Abstract
In the field of biochemical process design, the accurate modeling of microbial growth is essential for the development and optimization of biological reactors used in the production of high-value compounds. Achieving this objective requires a detailed understanding of how environmental factors-such as pH and nutrient availability-influence microbial dynamics across the four distinct growth phases: lag, exponential, stationary, and death. Traditionally, reactor design relies heavily on the Monod model, which provides a simplified representation of microbial growth, focusing primarily on the exponential phase under constant operating conditions (1). However, this model presents substantial limitations when applied to dynamic environments where key parameters vary over time. To overcome these constraints, the present study proposes a data-driven modeling approach using a multilayer perceptron (MLP) artificial neural network for the prediction of microbial growth trajectories under varying pH conditions and substrate compositions. The yeast strain Candida guilliermondii was selected as the model microorganism due to its industrial relevance. Experimental growth data were collected through optical density measurements using a Multiskan™ FC Microplate Photometer (Thermo Scientific), covering a pH range from 6.0 to 8.5 and two substrate scenarios: pure xylose, and a 1:2 glucose-xylose mixture. The experimental data were used to train the MLP neural network, which generated predictive models capable of estimating growth behavior under the specified input conditions. This modeling approach enables the simulation of microbial growth curves at any given time point within the defined parameter space, providing a more flexible and comprehensive tool compared to classical models. The results of this study demonstrate that the proposed MLP-based model is a powerful computational tool for both the design and real-time control of bioprocesses. The integration of these tools into predictive control strategies is a key aspect of the bioprocess research. The model's versatility and its ability to predict microbial behavior make it a very important tool for bioreactor control.