During the anaerobic digestion (AD) of fruit and vegetable waste (FVW), excessive particle size reduction can lead to the overproduction and inhibition of methanogenic microorganisms. This paper presents an in-depth analysis through experimental assays, modeling, and response surface analysis of the effect of particle size on methane production. A simple model was proposed considering the inhibition of the growth of methanogenic microorganisms and surface-based hydrolysis kinetics. The model parameters were estimated using experimental data from batch systems fed with FVW of varying particle sizes (ranging from 1.8 to 1000 μm). Response surface methodology establishes a statistical model for estimating methane production based on particle size and concentration. Numerical and statistical analyses were conducted using Matlab R2024a and Minitab 24 software. A model with an R² of 0.89 was obtained, which determined an optimal concentration of 8.2 $\mathrm{k}\mathrm{g}·{\mathrm{m}}^{-3}$ and a particle size of 742.3 $\mathsf{\mu }\mathrm{m}$, yielding a methane production of 303.3 ${\mathrm{m}}^3·\mathrm{k}{\mathrm{g}}^{-1} \mathrm{V}\mathrm{S}$, similar to the experimentally obtained range of 300.95 to 316.7 ${\mathrm{m}}^3·\mathrm{k}{\mathrm{g}}^{-1} \mathrm{V}\mathrm{S}$.

Microalgae are used to produce renewable biofuels (biodiesel, bioethanol, biogas, and biohydrogen) and high-value-added products, as well as in bioremediation and CO₂ sequestration tasks. In the case of anaerobic digestion of microalgae, biogas can be produced from mainly proteins and carbohydrates. Anaerobic digestion is a complex process that involves several stages and is susceptible to operational instability due to various factors. Robust controllers with simple structure and design are necessary for practical implementation purposes and to achieve a proper process operation despite process variabilities, uncertainties, and complex interactions. This paper presents the application of a control design based on the modeling error compensation technique for the anaerobic digestion of microalgae. The control design departs from a low-order input–output model by enhancement with uncertainty estimation. The results show that achieving desired organic pollution levels and methanogenic biomass concentrations as well as minimizing the effect of external perturbations on a benchmark case study of the anaerobic digestion of microalgae is possible with the proposed control design.