Prediction of the biogas production using GA and ACO input features selection method for ANN model
This paper presents a fast and reliable approach to analyze the biogas production process with respect to the biogas production rate. The experimental data used for the developed models included 15 process variables measured at an agricultural biogas plant in Germany. In this context, the concentrat...
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| Published in | Information processing in agriculture Vol. 6; no. 3; pp. 349 - 356 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
01.09.2019
Elsevier |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2214-3173 2214-3173 |
| DOI | 10.1016/j.inpa.2019.01.002 |
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| Abstract | This paper presents a fast and reliable approach to analyze the biogas production process with respect to the biogas production rate. The experimental data used for the developed models included 15 process variables measured at an agricultural biogas plant in Germany. In this context, the concentration of volatile fatty acids, total solids, volatile solids acid detergent fibre, acid detergent lignin, neutral detergent fibre, ammonium nitrogen, hydraulic retention time, and organic loading rate were used. Artificial neural networks (ANN) were established to predict the biogas production rate. An ant colony optimization and genetic algorithms were implemented to perform the variable selection. They identified the significant process variables, reduced the model dimension and improved the prediction capacity of the ANN models. The best prediction of the biogas production rate was obtained with an error of prediction of 6.24% and a coefficient of determination of R2 = 0.9. |
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| AbstractList | This paper presents a fast and reliable approach to analyze the biogas production process with respect to the biogas production rate. The experimental data used for the developed models included 15 process variables measured at an agricultural biogas plant in Germany. In this context, the concentration of volatile fatty acids, total solids, volatile solids acid detergent fibre, acid detergent lignin, neutral detergent fibre, ammonium nitrogen, hydraulic retention time, and organic loading rate were used. Artificial neural networks (ANN) were established to predict the biogas production rate. An ant colony optimization and genetic algorithms were implemented to perform the variable selection. They identified the significant process variables, reduced the model dimension and improved the prediction capacity of the ANN models. The best prediction of the biogas production rate was obtained with an error of prediction of 6.24% and a coefficient of determination of R2 = 0.9. This paper presents a fast and reliable approach to analyze the biogas production process with respect to the biogas production rate. The experimental data used for the developed models included 15 process variables measured at an agricultural biogas plant in Germany. In this context, the concentration of volatile fatty acids, total solids, volatile solids acid detergent fibre, acid detergent lignin, neutral detergent fibre, ammonium nitrogen, hydraulic retention time, and organic loading rate were used. Artificial neural networks (ANN) were established to predict the biogas production rate. An ant colony optimization and genetic algorithms were implemented to perform the variable selection. They identified the significant process variables, reduced the model dimension and improved the prediction capacity of the ANN models. The best prediction of the biogas production rate was obtained with an error of prediction of 6.24% and a coefficient of determination of R2 = 0.9. Keywords: Ant colony optimization, Artificial neural networks, Biogas, Genetic algorithm |
| Author | Klocke, Michael Hitzmann, Bernd Beltramo, Tanja |
| Author_xml | – sequence: 1 givenname: Tanja surname: Beltramo fullname: Beltramo, Tanja email: tanja.beltramo@gmail.com organization: Institute of Food Science and Biotechnology, University of Hohenheim, Stuttgart 70599, Germany – sequence: 2 givenname: Michael surname: Klocke fullname: Klocke, Michael organization: Leibniz Institute for Agricultural Engineering and Bioeconomy, Potsdam 14469, Germany – sequence: 3 givenname: Bernd surname: Hitzmann fullname: Hitzmann, Bernd organization: Institute of Food Science and Biotechnology, University of Hohenheim, Stuttgart 70599, Germany |
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| Cites_doi | 10.1016/0003-2670(94)80307-2 10.1002/rnc.727 10.1016/j.biosystemseng.2016.01.006 10.1023/A:1022602019183 10.1016/j.knosys.2012.11.005 10.2166/wst.2002.0292 10.1186/1471-2105-6-30 10.1093/bioinformatics/btl474 10.1002/bit.21282 10.1016/j.renene.2012.03.027 10.1016/j.resconrec.2009.08.012 10.1002/cem.1180080107 10.1016/S0168-1656(98)00118-7 10.1016/j.envsoft.2006.03.004 10.1016/j.tcs.2005.05.020 10.1007/s00253-015-6627-9 10.1007/s10666-008-9150-x 10.1016/j.jtbi.2005.05.016 10.1016/j.chemolab.2015.02.002 10.1016/S0169-7439(01)00155-1 10.1016/j.chemolab.2014.01.012 10.1016/j.envsoft.2004.09.006 10.1111/1751-7915.12263 10.1016/j.cam.2004.07.034 |
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| Keywords | Ant colony optimization Biogas Genetic algorithm Artificial neural networks |
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| SubjectTerms | acid detergent fiber acid insoluble lignin algorithms ammonium nitrogen Ant colony optimization Artificial neural networks Biogas gas production (biological) Genetic algorithm Germany neural networks neutral detergent fiber prediction system optimization total solids volatile fatty acids |
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| Title | Prediction of the biogas production using GA and ACO input features selection method for ANN model |
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