Comparison study of the effect modeling of flow parameters on the membrane clarification efficiency for pomegranate juice

Q2 Engineering

Engineering in Agriculture, Environment and Food Pub Date : 2019-10-01 DOI:10.1016/j.eaef.2019.04.005

Marzieh Toupal Poudineh , Payam Zarafshan , Hossein Mirsaeedghazi , Mohammad Dehghani

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引用次数: 1

Abstract

In recent years, several studies have indicated that modeling techniques based on artificial intelligence can be used for efficient prediction of food industry-related variables. In this study, machine learning methods were used to predict the permeate flux of pomegranate juice in a membrane clarification system based on membrane material, pore size, pressure, flow rate, and processing time. The experimental data were modeled using curve fitting, fuzzy inference system (FIS), artificial neural networks (ANN), and adaptive neuro-fuzzy inference system (ANFIS). Results showed that the permeate flux is a function of time and a power equation can predict the permeate flux with MSE of 0.0136. FIS, ANN and ANFIS models resulted in MSEs equal to 0.0495, 0.0145, and 0.0045 for permeate flux prediction, respectively. According to these findings, ANFIS has resulted in more reliable performance which can be used as an acceptable model in the prediction of permeate flux. The optimum architecture for the ANN was obtained 5-22-1 whilst the architecture of ANFIS models for PVDF and MCE membranes were 3-7-12-12-1 and 4-9-24-24-1, respectively. The results of this study can be used to predict the amount of permeate flux in the absence of experimental data and/or for interpolation and extrapolation of the permeate flux.

Practical applications

One of the problems in juice membrane clarification is the accumulation and deposition of rejected compounds on membrane surfaces or inside its pores which results in a membrane fouling. On the other hand, several parameters can have influence on fouling and predictions of juice permeate flux during the membrane processing whereas they are important in industrial applications. Therefore, providing a model which able to predict the permeate flux having the value of effective input parameters seems to be useful. In this regard, several artificial methods can be used.

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流动参数对石榴汁清膜效率影响模型的比较研究

近年来，一些研究表明，基于人工智能的建模技术可以用于食品工业相关变量的有效预测。在本研究中，采用机器学习方法，基于膜材料、孔径、压力、流速和处理时间，预测石榴汁在膜澄清系统中的渗透通量。采用曲线拟合、模糊推理系统(FIS)、人工神经网络(ANN)和自适应神经模糊推理系统(ANFIS)对实验数据进行建模。结果表明，渗透通量是时间的函数，幂函数方程可以预测渗透通量，MSE为0.0136。FIS、ANN和ANFIS模型预测渗透通量的均方根误差分别为0.0495、0.0145和0.0045。结果表明，ANFIS模型的性能更加可靠，可以作为一种可接受的渗透通量预测模型。PVDF膜和MCE膜的ANFIS模型的最佳结构分别为3-7-12-12-1和4-9-24-24-1。本研究结果可用于在没有实验数据的情况下预测渗透通量的大小和/或对渗透通量进行插值和外推。实际应用果汁膜澄清的问题之一是被拒绝的化合物在膜表面或膜孔内的积累和沉积，从而导致膜污染。另一方面，在膜处理过程中，有几个参数会影响污染和果汁渗透通量的预测，而它们在工业应用中是重要的。因此，提供一个具有有效输入参数值的模型来预测渗透通量似乎是有用的。在这方面，可以使用几种人工方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Engineering in Agriculture, Environment and Food Engineering-Industrial and Manufacturing Engineering

CiteScore

1.00

自引率

0.00%

发文量

期刊介绍： Engineering in Agriculture, Environment and Food (EAEF) is devoted to the advancement and dissemination of scientific and technical knowledge concerning agricultural machinery, tillage, terramechanics, precision farming, agricultural instrumentation, sensors, bio-robotics, systems automation, processing of agricultural products and foods, quality evaluation and food safety, waste treatment and management, environmental control, energy utilization agricultural systems engineering, bio-informatics, computer simulation, computational mechanics, farm work systems and mechanized cropping. It is an international English E-journal published and distributed by the Asian Agricultural and Biological Engineering Association (AABEA). Authors should submit the manuscript file written by MS Word through a web site. The manuscript must be approved by the author''s organization prior to submission if required. Contact the societies which you belong to, if you have any question on manuscript submission or on the Journal EAEF.