Prediction of the biogas production using GA and ACO input features selection method for ANN model

被引：21

作者：

Beltramo T. ^{[1
]}

Klocke M. ^{[2
]}

Hitzmann B. ^{[1
]}

机构：

[1] Institute of Food Science and Biotechnology, University of Hohenheim, Stuttgart

[2] Leibniz Institute for Agricultural Engineering and Bioeconomy, Potsdam

来源：

Information Processing in Agriculture | 2019年 / 6卷 / 03期

关键词：

Ant colony optimization; Artificial neural networks; Biogas; Genetic algorithm;

D O I：

10.1016/j.inpa.2019.01.002

中图分类号：

学科分类号：

摘要：

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. © 2019 China Agricultural University

引用

页码：349 / 356

页数：7

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