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May, G. S. Intelligent SOP manufacturing. IEEE Trans. Advanced Packag. 2004, 27, pp 426–437. 17. ; May, G. S. Modeling and Optimization of Via Formation in Dielectrics by Laser Ablation Using Neural Networks and Genetic Algorithms. IEEE Trans. Electron. Packaging Manufactur. 2004, 27, pp 133–144. 18. Zadeh, L. A. Fuzzy Sets. Inform. Control 1965, 8, pp 338– 353. 19. Geisler, J. ; Lee, C. S. ; May, G. S. Neurofuzzy Modeling of Chemical Vapor Deposition Processes. IEEE Trans. Semiconductor Manufactur.

2005, 18, pp 584–591. 81. Kim, T. ; May, G. S. Intelligent Control of Via Formation by Photosensitive BCB for MCM-L/D Applications. IEEE Trans. Semiconductor Manufactur. 1999, 12, pp 503–515. 82. ; Koivo, H. ; Koivisto, H. Neural Networks in Process Fault Diagnosis. IEEE Trans. Systems, Man Cybernet. 1991, 21, pp 815–825. 83. Burke, L. ; Rangwala, S. Tool Condition Monitoring in MetalCutting—a Neural Network Approach. J. Intell. Manufactur. 1991, 2, pp 269–280. 84. Wasserman, P. ; Haddad, S. Neural Networks for On-Line Machine Condition Monitoring.

This development requires a certain length of reactor that depends on the gas properties, pressure, velocity, and composition. The provision required for a welldeveloped and stable fluid flow behavior is a critical design consideration for many reactor designs. MIXED CONVECTION PHENOMENA The superimposition of buoyancy-driven flows due to gasphase density gradients, resulting from temperature gradients as well as compositional variations on the forced flow entering the reactor leads to a mixed convection type of flow.

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