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Updated: Jan 29, 2026

Ammonia Synthesis at Low Pressure
Published on: August 23, 2017
预测使用人工神经网络对用二硫酸处理的肉的氨缓解效率
1Department of Biosystems Engineering, Faculty of Agriculture, Bursa Uludag University, 16059 Bursa, Türkiye.
人工智能准确地预测了从肉垃圾中去除氨的效率. 人工神经网络 (ANN) 中的Levenberg-Marquardt算法在家禽养殖中显示出对氨减缓策略的卓越性能.
科学领域:
- 环境科学 环境科学
- 农业工程 农业工程
- 人工智能的人工智能
背景情况:
- 增加家禽生产加剧了由于高蛋白质含量导致的氨 (NH) 排放.
- 传统的NH3去除方法通常是反应性的;预测方法提供主动的解决方案.
- 准确估计NH3去除效率对于家禽养殖环境管理至关重要.
研究的目的:
- 使用人工神经网络 (ANN) 预测 brojiler litter 的氨去除效率.
- 为了比较不同ANN学习算法的NH3排放预测的性能.
- 确定用于氨减缓策略的可靠数据为基础的方法.
主要方法:
- 开发了四个人工神经网络 (ANN) 模型,使用莱文伯格-马奎特 (LM),弗莱彻-里夫斯 (FR),缩放结合梯度 (SCG) 和贝叶斯规范化 (BR) 算法.
- 在受过训练的ANN中,在受控的实验室条件下,从肉垃圾中获取数据.
- 基于预测准确性和概括能力评估模型性能.
主要成果:
- 基于LM的ANN模型与12个隐藏的神经元实现了最高的预测性能 (R2 = 0.9777).
- 用10个神经元的贝叶斯规范化 (BR) 模型显示了可比的准确性.
- 在预测准确性和概括性方面,LM算法超过了FR和SCG模型.
结论:
- 基于ANN的建模为估计NH3去除效率提供了一种可靠和基于数据的方法.
- LM算法是一种高效的学习方法,用于预测家禽垃圾中的氨去除.
- 这种方法可以支持在密集的家禽生产系统中减轻氨的决策.
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