A three-dimensional numerical model verified by previous experimental data is developed to simulate the fluidized bed gasification of refuse derived fuel （RDF）. The CaO dechlorination model obtained by the thermal gravity analysis （TGA） is coupled to investigate the process of CaO dechlorination. An Eulerian-Eulerian method is adopted to simulate the gas-solid flow and self-developed chemical reaction modules are used to simulate chemical reactions. Flow patterns, gasification results and dechlorination efficiency are obtained by numerical simulation. Meanwhile, simulations are performed to evaluate the effects of Ca/Cl molar ratio and temperature on dechlorination efficiency. The simulation results show that the presence of bubbles in the gasifier lowers the CaO dechlorination efficiency. Increasing the Ca/Cl molar ratio can enhance the dechlorination efficiency. However, with the temperature increasing, the dechlorination efficiency increases initially and then decreases. The optimal Ca/Cl molar ratio is in the range of 3. 0 to 3. 5 and the optimal temperature is 923K.
In order to investigate the influence of hybrid coupling on the synchronization of delayed neural networks, by choosing an improved delay-dependent Lyapunov-Krasovskii functional, one less conservative asymptotical criterion based on linear matrix inequality （LMI） is established. The Kronecker product and convex combination techniques are employed. Also the bounds of time-varying delays and delay derivatives are fully considered. By adjusting the inner coupling matrix parameters and using the Matlab LMI toolbox, the design and applications of addressed coupled networks can be realized. Finally, the efficiency and applicability of the proposed results are illustrated by a numerical example with simulations.
