In this work, simple tuning rules for feedforward compensators were applied to design a control strategy to regulate the inside air temperature of a greenhouse during daytime by means of a natural ventilation system. The developed control strategy is based on a PI (Proportional-Integral) controller combined with feedforward compensators to improve the performance against measurable external disturbances such as outside air temperature, solar radiation, and wind velocity. Since the greenhouse process dynamics is very complex and physical non-linear models are mathematically complicated, a system identification methodology was proposed to obtain simpler models (high-order polynomial and low-order transfer functions). Thus, an easier procedure was completed to tune the PI controller parameters and to obtain the feedforward compensators expressions by following a series of modern and simple tuning rules. Simulations with real data were executed to compare the control performance of a PI controller with or without the addition of feedforward compensators. Moreover, real tests for the developed control strategy were carried out in an experimental greenhouse. Results demonstrate an enhanced control performance with the presence of the feedforward compensators under different weather conditions.
Production of microalgae is one of the emerging biotechnological processes due to its potential applications to produce high value-added compounds. In photobioreactors for microalgae production, the biomass concentration is a desirable variable to be measured on-line to optimize the yield of the systems. However, biomass concentration can hardly be monitored in real time. There are few expensive commercial sensors that in fact provide uncertain measurements. State estimators, also known as software sensors, are algorithms designed to estimate unmeasured (or non-easily measurable) variables of a process. In this work, a state estimator using the extended Kalman filter algorithm is developed to estimate biomass concentration for an outdoor industrial raceway photobioreactor. The state estimator is based on a dynamic model for microalgae production specifically designed for this type of photobioreactor. Results demonstrate that, despite the complex non-linear dynamics that characterise this kind of bioprocess, a state estimator can provide a relatively accurate estimation of the biomass concentration. Furthermore, a state estimator could be used to optimize the operation of industrial photobioreactors by utilizing the estimated biomass concentration for automatic control of the process.
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