Optimizing combustion process by adaptive tuning technology based on Integrated Genetic Algorithm and Computational Fluid Dynamics

Liu, X.; Bansal, R.C.

doi:10.1016/j.enconman.2011.11.013

Cited by 5 publications

(1 citation statement)

References 29 publications

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“…Genetic algorithm (GA) methodologies have been investigated as efficient approaches for obtaining solutions very close to optimum points [35,36]. GA is a nongradient-based stochastic technique, which is capable of exploring and exploiting the search space for the optimal solution.…”

Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

Flame Failures and Recovery in Industrial Furnaces: A Neural Network Steady-State Model for the Firing Rate Setpoint Rearrangement

Tahmineh

Rostamnezhad

Chaibakhsh

et al. 2018

International Journal of Chemical Engineering

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Burner failures are common abnormal conditions associated with industrial fired heaters. Preventing from economic loss and major equipment damages can be attained by compensating the lost heat due to burners' failures, which can be possible by defining appropriate setpoints to rearrange the firing rates for healthy burners. In this study, artificial neural network models were developed 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 different failure scenarios, the best possible excess firing rates for healthy burners to recover the furnace from abnormal conditions were obtained and data were recorded for training and testing stages. e performances of the developed neural steady-state models were evaluated through simulation experiments. e obtained results indicated the feasibility of the proposed technique to deal with the failures in the combustion system.

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Section: Genetic Algorithm (Ga)mentioning

confidence: 99%