Based Optimization

Typically, the optimization process is conducted in iterative fashion (Fig. 5), beginning with the development of a model of the process. The model can be statistical (6) or mathematical. In early stages of product or process design, relatively little is known, and only a preliminary version of 

Oil refining is perhaps the best-developed example of a process operated in this continuous optimization mode. A refinery receives a different mixture of petroleum every day, and the prices of its various products fluctuate continuously. Exquisite knowledge of the process is used to determine the precise conditions (temperatures, pressures, recycle rates, etc.) that would product the optimum product mix for the available raw materials and market conditions. As the factory is operated, model predictions are compared to actual performance, and deviations are used to optimize model performance. 

Current practices in industrial pharmacy can now be put in perspective. Typically, the method of choice is univariate one variable at a time (OVAT). One variable is examined for a few conditions, which, in practice, are selected within a safe subset of the permissible design space. A value of this parameter is selected and kept subsequently constant. Another variable is then examined, a value is chosen, and the process continues sequentially. Intuitively, unless the target function is essentially a plane, if the end result is anywhere near the global optimum, it is only by chance. A historical reason for this dated practice is that the regulatory framework greatly discouraged implementation of the virtuous cycle mentioned above, which 

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Summarizing, the efficiency of Newton-Raphson based optimizations depends on the following factors ... 

The most common way to obtain a basis is via energy optimization. However, it is well known that bases optimized for a particular property such as energy are not always good for calculation of other, perhaps only tangentially related, properties. It is thus useful to have another figure of merit for a basis in connection with a particular application. We outline here a method that may be useful in the context of the calculation of GOS s. 

Krier M, de Araujo-Junior JX, Schmitt M, Duranton J, Justiano-Basaran H, Lugnier C, Bourguignon JJ, Rognan D. Design of small-sized libraries by combinatorial assembly of linkers and functional groups to a given scaffold application to the structure-based optimization of a phosphodiesterase 4 inhibitor. J Med Chem 2005 48 3816-22. 

Model-based optimization of a sequencing batch reactor for advanced biological wastewater treatment... 

Martin BR, Giepmans BNG, Adams SR, Tsien RY (2005) Mammalian cell—based optimization of the biarsenical-binding tetracysteine motif for improved fluorescence and affinity. Nat Biotechnol 23 1308-1314... 

Direct InhA inhibitors have also been sought to avoid isoniazid resistance mediated by catalase-peroxidase mutation. Lipophilic analogs of triclosan such as 36 show a nanomolar K on the enzyme with an MIC of 1-2 pg/mL on isoniazid-resistant strains [56]. Structure-based optimization of two separate HTS leads afforded 37 and 38, both submicromolar inhibitors of InhA but devoid of any significant antibacterial activity [57,58],... 

Comparison of the results of equation-based and simultaneous modular-based optimization for two connected distillation columns... 

Wolbert et al. in 1991 proposed a method of obtaining accurate analytical first-order partial derivatives for use in modular-based optimization. Wolbert (1994) showed how to implement the method. They represented a module by a set of algebraic equations comprising the mass balances, energy balance, and phase relations ... 

Simulation Sciences, Inc. Documentation for ROMEO (Rigorous On-line Modeling with Equation-based Optimization. Brea, CA (1999). 

Forbes, F. T. Marlin and J. MacGregor. Model Selection Criteria for Economics-Based Optimizing Control. Comput Chem Eng 18 497-510 (1994). 

Presented methods can be combined to provide advanced decision support. Simulation-based optimization combines simulation and optimization in order to use simulation no longer as descriptive but as a prescriptive method and decision support (Tekin/Sabuncuoglu 2004). Tekin and Sabuncuoglu provide a classification on advanced simulation-based optimization methods (Tekin/Sabuncuoglu 2004, p. 1068) as illustrated in fig. 23. 

Several simulation-based optimization models in the context of supply chain management can be found e.g. in the area of supply chain network optimization (Preusser et al. 2005) or to simulate rescheduling of production facing demand uncertainty or unplanned shut-downs (Tang/Grubbstrom 2002 Neuhaus/Giinther 2006). A basic approach of simulation-based optimization is presented by Preusser et al. 2005, p. 98 illustrated in fig. 24. 

Price Planning Using Simulation-based Optimization... 

Utilization-optimal prices for these businesses can be systematically identified using simulation-based optimization of prices with contributions from Kurus (2006). 

In literature simulation and simulation-based optimization is focused on supply chain management areas such as production (Smith 2003 Wullink et al. 2004), inventory (Siprelle et al. 2003), transportation or integrated supply chain networks (Preusser et al. 2005). 

The simulation-based optimization approach is illustrated in fig. 104 (see also Preusser et al. 2005, p. 98). 

These simple examples can only show the opportunity to further extend the value chain planning model usage for decision support integrated in simulation-based optimization architecture. There is an opportunity for further industry-oriented research to better understand production-price dynamics in different types of value chain networks. 

Jung JY, Blau G, Pekny JF, Reklaitis GV, Eversdyk D (2004) A simulation based optimization approach to supply chain management under demand uncertainty. Computers Chemical Engineering 28 2087-2106... 

Kurus D (2006) Simulation-based optimization model for supply chain planning of commodities in the chemical industry Technical University of Berlin Lababidi HMS, Ahmed MA, Alatiqi IM, Al-Enzi AF (2004) Optimizing the Supply Chain of a Petrochemical Company under Uncertain Operating and Economic Conditions. Industrial Engineering Chemistry Research 43 63-73 Labys WC (1973) Dynamic Commodity Models Specification, Estimation and Simulation, Lexington Books, Lexington... 

The hybrid approach tries to combine the model-based optimization approach with the heuristic approach, thereby, avoiding the problems of unavailability of models. In the initial stages, a property-based approach (where properties are obtained through model or experimental measurements) is applied and in the final stages (where models are usually easy to develop), a model-based optimization approach is applied (see for example, Gani (2004)). More work is needed to establish this technique for food-process applications. 

In this chapter, the genesis of SMILES-based descriptors (as well as perspectives of utilization of these characteristics for QSPR/QSAR analyses) is discussed. We concluded that in fact the SMILES-based optimal descriptors are derivatives of the graph-based optimal descriptors. In fact the SMILES-based descriptors are calculated with scheme that is similar to the well-known additive scheme (Zinkevich et al., 2004), but instead of contributions for the molecular fragments (chemical elements, different kinds of cycles, covalent bonds, etc.) contributions for the SMILES fragments (c, C, n, N, Cl, Br, =,, etc.) are using. 

SMILES-based optimal descriptors can be utilized as a tool for prediction of the fullerene C60 solubility. 

Secanell et al. [125] presented a gradient-based optimization of fuel cell performance. They found that a significant increase in performance could be achieved by increasing Pt loading and reaching a Nafion mass fraction around 20-30 wt% in the CL. 

Relative purity measurement and the relative purity-based reaction optimization have long been used in combinatorial synthesis. In order to make high-through-put purification a success, the yield-based optimization is essential. Chemiluminescent nitrogen detection (CLND) [4] with HPLC determines the quantitative yield after each reaction step during the library feasibility and rehearsal stages. The yield of each synthetic step provides guidance for the final library synthesis. 

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