A fast linear predictive adaptive model of packed bed coupled with UASB reactor treating onion waste to produce biofuel

Abstract

BackgroundAgro-industrial wastes are an energy source for different industries. However, its application has not reached small industries. Previous and current research activities performed on the acidogenic phase of two-phase anaerobic digestion processes deal particularly with process optimization of the acid-phase reactors operating with a wide variety of substrates, both soluble and complex in nature. Mathematical models for anaerobic digestion have been developed to understand and improve the efficient operation of the process. At present, lineal models with the advantages of requiring less data, predicting future behavior and updating when a new set of data becomes available have been developed. The aim of this research was to contribute to the reduction of organic solid waste, generate biogas and develop a simple but accurate mathematical model to predict the behavior of the UASB reactor.ResultsThe system was maintained separate for 14 days during which hydrolytic and acetogenic bacteria broke down onion waste, produced and accumulated volatile fatty acids. On this day, two reactors were coupled and the system continued for 16 days more. The biogas and methane yields and volatile solid reduction were 0.6 ± 0.05 m3 (kg VSremoved)−1, 0.43 ± 0.06 m3 (kg VSremoved)−1 and 83.5 ± 9.8 %, respectively. The model application showed a good prediction of all process parameters defined; maximum error between experimental and predicted value was 1.84 % for alkalinity profile.ConclusionsA linear predictive adaptive model for anaerobic digestion of onion waste in a two-stage process was determined under batch-fed condition. Organic load rate (OLR) was maintained constant for the entire operation, modifying effluent hydrolysis reactor feed to UASB reactor. This condition avoids intoxication of UASB reactor and also limits external buffer addition.

Improvement in Methane Production from Pelagic Sargassum Using Combined Pretreatments

“…The higher adsorption of volatile fatty acids from GAC has been observed with substrates whose volatile fatty acid (VFA) profile consists of hydrophobic compounds and long chains (above C5), for which a decreasing adsorption affinity was determined in the order of butyric, propionic, and acetic acid, which can cause a decrease in methane yields [ 79 , 80 ]. On the other hand, this can help keep VFAs below the inhibitory limits (less than 1.5 g/L) [ 81 , 82 ]; Valero et al determined that the adsorption or promotion of DIET when a conductive material was used depended on the characteristics of the substrate; in the case of pot ale whiskey, the use of activated carbon did not increase anaerobic digestion due to the adsorption of organic matter, which in turn reduced the production of methane, while the powdered activated carbon (PAC) improved anaerobic digestion using brewery spent yeast as substrate and provided better resistance to inhibitory conditions with a yield of 699 L CH 4 /kg VS [ 34 ]. Conductive materials prevent the accumulation of volatile fatty acids, maintaining the optimal pH for biogas production (6.8 to 7.4) [ 83 ].…”

The Advantage of Citrus Residues as Feedstock for Biogas Production: A Two-Stage Anaerobic Digestion System

“…It was inoculated with a mixture of activated sludge from a WWTP (wastewater treatment plant) active in Bogotá and kept at a temperature of 30-35 • C by means of a water bath. Synthetic wastewater (SW) was used, as mentioned [26]. The biogas obtained in this unit passed to a 2 L gasometer through which the determination of the daily volume was carried out.…”

Fermentation of Biodegradable Organic Waste by the Family Thermotogaceae

“…An alternative solution could be their biological conversion into bioenergy and high value-added products (food and pharmacological ingredients, biogas, fertilizers, etc.) [41,[97][98][99]. Up to 65% of the dry weight of onion waste is composed of nonstructural carbohydrates, including fructose (114 ± 1.4 mmol/L), glucose (137.5 ± 0.9 mmol/L) and sucrose (21 ± 0.7 mmol/L) [41].…”