Unable to Login Due to Unstable Local Pc Time or a Firebox System Time Burner failures are common abnormal weather condition associated with industrial fired heaters. Preventing from economic loss and major equipment damages tin be attained past compensating the lost heat due to burners' failures, which can exist possible by defining advisable setpoints to rearrange the firing rates for salubrious burners. In this study, artificial neural network models were adult for estimating the appropriate setpoints for the combustion control system to recover an industrial fired-heater furnace from abnormal conditions. For this purpose, based on an accurate high-order mathematical model, constrained nonlinear optimization problems were solved using the genetic algorithm. For unlike failure scenarios, the best possible excess firing rates for healthy burners to recover the furnace from abnormal weather condition were obtained and data were recorded for training and testin