Exploring Mathematical Modelling in the Photo2Fuel Project

Mathematical modelling is at the heart of the Photo2Fuel project. Dive into the fascinating world where equations and simulations drive the future of renewable energy.



In the world of scientific and technological innovation, mathematical modelling serves as a powerful tool, enabling scientists to articulate and understand complex natural processes through equations and simulations. It bridges theory with real-world applications, providing a quantitative framework that can guide decision-making and optimisation. Within the context of the Photo2Fuel project, mathematical modelling plays a pivotal role in shaping the future of sustainable energy solutions.

What is Mathematical Modelling?

Mathematical modelling involves using mathematical language to describe, understand, and predict phenomena in the natural world. It transforms intricate systems into manageable representations, fostering insights and discoveries that might not be evident from observation alone. Whether through deterministic equations, probabilistic models, or simulations, mathematical modelling integrates data and theory to illuminate the underlying mechanisms driving complex processes. To sum up, mathematical modelling is a mathematical representation of a system used to make predictions and provides insights about a real-world scenario.

The Biochemical Model: Monod Equation

The Monod equation, a cornerstone in microbiology and biochemistry, describes the growth of microorganisms, such as Moorella thermoacetica and Methanosarcina barkeri, in response to nutrient availability1. This relationship between nutrient levels and microbial growth rates is crucial for optimizing bioprocesses.

In this context, bacterial growth serves as a critical parameter within the Photo2Fuel framework, influencing the efficiency and scalability of the technology. As microorganisms metabolize substrates to produce desired compounds, understanding and manipulating their growth dynamics are essential for maximizing productivity while minimizing resource consumption2. This parameter thus guides decisions on system design and operational strategies.

Photo2Fuel Multidisciplinary Design Optimisation (MDO)

Multidisciplinary Design Optimisation (MDO) emerges as a strategic methodology within the Photo2Fuel project, integrating diverse models from biochemistry, chemical engineering, environmental science, and economics. MDO seeks to harmonize conflicting objectives and constraints across these disciplines to identify the most sustainable and economically viable pathway forward. By evaluating and optimizing multiple variables simultaneously, MDO enables informed decisions on scaling up the most promising technology.

The decision-making process in Photo2Fuel hinges significantly on the outcomes derived from MDO. By leveraging mathematical models, experimental data, and insights from interdisciplinary collaborations, MDO evaluates and contrasts different system configurations. This holistic approach ensures that the chosen technology aligns with sustainability and efficiency criteria, thereby guiding future project directions and investments.

In this sense, by combining theory and experimentation, the Photo2Fuel project exemplifies how mathematical modelling can revolutionize the optimisation of renewable energy technologies, paving the way for a brighter tomorrow.


  1. Monod, J. (1949). The growth of bacterial cultures. Annual Review of Microbiology, 3(1), 371-394.
  2. De Jong Hidde, Casagranda Stefano, Giordano Nils, Cinquemani Eugenio, Ropers Delphine, Geiselmann Johannes and Gouzé (Jean-Luc 2017). Mathematical modelling of microbes: metabolism, gene expression and growth. J. R. Soc. Interface.1420170502

Written by Francisco López , María Cámara 

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