MODELING DEAD-END ULTRAFILTRATION OF APPLE JUICE USING ARTIFICIAL NEURAL NETWORK


GÖKMEN V., Acar O. C. , Serpen A., Sugut I.

JOURNAL OF FOOD PROCESS ENGINEERING, vol.32, no.2, pp.248-264, 2009 (Peer-Reviewed Journal) identifier identifier

  • Publication Type: Article / Article
  • Volume: 32 Issue: 2
  • Publication Date: 2009
  • Doi Number: 10.1111/j.1745-4530.2007.00214.x
  • Journal Name: JOURNAL OF FOOD PROCESS ENGINEERING
  • Journal Indexes: Science Citation Index Expanded, Scopus
  • Page Numbers: pp.248-264

Abstract

An artificial neural network (ANN) was developed to model the dead-end ultrafiltration process of apple juice. Molecular weight cutoff, transmembrane pressure, gelatin-bentonite concentration and time were the input variables, while filtrate flux and filtrate volume were the output variables of the ultrafiltration process. According to error results and correlation values for two types of network (one or two hidden layer configurations), configurations with two hidden layers had comparatively better performance. The highest correlation coefficient with the minimum prediction error was calculated for two hidden layers with 6-5 nodes configuration. Trained ANN (4-6-5-2) predicted filtrate flux and filtrate volume with 2.33 and 1.38% mean relative error, respectively. The results suggest that the ANN modeling can be effectively used to optimize filtration process.