A Neuro-Fuzzy Model for a Dynamic Prediction of Milk Ultrafiltration Flux and Resistance

Authors

  • Javad Sargolzaei Department of Chemical Engineering, University of Ferdowsi, P.O. Box 9177948944 Mashhad, I. R. IRAN
  • Mahmood Mousavi Department of Chemical Engineering, University of Ferdowsi, P.O. Box 9177948944 Mashhad, I. R. IRAN
  • Mohammad Khoshnoodi Department of Chemical Engineering, University of Sistan and Baluchestan, P.O. Box 98164 -161 Zahedan, I. R. IRAN
  • Nasser Saghatoleslami Department of Chemical Engineering, University of Ferdowsi, P.O. Box 9177948944 Mashhad, I. R. IRAN
Abstract:

A neuro-fuzzy modeling tool (ANFIS) has been used to dynamically model cross flow ultrafiltration of milk. It aims to predict permeate flux and total hydraulic resistance as a function of transmembrane pressure, pH, temperature, fat, molecular weight cut off, and processing time. Dynamic modeling of ultrafiltration performance of colloidal systems (such as milk) is very important for designing of a new process and better understanding of the present process. Such processes show complex non-linear behavior due to unknown interactions between compounds of a colloidal system. In this paper, ANFIS, Multilayer Perceptron (MLP) and FIS were applied to compare results. The ANFIS approximation gave some advantage over the other methods. The results reveal that there is an excellent agreement between the tested (not used in training) and modeled data, with a good degree of accuracy. Furthermore, the trained ANFIS are capable of accurately capture the non-linear dynamics of milk ultrafiltration even for a new condition that has not been used in the training process (tested data). In addition, ANFIS and Multilayer Perceptron (MLP) are compared and the Matlab software was adopted to implement the method.  

Upgrade to premium to download articles

Sign up to access the full text

Already have an account?login

similar resources

a neuro-fuzzy model for a dynamic prediction of milk ultrafiltration flux and resistance

a neuro-fuzzy modeling tool (anfis) has been used to dynamically model cross flow ultrafiltration of milk. it aims to predict permeate flux and total hydraulic resistance as a function of transmembrane pressure, ph, temperature, fat, molecular weight cut off, and processing time. dynamic modeling of ultrafiltration performance of colloidal systems (such as milk) is very important for designing ...

full text

Multidimensional Dynamic Modeling of Milk Ultrafiltration Using Neuro-Fuzzy Method and a Hybrid Physical Model

Prediction of the dynamic crossflow ultrafiltration rate of a protein solution such as milk poses a complex non-linear problem a...

full text

the innovation of a statistical model to estimate dependable rainfall (dr) and develop it for determination and classification of drought and wet years of iran

آب حاصل از بارش منبع تأمین نیازهای بی شمار جانداران به ویژه انسان است و هرگونه کاهش در کم و کیف آن مستقیماً حیات موجودات زنده را تحت تأثیر منفی قرار می دهد. نوسان سال به سال بارش از ویژگی های اساسی و بسیار مهم بارش های سالانه ایران محسوب می شود که آثار زیان بار آن در تمام عرصه های اقتصادی، اجتماعی و حتی سیاسی- امنیتی به نحوی منعکس می شود. چون میزان آب ناشی از بارش یکی از مولفه های اصلی برنامه ...

15 صفحه اول

investigating the feasibility of a proposed model for geometric design of deployable arch structures

deployable scissor type structures are composed of the so-called scissor-like elements (sles), which are connected to each other at an intermediate point through a pivotal connection and allow them to be folded into a compact bundle for storage or transport. several sles are connected to each other in order to form units with regular polygonal plan views. the sides and radii of the polygons are...

Time Prediction Using a Neuro-Fuzzy Model for Projects in the Construction Industry

This paper presents a prediction model based on a new neuro-fuzzy algorithm for estimating time in construction projects. The output of the proposed prediction model, which is employed based on a locally linear neuro-fuzzy (LLNF) model, is useful for assessing a project status at different time horizons. Being trained by a locally linear model tree (LOLIMOT) learning algorithm, the model is int...

full text

A neuro-fuzzy approach to vehicular traffic flow prediction for a metropolis in a developing country

Short-term prediction of traffic flow is central to alleviating congestion and controlling the negative impacts of environmental pollution resulting from vehicle emissions on both inter- and intra-urban highways. The strong need to monitor and control congestion time and costs for metropolis in developing countries has therefore motivated the current study. This paper establishes the applicatio...

full text

My Resources

Save resource for easier access later

Save to my library Already added to my library

{@ msg_add @}


Journal title

volume 26  issue 2

pages  53- 61

publication date 2007-06-01

By following a journal you will be notified via email when a new issue of this journal is published.

Hosted on Doprax cloud platform doprax.com

copyright © 2015-2023