使用深度学习对LPG过程进行软传感
Nikolaos Sifakis1, Nikolaos Sarantinoudis1, George Tsinarakis1
1Industrial and Digital Innovations Research Group (INDIGO), School of Production Engineering and Management, Akrotiri Campus, Technical University of Crete, 73100 Chania, Greece.
Sensors (Basel, Switzerland)
|September 28, 2023
概括
这项研究整合了软传感器和深度学习,以加强炼油厂的监控. 它提高了对脱乙化和脱乙化等过程的预测准确性,优化了液化石油气 (LPG) 生产.
科学领域:
- 化学工程是化学工程的重要组成部分.
- 数据科学数据科学数据科学
- 工业过程控制 工业过程控制
背景情况:
- 炼油厂依赖定期采样进行质量控制,导致延误.
- 复杂的蒸过程需要高效的监控以实现最佳性能.
- 准确估计成分度对于产品规格至关重要.
研究的目的:
- 整合软传感器和深度学习,以改善炼油监控.
- 开发用于估计液化天然气和蒸柱能耗中的C2/C5含量的模型.
- 为了提高预测准确性和效率在去乙化和脱乙化过程中.
主要方法:
- 使用深度学习技术开发软传感器模型.
- 人工神经网络 (ANN) 和随机森林回归器 (RFR) 模型的实施.
- 用真实炼油厂运营数据测试模型,解决可扩展性和数据质量问题.
主要成果:
- 成功估计了LPG中的C2和C5含量等关键变量.
- 精确预测蒸柱的能源消耗.
- 在炼油厂应用中证明了深度学习模型的有效性.
结论:
- 软传感器和深度学习为炼油厂实时监控提供了强大的解决方案.
- 开发的模型显示出在类似的工业环境中具有很高的适用性和复制潜力.
- 强调模型的可解释性和在线学习能力,以实现持续改进.
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