Akademik Veri Yönetim Sistemi
Process Safety and Environmental Protection, cilt.177, ss.1403-1414, 2023 (SCI-Expanded)
Process uncertainties have been a big challenge to the stable operation and control of process industries. The current study is based on the use of an artificial intelligence model as a surrogate in the online optimization of process conditions of reactive units of a petroleum refinery under uncertainty. Initially, a steady-state Aspen model was used to perform exergy analysis for quantifying the 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., mass flow rate, pressure, and temperature, to generate a dataset of 216 samples. An artificial neural network (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 approaches were fed to Aspen model for cross-validation. The Fourier Amplitude Sensitivity Test (FAST) was performed to identify the most influential process conditions in terms of their effect on process exergy efficiency. The overall plant had an exergy efficiency, irreversibility, and improvement potential of 50.57 %, 34,955.55 kW, and 17,276.98 kW, respectively. The correlation coefficient of ANN model was 0.97432. The use of ANN as a surrogate in the optimization frameworks outperformed the standalone (SA) Aspen model of the process in attaining the highest exergy efficiency. Overall the performance of both the GA and the PSO based approaches were comparable. Based on sensitivity analysis, inlet temperatures of reactors were the most sensitive variables affecting the process exergy efficiency. The current study helps in laying a foundation for the simulation of Refinery 4.0.