Multilevel Flow Modeling is a methodology for inferring causes or effects of process system anomalies. A procedure for validating model causality is proposed, as interest has increased from industry in applications to safety-critical systems.

A series of controlled experiments are conducted as simulations in K-Spice, a dynamic process simulator, by manipulating actuators to analyse the response of process variables. The system causality is analysed stochastically under a defined range of randomly sampled process conditions. The causal influence of an actuator on a process variable is defined as a probability of a qualitative and discrete causal state.

By testing an MFM model, and interpreting the propagation paths produced by MFM, the results from MFM are compared to the stochastic causality analysis to determine the model accuracy. The method has been applied to a produced water treatment system for separation of liquid and gas, to revise the causal relations of the model.

多级流建模是一种用于推断过程系统异常的原因或影响的方法。提出了一种验证模型因果关系的方法，因为业界对安全性至关键系统的应用越来越感兴趣。

通过操纵执行器以分析过程变量的响应，在动态过程仿真器K-Spice中进行了一系列受控实验作为仿真。在确定范围的随机采样过程条件下随机分析系统因果关系。致动器对过程变量的因果影响定义为定性和离散因果状态的概率。

通过测试MFM模型并解释MFM产生的传播路径，将MFM的结果与随机因果关系分析进行比较，以确定模型的准确性。该方法已被应用于采出水处理系统以分离液体和气体，以修正该模型的因果关系。