Abstract
Fired heaters are energy intensive units in petroleum refineries. For the economical operations,
energy conservation is of prime importance that bring focus to combustion in fired heaters as
a major energy consumption process. Any improvement in the heater efficiency can result in
large reductions in energy consumption, emissions, and associated costs. The aim of this
research is to develop a practical computational tool that can predict and optimize the thermal
efficiency and NOx emission rate of fired heaters, based on artificial Intelligence application.
An Artificial neural networks (ANNs) and genetic algorithms (GAs) model has been developed
and used based historical operational data to predict and optimize efficiency and emissions
from a fired-heater unit in petroleum refinery., Thermal efficiency was predicted and optimized
as a function of three operating parameters: percentage excess oxygen, heater draft and the
enthalpy change (energy transferred) to the process fluid . The model accuracy was found to
be acceptable on the data collected from the five-year operation of a fired heater. The
effectiveness of the optimization method was demonstrated through trial runs practically for
different seasons. In the second part of the study, extensive operational data with emissions
were collected to predict the thermal efficiency and NOx emissions using the earlier adopted
concept of ANN. The network configured using four operating parameters: excess Percentage
oxygen, heater draft, the enthalpy change of the process fluid and fuel gas to predict two outputs
(efficiency and NOx emission rate). Multi Objective Genetic Algorithm (MOGA) was used to
optimize both outputs. The models accuracy were found to be acceptable in terms of RMSE
and showed good prediction of the outputs (efficiency and emission).