通过实时预测建模,提高炼油厂重油分量的分析性能
Emad Al-Shafei1, Ali Aljishi2, Mohammed Albahar2
1Research and Development Center, Saudi Aramco, Dhahran, Saudi Arabia. emadnaji@gmail.com.
概括
这项研究开发了强大的模型,可以使用单个分析仪实时预测重油的性能,例如硫含量和金属度. 这些模型提高了炼油厂分析效率和重油加工优化.
科学领域:
- 石油工程是石油工程中的一个.
- 分析化学 分析化学
- 过程控制 过程控制
背景情况:
- 重型石油精炼需要准确的实时监测物理化学性质.
- 确定硫含量,MCR和金属度等属性的现有方法可能耗时.
- 优化重油加工需要先进的分析技术,以便立即反.
研究的目的:
- 开发和验证一套可靠的模型套件,用于实时测定重油炼油厂碎片中的物理化学性质.
- 集成一个单一的分析仪,使用部分最小平方回归来进行增强的炼油厂分析.
- 为了为重油料提供关键石油性质的精确实时预测.
主要方法:
- 在单个分析器中集成的六个部分最小平方回归方程的开发.
- 在炼油厂运营中应用实时分析技术.
- 模型预测与美国材料测试协会 (ASTM) 标准的验证.
主要成果:
- 成功实时预测硫含量,微碳残留量 (MCR),青含量,加热值,和的度.
- 模型是针对具有API重力 (3°-32°) 和硫含量 (2.8-5.5 wt%) 的重油料量身定制的.
- 证明了物理化学预测模型的准确性和精度.
结论:
- 开发的模型显著提高了炼油厂分析重油的效率.
- 这种方法为重油加工实时监控和优化制定了新的标准.
- 集成分析仪简化了分析,弥合了不同炼油厂过程之间的差距.
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