对于基纤维素生物质造性能分析的确定性模型
Abbas Azarpour1, Sohrab Zendehboudi2, Noori M Cata Saady3
1Department of Engineering and Physics, Southern Arkansas University, Magnolia, Arkansas 71753, United States.
ACS omega
|March 3, 2025
概括
这项研究优化了使用先进的混合人工智能模型优化纤维纤维化生物质烧烤. 结合模拟回火最小方形支向量机 (CSA-LSSVM) 实现了最高的准确性,确定温度是有效生产生物能源的关键因素.
科学领域:
- 可再生能源和生物燃料的使用
- 化学工程和工艺优化 化学工程和工艺优化
- 人工智能在能源中的作用
背景情况:
- 全球日益增长的能源需求需要可持续的替代化石燃料.
- 可再生能源,特别是生物质,为缓解气候变化提供了一个有希望的解决方案.
- 造是一种有效的热化学过程,用于提高生物质的性能,用于能源应用.
研究的目的:
- 分析和建模石纤维素生物质的化过程.
- 开发基于生物质特性和操作条件的固体产量的预测模型.
- 确定影响生物能源优化化效率的关键参数.
主要方法:
- 混合机器学习模型:人工神经网络-粒子群集优化 (ANN-PSO),自适应神经模糊推理系统 (ANFIS) 和合模拟化最小方形支持矢量机 (CSA-LSSVM).
- 基因表达编程 (GEP) 用于开发生物质特征,操作条件和固体产量之间的相关性.
- 参数灵敏度分析,以确定各种因素对炼过程的影响.
主要成果:
- 在CSA-LSSVM模型中,R2 = 0.98,MSE = 0.00082和AARE% = 2.61%的精度更高.
- 确定的主要影响变量包括居住时间,温度和水分含量.
- 温度被发现是基纤维素生物质化过程中最关键的参数.
结论:
- 开发的混合人工智能模型准确地预测了生物质烧烤产量,有助于过程优化.
- 调查结果为生物能源行业提供了关键的见解,以实现成本效益和能源效率高的运营.
- 优化造可以大大减少二氧化碳排放和推进可持续能源解决方案.
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