of Mechanical Engineering . pp. 36-47. ISSN 1823-5514

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Palm oil refining is a process of removing the unwanted compounds from crude palm oil to produce a refined, bleached and deodorized (RBD) palm oil. It consists of three main processes which are degumming, bleaching and deodorization. In this study, an adaptive neuro-fuzzy inference system was developed to predict the FFA content in the RBD palm oil. The FFA content is non-linearly correlated to the input quality and the operating condition at the refining processes. Feed flow rate, FFA content (input), moisture, IV, phosphoric acid, bleaching earth, bleaching temperature, bleaching vacuum pressure, deodorizer temperature, deodorizer vacuum pressure, heating temperature, sparging steam pressure, and pre-stripper sparging steam were used to build the ANFIS model. Subtractive clustering method was selected for the ANFIS model. The performance of the model was evaluated using RMSE and R2 value. This study demonstrates that ANFIS are useful for estimating the FFA quality at the palm oil refining process.

Item Type:Article
Uncontrolled Keywords:quality estimation, palm oil, refinery
Subjects:T Technology > TJ Mechanical engineering and machinery
Divisions:Mechanical Engineering
ID Code:90271
Deposited By: Narimah Nawil
Deposited On:31 Mar 2021 13:03
Last Modified:31 Mar 2021 13:03

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