Combination of genetic algorithm and computational fluid dynamics in combustion process emission minimization

Computational fluid dynamics and interactive multiobjective optimization in the development of low-emission industrial boilers

“…In an earlier study (Saario et al 2006), it was shown that the boiler NO emission can be decreased significantly using optimization. However, the same study revealed that single-objective optimization (SOO) is not sufficient in the SNCR process optimization, since low NO emission was achieved at the expense of high NH 3 emission.…”

“…The application of both SOO and CFD in combustion-related problems is less common. Johnson et al (2001) minimized the CO emission of a burner and Saario et al (2006) minimized the NO emission of a fluidized bed boiler.…”

Optimizing the heat transfer performance of the recovery boiler superheaters using simulated annealing, surrogate modeling, and computational fluid dynamics

“…The previous studies have been related to coal-fired boilers [63,64], biomass-fired or co-fired boilers [65][66][67][68], and aluminum furnaces [69,70]. The main focus in many of these studies was the minimization of NO x emissions [63,[65][66][67]. To the author's knowledge, CFDoptimization has not been utilized before the present research in the context of recovery boilers.…”

“…Stochastic optimization methods, which utilize random variables in the optimization process, have been widely used in CFD-optimization [58][59][60][62][63][64][65][66][67]. Their main advantages are considered to be the following: 1) they can easily use both discrete and continuous variables, and can handle non-linear, non-convex, and non-continuous objective functions, as well as both single-objective and multi-objective problems, 2) their stochastic nature offers high robustness and should prevent them from getting stuck in local extrema, which increases the likelihood of finding a global optimum, 3) they typically have the ability to find multiple optimal solutions and design trade-offs (Pareto set), and 4) because they typically operate on the basis of design point locations and objective function values only, it is straightforward to combine them CFD models [53,95,96].…”

Combining Support Vector Regression and Ant Colony Optimization to Reduce NOx Emissions in Coal-Fired Utility Boilers

“…GA has been successfully applied to optimize NOx emissions in combustion system. These applications include optimizing waste incineration plant operation and providing a decision support tool for plant operators, 22 finding the optimum settings for NOx emission minimization in the bubbling fluidized bed boiler, 23 providing a viable way to realize low NOx emissions in engine, 24 the palm oil mill, 25 and utility boilers. 2,3,5 Nevertheless, despite its benefits, GA may require long processing time for a near optimum solution to evolve.…”