Automated Compartment Model Development Based on Data from Flow-Following Sensor Devices

Abstract

Due to the heterogeneous nature of large-scale fermentation processes they cannot be modelled as ideally mixed reactors, and therefore flow models are necessary to accurately represent the processes. Computational fluid dynamics (CFD) is used more and more to derive flow fields for the modelling of bioprocesses, but the computational demands associated with simulation of multiphase systems with biokinetics still limits their wide applicability. Hence, a demand for simpler flow models persists. In this study, an approach to develop data-based flow models in the form of compartment models is presented, which utilizes axial-flow rates obtained from flow-following sensor devices in combination with a proposed procedure for automatic zoning of volume. The approach requires little experimental effort and eliminates the necessity for computational determination of inter-compartmental flow rates and manual zoning. The concept has been demonstrated in a 580 L stirred vessel, of which models have been developed for two types of impellers with varying agitation intensities. The sensor device measurements were corroborated by CFD simulations, and the performance of the developed compartment models was evaluated by comparing predicted mixing times with experimentally determined mixing times. The data-based compartment models predicted the mixing times for all examined conditions with relative errors in the range of 3–27%. The deviations were ascribed to limitations in the flow-following behavior of the sensor devices, whose sizes were relatively large compared to the examined system. The approach provides a versatile and automated flow modelling platform which can be applied to large-scale bioreactors.

Mathematical Analysis of a Low Cost Mechanical Ventilator Respiratory Dynamics Enhanced by a Sensor Transducer (ST) Based in Nanostructures of Anodic Aluminium Oxide (AAO)

“…In which f is the air flow from a source, the section area of the hose is a, and the flow reduces its section area b while crossing the orifice plate. Thus, a pressure difference can be measured between points P1 and P2, moreover 𝑣 and 𝑣 represent the fluid flow speed of the air flow in the direction depicted by the red arrow [11,12]. Figure 4 shows the compact air flow sensor developed for the integrated design, which is composed of an upstream and downstream pipe welded to an orifice plate.…”

“…Figure 4 shows the compact air flow sensor developed for the integrated design, which is composed of an upstream and downstream pipe welded to an orifice plate. Dimensions are established based on the analytic and experimental results, which guarantee the pressure difference measurement over time for this particular medical application with a mechanical ventilation system OxygenIP.PE, whose nominal working conditions correspond to a transient flow [11]. Figure 4 shows the compact air flow sensor developed for the integrated design, which is composed of an upstream and downstream pipe welded to an orifice plate.…”

“…In which f is the air flow from a source, the section area of the hose is a, and the flow reduces its section area b while crossing the orifice plate. Thus, a pressure difference can be measured between points P1 and P2, moreover v 1 and v 2 represent the fluid flow speed of the air flow in the direction depicted by the red arrow [11,12]. In order to find the pressure, airflow and volume equations, the Stokes equations were analyzed using Equation (1), in which, "f" is the function as the consequence of airflow and "µ" and "D" are the average velocity and dispersion coefficient, respectively [13,14].…”

“…Moreover, using the costing function "J" the optimal estimation was analyzed, as it is described by Equation (11) after replacing Equation (10) in Equation (9).…”

“…Looking for the minimal error, Equation (11) was derived as the dependence of "∆U" in Equation (12).…”