A comparative study of soft sensor design for lipid estimation of microalgal photobioreactor system with experimental validation

On-Line Monitoring of Biological Parameters in Microalgal Bioprocesses Using Optical Methods

Microalgae are promising sources of fuels and other chemicals. To operate microalgal cultivations efficiently, process control based on monitoring of process variables is needed. On-line sensing has important advantages over off-line and other analytical and sensing methods in minimizing the measurement delay. Consequently, on-line, in-situ sensors are preferred. In this respect, optical sensors occupy a central position since they are versatile and readily implemented in an on-line format. In biotechnological processes, measurements are performed in three phases (gaseous, liquid and solid (biomass)), and monitored process variables can be classified as physical, chemical and biological. On-line sensing technologies that rely on standard industrial sensors employed in chemical processes are already well-established for monitoring the physical and chemical environment of an algal cultivation. In contrast, on-line sensors for the process variables of the biological phase, whether biomass, intracellular or extracellular products, or the physiological state of living cells, are at an earlier developmental stage and are the focus of this review. On-line monitoring of biological process variables is much more difficult and sometimes impossible and must rely on indirect measurement and extensive data processing. In contrast to other recent reviews, this review concentrates on current methods and technologies for monitoring of biological parameters in microalgal cultivations that are suitable for the on-line and in-situ implementation. These parameters include cell concentration, chlorophyll content, irradiance, and lipid and pigment concentration and are measured using NMR, IR spectrophotometry, dielectric scattering, and multispectral methods. An important part of the review is the computer-aided monitoring of microalgal cultivations in the form of software sensors, the use of multi-parameter measurements in mathematical process models, fuzzy logic and artificial neural networks. In the future, software sensors will play an increasing role in the real-time estimation of biological variables because of their flexibility and extendibility.

Noninvasive online monitoring of Corynebacterium glutamicum fed-batch bioprocesses subject to spent sulfite liquor raw material uncertainty

In this study the use of a particle filter algorithm to monitor Corynebacterium glutamicum fed-batch bioprocesses with uncertain raw material input composition is shown. The designed monitoring system consists of a dynamic model describing biomass growth on spent sulfite liquor. Based on particle filtering, model simulations are aligned with continuously and noninvasively measured carbon evolution and oxygen uptake rates, giving an estimate of the most probable culture state. Applied on two validation experiments, culture states were accurately estimated during batch and fed-batch operations with root mean square errors below 1.1 g L^-1 for biomass, 0.6 g L^-1 for multiple substrate concentrations and 0.01 g g^-1 h^-1 for biomass specific substrate uptake rates. Additionally, upon fed-batch start uncertain feedstock concentrations were corrected by the estimator without the need of any additional measurements. This provides a solid basis towards a more robust operation of bioprocesses utilizing lignocellulosic side streams.