Abstract

In this study, the potential of two software sensors for on-line estimation of biomass concentration during cultivation of filamentous microorganisms is examined. The first sensor is based on common bioreactor off-gas analyses, and uses the assumption of the biomass concentration linear dependence on the square root of cumulative O2 consumption. Parameters of the semi-empirical data-driven software sensor based on off-gas analysis were calculated from experimental cultivation data using linear regression. The second sensor is based on biocalorimetry, i.e., the on-line calculation of metabolic heat flux from general enthalpy balance of the bioreactor. The software sensor based on biocalorimetry thus essentially represents a model-driven approach, making use of a fundamental process model based on the enthalpy balance around the bioreactor. This approach has been combined with the experimental identification of the specific biomass heat production, which represents the main process-specific parameter of the software sensor based on biocalorimetry. For this sensor, the accuracy requirements on the process variable on-line measurements were also analysed. The experimental data from the pilot-scale antibiotics Nystatin production by a bacterium Streptomyces noursei were used to calculate the specific bioprocess heat production value using linear regression. The achieved results enabled us to propose a new on-line indicator calculated as the ratio of the outputs of both sensors, which can serve as a timely warning of the risk of undesired nutritional conditions of a culture characterized as underfeeding.

Highlights

  • The software sensor (“soft sensor” or “software sensor”) already represents an established concept in the field of production process monitoring

  • The aim of this study was to explore the potential of two different approaches to the task of online estimation of biomass concentration in a filamentous microorganism cultivation process by software sensors

  • Whereas the first approach is based on the on-line measurement of the composition of the bioreactor off-gases, the second approach uses the on-line measurement of process variables related to the enthalpy balance of the bioreactor

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Summary

Introduction

The software sensor (“soft sensor” or “software sensor”) already represents an established concept in the field of production process monitoring. The term “sensor” means that the entire software sensor provides on-line information about the monitored process, to traditional hardware sensors[1]. The basic principle of software sensors is the use of one or more relatively easy on-line measurable process variables to estimate other variables or process indicators that are difficult to measure in on-line. – “grey-box” sensor, referred to as “model-driven” – based on a mathematical model of a process based on physical, chemical or biological relationships with experimental identification of unknown parameters from historical process data;. – “black-box” sensor, referred to as “data-driven” – is used in cases where a mathematical model of the relation between inputs and outputs of the software sensor is not known. The mathematical description of this relation is derived from historical process data using appropriate computational tools (regression analysis, neural networks, etc.).

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