Abstract:
In this work, integrated frameworks of the artificial neural networks (ANN) with
genetic algorithm (GA) and particle swarm optimization (PSO) were developed to
realize higher exergy efficiency of reactive units of a refinery under uncertainty in
process conditions. Initially, a steady-state Aspen model was used to perform exergy
analysis for quantifying exergy efficiency, irreversibility and improvement potential
of the plant. The process model was then transformed to a dynamic mode by inserting
±5% uncertainty in process conditions, i.e., temperature, pressure, and mass flow rate,
to generate a dataset of 216 samples for integrated naphtha and isomerization process
and 200 for delayed coking process. An ANN model was developed using the dataset
to predict exergy efficiency. The ANN model was used as a surrogate in GA and PSO
environments to achieve higher exergy efficiency under uncertainty. The optimized
process condition derived through GA and PSO based approach were fed to Aspen
model for cross-validation. The integrated naphtha and isomerization process had an
overall exergy efficiency, irreversibility, and improvement potential of 50.57%,
34955.55 kW, and 17276.98 kW, respectively. Whereas the delayed cocking process
had an overall exergy efficiency, irreversibility, and improvement potential of 77.61%,
29204.035 kW, and 6539.51 kW, respectively. The correlation coefficient of ANN
model was 0.97432 for integrated naphtha and isomerization process and 0.99051 for
delayed coking process. Performance of the GA and the PSO based approaches were
comparable, and they significantly enhanced the exergy efficiency of the plant when
compared to standalone Aspen model of the process.
