An integrated prediction and optimization model of biogas production system at a wastewater treatment facility

Akbaş, Halil Emre; Bilgen, Bilge

doi:10.1016/j.biortech.2015.08.017

Cited by 75 publications

(19 citation statements)

References 22 publications

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“…Offline estimation of yield and kinetic coefficients in anaerobic wastewater treatment by reducing the error between actual measured response and simulated response has been done using PSO [56], and PSO has been used for parameter estimation in a modified ADM1 model for modeling volatile fatty acids (VFA), showing its advantage in directly seeking the optima in a multidimensional space without crossover and mutation [57]. Multilayer perceptron neural network (MLPNN) and PSO were combined [58] to obtain maximum methane percentage in biogas, biogas quantity, and biogas quality. MLPNN was used for model prediction, giving a regression coefficient as high as 0.91, providing good prediction of modeled outputs, and PSO used for model optimization helped in utilization of biogas production at maximum output levels.…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Review of Anaerobic Digestion Modeling and Optimization Using Nature-Inspired Techniques

2019

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Although it is a well-researched topic, the complexity, time for process stabilization, and economic factors related to anaerobic digestion call for simulation of the process offline with the help of computer models. Nature-inspired techniques are a recently developed branch of artificial intelligence wherein knowledge is transferred from natural systems to engineered systems. For soft computing applications, nature-inspired techniques have several advantages, including scope for parallel computing, dynamic behavior, and self-organization. This paper presents a comprehensive review of such techniques and their application in anaerobic digestion modeling. We compiled and synthetized the literature on the applications of nature-inspired techniques applied to anaerobic digestion. These techniques provide a balance between diversity and speed of arrival at the optimal solution, which has stimulated their use in anaerobic digestion modeling.

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Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Review of Anaerobic Digestion Modeling and Optimization Using Nature-Inspired Techniques

2019

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“…Las redes neuronales artificiales se han utilizado con éxito en procesos biológicos para el modelado, la predicción y el control de dinámicas no lineales [6]. Las Redes Neuronales Recurrentes (RHONN) han demostrado ser factibles en aplicaciones de identificación y control debido a su arquitectura flexible y robustez [7].…”

Section: Iiiidentificador Neuronalunclassified

“…Esta red es entrenada con un algoritmo basado en un Extendend Kalman Filter (EKF). [13][14][15][16], el objetivo filtro de Kalman es diseñar un observador óptimo que estime los estados de un sistema con ruido blanco en la salida y en los estados. Considerando el sistema discreto siguiente:…”

Section: Iiiidentificador Neuronalunclassified

Modelado neuronal de un proceso de digestión aeróbica de aguas residuales

Colmenares¹,

Tún²

2019

BISTUA

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Los procesos biológicos para el tratamiento de agua residual son sistemas altamente no lineales que están sometidos a incertidumbres y perturbaciones externas, se ha creído ampliamente que los modelos que describen las reacciones bioquímicas y procesos de sedimentación son muy complejos y difíciles de manejar exactamente con fines de control. Por tal motivo se requieren estrategias avanzadas para la identificación, estimación de parámetros no medibles del proceso y control.En este trabajo se propone una red neuronal no lineal en tiempo discreto en presencia de perturbaciones externas y estimación de los estados dinámicos difíciles de medir en un proceso de digestión aerobia para tratamiento de agua residual. Se presentan resultados de estimación vía simulación, donde se demuestra que el modelo neuronal propuesto es eficiente para la estimación de las dinámicas de los estados en presencia de perturbaciones. Con este modelo neuronal será posible diseñar una estrategia de control para optimizar el rendimiento de la degradación de materia orgánica en un proceso de digestión aerobia usando técnicas de control óptimo y estimación neuronal.Keywords— Modelo ASM1, Red neuronal, dinámicas no lineales, agua residual.

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“…As a result, around 14 characteristics are predicted, of which 10 resembled so close to the experimental data, thereby the model is validated. [42] Using multi-layer neural and PSO model, optimization of CH 4 and biogas with 1-output and optimization of biogas quality (CH 4 , CO 2 , etc) and yield with 2-outputs were structured. Ideal input and output values are obtained with respect to the structured models and values from wastewater processing that is expected to enhance biofuel quantity and quality-wise.…”

Section: Mathematical and Neuralmentioning

confidence: 99%

A Comprehensive analysis on Bioenergy Yield via Anaerobic Disintegration of Organic Matter

Diaz¹

2017

SIJST

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Solid and liquid waste disposal by anaerobic digestion is feasible enough to produce biofuel by reducing volume of sludge. The rate of waste conversion to biogas in activated sludge process has been increasing as it reduces negative environmental effect and stabilizes ecological balance. A brief overview of substrates that is suitable for biogas yield has been presented, where it is clear that the feedstock with higher total solid concentration results to increase biofuel generation potential. It intends to provide various anaerobic disintegration techniques that emphasized on bioenergy development. Further, the recommendations over future scope have also been dealt with.

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An integrated prediction and optimization model of biogas production system at a wastewater treatment facility

Cited by 75 publications

References 22 publications

Review of Anaerobic Digestion Modeling and Optimization Using Nature-Inspired Techniques

Review of Anaerobic Digestion Modeling and Optimization Using Nature-Inspired Techniques

Modelado neuronal de un proceso de digestión aeróbica de aguas residuales

A Comprehensive analysis on Bioenergy Yield via Anaerobic Disintegration of Organic Matter

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