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# Validation of a simple and adaptive prediction model for temperature in outdoor microalgae cultivation systems

Ali Gharib, Francesca Casagli, Walid Djema, Pedro Moñino Fernandez, Rocca

Chin-On, Olivier Bernard

► To cite this version:

Ali Gharib, Francesca Casagli, Walid Djema, Pedro Moñino Fernandez, Rocca Chin-On, et al. Validation of a simple and adaptive prediction model for temperature in outdoor microalgae cultivation systems. ECCE 14 & ECAB 7 - 14th European Congress of Chemical Engineering and 7th European Congress of Applied Biotechnology, Sep 2023, Berlin, Germany. hal-04390889

HAL Id: hal-04390889

https://inria.hal.science/hal-04390889v1

Submitted on 12 Jan 2024

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Distributed under a Creative Commons Attribution 4.0 International License
Validation of a simple and adaptive prediction model for

temperature in outdoor microalgae cultivation systems

Gharib A.<fnref n="1" />, Casagli F.<fnref n="1" />, Djema W.<fnref n="1" />, Moñino Fernandez P.<fnref n="2" />, Chin-On R.<fnref n="2" />, Bernard O.<fnref n="1" />,

Microalgae are unicellular autotrophic organisms that have the potential to be used for various biotechnological applications like biofuel or wastewater treatment in large scale production systems. The dynamics of microalgae growth in outdoor systems are strongly dependent on light and temperature and thus on meteorology. In fact, they are the main drivers of the growth rate of microalgae and further of the process productivity. Based on heat transfer modeling, numerous thermal models have been created for simple reactor geometries (usually for raceway ponds), that deliver accurate temperature predictions. However, these models require a lot of physical constants and a costly setup process. As a consequence, we developed a new generic non-linear model which overcomes these difficulties and is more suitable for control purposes. The novelty of this model is the flexibility to adapt to any cultivation system by using a small data set to identify the parameters of the model.

<figure><img src="image_3.png" /><figcaption>Figure 1: Using a data-set of 4 days (200 points) to fit the reduced-model to measured data from a raceway pond reactor in south of France</figcaption></figure>

<footnote n="1">¹Université Côte d'Azur, Inria, INRAE, CNRS, Sorbonne Université, Biocore Team, France</footnote>

<footnote n="2">²Wageningen University, Bioprocess Engineering, Netherlands,</footnote>
The crucial step of this work is the calibration procedure to adapt the model to a new
process geometry. Here, we propose a validation of the new model, by using several
sets of accessible experimental data. To calibrate the model for precise temperature
forecasts over several weeks for the four scenarios, four days of data were needed.
The results show, that the model turned out to be accurate for four different cultivation
systems. Two raceway ponds, a V-shaped outdoor panel photobioreactor, and a
tubular photobioreactor underneath a greenhouse. The next objective is to combine
this temperature model to a light model to predict productivity and perform model
predictive control.