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 ${\mathrm{m}}^3·\mathrm{k}{\mathrm{g}}^{-1} \mathrm{V}\mathrm{S}$.

This study examines the anaerobic digestion (AD) of fruit and vegetable waste (FVW) and digestates to assess the effect of particle size on anaerobic biodegradability (AB) and process rate at different stages of digestion progress. Batch assays were conducted with FVW mixtures and digestates from 5, 10, and 15 days of digestion, using four particle size ranges: Ø1 < 1.8 µm, 1.8 < Ø2 < 500 µm, 500 < Ø3 < 1000 µm, and Ø4 > 1000 µm. While AB and specific methanogenic activity (SMA) showed no significant differences among FVW mixtures, particle size significantly influenced these variables. Methane yields were 298.2 and 309.8 $\mathrm{m}\mathrm{L} \mathrm{C}{\mathrm{H}}_4·{\mathrm{g}}^{-1}\mathrm{V}\mathrm{S}$ for Ø4 and Ø3 particles, exceeding the 186.7 and 161.8 $\mathrm{m}\mathrm{L} \mathrm{C}{\mathrm{H}}_4·{\mathrm{g}}^{-1}\mathrm{V}\mathrm{S}$ obtained for Ø2 and Ø1, respectively. These results indicate that particle size reduction enhanced methane production; however, reducing the particle size below 500 µm inhibits methanogenesis due to intermediate accumulation. Compared to FVW, digestates exhibited a 23% reduction in AB, a 73.9% decrease in SMA, and methane yields of 55.8–294 $\mathrm{m}\mathrm{L} \mathrm{C}{\mathrm{H}}_4·{\mathrm{g}}^{-1}\mathrm{V}\mathrm{S}$. Additionally, the surface-based kinetic constant ($K_{SBK}$) decreased from 0.4523 $\mathrm{g}·\mathrm{d}{\mathrm{m}}^{-2}·{\mathrm{d}}^{-1}$for FVW to 0.0437 $\mathrm{g}·\mathrm{d}{\mathrm{m}}^{-2}·{\mathrm{d}}^{-1}$for digestates. These differences are attributed to the rapid consumption of easily biodegradable fractions within the first 5 days of digestion.

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.

The main commercial variety of coffee is the Coffea arabica specie, however, it is very susceptible to damage caused by nematodes, for this reason scions of the C. arabica specie are used on ro-busta (Coffea canephora) rootstocks to improve tolerance to these microorganisms, because they modify the organoleptic, nutraceutical and nutritional characteristics. The objective of this work was to assess the effect of five roasting degrees on commercial beans (light, medium light, medi-um, medium, medium dark and dark) in two varieties of Coffea arabica species (Colombia and Costa Rica) and their respective scions on C. canephora of the Romex variety. The scion of the Co-lombia variety with the medium dark roast had the highest concentration of caffeine, while the coffee beans of the Costa Rica variety with the light roast grade had the highest concentration of chlorogenic acid (5-CQA), while the highest acrylamide content was found in the scion of the Co-lombia variety with the medium light roast beans and nutritional content differs considerably between varieties, however, there is similarity between roasting degrees. The degree of roasting, variety and scion of coffee influences the organoleptic, nutraceutical and nutritional content of the coffee infusion.

Anaerobic digestion (AD) of food waste is strongly influenced by substrate characteristics and operating conditions, particularly substrate composition, particle size, and substrate-to-inoculum (S/I) ratio. In this study, a modified ADM1-R3 model, incorporating a surface-based disintegration kinetics approach, was developed to evaluate the simultaneous influence of these factors on methane production. Sensitivity analysis identified the disintegration and hydrolysis constants as the most influential parameters, while principal component analysis supported a sequential calibration strategy. The model was calibrated using literature data and verified against independent datasets, achieving $R^2$ values of 0.9967 (calibration) and 0.9745–0.9880 (verification). Simulation results showed that optimal performance was observed at low S/I ratios (0.5–1) and intermediate particle sizes (1.4–4 mm), with maximum yields of 419, 744, and 581 $\mathrm{m}\mathrm{L}·{\mathrm{g}}^{-1} \mathrm{V}\mathrm{S}$ for carbohydrate-rich, protein- and lipid-rich, and mixed substrates, respectively. Overall, the model provides a consistent framework for analyzing AD behavior and identifying favorable operational conditions, although further validation under unstable operating conditions is required to fully assess its predictive capability.