通过快速图像识别和视觉分析,通过机器学习协助评估城市固体废物热处理效率
Zixiao Wu1, Jia Jia2, Xiaohui Sun3
1School of Environmental Science and Engineering, Zhejiang Provincial Key Laboratory of Solid Waste Treatment and Recycling, Zhejiang Engineering Research Center of Non-ferrous Metal Waste Recycling, Zhejiang Gongshang University, Hangzhou 310012, China.
Waste management (New York, N.Y.)
|January 14, 2025
概括
本研究引入了一种使用神经网络的快速图像识别方法,用于评估城市固体废物 (MSW) 热处理效应. BAEVA 1.0软件根据底层灰色准确评估焚烧情况,为传统测量提供了方便的替代方案.
科学领域:
- 环境科学 环境科学
- 废物管理 废物管理
- 人工智能的人工智能
背景情况:
- 分散的热处理是农村城市固体废物 (MSW) 处置的常见方法.
- 由于复杂和耗时的测量,评估焚烧效率具有挑战性.
研究的目的:
- 开发一种快速图像识别方法,用于评估MSW热处理效果.
- 为了确定底灰点火损失和颜色特性之间的相关性.
主要方法:
- 利用皮尔森相关性分析将点火损失与R,G,B颜色值联系起来.
- 实现了反向传播人工神经网络 (BPANN) 算法,以提高准确性.
- 开发了BAEVA 1.0软件,将BPANN集成到实际应用中.
主要成果:
- 在点火损失和RGB颜色值之间观察到强烈的相关性 (R2>0.80).
- 在BPANN模型中,平均评估误差为3.21,比线性回归高出27.9%.
- 与现有方法相比,BAEVA 1.0表现出优越的功能,方便和准确性.
结论:
- 开发的图像识别方法为评估MSW焚烧提供了快速而准确的方法.
- BAEVA 1.0软件为评估热处理效应提供了一个实用的工具,特别是当传统测量很困难时.
- 这项研究为资源有限的环境中MSW管理提供了有价值的评估策略.
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