气生产和污染减缓:通过机器学习和储存来加强塑料废物和生物质的气化,以实现可持续的未来
Abu Danish Aiman Bin Abu Sofian1, Hooi Ren Lim1, Kit Wayne Chew2
1Department of Chemical and Environmental Engineering, Faculty of Science and Engineering, University of Nottingham Malaysia, Jalan Broga, 43500 Semenyih, Selangor Darul Ehsan, Malaysia.
Environmental pollution (Barking, Essex : 1987)
|November 29, 2023
概括
从塑料废物和生物质通过气化生产,为清洁能源和减少废物提供了一个可持续的解决方案. 机器学习集成提高了效率和经济可行性,以实现更绿色的未来.
科学领域:
- 可持续能源 可持续能源
- 废物管理 废物管理
- 化学工程是化学工程的重要组成部分.
背景情况:
- 实现碳中和需要平衡能源需求与减少污染.
- 塑料废物和生物质为可持续能源生产提供了未充分利用的资源.
- 是环境改革和绿色经济的关键推动因素.
研究的目的:
- 审查塑料废弃物和生物质气化以生产气的潜力.
- 分析机器学习在优化气化过程中的作用.
- 评估这些生产方法的经济可行性和环境效益.
主要方法:
- 关于塑料废物和生物质的联合气化技术的综合文献综述.
- 机器学习应用在优化气化参数方面的分析.
- 评估气储存解决方案和经济可行性研究.
主要成果:
- 塑料废物和生物质的联合气化为减少废物和产生清洁气提供了一个有前途的途径.
- 机器学习显著提高了气化过程的效率,提高了产量和经济成果.
- 先进的储存对于强大的可持续能源基础设施至关重要.
结论:
- 从非传统原料中整合生产,通过机器学习进行优化,并支持有效的储存,对于可持续的,无污染的未来至关重要.
- 这种方法支持向绿色经济过渡,并促进循环能源实践.
- 需要进一步的研究和技术进步来有效地扩展这些解决方案.
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