A novel mathematical model for the dynamic assessment of gas composition and production in closed or vented fermentation systems

- A versatile and scalable fermentation model for describing gas production processes is proposed.
- Comprehensive production profiling of the gases is obtained through a novel mass-flow equation.
- Performance of the model is demonstrated using an in-vitro fermentation system with fecal samples of healthy humans incubated with a highly fermentable fiber.
- The contributions of different components incorporated in the model are investigated and compared to the existing methods.

Closed and pressure regulated fermentation systems with no gas inlets appear in many farming, medical and industrial applications. However, many of such systems still lack the capacity to obtain a complete profile of the gas production pattern or the ability to provide parameters that describe the patterns of gas production. Consequently, these models do not exploit the full potential for providing insights into the attributes of the resulted fermentation patterns. To date still there is no rigorous presentation that includes the outputs from gas, pressure and temperature sensors, while considering the effect of vented gases. Here, we introduce a versatile and scalable fermentation model for describing gas production processes. This new model uses a novel mass-flow equation for comprehensive profiling of the gases produced. The performance of this model is demonstrated by revealing the gas kinetic patterns that resulted when fecal matter, together with highly fermentable fiber, was incubated in an in-vitro system. The new model shows differences of up to 7% and 9.1% in cumulative gas production and gas production rate, respectively, with respect to existing methods The model provides the first comprehensive solution for gas production profiling by accurately taking into account the measured data from gas, pressure and temperature sensors for common fermentation systems that can potentially be of great importance for analyzing incubation units.