Light variation in temporal and spatial domains is a key constraint on the photosynthetic performance of phytoplankton. The most obvious responses are the modification of cell pigment content either to improve photocapture or to mitigate photo-damage. Very few studies have analyzed whether light variation significantly alters carbon assimilation, especially in a fluctuating light environment as in the mixed layer of the ocean. We addressed the question using a modeling approach, which allows the reproduction of most of the possible scenarios, obtained with great difficulty from laboratory or field experiments. The complete model is based on the dynamic coupling of a photoacclimation and photodamage-repair responses. In this combined model the virtual phytoplankton is exposed to different light regimes (steady, square wave, sinusoidal light-dark cycles and fluctuating regimes). The results reconcile controversial results on different photoacclimation states achieved during fluctuating light regimes. The model produces a depression of carbon assimilation in any light fluctuating scenario, as compared to steady light regimes, due to the temporal delay between light fluctuations and photoresponses. These results suggest the possibility of selective pressure during evolution, more effective on photoprotective mechanisms than on optimization of light harvesting.
Summary
The production of microalgae represents a large and rapidly expanding market with several applications in the fields of food, pharmaceutics, cosmetics, and energy. Microalgae are photosynthetic aquatic microorganisms whose growth is mainly controlled by a few environmental parameters: temperature, light, pH, and nutrient availability. For this reason, monitoring and controlling such parameters is crucial for their production. At the same time, the development of mathematical models to simulate the behavior of biological systems has become a major predictive and control tool of production processes. In this paper, we present an Internet of Things (IoT) system that can couple sensor data collected directly by biotechnological cultivations with a predictive simulation model. The IoT system constitutes the core of a Decision Support System developed to help the end‐user in the management of industrial production processes.
Microalgae cultivation is an emerging and interesting field with an increasing market and with Great prospects for the future both in the food, pharmaceutical, cosmetic and energetic field as well as for other new applications. Microalgae are photosynthetic aquatic microorganism which growth is regulated in-primis by light, temperature, pH and nutrients availability. The monitoring and control of these parameters is crucial in Microalgae cultivation plants because it allows to increase the production and the quality of microalgae biomass feedstock and to prevent the critical conditions for the culture which can compromise the cultivation.\ud
\ud
In the present work, we present an affordable and easy to use application of Internet of Things (IoT) for the monitoring and control of a microalgae cultivation coupled with a biological modeling for Decision Support System (DSS) to the operator for managing a plant sited in Southern Italy
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.