This study evaluates the removal of COD and nitrogen from poultry wastewater in photobioreactors. Cell growth, the effect of light intensity (3200, 9800, and 12000 lux) and air flow (1.6, 3.2, and 4.8 L min⁻¹) as a source of CO₂ in bold basal medium and wastewater with different concentrations of COD were evaluated. The growth kinetics were modeled by using the Gompertz model and logistic model for both culture media. COD removals of up to 95% were achieved, and poultry wastewater was found to be a viable growing medium for Chlorella vulgaris. Finally, the wastewater met Mexican standards, and biomass was obtained with products valued as lipids (3.2 g lipid/100 g biomass) and proteins (342.94 mg L⁻¹). The culture was found to have a dilatory behavior, and the rheological models of Ostwald de Waele, Ostwald de Waele linealized and Herschel Bulkley were utilized, showing a laminar behavior.

This article presents a study that identifies the variables with greatest impact on the biogas and methane production over a process with thermal pretreatment, to accelerate anaerobic digestion process in sewage sludge in a water treatment plant, for a poultry processing factory, by using fuzzy logic. The designed fuzzy logic model includes 688 inference rules, with a correlation of 99.3% between prediction data against experimental data, for the biogas variable; and 97% for the methane variable. The predictions of the fuzzy logic model were analyzed with response surface models, and it is concluded that the temperature and operating time variables are mutually determining in the biogas and methane production. Likewise, this research provides a methodology for the design of an expert decision support system that allows to evaluate and optimize a mesophilic anaerobic digestion process through a previous thermal treatment in order to improve the yields of biogas and methane in the treatment of effluent sludge from agroindustry. These results propose to diffuse logic as a reliable tool to make comparisons, and predictions for operation variables management on the treatment of residual sludge with thermal pretreatment on anaerobic digestion.

Tomato waste, characterized by high organic matter and moisture content, offers a promising substrate for anaerobic digestion, though rapid acidification can inhibit methanogenic activity. This study investigated the performance of a 10.25 L anaerobic fixed biofilm reactor for biogas production using liquid tomato waste, processed through grinding and filtration, at high organic loading rates, without external pH control or co-digestion. Four scouring pads were vertically installed as a fixed bed within a fiberglass structure. Reactor performance and buffering capacity were assessed over three stages with progressively increasing organic loading rates (3.2, 4.35, and 6.26 gCOD/L·d). Methane yields of 0.419 LCH4/gCOD and 0.563 LCH4/g VS were achieved during the kinetic study following stabilization. Biogas production rates reached 1586 mL/h, 1804 mL/h, and 4117 mL/h across the three stages, with methane contents of 69%, 65%, and 72.3%, respectively. Partial alkalinity fluctuated, starting above 1500 mg CaCO₃/L in Stage 1, dropping below 500 mg CaCO₃/L in Stage 2, and surpassing 3000 mg CaCO₃/L in Stage 3. Despite periods of forced acidification, the system demonstrated significant resilience and high buffering capacity, maintaining stability through hydraulic retention time adjustments without the need for external pH regulation. The key stability indicators identified include partial alkalinity, effluent chemical oxygen demand, pH, and one-day cumulative biogas. This study highlights the effectiveness of anaerobic fixed biofilm reactors in treating tomato waste and similar fruit and vegetable residues for sustainable biogas production.

Sugarcane is one of the main agro-industrial products consumed worldwide, and, therefore, the use of suitable soils is a key factor to maximize its production. As a result, the need to evaluate soil matrices, including many physical, chemical, and biological parameters, to determine the soil’s aptitude for growing food crops increases. Machine learning techniques were used to perform an in-depth analysis of the physicochemical indicators of vertisol-type soils used in sugarcane production. The importance of the relationship between each of the indicators was studied. Furthermore, and the main objective of the present work, was the determination of the minimum number of the most important physicochemical indicators necessary to evaluate the agricultural suitability of the soils, with a view to reducing the number of analyses in terms of physicochemical indicators required for the evaluation. The results obtained relating to the estimation of agricultural capability using different numbers of parameters showed accuracy results of up to 91% when implementing three parameters: Potassium (K), Calcium (Ca) and Cation Exchange Capacity (CEC). The reported results, relating to the estimation of the physicochemical parameters, indicated that it was possible to estimate eleven physicochemical parameters with an average accuracy of 73% using only the data of K, Ca and CEC as input parameters in the Machine Learning models. Knowledge of these three parameters enables determination of the values of soil potential in regard to Hydrogen (pH), organic matter (OM), Phosphorus (P), Magnesium (Mg), Sulfur (S), Boron (B), Copper (Cu), Manganese (Mn), and Zinc (Zn), the Calcium/Magnesium ratio (Ca/Mg), and also the texture of the soil.