Modeling anaerobic digestion of dairy manure using the IWA Anaerobic Digestion Model no. 1 (ADM1)

Page, Demi; Hickey, K. L.; Narula, Rajiv; Main, A. L.; Grimberg, Stefan J.

doi:10.2166/wst.2008.678

Cited by 45 publications

(20 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The changes of the input concentrations depending on the residue used (i.e., agrowastes, wastewater). For this reason, a specific range of variation was defined using the values of parameter extracted from several references [6], [17], [18], [21]- [25]. Although this review allowed identifying the maximum and minimum possible values for many of the variables, some input concentrations like S ch4 and S h2 were not documented, so it was decided to vary them in a wide range for academic purposes.…”

Section: Methodsmentioning

confidence: 99%

“…However, microorganisms that develop the AD process are highly sensitive so any variation in the reactor conditions or changes in the residue that feed them could inhibit or damage the biological process [2], [5]. For these reasons, it is necessary to have a reliable characterization of each waste that enters to the reactor and control many of the variables involved in the process, such as: temperature, pH, carbon/nitrogen ratio (C/N), organic leading rate and hydraulic and solid retention times [5], [6].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Assessment of the Input Substrate Characteristics Included in the Anaerobic Digestion Model No. 1 (ADM1)

Morales

Rodriguez

Rojas

2017

Ing.

View full text Add to dashboard Cite

Context: Anaerobic Digestion (AD) is a well-established process and widely used for wastewater treatment and power generation worldwide. In engineering, the Anaerobic Digestion Model number 1 (ADM1) is the preferred tool for predicting the behavior of AD. However, this model requires a detailed characterization of the input substrate, which is often a difficult and expensive process. This paper presents an analysis of the input waste variables in the ADM1 and its effects on the biogas production, in order to identify on which variables the characterization should be centered. Method: A sensitivity analysis was performed using a simple methodology that consisted of establishing a reference case and change the value of each input concentrations one at a time leaving the other parameters equal. For this a specific range of variation was defined using the values of parameter extracted from several references. Simulations were done in MATLAB/Simulink R using a routine developed by the authors based on the ADM1 implementation proposed in the Lund University, Sweden. Results: The analysis reveals that most critical variables involved in the methane production are temperature, volumetric flow rate of substrates and the concentrations of proteins, lipids and carbohydrates. For the case analyzed, variations in concentrations of lipids and carbohydrates increase the production of methane by more than 500 %. Conclusions: In the concentrations of proteins, lipids and carbohydrates should focus the characterization of waste input when the ADM1 is implemented. Also, simulations shows that the input concentrations should be carefully estimated because oversizing of these can cause erroneous results. Keywords: ADM1; anaerobic digestion; biogas production; sensitivity analysis. ResumenContexto: La digestión anaeróbica (DA) es un proceso bien establecido y ampliamente utilizado para el tratamiento de aguas residuales y generación de energía en todo el mundo. En ingeniería, el modelo de digestión anaeróbica número 1 (ADM1) es la herramienta preferida para predecir el comportamiento de la DA. Sin embargo, este modelo requiere una caracterización detallada del sustrato de entrada, que a menudo es un proceso difícil y costoso. En este trabajo se presenta un análisis de las variables que caracterizan el residuo de entrada en el ADM1 y sus efectos en la producción de biogás, con el fin de identificar cuáles son las variables en las que la caracterización debería centrarse. Método: Se realizó un análisis de sensibilidad utilizando una metodología simple que consistió en establecer un caso de referencia y cambiar el valor de cada una de las concentraciones de entrada una a la vez dejando los otros parámetros iguales. Para ello se definió un rango específico de variación utilizando los valores de parámetros extraídos de varias referencias. Las simulaciones se realizaron en MATLAB / Simulink R mediante una rutina desarrollada por los autores y basada en la implementación de ADM1 propuesta en la Universidad de Lund, Suecia. Resultad...

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessment of the Input Substrate Characteristics Included in the Anaerobic Digestion Model No. 1 (ADM1)

Morales

Rodriguez

Rojas

2017

Ing.

View full text Add to dashboard Cite

show abstract

“…ADM1 is very popular and the nowadays most complex mathematical model used to simulate the anaerobic digestion process (for a review see [3]). In several publications it is utilized to dynamically model full-scale agricultural and industrial biogas plants [5,23,29]. ADM1 is a structured model incorporating disintegration and hydrolysis, acidogenesis, acetogenesis, and methanogenenesis steps.…”

Section: Biogas Plant Simulationmentioning

confidence: 99%

Multi-fidelity modeling and optimization of biogas plants

Zaefferer

Gaida

Bartz-Beielstein

2016

Applied Soft Computing

View full text Add to dashboard Cite

An essential task for operation and planning of biogas plants is the optimization of substrate feed mixtures. Optimizing the monetary gain requires the determination of the exact amounts of maize, manure, grass silage, and other substrates. Accurate simulation models are mandatory for this optimization, because the underlying chemical processes are very slow. The simulation models themselves may be time-consuming to evaluate, hence we show how to use surrogatemodel-based approaches to optimize biogas plants efficiently. In detail, a Kriging surrogate is employed. To improve model quality of this surrogate, we integrate cheaply available data into the optimization process. Doing so, multifidelity modeling methods like Co-Kriging are employed. Furthermore, a two-layered modeling approach is employed to avoid deterioration of model quality due to discontinuities in the search space. At the same time, the cheaply available data is shown to be very useful for initialization of the employed optimization algorithms.Overall, we show how biogas plants can be efficiently modeled using data-driven methods, avoiding discontinuities as well as including cheaply available data. The application of the derived surrogate models to an optimization process is shown to be very difficult, yet successful for a lower problem dimension.

show abstract

“…Modelling anaerobic digestion using the complex ADMl has proven to be a very good and all-round approach Page et al 2008;Koch et al 2010). The ADMl is commonly implemented as a non-linear differential equation system of the form:…”

Section: The Biogas Plantmentioning

confidence: 99%

State estimation for anaerobic digesters using the ADM1

Gaida

Wolf

Meyer

et al. 2012

Water Science and Technology

View full text Add to dashboard Cite

The optimization of full-scale biogas plant operation is of great importance to make biomass a competitive source of renewable energy. The implementation of innovative controi and optimization algorithms, such as Nonlinear Model Predictive Control, requires an online estimation of operating states of biogas plants. This state estimation allows for optimal control and operating decisions according to the actual state of a plant. In this paper such a state estimator is developed using a calibrated simulation model of a full-scale biogas plant, which is based on the Anaerobic Digestion Model N0.1. The use of advanced pattern recognition methods shows that model states can be predicted from basic online measurements such as biogas production, CH4 and CO2 content in the biogas, pH value and substrate feed volume of known substrates. The machine learning methods used are trained and evaluated using synthetic data created with the biogas plant model simulating over a wide range of possible plant operating regions. Results show that the operating state vector of the modelled anaerobic digestion process can be predicted with an overall accuracy of about 90%.This facilitates the application of state-based optimization and control algorithms on full-scale biogas plants and therefore fosters the production of eco-friendly energy from biomass.

show abstract

Modeling anaerobic digestion of dairy manure using the IWA Anaerobic Digestion Model no. 1 (ADM1)

Cited by 45 publications

References 12 publications

Assessment of the Input Substrate Characteristics Included in the Anaerobic Digestion Model No. 1 (ADM1)

Assessment of the Input Substrate Characteristics Included in the Anaerobic Digestion Model No. 1 (ADM1)

Multi-fidelity modeling and optimization of biogas plants

State estimation for anaerobic digesters using the ADM1

Contact Info

Product

Resources

About