Biogas plant optimization using genetic algorithms and particle swarm optimization

Wolf, Christian; McLoone, Seán; Bongards, Michael

doi:10.1049/cp:20080670

Cited by 11 publications

(19 citation statements)

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“…Biogas plant substrate feed mixtures have previously been optimized with a Genetic Algorithm and Particle Swarm Optimization by Wolf et al [35]. More recently Ziegenhirt et al [39] used state of the art evolution strategies like Covariance Matrix Adaption Evolution Strategy (CMAES) [18,17] or Differential Evolution (DE) [34] to reduce the number of needed simulations.…”

Section: Biogas Substrate Feed Optimizationmentioning

confidence: 99%

“…The herein presented research on the other hand is based on the MATLAB R Toolbox for Biogas Plant Simulation [16]. In contrast to earlier works by Wolf et al [35] and Ziegenhirt et al [39] our approach is not limited to the ADM1. A simple estimate of a substrate mixtures quality is derived from the biogas potential of each ingredient.…”

Section: Biogas Substrate Feed Optimizationmentioning

confidence: 99%

“…In the same way, there are other details of additional parameters which may require to be included. In previous studies [35,39] these were included in a weighted sum of objectives. Since the weights are hard to set, a multi-objective approach may make more sense.…”

Section: Optimum Of Bmp Model As Start Pointmentioning

confidence: 99%

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Multi-fidelity modeling and optimization of biogas plants

Zaefferer

Gaida

Bartz-Beielstein

2016

Applied Soft Computing

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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.

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Section: Biogas Substrate Feed Optimizationmentioning

confidence: 99%

Section: Biogas Substrate Feed Optimizationmentioning

confidence: 99%

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Multi-fidelity modeling and optimization of biogas plants

Zaefferer

Gaida

Bartz-Beielstein

2016

Applied Soft Computing

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“…Ward et al (2008) discussed the optimization of environmental conditions within the digester, e.g., temperature, pH, buffering capacity and fatty acid concentrations. Wolf et al (2008) aimed at the optimization of biogas plant operation. For this reason, they used GA and PSO models, which were integrated with a dynamical simulation model for anaerobic digestion.…”

Section: Introductionmentioning

confidence: 99%

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

Akbaş

Bilgen

2015

Bioresource Technology

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“…Optimising an anaerobic sequencing batch reactor with the use of artificial neural networks and genetic algorithms demonstrated a clear improvement in biogas production [13]. Using Particle Swarm Optimization in optimising substrate feed mix resulted in a 20% improvement in biogas production [14]. Another use of particle swarm optimization for optimising values of certain biogas production process variables (e.g., temperature, pH value) in a multi-layer perceptron neural network model resulted in a 20.8% increase in biogas production [15].…”

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confidence: 99%