A novel 3-layer mixed cultural evolutionary optimization framework for optimal operation of syngas production in a Texaco coal-water slurry gasifier

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 algorithm framework which has a population space, a medium space 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.

Modeling and optimization for oil well production scheduling

In this paper,an oil well production scheduling problem for the light load oil well during petroleum field exploitation was studied.The oil well production scheduling was to determine the turn on/off status and oil flow rates of the wells in a given oil reservoir,subject to a number of constraints such as minimum up/down time limits and well grouping.The problem was formulated as a mixed integer nonlinear programming model that minimized the total production operating cost and start-up cost.Due to the NP-hardness of the problem,an improved particle swarm optimization(PSO) algorithm with a new velocity updating formula was developed to solve the problem approximately.Computational experiments on randomly generated instances were carried out to evaluate the performance of the model and the algorithm's effectiveness.Compared with the commercial solver CPLEX,the improved PSO can obtain high-quality schedules within a much shorter running time for all the instances.

OPTIMIZING PRODUCT MIX AND BOOSTING COMPETITIVE POWER

Since the decision of the State Council in 1985 on expanding the export of electromechanical products, China’s exports of electrome-chanical products has freed itself from long fluctuation and realized fast growth. According to statistics from the Customs Office, China’s exports of electro-mechanical products in 1995 reached US$43.86 billion, increasing 25 times in 10 years, and becoming China’s first pillar products for export. While achieving fast growth in exports, product mix has also seen sig-