Dynamic optimization

The third requirement is a scoring scheme to identify optimal dynamic programming alignments used in the above iterative multiple alignment... 

Buryak, A. Severin, K. Easy to optimize Dynamic combinatorial libraries of metal-dye complexes as flexible sensors for tripeptides. J. Comb. Chem. 2006, 8, 540-543. 

Ladame, S. Whimey, A. Balasubramanian, S. Targeting nucleic acid secondary structures with polyamides using an optimized dynamic combinatorial approach. Angew. Chem. Int. Ed. 2005,44, 5736-5739. 

Optimized Dynamical Control of State Transfer Through Noisy... 

Structure building, geometry optimization, dynamics, semiempirical calculations. AUTONOM for computerized assignment of chemical nomenclature to structures. PC. 

In the CP scheme, the electron variables are optimized dynamically so that the quantum SCF calculations are avoided. However, similar to conventional... 

The application of model reduction techniques in the context of dynamic optimization of chemical plants operation is investigated. The focus is on the derivation and use of reduced models for the design and implementation of optimal dynamic operation in large-scale chemical plants. The recommended procedure is to apply the model reduction to individual units or groups of units, followed by the coupling of these reduced models, to obtain the reduced model of the plant. The procedure is flexible and accurate and leads to a major reduction of the simulation time. 

The strong competition in the industrial environment nowadays demands for economical operation of chemical plants. This goal can be achieved in two ways, which do not exclude each other. One approach is to continuously respond to the market conditions through dynamic operation. A second approach is to develop control systems that maintain the steady state or implement the optimal dynamic behaviour. For the first approach, the economical optimality is achieved through dynamic optimization. For the second approach, the development of the plantwide control structures to achieve stable operation is of paramount importance. 

S.P. Asprey, S. Macchietto, 2000, Statistical Tools for Optimal Dynamic Model Building, Comput. Chem. Engng., 24, 1261-1267. 

Kawajiri, Y. and Biegler, L. T. (2006). Nonlinear programming superstructure for optimal dynamic operations of Simulated Moving Bed processes, Ind. Eng. Chem. Res., 45, pp. 8503-8513. 

Structure building, geometry optimization, dynamics, semiempirical calculations. PC. 

Tel. 212-460-1622, e-mail griepke spint.compuserve.com Structure building. Stick and dot surface display. Geometry optimization dynamics by AMBER parameters. MNDO and AMI semiempirical calculations. AUTONOM for computerized assignment of chemical nomenclature to structures from graphical input. Beilstein and Brookhaven chemical databases in CD-ROM format. PC. 

The designed experiment was carried out and the products were obtained. The response values of the products were determined. Then, the results were analyzed, a quadratic polynomial was chosen as the simulation equation and the optimized dynamic parameter equation of response values are shown in Table 5.3. 

In this section, we suggest a method to provide an optimized, dynamic and well-suited maintenance planning to a multi-components system like a commercial heavy vehicle. This method uses degradation models with a maintenance optimization using a rolling horizon. 

In this section we present a d5mamic program to find the optimal dynamic transshipment strategy. 

Chatwin, R.E. 2000. Optimal Dynamic Pricing of Perishable Products with Stochastic Demand and a Finite Set of Prices. European Journal of Operational Research 125(1), 149-174. 

Gallego, G., and G. van Ryzin. 1994. Optimal Dynamic Pricing of Inventories with Stochastic Demand over Finite Horizons. Management Science 40(8), 999-1020. 

Zhao, W., YS. Zheng. 2000. Optimal Dynamic Pricing for Perishable Assets with Nonhomogeneous Demand. Management Science 46(3), 375-388. 

G. Gallego and G. Van Ryzin. Optimal dynamic pricing of inventories with stochastic demand over finite horizons. Management Science, 40(8) 999-1020, 1994. 

W. Zhao and Y. S. Zheng. Optimal dynamic pricing for perishable assets with nonhomogeneous demand. Management Science, 46(3) 375-388, 2000. 

Ali, S. F, Padhi, R. (2009). Active vibration suppression ofnonlinear beams using optimal dynamic inversion. Journal of Systems and Control Engineering, 223(5), 657-672. 

Ali, S. F., Ramaswamy, A. (2009c). Optimal dynamic inversion based semi-active control of benchmark bridge using MR dampers. Structural Control and Health Monitoring, 7(5(5), 564-585. doi 10.1002/stc.325... 

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