determination of suitable operating conditions of fluid catalytic cracking process by application of artificial neural network and firefly algorithm

Fluid catalytic cracking (fcc) process is a vital unit to produce gasoline. in this research, a feed forward ann model was developed and trained with industrial data to investigate the effect of operating variables containing reactor temperature feed flow rate, the temperature of the top of the main column and the temperature of the bottom of the debutanizer tower on quality and quantity of gasoline, lpg flow rate and process conversion. eventually, validated ann model and firefly algorithm which is an evolutionary optimization algorithm were applied to optimize the operating conditions. three different optimization cases including maximization of ron (as the parameter which demonstrates the quality of the gasoline), gasoline flow rate and conversion were investigated. in order to obtain the maximum level of targeted output variables, inlet reactor temperature, temperature of the top of the main column, temperature of the bottom of debutanizer column and feed flow rate should respectively set at 525,138, 169ºc and 43000 bbl/day. also, sensitivity analysis between the input and output variables were carried out to derive some effective ruleof- thumb to facilitate the operation of the process under unsteady state conditions. the result introduces a methodology to compensate for the negative effect of undesirable variation in some operating variables by manipulating the others.