Microalgae are photoautotrophic microorganisms capable of producing compounds with potential bioenergetic applications as an alternative energy source due to the imminent exhaustion of fossil fuels, their impact on the environment, and the constant population increase. The mass cultivation of these microorganisms requires high concentrations of nutrients, which is not profitable if analytical grade culture media are used. A viable alternative is the use of agro-industrial wastewater, due to the metabolic flexibility of these microorganisms and their ability to take advantage of the nutrients present in these substrates. For the reasons mentioned above, the effect of the cultivation in wastewater from cheese processing on the growth parameters and biomass composition of Scenedesmus sp. was evaluated, and its nutrient removal capacity determined. A high lipid concentration was obtained in the cultures with the dairy effluent (507.81 ± 19.09 mg g⁻¹) compared to the standard culture medium, while the growth parameters remained similar to the control medium. Scenedesmus sp. achieved high percentages of nutrient assimilation of the wastewater used (88.41% and 97.07% for nitrogen and phosphorus, respectively). With the results obtained, the feasibility of cultivating microalgae in agro-industrial wastewater as an alternative culture medium that induces the accumulation of compounds with potential bioenergetic applications was verified.

The unique perspective that microalgae biomass presents for bioenergy production is currently being strongly considered. This type of biomass production involves large amounts of nutrients, due to nitrogen and phosphorous fertilizers, which impose production limitations. A viable alternative to fertilizers is wastewater, rich in essential nutrients (carbon, nitrogen, phosphorus, potassium). Therefore, Arthrospira platensis was cultivated in 150 mL photobioreactors with 70% (v/v) with the wastewater from a dairy industry, under a regime of light:dark cycles (12 h:12 h), with an irradiance of 140 μmol m−2 s−1 photon. The discontinuous cultures were inoculated with an average concentration of chlorophyll-a of 13.19 ± 0.19 mg L−1. High biomass productivity was achieved in the cultures with wastewater from the dairy industry (1.1 ± 0.02 g L−1 d−1). This biomass was subjected to thermal and physical treatments, to be used in co-digestion with cattle manure. Co-digestion was carried out in a mesophilic regime (35 °C) with a C: N ratio of 19:1, reaching a high methane yield of 482.54 ± 8.27 mL of CH4 g−1 volatile solids (VS), compared with control (cattle manure). The results demonstrate the effectiveness of the use of cyanobacterial biomass grown in wastewater to obtain bioenergy.

(1) Background: This research examines the contamination levels of cadmium (Cd) and lead (Pb) in sugarcane and its derived products in Ecuador, addressing the significant issue of heavy metal pollution in the country’s agricultural lands. The primary aim of this study was to quantify the concentrations of Cd and Pb in sugarcane and the products derived from it, which are available to the Ecuadorian market. (2) Methods: Samples of the most-cultivated sugarcane varieties in the country, including ECU-01, ECU-02, Cenicaña (CC85-92), and Ragnar, were obtained. To ascertain the source of contamination in the derived products, the brands of panela, white sugar, and brown sugar that are most widely consumed in the country were selected. An atomic absorption spectrophotometry analysis was performed with a graphite furnace. (3) Results: All cane varieties presented Cd and Pb contamination. The variety with the highest levels of heavy metals was Ragnar, with average values of 4.32 mg kg⁻¹ of Pb and 0.15 mg kg⁻¹ of Cd. In the derived products, Pb was found to exceed the maximum limits stipulated in national and international regulations (0.5 mg kg⁻¹) in panela (2.3 mg kg⁻¹) and white sugar (1.6 mg kg⁻¹) sold by one of the brands. (4) Conclusions: It was found that lead (Pb) contamination is directly linked to the use of contaminated sugarcane as a raw material, along with bleaching agents.

The thermogravimetric analysis (TGA) data of wood present a very similar layout, which makes it difficult to obtain information on components or types of biomass with traditional techniques, which are generally based on the decrease in weight of the sample at specific temperature values. In this work, artificial neural networks are applied as an innovative technique to identify species or differences in components more effectively. This work evaluates the use of TGA data markers (similar to genetic markers) to obtain information on biomass. That is multiple values of the percentage of residual weight with respect to the initial one at specific temperatures of the TGA data. These networks achieve classification by automatically adjusting weights based on training patterns with input marker data. By replicating the curve for the same sample and having unique characteristics, TGA becomes a valuable tool to identify species and characterize biomass composition. The application of artificial intelligence techniques makes it possible to provide detailed information about the components and improve the accuracy of sample classification. The results demonstrate that the neural network successfully classified 95% of wood samples from eight different species and accurately determined the percentage composition with 98% precision.