Artificial Neural Network (ANN)-based Prediction of Depth Filter Loading Capacity for Filter Sizing
Artificial Neural Network (ANN)-based Prediction of Depth Filter Loading Capacity for Filter Sizing
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DOI：
10.1002/btpr.2329
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发表时间：
2016-11-01
影响因子：
2.9
通讯作者：
Alva, Solomon J.
中科院分区：
文献类型：
--
作者：
Agarwal, Harshit;Rathore, Anurag S.;Alva, Solomon J.
本文介绍了人工神经网络（ANN）建模在商业生产过程中用于澄清单克隆抗体（mAb）产品的深度过滤器负载能力预测中的应用。通过分析压差（DP）随时间的变化，评价了运行参数对过滤器负荷能力的影响。提出的人工神经网络模型使用入口流特性（饲料浊度，饲料细胞计数，饲料细胞活力），通量和时间来预测相应的DP。该神经网络包含一个输出层，隐藏层包含10个神经元，并采用s型激活函数。该网络使用174个训练点、37个验证点和37个测试点进行训练。此外，在每个操作条件下，使用1.1 bar的压力截止来确定所需的过滤面积。模型结果表明，预测值与实验值吻合良好，回归系数R-2为0.98。所开发的人工神经网络模型用于对不同澄清批次进行变深度过滤。通过蒙特卡罗模拟来估计不同的澄清批次使用不同的过滤区域而不是使用相同的过滤区域所节省的成本。这一操作节省了10%的货物成本。(C) 2016年美国化学工程师学会
This article presents an application of artificial neural network (ANN) modelling towards prediction of depth filter loading capacity for clarification of a monoclonal antibody (mAb) product during commercial manufacturing. The effect of operating parameters on filter loading capacity was evaluated based on the analysis of change in the differential pressure (DP) as a function of time. The proposed ANN model uses inlet stream properties (feed turbidity, feed cell count, feed cell viability), flux, and time to predict the corresponding DP. The ANN contained a single output layer with ten neurons in hidden layer and employed a sigmoidal activation function. This network was trained with 174 training points, 37 validation points, and 37 test points. Further, a pressure cut-off of 1.1 bar was used for sizing the filter area required under each operating condition. The modelling results showed that there was excellent agreement between the predicted and experimental data with a regression coefficient (R-2) of 0.98. The developed ANN model was used for performing variable depth filter sizing for different clarification lots. Monte-Carlo simulation was performed to estimate the cost savings by using different filter areas for different clarification lots rather than using the same filter area. A 10% saving in cost of goods was obtained for this operation. (C) 2016 American Institute of Chemical Engineers