title: Artificial neural network (ANN)-based prediction of depth filter loading capacity for filter sizing.
abstract: 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 (R2) 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. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:1436-1443, 2016.
authors.affiliation: Dept. of Chemical Engineering, Indian Institute of Technology, Hauz Khas, New Delhi, 110016, India.
authors.forename: Harshit
authors.initials: H
authors.lastname: Agarwal
authors.affiliation: Dept. of Chemical Engineering, Indian Institute of Technology, Hauz Khas, New Delhi, 110016, India.
authors.forename: Anurag S
authors.initials: AS
authors.lastname: Rathore
authors.affiliation: Research and development, Biocon research limited, Bangalore 560100, India.
authors.forename: Sandeep Ramesh
authors.initials: SR
authors.lastname: Hadpe
authors.affiliation: Research and development, Biocon research limited, Bangalore 560100, India.
authors.forename: Solomon J
authors.initials: SJ
authors.lastname: Alva
mesh_terms: D000911:Antibodies, Monoclonal / Q000096:biosynthesis; D005374:Filtration / Q000295:instrumentation; D009010:Monte Carlo Method; D016571:Neural Networks, Computer; D010316:Particle Size
chemical_list: D000911:Antibodies, Monoclonal
keywords: artificial neural network; bioprocessing; depth filtration; filter loading capacity; monoclonal antibody