Application of Predictive Feedforward Compensator to Microalgae Production in a Raceway Reactor: A Simulation Study

In this work, the evaluation of a predictive feedforward compensator is provided in order to highlight its most important advantages and drawbacks. The analyzed technique has been applied to microalgae production process in a raceway photobioreactor. The evaluation of the analyzed disturbance rejection schemes were performed through simulation, considering a nonlinear process model, whereas all controllers were designed using linear model approximations resulting in a realistic evaluation scenario. The predictive feedforward disturbance compensator was coupled with two feedback control techniques, PID (Proportional-Integral-Derivative) and MPC (Model Predictive Control) that are widely used in industrial practice. Moreover, the classical feedforward approach has been used for the purpose of comparison. The performance of the tested technique is evaluated with different indexes that include control performance measurements as well as biomass production performance. The application of the analyzed compensator to microalgae production process allows us to improve the average photosynthesis rate about 6% simultaneously reducing the energy usage about 4%.

[1]  José Luis Guzmán,et al.  The Comparison Study of Short-Term Prediction Methods to Enhance the Model Predictive Controller Applied to Microgrid Energy Management , 2017 .

[2]  Antonio Visioli,et al.  Automatic Tuning of Feedforward Controllers for Disturbance Rejection , 2014 .

[3]  F. G. Acién,et al.  Dynamic model of an industrial raceway reactor for microalgae production , 2016 .

[4]  José Luis Guzmán,et al.  Robust design methodology for simultaneous feedforward and feedback tuning , 2016 .

[5]  F. G. Fernández,et al.  Photobioreactors for the production of microalgae , 2013 .

[6]  F. G. Acién,et al.  Fluid-dynamic characterization of real-scale raceway reactors for microalgae production , 2013 .

[7]  Montserrat Gil-Martínez,et al.  Quantitative feedback-feedforward control for model matching and disturbance rejection , 2013 .

[8]  Sigurd Skogestad,et al.  Simple analytic rules for model reduction and PID controller tuning , 2003 .

[9]  F. G. Acién,et al.  Evaluation of carbon dioxide mass transfer in raceway reactors for microalgae culture using flue gases. , 2014, Bioresource technology.

[10]  José Luis Guzmán,et al.  Improving feedforward disturbance compensation capabilities in Generalized Predictive Control , 2012 .

[11]  O. Bernard.  Hurdles and challenges for modelling and control of microalgae for CO2 mitigation and biofuel production , 2011 .

[12]  F. G. Acién,et al.  Effective utilization of flue gases in raceway reactor with event-based pH control for microalgae culture. , 2014, Bioresource technology.

[13]  D. Cooper,et al.  A Tuning Strategy for Unconstrained SISO Model Predictive Control , 1997 .

[14]  José Luis Guzmán,et al.  Measurable Disturbances Compensation: Analysis and Tuning of Feedforward Techniques for Dead-Time Processes , 2016 .

[15]  Tore Hägglund,et al.  Simple tuning rules for feedforward compensators , 2011 .

[16]  F. G. Acién,et al.  Selective pH and dissolved oxygen control strategy for a raceway reactor within an event-based approach , 2015 .

[17]  Manuel Berenguel,et al.  Model predictive control of pH in tubular photobioreactors , 2004 .

[18]  E. Grima,et al.  Prediction of dissolved oxygen and carbon dioxide concentration profiles in tubular photobioreactors for microalgal culture , 1999, Biotechnology and bioengineering.

[19]  J. M. Fernández-Sevilla,et al.  Comprehensive model of microalgae photosynthesis rate as a function of culture conditions in photobioreactors , 2013, Applied Microbiology and Biotechnology.

[20]  J. Sevilla,et al.  A study on simultaneous photolimitation and photoinhibition in dense microalgal cultures taking into account incident and averaged irradiances , 1996 .