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Artificial Neural Network (ANN)-based Prediction of Depth Filter Loading Capacity for Filter Sizing

机译：基于人工神经网络（ANN）的深度过滤器装料容量预测

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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
机译：本文介绍了人工神经网络（ANN）建模在预测深度过滤器负载能力以澄清商业化生产过程中单克隆抗体（mAb）产品方面的应用。基于对压差（DP）随时间变化的分析，评估了运行参数对过滤器负载能力的影响。拟议的人工神经网络模型使用入口流的性质（饲料浊度，饲料细胞数，饲料细胞活力），通量和时间来预测相应的DP。人工神经网络包含一个单独的输出层，在隐藏层中具有十个神经元，并采用了S型激活函数。该网络接受了174个训练点，37个验证点和37个测试点的训练。此外，使用1.1 bar的压力截止来确定每种操作条件下所需的过滤面积。建模结果表明，预测数据与实验数据之间具有极好的一致性，回归系数（R-2）为0.98。所开发的ANN模型用于对不同的澄清批次执行可变深度过滤器大小调整。通过对不同的澄清批次使用不同的过滤面积，而不是使用相同的过滤面积，进行了蒙特卡洛模拟以估算成本节省。此操作节省了10％的商品成本。 （C）2016美国化学工程师学会

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