Analysis of Multivariate Sensor Data for Monitoring of

Improved monitoring of cultivations has the potential to increase process understanding, enable immediate quality assessment, improve process control, and facilitate optimization of the process. Over the past decades various advanced sensor technologies have been developed, which can facilitate rapid estimations of important biological or chemical variables in the cultivation processes. Despite a large number of academic studies, most of the advanced sensors have not found widespread use in the industry until now. The main aim of this thesis has been to address this apparent discrepancy. The thesis consists of four case studies, which have provided critical evaluations of various sensor technologies and have served to elucidate some of the specific challenges of advanced sensor application in filamentous and industrially relevant cultivation processes. The first two case studies were focused on quantification of filamentous morphology using off-line laser diffraction and the relation of the size distribution to the rheological properties of the cultivation broth. Firstly, the method of laser diffraction was compared to simple image analysis for quantification of clumps and pellets in Streptomyces coelicolor cultivation broth. The laser diffraction technique and image analysis yielded size distributions with similar shape, i.e. unimodal or bimodal distributions. The two techniques produced similar estimations of the population means, whereas the estimates of the standard deviations were generally higher using the laser diffraction technique compared to image analysis. In the second study, laser diffraction was used to quantify the morphology in industrial Aspergillus oryzae cultivations. Systematic changes in the size distribution were correlated to (1) the change in morphology as the cultivation progressed and (2) the difference in morphologies observed between continuously fed batches and the batches with pulse-pause feeding. Models were calibrated for prediction of the rheological parameters yield stress, consistency index and the apparent viscosity of A. oryzae broth based on the size distribution. Validation on an independent test set yielded a root mean square error of 1.20 Pa for the yield stress, 0.209 Pa sn for the consistency index and 0.0288 Pa s for apparent viscosity, corresponding to R2=0.95, R2=0.94, and R2=0.95 respectively. In the third case study, in situ NIR spectroscopy was evaluated for quantification of of two important analytes: glucose and ammonium in S. coelicolor batch cultivations. In order to critically evaluate the ability of NIR to measure the two specific analytes, the inherent correlations in the batch process were broken by the use of semi-synthetic samples measured in situ. In addition, samples were collected and analyzed using off-line NIR, to highlight the specific challenges of in situ monitoring. The results showed that both off-line and in situ NIR are suitable for the measurement of glucose concentration in the range 1-40 g/L. Using in situ NIR spectroscopy, a prediction error or 1.1 g/L glucose was obtained corresponding to 5.4 % of the mean value in a validation batch. For measurement of ammonium in the concentration range 40-110 mM, a large increase in prediction error was observed for in situ measurements (RMSEP= 11 mM corresponding to 16% of the mean value) compared to off-line measurements (RMSEP = 5.1 mM corresponding to 7.1 % of the mean value). This may be partly explained by a reduction in usable wavelength regions in the in situ NIR spectra compared to the spectra collected off-line, as well as increased noise in the in situ measurements. In the fourth case study, multi-wavelength fluorescence, scanning dielectric spectroscopy (DE), on-line turbidity (OD), and simple software sensors were evaluated for the on-line estimation of biomass concentration in S. coelicolor fed-batch cultivations. The experiments were designed to imitate an industrial process development, thereby introducing significant variation in the process parameters. In this highly challenging system, it was shown that the more advanced sensors with multivariable output did not improve predictions of dry cell weight compared to the more simple measurements of dual frequency DE, on-line turbidity and measurements of carbon dioxide evolution rate (CER). However, the predictions of dry cell weight were greatly improved by combining selected sensors. Thus a prediction error of 1.5 g/L, corresponding to 6 % of the covered biomass range was obtained by using a combination of dual frequency DE, on-line OD and CER measurements. Furthermore, the use of multiple sensors enabled supervision of the predictions in real-time, facilitating an operator intervention such as a change between individual sensors or the collection of an off-line sample for recalibration. It was thereby demonstrated that the on-line biomass sensors fulfill the demanding requirements of process development and can be of great industrial use in both pilot plant and/or industrial production scale.