Abstract:
Furnaces have been known as the central preliminary units employed for
hydrocarbon processing in petrochemical industries. The most significant energy
consumption part in refineries is associated with furnace units. Therefore,
achieving higher thermal efficiencies is the primary concern in designing and
during operation. Due to significant energy consumption in such systems, a minor
improvement in thermal efficiency would lead to considerable savings. Much
work has been done in the literature on the design and optimization of the furnace
using different optimization methods. However, no one has focused on the
optimization of the furnace under uncertain process conditions. The current work
developed an Integrated Framework of Artificial Intelligence and Genetic
Algorithm for the furnace of a petroleum refinery to predict the optimum amount
of excess air and mass flow rates of crude oil and fuel stream in the presence of
uncertainty in process conditions. Using optimized industrial data, a furnace model
was regenerated in Aspen EDR. The COM server was used to build the interface
between Aspen HYSYS and MATLAB. The data set was generated by inserting
the variation of ±1, ±2, ±3, ±4, and ±5 in the crude oil composition as well as in
the inlet temperature and pressure of cold crude oil, fuel, and air stream. The
optimum amount of excess air and mass flow rates for each variation was
determined using a single objective genetic algorithm. A total of 360 data points
were generated. 70% were used for the Feedforward neural network (ANN) and
the remaining data points were equally divided for the validation and testing of the
model. The proposed artificial neural network (ANN) model achieved a correlation
coefficient value of 0.99984. The high accuracy and robustness of the ANN model
make it suitable for real-time industrial applicationsto reduce energy consumption.
