Reassessing the out-of-phase phenomenon in shake flasks by evaluating the angle-dependent liquid distribution relative to the direction of the centrifugal acceleration.

Shake flasks are still the most relevant experimental tool in the development of viscous fermentation processes. The phase number, which defines the onset of the unfavorable out-of-phase (OP) phenomenon in shake flasks, was previously defined via specific power input measurements. In the OP state, the bulk liquid no longer follows the orbital movement of the imposed centrifugal force, which is for example, detrimental to oxygen transfer. In this study, an optical fluorescence technique was used to measure the three-dimensional liquid distribution in shake flasks. Four new optically derived evaluation criteria for the phase transition between the in-phase and OP condition were established: (a) thickness of the liquid film left on the glass wall by the rotating bulk liquid, (b) relative slope of the leading edge of bulk liquid (LB) lines, (c) trend of the angular position of LB, and (d) very high angular position of the leading edge. In contrast to the previously applied power input measurements, the new optical evaluation criteria describe the phase transition in greater detailed. Instead of Ph = 1.26, a less conservative value of Ph = 0.91 is now suggested for the phase transfer, which implies a broader operating window for shake flask cultivations with higher viscosities.

Batch process monitoring based on multiple-phase online sorting principal component analysis.

Existing phase-based batch or fed-batch process monitoring strategies generally have two problems: (1) phase number, which is difficult to determine, and (2) uneven length feature of data. In this study, a multiple-phase online sorting principal component analysis modeling strategy (MPOSPCA) is proposed to monitor multiple-phase batch processes online. Based on all batches of off-line normal data, a new multiple-phase partition algorithm is proposed, where k-means and a defined average Euclidean radius are employed to determine the multiple-phase data set and phase number. Principal component analysis is then applied to build the model in each phase, and all the components are retained. In online monitoring, the Euclidean distance is used to select the monitoring model. All the components undergo online sorting through a parameter defined by Bayesian inference (BI). The first several components are retained to calculate the T(2) statistics. Finally, the respective probability indices of [Formula: see text] is obtained using BI as the moving average strategy. The feasibility and effectiveness of MPOSPCA are demonstrated through a simple numerical example and the fed-batch penicillin fermentation process.