MATEC Web Conf.
Volume 277, 20192018 International Joint Conference on Metallurgical and Materials Engineering (JCMME 2018)
|Number of page(s)||7|
|Section||Metallurgy & Control and Manufacturing|
|Published online||02 April 2019|
Forecasting N2O emission and nitrogen loss from swine manure composting based on BP neural network
College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, China
* Corresponding author: email@example.com
Nitrogen loss and greenhouse gas emission during compost will cause secondary pollution and waste nutrients. To address this issue, a predictive model was set up to obtain a clear knowledge of the N2O emission and nitrogen loss from swine manure composting. This paper collected 68 group data from 11 published papers about pig manure composting N2O emission and total nitrogen loss. Select 4 indexes were taken as predicted indexes include aeration rate, moisture content, C/N, and the amount of superphosphate to establish a BP neural network for forecasting the N2O emission and total nitrogen loss from composting. The analyses show that the mean error of N2O emission forecasting model is 1.17; the value of MAPE is 138.85%. As for nitrogen loss, the mean error is 24.72 and the mean absolute percentage error is 11.06%. Compare to the traditional linear regression, the BP neural network model has good accuracy on forecasting N2O emission and TN loss from manure composting. BP neural network has considerable application prospect in forecast nitrogen loss and greenhouse gas emission from composting.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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