Abstract:
In this work, we utilized advanced exergy analysis in conjunction with an integrated framework
that combines artificial neural network (ANN) with particle swarm optimization (PSO) and
genetic algorithm (GA) to evaluate performance of heat exchangers within a crude oil
distillation unit under uncertainty. At the beginning, we constructed an equilibrium-state Aspen
model, which subsequently used to conduct both traditional and advanced exergy analyses.
This exergy analysis enabled us to quantify exergy efficiency and the level of irreversibility
within the heat exchanger network (HEN). Subsequently, we calculated four components of
irreversibility—endogenous, exogenous, avoidable, and unavoidable—for the equipment
exhibiting significant incompetence with advanced exergy analyses. The operational model
was subsequently converted into a dynamic configuration by introducing ±10% fluctuation into
system variables, such as temperature, and mass flow rate, resulting in the creation of a dataset
comprising 600 samples. Five ANN models were developed using this dataset, each designed
to predict various aspects, including overall exergy efficiency, exergy destruction, modified
exergy efficiency, unavoidable exergy destruction and avoidable exergy destruction. ANN
model served as a substitute within the PSO and GA environment to optimize HEN under
uncertain condition. The optimized operational parameters obtained within the PSO and GA
methods were further validated by feeding them into Aspen model for validation through crossreferencing. The exergy analysis revealed that HEN had 66.16% exergy efficiency, and the
exergy destruction was 5403.166 kW. Advanced exergy analysis further revealed that avoidable
exergy destruction amounted to 1,759.80 kW, while unavoidable exergy destruction stood at
3,643.35 kW. Seven heat exchangers, displaying the highest exergy destruction rates, were
identified as priority candidates for intervention due to their significant impact on the network.
The effectiveness of both the GA and PSO optimization methods exhibited similar results, and
they notably enhanced the exergy performance of the facility when correlate to the standalone
Aspen model of the process.
