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Figure 17.3 POH and the STS-1 payload capacity within the STS-1 SPE. Note that the net payload capacity in the STS-1 frame is only 84 columns.
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where K is the coef cient of proportionality mentioned above, which is nowadays generally referred to as hydraulic conductivity (K has units of length per unit time, i.e. the same units as
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4. Type tetris and press Return. You should see something like Figure 9-5.
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EC (2007). NMP-2008-3.2 2 Self-Learning Production Systems: Work Programme on Nanosciences, Nanotechnologies, Materials, and New Production Technologies, available at ftp:// ard.huji.ac.il/pub/251/NMP_Fiche_small__2008_02.pdf. ERTC (2004). Conf. Proc. ERTC 2004, London, May 2004. Filev, D. P., T. Larsson, L. Ma (2000). Intelligent Control for Automotive Manufacturing RuleBased Guided Adaptation. IEEE Conf. IECON-2000, Nagoya, Japan, October, pp. 283 288. Filev, D., T. Tseng (2006). Novelty Detection-Based Machine Health Prognostics. Proc. 2006 Int. Symp. on Evolving Fuzzy Systems, IEEE Press, pp. 193 199. Fortuna, L., S. Graziani, A. Rizzo, M. G. Xibilia (2007). Soft Sensor for Monitoring and Control of Industrial Processes. Advances in Industrial Control Series ( M. J. Grimble, M. A. Johnson, eds.). Berlin: Springer-Verlag. Goel, K., Prashant, Y. (1999). Leveraging Knowledge Capital in Oil Re neries through Intelligent Systems. Proc. Int. Conf. PETROTECH 99. Kordon, A., G. Smits (2001). Soft Sensor Development Using Genetic Programming. Proc. GECCO 2001, San Francisco, pp. 1346 1351. Kordon, A., G. Smits, A. Kalos, E. Jordaan (2003). Robust Soft Sensor Development Using Genetic Programming. Nature-Inspired Methods in Chemometrics ( R. Leardied.). Amsterdam: Elsevier, pp. 69 108. Li, W., H. H. Yue, S. Valle-Cervantes, S. J. Qin (2000). Recursive PCA for Adaptive Process Monitoring. J. Process Control, Vol. 10, No. 5, pp. 471 486. Mac J. (1998). Application de la tcnica de calibracion PLS al control y modelado de as, e Processos . PhD Thesis. University of La Laguna, Tenerife, Spain. Mac as-Hernndez, J. (2001). Design of a Production Expert System to Improve Re nery Process a Operations. European Symposium on Intelligent Technologies, Hybrid Systems, and Their Implementation on Smart Adaptive Systems, Puerto de la Cruz, Tenerife, Spain, December 13 14, EUNITE 2001, pp. 63 68. Macias, J., J. Feliu, A. Bosch (2003). Simulation Workbench between HYSYS Rigorous Model and Its NN Extension: Case Study of a Complex Simulation with Re-circulation, Proc. 4th European Congress on Chemical Engineering, ECCE 4, ISBN: 84-88233-31-0. Qin, S. J., H. Yue, R. Dunia (1997). Self-Validating Inferential Sensors with Application to Air Emission Monitoring, Industrial Engineering Chemistry Research, Vol. 36, pp. 1675 1685. Rallo, R., J. Ferre-Gine, A. Arena, F. Girault (2004). Neural Virtual Sensor for the Inferential Prediction of Product Quality from Process Variables, Computers and Chemical Engineering, Vol. 26, pp. 1735 1754. Rodriguez, M., J. J. Rubio, A. Jimenez (1997). Naphta Cut Point Inference Using Neural Networks. In Neural Networks in Engineering Systems (A. B. Bulsari, S. Kallio,eds.), Stockholm, Sweden, June, www.diquima.upm.es/recursos/docs/homme.pdf. Smits, G., A. Kordon, E. Jordaan, C. Vladislavleva, M. Kotanchek (2006). Variable Selection in Industrial Data Sets Using Pareto Genetic Programming. In Genetic Programming Theory and Practice III (T. Yu, R. Riolo, B. Worzel,eds.). New York: Springer, pp. 79 92. White, D. C. (2001). The Smart Plant Re nery: Economics and Technology. NPRA Annual Meeting, San Antonio, Texas, March 23 25, AM-03-19. White, D. C. (2003). Creating the Smart Plant. Hydrocarbon Processing, October 2003, pp. 41 50. Yan, W., H. Shao, X. Wang (2004). Soft Sensing Modelling Based on Support Vector Machine and Bayesian Model Selection, Computers and Chemical Engineering, Vol. 28, pp. 1489 1498.
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point. (Hydraulic head is essentially a summation of the elevation and pressure of water at any given point; see Section 3.1.2.)
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