Modeling and optimization of process fired heaters

Abstract

In this study, a mathematical model associated with genetic algorithms (GAs) is developed to optimize the design of process fired heaters where the objective function is the total annual cost. The mathematical model considering operational and geometric constraints is used to design all subsections of a furnace including the radiant chamber, the convection and stack sections. The proposed model allows the economic optimization using the MATLAB genetic algorithm toolbox. This procedure is implemented to design and optimize fired heaters. Two case studies are considered. First, by comparing the results with literature, it is shown that the developed model can be successfully used in design with acceptable accuracy. In the first case study, based on TAC (total annual cost) minimization, two optimization scenarios with two sets of decision variables using GA are applied to determine the optimal economic design. It is demonstrated that by considering the tube diameter as a decision variable (scenario B), the TAC minimization approach does not present a realistic optimal design, because the crude oil pressure drop is out of a permissible range. Finally, by including the pumping cost into the operating cost and modifying the objective function, another optimization approach based on MTAC minimization is applied for the same optimization scenarios. In this case, scenario B results in the economic/realistic optimal design by up to 2.48% cheaper than Original Design.