This work presents the application of an event-based model predictive control algorithm to regulate the pH in a microalgae production process. The control aim is to maintain the pH within specific limits and to minimize CO2 losses. The control scheme is based on a Generalized Predictive Control (GPC) algorithm with sensor deadband approach. In this algorithm, the controller execution frequency is adapted to the process dynamics. The event-based scheme works with low sampling frequency if controlled variable, pH in this case, is inside an established band. Otherwise, when the pH value is outside the band, the controller actuation frequency is increased to try to drive it quickly near the setpoint based on the selected tolerance. In such a way, the event-based control algorithm allows to establish a tradeoff between control performance and number of process update actions. This fact can be directly related with reduction of CO2 injection times, what is also reflected in CO2 losses. The control structure is first evaluated through simulations using a nonlinear model for microalgal production in tubular photobioreactors. Afterwards, real experiments are presented on an industrial photobioreactor in order to verify the results obtained through simulations. Additionally, the real tests on the industrial plant are used to verify the event-based control scheme in the case of plant-model mismatch, presence of measurement noise and disturbances. Moreover, the control results are compared with classical time-based solutions using well-known control performance indexes.