Optimizing operational parameters for syngas production of Texaco coal-water slurry gasifier studied in this paper is a complicated nonlinear constrained problem concerning 3 BP (Error Back Propagation) neural networks. To solve this model

a new 3-layer cultural evolving algorithmframeworkwhich has a population space

amediumspace and a belief space is firstly conceived. Standard differential evolution algorithm (DE)

genetic algorithm (GA)

and particle swarm optimization algorithm (PSO) are embedded in this framework to build 3-layer mixed cultural DE/GA/ PSO (3LM-CDE

3LM-CGA

and 3LM-CPSO) algorithms. The accuracy and efficiency of the proposed hybrid algorithms are firstly tested in 20 benchmark nonlinear constrained functions. Then

the operational optimization model for syngas production in a Texaco coal-water slurry gasifier of a real-world chemical plant is solved effectively. The simulation results are encouraging that the 3-layer cultural algorithm evolving framework suggests ways in which the performance of DE

GA

PSO and other population-based evolutionary algorithms (EAs) can be improved

and the optimal operational parameters based on 3LM-CDE algorithm of the syngas production in the Texaco coalwater slurry gasifier shows outstanding computing results than actual industry use and other algorithms.