Prafful Kumar Meena, Jai Gopal Sharma, Manish Jain
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The predicted values from both ANNs well fitted with the experimental results with <i>R</i><sup>2</sup> < 0.99. Simulations showed that membrane fouling increased as flux reduction increased from 36.3% to 76.39% when trans-membrane pressure increased from 0.5 to 2 bar. In contrast, a 19.96% reduction in flux was observed by increasing the Reynolds number from 750 to 2500. An increment of 77.37% of flux reduction was observed with increasing feed temperature from 30°C to 40°C. Simulations confirmed that transmembrane pressure, Reynolds number, and feed temperature strongly influence membrane fouling. 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An increment of 77.37% of flux reduction was observed with increasing feed temperature from 30°C to 40°C. Simulations confirmed that transmembrane pressure, Reynolds number, and feed temperature strongly influence membrane fouling. 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引用次数: 0
摘要
微过滤能耗低,无需使用热量和化学品,是最适合从乳清中回收蛋白质的工艺之一。然而,膜堵塞是微过滤工艺的限制因素之一,阻碍了其商业应用。在这项研究中,采用了基于人工神经网络(ANN)的模型来研究不同操作参数对乳清浓缩膜污垢的影响。输入参数包括跨膜压力、雷诺数和进料温度。现有研究的实验数据被用于训练 ANN。在跨膜压力和雷诺数方面,23 个神经元的 ANN 的均方误差(MSE)最小。在进料温度方面,有 7 个神经元的 ANN 的平均平方误差最小。两个方差网络的预测值与实验结果非常吻合,R2 为 0.99。模拟结果表明,当跨膜压力从 0.5 巴增加到 2 巴时,膜污垢会随着通量减少从 36.3% 增加到 76.39%。相反,当雷诺数从 750 增加到 2500 时,通量减少了 19.96%。进料温度从 30°C 提高到 40°C,流量减少了 77.37%。模拟证实,跨膜压力、雷诺数和进料温度对膜污垢有很大影响。基于 ANN 的方法是为乳清蛋白分离建立膜污垢模型的最准确方法。
Recovery of Whey Protein by Using Microfiltration: Artificial Neural Network–Based Modeling and Effects of Different Operating Parameters
Microfiltration is one of the most suitable processes for protein recovery from whey due to its low energy consumption and lack of use of heat and chemicals. However, membrane fouling is one of the limiting factors in the microfiltration process, preventing its commercial use. In this study, an artificial neural network (ANN) based model was employed to study the effects of different operating parameters on membrane fouling in whey concentration. Trans-membrane pressure, Reynolds number, and feed temperature were selected as the input parameters. Experimental data from the available studies were used to train the ANN. The ANN with 23 neurons gave a minimum mean squared error (MSE) for trans-membrane pressure and Reynolds number. The ANN with seven neurons gave the minimum MSE for feed temperature. The predicted values from both ANNs well fitted with the experimental results with R2 < 0.99. Simulations showed that membrane fouling increased as flux reduction increased from 36.3% to 76.39% when trans-membrane pressure increased from 0.5 to 2 bar. In contrast, a 19.96% reduction in flux was observed by increasing the Reynolds number from 750 to 2500. An increment of 77.37% of flux reduction was observed with increasing feed temperature from 30°C to 40°C. Simulations confirmed that transmembrane pressure, Reynolds number, and feed temperature strongly influence membrane fouling. An ANN-based approach was the most accurate method to model membrane fouling for whey protein separation.
期刊介绍:
This international research journal focuses on the engineering aspects of post-production handling, storage, processing, packaging, and distribution of food. Read by researchers, food and chemical engineers, and industry experts, this is the only international journal specifically devoted to the engineering aspects of food processing. Co-Editors M. Elena Castell-Perez and Rosana Moreira, both of Texas A&M University, welcome papers covering the best original research on applications of engineering principles and concepts to food and food processes.