Membrane Research Laboratory, Department of Chemical Engineering, National Institute of Technology, Tiruchirappalli, 620 015, India; Advanced Membrane Technology Research Centre (AMTEC), Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia.
Department of Chemical and Materials Engineering, Donadeo Innovation Center for Engineering, University of Alberta-T6G 1H9, Edmonton, Canada.
J Environ Manage. 2022 Jan 1;301:113872. doi: 10.1016/j.jenvman.2021.113872. Epub 2021 Oct 1.
Effluent originating from cheese production puts pressure onto environment due to its high organic load. Therefore, the main objective of this work was to compare the influence of different process variables (transmembrane pressure (TMP), Reynolds number and feed pH) on whey protein recovery from synthetic and industrial cheese whey using polyethersulfone (PES 30 kDa) membrane in dead-end and cross-flow modes. Analysis on the fouling mechanistic model indicates that cake layer formation is dominant as compared to other pore blocking phenomena evaluated. Among the input variables, pH of whey protein solution has the biggest influence towards membrane flux and protein rejection performances. At pH 4, electrostatic attraction experienced by whey protein molecules prompted a decline in flux. Cross-flow filtration system exhibited a whey rejection value of 0.97 with an average flux of 69.40 L/mh and at an experimental condition of 250 kPa and 8 for TMP and pH, respectively. The dynamic behavior of whey effluent flux was modeled using machine learning (ML) tool convolutional neural networks (CNN) and recursive one-step prediction scheme was utilized. Linear and non-linear correlation indicated that CNN model (R - 0.99) correlated well with the dynamic flux experimental data. PES 30 kDa membrane displayed a total protein rejection coefficient of 0.96 with 55% of water recovery for the industrial cheese whey effluent. Overall, these filtration studies revealed that this dynamic whey flux data studies using the CNN modeling also has a wider scope as it can be applied in sensor tuning to monitor flux online by means of enhancing whey recovery efficiency.
奶酪生产产生的废水由于其高有机负荷而对环境造成压力。因此，这项工作的主要目的是比较不同工艺变量（跨膜压力（TMP）、雷诺数和进料 pH 值）对使用聚醚砜（PES 30 kDa）膜在死端和错流模式下从合成和工业奶酪乳清中回收乳清蛋白的影响。对污垢机理模型的分析表明，与评估的其他孔堵塞现象相比，滤饼层形成占主导地位。在输入变量中，乳清蛋白溶液的 pH 值对膜通量和蛋白质截留性能的影响最大。在 pH 4 时，乳清蛋白分子所经历的静电吸引力促使通量下降。错流过滤系统在实验条件为 250 kPa 和 8 时，对乳清的截留率为 0.97，平均通量为 69.40 L/mh。使用机器学习（ML）工具卷积神经网络（CNN）对乳清废水通量的动态行为进行建模，并利用递归一步预测方案进行。线性和非线性相关性表明，CNN 模型（R - 0.99）与动态通量实验数据相关性良好。PES 30 kDa 膜对工业奶酪乳清废水的总蛋白截留系数为 0.96，水回收率为 55%。总的来说，这些过滤研究表明，使用 CNN 建模的动态乳清通量数据研究也具有更广泛的应用范围，因为它可以应用于传感器调谐，通过提高乳清回收率来在线监测通量。
