Robust Control Based on Modeling Error Compensation of Microalgae Anaerobic Digestion
Abstract
:1. Introduction
2. Materials and Methods
2.1. AD of Microalgae
2.2. MAD Model
2.3. Robust Control Design Based on MEC
- A.
- For optimization and control purposes of AD processes, usually in practice, only a relatively limited number of control actions are possible. These are mostly restricted to the input flow rate or the input of a particular substrate in the feed [19,20]. Therefore, this paper selects the dilution rate (directly related to the input flow rate) as the control input variable.
- B.
- The minimum and maximum control inputs are selected following previous studies on the operational behavior of the MAD model [30]. The maximum input flow rate must be chosen to prevent the washout condition.
3. Results
3.1. Control of the Organic Pollution Level
3.1.1. Control Problem
3.1.2. Numerical Results
3.2. Control of the Methanogenic Biomass Concentration
3.2.1. Control Problem
3.2.2. Numerical Results
4. Discussion
- Dilution and Sin values: The AD operation is markedly influenced by the dilution rate and the substrate feed [19,20]. The observed values in the base and controlled process numerical simulation correspond to the region of lower organic pollution level and higher methanogenic biomass concentration according to the in-depth study presented by Khedim et al. [30] for the selected parameter values. As the substrate input feed increases and the dilution rate decreases, an increase in the methanogenic biomass concentration can achieve, as is shown in Figure 3 when the external perturbation is applied. Khedim et al. [30] suggest that the optimum yield of the MAD model in terms of biogas production was obtained for the following ranges [0.001–0.05] d−1, [0.03–30] gCOD/L of D, and Sin, respectively. Low dilution rates correspond to high HRT, allowing the active biomass population to remain in the reactor and not limiting the hydrolysis step.
- MEC closed-loop performance: The numerical results of the proposed controller on the benchmark MAD model demonstrate the capabilities and versatility of the MEC control approach when controlling the complex operation of AD. It is also noted that only two papers have addressed control designs for the AD of microalgae for the organic pollution level control problem [22,43]. In both cases, considering a possible error in that references in the time units (from hours to days) and sight differences between the values of some variables, the magnitude of the computed dilution rate is similar to the numerical assessment of two proposed nonlinear controllers based on feedback linearization and robust adaptive controllers. However, since both contributions include state and uncertain kinetic estimators, a fair comparison is not possible.
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Rodríguez-Jara, M.; Velasco-Pérez, A.; Vian, J.; Vigueras-Carmona, S.E.; Puebla, H. Robust Control Based on Modeling Error Compensation of Microalgae Anaerobic Digestion. Fermentation 2023, 9, 34. https://doi.org/10.3390/fermentation9010034
Rodríguez-Jara M, Velasco-Pérez A, Vian J, Vigueras-Carmona SE, Puebla H. Robust Control Based on Modeling Error Compensation of Microalgae Anaerobic Digestion. Fermentation. 2023; 9(1):34. https://doi.org/10.3390/fermentation9010034
Chicago/Turabian StyleRodríguez-Jara, Mariana, Alejandra Velasco-Pérez, Jose Vian, Sergio E. Vigueras-Carmona, and Héctor Puebla. 2023. "Robust Control Based on Modeling Error Compensation of Microalgae Anaerobic Digestion" Fermentation 9, no. 1: 34. https://doi.org/10.3390/fermentation9010034