Abstract
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.
Highlights
Microalgal biomass contains significant amounts of valuable components including lipids, proteins, carbohydrates, pigments and vitamins that can be separated and upgraded to various products in the biofuel, food, fodder, cosmetic and pharmaceutical industries [1,2]
The support in signal processing using chemometric models and machine learning has grown. These developments are summarized with focus on monitoring the biological phase using non-destructive, real-time, on-line, or in-line methods that avoid contamination of the running cultivation in closed photobioreactor systems, minimize measurement delays and supply the information required for successful process control without physically affecting the cultivation
Measurement methods used for online monitoring of biological variables are reviewed in Section 2, arranged according to the measured variable and the employed method: biomass concentration measured by optical density, fluorescence, color and reflectance; mixed culture discrimination, cell number concentration, cell morphology, culture health monitoring, microalgal species identification, photosynthetic efficiency and quantum yield, and biomass composition in terms of lipid, carbohydrate, pigment and protein concentration
Summary
Microalgal biomass contains significant amounts of valuable components including lipids, proteins, carbohydrates, pigments and vitamins that can be separated and upgraded to various products in the biofuel, food, fodder, cosmetic and pharmaceutical industries [1,2]. The support in signal processing using chemometric models and machine learning has grown In this review, these developments are summarized with focus on monitoring the biological phase (biomass concentration and composition, physiological state, morphology, biological contaminants) using non-destructive, real-time, on-line, or in-line methods that avoid contamination of the running cultivation in closed photobioreactor systems, minimize measurement delays and supply the information required for successful process control without physically affecting the cultivation. Measurement methods used for online monitoring of biological variables are reviewed, arranged according to the measured variable and the employed method: biomass concentration measured by optical density, fluorescence, color and reflectance; mixed culture discrimination, cell number concentration, cell morphology, culture health monitoring, microalgal species identification, photosynthetic efficiency and quantum yield, and biomass composition in terms of lipid, carbohydrate, pigment and protein concentration. The same principle applies to publications cited more than once, both in sections covering the measurement method and in sections covering the data processing approach
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have