Input significance ranking of microalgae continuous culture models

Background: Microalgal cultures are evolving into a promising ecofriendly technology for a host of applications. To be sustainable, culture conditions need to be optimized and then controlled. One way to develop robust controllers for a cultivation system is by using mathematical growth models to simulate microalga-based production. In this scenario, engineering design tasks begin by selecting the critical variables of these models. Results: A new methodology for determining the significance ranking of a model's input factors under steady-state operation (parameters (e.g. biological, geometrical) and/or process variables) was designed. The sensitivity of biomass response to its inputs was investigated in four different photobioreactor growth models within a nominal operational region. The methodology ranks models' input factors based on the one-at-a-time Morris method of elementary effects and variance-based Sobol's method. Such information provided by the presented procedure is valuable as it reveals which input parameters explain most of the variance in model predictions. Conclusion: The methodology allowed the identification of controlled variables and biological parameters to be targeted for enhanced calibration. Furthermore, the presented methodology showed that in continuous reactors the dilution rate is a critical variable of the process. Therefore, it should be controlled. Additionally, most surprisingly, it is observed that controlling the light intensity within the optimum point of operation is not necessarily a crucial task. However, although its manipulation is still important, the accurate calibration of the parameters of the model may represent a greater influence on the biomass response. (c) 2023 Society of Chemical Industry (SCI).

Automated summary

For microalgal cultures, using mathematical growth models to simulate production and determine the significance of input factors is crucial for optimal control and sustainability.