Abstract:
Energy efficiency studies are significant in the petroleum refinery because of their
environmental impact and cost. In this context, crude distillation along with downstream units
play an important role in refinery operations. This study established integrated frameworks that
combine artificial neural networks (ANN) with particle swarm optimization (PSO) and genetic
algorithm (GA). The objective was to achieve improved exergy efficiency in petroleum refinery
operations under uncertainty. At first, a steady state Aspen HYSYS model was taken to execute
the exergy analysis in order to measure the exergy efficiency, exergy destruction or
irreversibility, and exergetic improvement potential of the overall plant model. The plant model
was subsequently converted into a dynamic mode by initiating a ±10% uncertainty in the
process parameters, such as pressure, temperature, and mass flow rates of 12 input streams.
This resulted in the creation of a dataset consisting of 500 samples. Those datasets were utilized
to create an Artificial Neural Network (ANN) model for the purpose of predicting the exergy
efficiency. The ANN model was employed as a surrogate in Particle Swarm Optimization
(PSO) and Genetic Algorithm (GA) environments to get superior exergy efficiency in the
presence of uncertainty. The optimum process condition obtained using GA and PSO approach
were fed into the Aspen HYSYS model for validation. The steady state exergy efficiency of
overall petroleum refinery model was 72.38%, while the irreversibility or exergy destruction
and improvement potential of the overall plant wide model was 7311.97 kW and 2037.85 kW
respectively. The ANN was trained using the scaled conjugate backpropagation (trainscg)
training algorithm, with the network's activity being regulated by the tansig activation function.
The RMSE was used to quantify the performance of the model architecture, having RMSE of
1.1349 for exergy efficiency. The R value for training is 0.99925, for validation is 0.93288, and
for testing 0.91209. The performance of the particle swarm optimization (PSO) and genetic
algorithm (GA) techniques were comparable, and they greatly improved the exergy efficiency
of the overall plant compared to the steady state Aspen HYSYS model of the process.
