Traditional methods used in the analysis of fermentation media suffer from a number of limitations. The search for more rapid and efficient methods has led to the development and application of near‐infrared spectroscopy. Near‐infrared spectroscopy has been applied successfully in a variety of industrial processes: agricultural, food, chemical and pharmaceutical, generally in the areas of raw material quality control but also including intermediate and finished product testing. The present research explores its potential for online fermentation monitoring of total cell count (TCC), specific gravity (SG), free amino nitrogen (FAN) and percentage alcohol by volume (% v v−1) in a 300 L pilot‐scale validation batch. Models that were generated from three calibration batches for each of these constituents exhibited overall favourable standard error of cross validation (SECV) and fit of predicted vs actual cross validated results (SECV, R2): SG (0.00072, 0.995), ethanol (0.17% v v−1, 0.990), FAN (16.5 mg L−1, 0.886) and TCC (1.24 × 106 cells mL−1, 0.640). The data that was most relevant to cell metabolism was determined to be sugar consumption rate, ethanol production rate, yield of ethanol and fermentation lag time. These ‘critical performance parameters’ were incorporated into a novel feed‐forward control strategy where yeast pitching rate was modified based on values of the critical performance parameters from the previous batch. Use of this feed‐forward strategy demonstrated how brewers can utilize near‐infrared monitoring for quality assurance through early detection of shifts in fermentation performance. Copyright © 2017 The Institute of Brewing & Distilling

No abstract

Control of biopharmaceutical processes is critical to achieve consistent product quality. The most challenging unit operation to control is cell growth in bioreactors due to the exquisitely sensitive and complex nature of the cells that are converting raw materials into new cells and products. Current monitoring capabilities are increasing, however, the main challenge is now becoming the ability to use the data generated in an effective manner. There are a number of contributors to this challenge including integration of different monitoring systems as well as the functionality to perform data analytics in real-time to generate process knowledge and understanding. In addition, there is a lack of ability to easily generate strategies and close the loop to feedback into the process for advanced process control (APC). The current research aims to demonstrate the use of advanced monitoring tools along with data analytics to generate process understanding in an Escherichia coli fermentation process. NIR spectroscopy was used to measure glucose and critical amino acids in real-time to help in determining the root cause of failures associated with different lots of yeast extract. First, scale-down of the process was required to execute a simple design of experiment, followed by scale-up to build NIR models as well as soft sensors for advanced process control. In addition, the research demonstrates the potential for a novel platform technology that enables manufacturers to consistently achieve "goldenbatch" performance through monitoring, integration, data analytics, understanding, strategy design and control (MIDUS control). MIDUS control was employed to increase batch-to-batch consistency in final product titers, decrease the coefficient of variability from 8.49 to 1.16%, predict possible exhaust filter failures and close the loop to prevent their occurrence and avoid lost batches.

No abstract