创新的模型优化机器学习,用于高精度预测和探索生物质热解中的转化
Xinran Zhou1, Zhenting Zha1, Bowen Li1
1School of Mechanical Engineering, Hefei University of Technology, Hefei, Anhui 230009, China; Institute of Thermo-Fluid Equipment and Energy Saving & Environmental Protection Engineering, Hefei University of Technology, Hefei, Anhui 230009, China.
Bioresource technology
|March 7, 2026
概括
本研究介绍了Pyro-SPIN模型,用于预测和优化生物质热解过程中的迁移,这对于减少废物到燃料过程中的有害氧化 (NOx) 排放至关重要.
科学领域:
- 生物质热化学转换生物质的热化学转换.
- 环境工程环境工程
- 计算化学是一种计算化学.
背景情况:
- 从生物质热解中生产零碳燃料需要尽量减少氧化 (NOx) 排放.
- 在热解过程中控制的行为是减少源的关键.
- 传统方法难以分析复杂的迁移途径.
研究的目的:
- 开发生物质热解过程中气迁移的预测模型.
- 为了优化 pyrolysis 条件以减少 NOx 的形成.
- 为了解和控制生物质转换中的基本行为提供一种新的方法.
主要方法:
- 开发Pyro-SPIN (基于源参数的综合迁移) 模型.
- 对模型预测与实验数据的协同分析.
- 机器学习技术包括链式建模和多输出联合培训.
主要成果:
- 温度,含量和氧含量是影响迁移的主要因素.
- 优化的条件 (颗粒大小<200微米,温度<500°C,持续时间<60分钟,加热缓慢) 有利于在固体炭中缩.
- 该模型实现了质量保存,并提高了预测的一致性.
结论:
- 该Pyro-SPIN模型准确地预测和优化生物质热解中的迁移途径.
- 这种方法提供了一种新的方法来了解和控制NOx在源头的形成.
- 开发的策略可以应用于生物质热化学转化中的其他元素.
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