Abstract
Abstract A multi-stream heat exchanger (MSHE) is the heart of LNG plant where 40% of the entire energy is consumed in this section. Moreover, the plant operation is subject to number of variations from the plant inlet such as ambient temperature, pressure, feed flow or composition. In industrial application, the mitigating of these variations is usually performed using trial and error approaches. Thus developing a competent and accurate model to predict the performance of the MSHE is an inevitable step to overcome those variations. In this study, a model for the MSHE operation is developed using artificial neural network. The modeling is made in such a way that the information about the internals of heat exchanger could allow the MSHEs from any variation that arises from the process itself or upstream conditions. A number of simulation runs have been made by taking a case study for the MSHE operation. The developed model can predict and provide prior information for the MSHE in order to take action during the plant performance.
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