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The industrial production of microalgae is a process as sustainable as it is interesting in terms of its diverse applications, especially for wastewater treatment. Its optimization requires an exhaustive knowledge of the system, which is commonly achieved through models that describe its dynamics. Although not widely used in this field, artificial neural networks are presented as an appropriate technique to develop this type of model, having the ability to adapt to complex and nonlinear problems solely from the process data. In this work, neural network models have been developed to characterize the pH dynamics in two different raceway reactors, one with freshwater and the other with wastewater. The models are able to predict pH profiles with a prediction horizon of up to eleven hours and only using available measurable process data, such as medimum level, CO<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mn>2</mn></msub></semantics></math></inline-formula> injection, and solar radiation. The results demonstrate the potential of artificial neural networks in the modeling of continuous dynamic systems in the field of industry, obtaining accurate, fast-running models that can adapt to different circumstances. Moreover, these models open the field to the design of data-driven model-based control algorithms to account for the nonlinear dynamics of this biological system.