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烃类危险源事故性泄漏过程的实时预测与预警技术一直是工业安全工程领域重要的研究课题。为此,如何实现真正意义上的过程实时预测,解决具有动态特性的过程模型初始特征参量的问题,不仅是动态过程预测的关键,而且客观上也是十分困难的。本文根据液态烃泄漏过程所涉及的因素繁多且过程动态特性复杂的特点,提出了基于“反求源”技术构思的实时预测方法。同时,通过人工智能技术架构烃类特征参量的动态数值预测系统,进而得到了与风洞实验较好的拟合效果。
Real-time prediction and warning technology of accidental leakage process of hydrocarbon hazards has been an important research topic in the field of industrial safety engineering. Therefore, how to realize real-time process prediction in real sense and solve the initial characteristic parameters of the process model with dynamic characteristics is not only the key to the dynamic process prediction, but also objectively difficult. In this paper, based on the characteristics of many factors involved in the process of liquid hydrocarbon leakage and the complex process dynamics, a real-time prediction method based on the concept of “reverse source” technology is proposed. At the same time, the dynamic numerical prediction system of hydrocarbon characteristic parameters is constructed by using artificial intelligence technology, and the better fitting effect with wind tunnel experiment is obtained.