Abstract:
In this work, a combined framework of the Artificial Neural Network and Genetic
Algorithm was developed to realize higher exergy efficiency of Heat Exchanger Network
of a Crude Distillation Unit, under uncertainty in process conditions. Initially, the steadystate exergy analysis was carried out using an Aspen HYSYS model to quantify the exergy
destructions and exergy efficiencies of all the individual heat exchangers and the overall
Heat Exchanger Network. Then the Aspen HYSYS model was changed to dynamic mode
by introducing uncertainty of ±5% in various process parameters, i.e., temperatures,
pressures, and mass flow rates of different process streams, to produce a dataset of 200
samples for HEN of CDU. Using the dataset, an ANN model was generated for the
prediction of overall exergy efficiency of HEN. The trained ANN model was then used as
a surrogate in the GA environment to attain improved overall exergy efficiency of the
HEN in the presence of uncertainty. Using a GA-based approach, the optimized process
conditions were found and then put into the Aspen HYSYS model for the purpose of crossvalidation. The overall exergy destruction and exergy efficiency of the HEN were
17611.21 kW and 63.34%, respectively. The trained ANN model had a correlation
coefficient (R) of 0.9996 and an RMSE of 0.0097 for overall exergy efficiency of HEN.
The performance of the GA-based approach was good enough, and it significantly
enhanced the overall exergy efficiency of HEN when compared to standalone Aspen
HYSYS model of the process.
