在碳峰值背景下,基于适应性遗传算法优化中国的货运结构
Hang Ke1, Guangyin Xu2, Chuntang Li1
1College of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou, 450002, China.
概括
到2030年,优化中国的货运运输可以大幅减少碳排放. 从公路转向铁路和水路的货运减少了超过10%的排放,有助于实现碳峰值目标.
科学领域:
- 环境科学 环境科学
- 运输工程 运输工程
- 运营研究 运营研究
背景情况:
- 交通是碳排放的主要来源,仅次于能源供应和工业生产.
- 实现碳峰值和碳中和目标需要大幅减少运输排放.
- 目前的货运结构需要优化,以满足未来的环境目标.
研究的目的:
- 到2030年,构建和解决中国货物运输的优化模型.
- 尽量减少运输的碳排放,同时考虑货运的实用价值和系统约束.
- 评估货物运输方式转变对能源消耗和排放的影响.
主要方法:
- 开发了一个优化模型,将碳排放作为主要目标,货运公用事业作为次要目标.
- 对于社会货运周转,经济/社会效益和生态因素的内在约束.
- 利用MATLAB中的自适应基因算法解决道路,铁路和非海洋水道的最佳货运周转问题.
主要成果:
- 预计到2030年,公路货运份额将减少8.07%,铁路 (0.93%) 和水路 (7.13%) 的份额将增加.
- 实现了能源消耗减少4247万,碳排放减少9138万标准煤 (两者约10.3%).
- 证明了自适应基因算法的高于传统方法的融合速度和准确性.
结论:
- 拟议的优化模型有效地减少了运输碳排放和能源消耗.
- 将货物从公路转移到铁路和水路,对于实现中国的环境目标至关重要.
- 适应性遗传算法为复杂的运输优化问题提供了有效的工具.
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