|dc.description.abstract||In this thesis, a surrogate model is aimed to be constructed for the separation-refrigeration (S-R) section in the ammonia plant. First, the variables defining the process are classified by input variables and output variables. Then the variables are reduced as many as possible using the dependency relationships. After the input and output variables are determined by variable identification, adaptive sampling is implemented to sample the input variables to obtain the input sample space with minimized number of sample points. The resultant input sample data is imported to HYSYS to obtain the corresponding output sample data. However, the HYSYS model was not able to calculate the corresponding output samples due to convergence issues. In order to address this issue, we divided the separation-refrigeration section furthermore into the HEx part, the separator part and the refrigeration section. Then we use the same approach to construct a surrogate model for the HEx part. We used two different variable identifications to define the output variables, of which one used the absolute output variables and the other one used variables differences. Sample spaces of both cases are obtained and surrogate models are generated using artificial neural network based on the sample spaces. The resultant surrogate models are validated by comparing their output predictions with the HYSYS' output results and the relative deviations are calculated.
It can be concluded that the surrogate models can be efficiently constructed based on the approach used in this thesis. Using the adaptive sampling, the number of sample points required to generate surrogate models can be successfully minimized, which saved computational expense of model construction effectively. In the approach of surrogate model construction, the variable identification is extremely crucial. It can affect both the computational expense of surrogate model generation and the accuracy of resultant surrogate models. Generally, the variable differences can save the computational expense but results in less accurate surrogate models than absolute variables. The approach used in this thesis can also be implemented to construct the surrogate models for other simulators.||