Estimación de Parámetros Cinéticos de la Degradación Aeróbica de Efluentes Lácteos usando AQUASIM v 2.1b

Rosa, Miguel A; Peralta, José María Margenat; Bosco, Diego M

doi:10.4067/s0718-07642010000300006

Cited by 4 publications

(3 citation statements)

References 8 publications

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“…Of the published AD models in literature, process simulation were accomplished by SIMBA under MATLAB/SIMULINK [15], AQUASIM v 2.1b® [16,17] and Aspen plus [18,19]. In order to overcome the challenge of interpreting and understanding the dynamic model of a complex biological system such as AD process due to lack of good background of mathematics, system dynamic modeling of biogas production system is considered.…”

Section: Delgadilo-mirquez Et Almentioning

confidence: 99%

Modeling Anaerobic Co-digestion of Food Wastes and Cattle Manure in an Industrial Plant: a System Dynamic Approach

Azeez

Gwandangaji

2022

Preprint

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Even though the Anaerobic Digestion Model number 1 (ADM1) is acceptable and widely used model for anaerobic digestion, it is associated with large number of experimental data and large number of model parameter identification thereby making it difficult to be utilized for optimization and control design. In this study, the applicability of Anaerobic Digestion Model number 2 (AM2) model to co-digestion of food wastes and cattle manure in an industrial biogas plant’s digester is explored. The model was modified and reconstructed in system dynamic modeling software (STELLA) for better interpretation and understanding. The predictive ability of the model using the model parameters estimated in Premium Solver was checked by comparing model results (biogas flow and compositions) with ADMI simulated results and the experimental data. In order to gain insight on variability of biogas flow and compositions as a result of change in some important process variables, sensitivity analysis was conducted. The simulation results of the modified AM2 model were found to correspond with the measured value with percentages errors of 1.42% and 0.6% for methane concentration and biogas flow respectively. The process variables associated with methanogenesis stage were obtained to be the most sensitive variables. The use of system dynamic modeling linked to AM2 model which is tractable, reduced and robust is therefore recommended for simulation of full scale biogas plant’s digester.

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Section: Delgadilo-mirquez Et Almentioning

confidence: 99%

Modeling Anaerobic Co-digestion of Food Wastes and Cattle Manure in an Industrial Plant: a System Dynamic Approach

Azeez

Gwandangaji

2022

Preprint

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“…On the one hand, when working with an aerobic activated sludge system, a nitrification process takes place, which causes the oxidation of ammonia and ammonium salts, and their subsequent conversion to nitrate. On the other hand, the diversity of microorganisms in raw wastewater generates competition in nitrogen assimilation, where the availability of nitrogen for microalgae is limited because the bacteria growth rate is higher than that of microalgae [ROSA et al 2010;TORRES et al 2017].…”

Section: Physicochemical Characterisation Of Effluents R1 and R2mentioning

confidence: 99%

Journal of Water and Land Development

Urbina-Suarez,

Barajas-Solano,

Garcia-Martinez

et al. 2022

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In recent years, the technical and economic feasibility of using microalgae and cyanobacteria has been explored for the removal and exploitation of domestic, agricultural and industrial residual effluents with high C, N and P compounds content. To contribute to the understanding of the process and its technical viability for microalgae growth, the article discusses monitoring, flow determination, and physicochemical characteristics of two types of effluents generated in an experimental farm located in the east of Colombia, before (R1) and after biological treatment (R2). In general, the results showed the reduction of different parameters, such as total dissolved solids (TDS), hardness, salinity and phosphates after treatment with activated sludge. However, the conductivity value obtained in R1 and R2 showed the presence of a pollutant load. These findings can be attributed to the highest concentration of fats and oils in the water during early hours of the day. Finally, although the concentration of nitrates increased from 46.63 to 225.21 mg•dm -3 and phosphate decreased slightly from 9.65 to 6.21 mg•dm -3 , no inhibition was generated in the microalgae, as evidenced in the growth of the microalgal biomass in effluents after nitrate and phosphate removal above 80%.

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“…Así, varias investigaciones para ver cómo reducir los impactos ambientales causados por la eliminación de residuos industriales favorece la preservación de los componentes del agua y del suelo en todo el mundo (Coria y Vegas, 2011;Passarini et al, 2012;Rodríguez et al, 2008;Rosa et al, 2010). Los mataderos generan efluentes líquidos compuestos de grasas, proteínas y restos de animales.…”

Section: Introductionunclassified

Elaboración y Clasificación Sensorial de Gelatinas de Patas de Pollos: Correlación usando Redes Neuronales Artificiales

et al. 2012

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ResumenSe presenta la estrategia para desarrollada para la clasificación sensorial de gelatinas obtenidas de patas de pollos utilizando redes neuronales basadas en los algoritmos de Kohonen. Estas redes muestran ser buenas herramientas para hacer las comparaciones sensoriales entre muestras, permitiendo identificar cuáles son las mejores muestras entre las gelatinas. De acuerdo a los resultados la Gelatina D, con 3,80 % (m/v) de polvo de gelatina y 28,6 (m/v) de azúcar fue a mejor entre las gelatinas de patas de pollo, recibiendo una aceptación sensorial entre 6 y 8 puntos en la escala hedónica. Palabras clave: redes neuronales, gelatinas de patas de pollos, análisis sensorial, calidad de los alimentosAbstract A strategy has been developed for the sensorial classification of jellies made from chicken feet using neural networks based on Kohonen algorithms. These networks showed to be good tools for sensorial comparison among samples, allowing identification of the best gelatins. The sample Gelatin D, con 3.80% (w/v) of gelatin powder and 28.6 (w/v) of sugar was the best than gelatin samples from chicken feet, with its sensorial qualities varying between 6 and 8 times in hedonic scale.

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Estimación de Parámetros Cinéticos de la Degradación Aeróbica de Efluentes Lácteos usando AQUASIM v 2.1b

Cited by 4 publications

References 8 publications

Modeling Anaerobic Co-digestion of Food Wastes and Cattle Manure in an Industrial Plant: a System Dynamic Approach

Modeling Anaerobic Co-digestion of Food Wastes and Cattle Manure in an Industrial Plant: a System Dynamic Approach

Journal of Water and Land Development

Elaboración y Clasificación Sensorial de Gelatinas de Patas de Pollos: Correlación usando Redes Neuronales Artificiales

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