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Design of experiments applications in bioprocessing: Concepts and approach
Author(s) -
Kumar Vijesh,
Bhalla Akriti,
Rathore Anurag S.
Publication year - 2013
Publication title -
biotechnology progress
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.572
H-Index - 129
eISSN - 1520-6033
pISSN - 8756-7938
DOI - 10.1002/btpr.1821
Subject(s) - bioprocess , akaike information criterion , design of experiments , biochemical engineering , computer science , process (computing) , selection (genetic algorithm) , unit operation , process engineering , machine learning , engineering , mathematics , statistics , chemical engineering , operating system
Most biotechnology unit operations are complex in nature with numerous process variables, feed material attributes, and raw material attributes that can have significant impact on the performance of the process. Design of experiments (DOE)‐based approach offers a solution to this conundrum and allows for an efficient estimation of the main effects and the interactions with minimal number of experiments. Numerous publications illustrate application of DOE towards development of different bioprocessing unit operations. However, a systematic approach for evaluation of the different DOE designs and for choosing the optimal design for a given application has not been published yet. Through this work we have compared the I‐optimal and D‐optimal designs to the commonly used central composite and Box–Behnken designs for bioprocess applications. A systematic methodology is proposed for construction of the model and for precise prediction of the responses for the three case studies involving some of the commonly used unit operations in downstream processing. Use of Akaike information criterion for model selection has been examined and found to be suitable for the applications under consideration. © 2013 American Institute of Chemical Engineers Biotechnol. Prog ., 30:86–99, 2014