人工智能用于模拟废物生物质气化的方法:一项审查
Fatma Alfarra1, H Kurtulus Ozcan2, Pınar Cihan3
1Engineering Faculty, Department of Environmental Engineering, Istanbul University-Cerrahpasa, 34320, Avcilar, Istanbul, Turkey. mayafarra90@gmail.com.
Environmental monitoring and assessment
|February 26, 2024
概括
人工智能 (AI),特别是机器学习 (ML) 和深度学习 (DL),为优化生物质气化提供了先进的解决方案. 这些人工智能模型提高了废物转化为能源的效率,预测和控制.
科学领域:
- 热化学转换过程中的热化学转换过程.
- 能源转换系统 能源转换系统
- 废物转化为能源的技术.
背景情况:
- 生物质气化对于将废物转化为合成气至关重要,但传统方法面临着复杂标准的挑战.
- 提高热化学过程的效率和生产率对于满足能源需求至关重要.
- 人工智能 (AI) 模型越来越多地用于加速这些过程并制定能源战略.
研究的目的:
- 提供机器学习 (ML) 和深度学习 (DL) 在生物质气化中的方法的概述.
- 评估ML和DL在热化学转换系统中的优缺点.
- 确定研究知识的差距,并推未来的研究方向.
主要方法:
- 对气化过程中应用的ML和DL技术的审查.
- 对人工智能应用在生产率预测,流程优化和实时监控方面的分析.
- 对混合模型和深度学习方法的评估,以提高准确性.
主要成果:
- ML和DL模型在热化学转换系统中显示出显著的有效性.
- 人工智能算法对于预测输出和优化能量转换至关重要.
- 混合模型和DL在复杂的建模和优化任务中取得了成功,提高了准确性.
结论:
- 人工智能,特别是ML和DL,是生物质气化和能源转换中的一个勃发展的公用事业.
- 这些先进的模型增强了生产率预测,流程优化和实时控制.
- 未来的研究应该专注于利用人工智能来解决该领域的复杂挑战和知识差距.
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