Optimization model-based

An optimal model-based PID tuning method for the control of poly-butadiene latex reactor... 

Preprocessing additional parameters required for the optimization model based on input and control data being calculated in preprocessing... 

Konno and Yamazaki (1991) proposed a large-scale portfolio optimization model based on mean-absolute deviation (MAD). This serves as an alternative measure of risk to the standard Markowitz s MV approach, which models risk by the variance of the rate of return of a portfolio, leading to a nonlinear convex quadratic programming (QP) problem. Although both measures are almost equivalent from a mathematical point-of-view, they are substantially different computationally in a few perspectives, as highlighted by Konno and Wijayanayake (2002) and Konno and Koshizuka (2005). In practice, MAD is used due to its computationally-attractive linear property. 

A remarkable advantage of the optimization models based on the use of thermodynamic space consists in the possibility, in case of reducing these models to CP problems, to transform the region of feasible solutions into a one-dimensional set (a graph in the form of a tree) and to study the specific features of the studied system behavior on this graph—"a thermodynamic tree."... 

This paper proposed a multiobjective optimization model based on semi-Markov decision processes and multiobjective GAs for the optimal replacement policy for monitored systems from oil industry. The proposed multiobjective GA with SMDP was validated by means of an exhaustive multiobjective algorithm and was able to find almost all solutions from the true non-dominated set. In addition, the time required to run the multiobjective GA jointly with the SMDP was much smaller than the needed by the exhaustive algorithm. 

The advantage of our methods is that they introduces formally for the first time discrete decisions and advanced optimizing model based controllers in a complete simultaneous process and control design framework. The benefits fi-om this approach include (i) improved economic performance, (ii) enhancement of the system s dynamic performance, (iii) guaranteed operability in the face of uncertainties and (iv) improved system stability characteristics. 

The following texts and articles provide an excellent discussion of optimization methods based on searching algorithms and mathematical modeling, including a discussion of the relevant calculations. 

Temperature, pH, and feed rate are often measured and controlled. Dissolved oxygen (DO) can be controlled using aeration, agitation, pressure, and/or feed rate. Oxygen consumption and carbon dioxide formation can be measured in the outgoing air to provide insight into the metaboHc status of the microorganism. No rehable on-line measurement exists for biomass, substrate, or products. Most optimization is based on empirical methods simulation of quantitative models may provide more efficient optimization of fermentation. 

Those based on strictly empirical descriptions Mathematical models based on physical and chemical laws (e.g., mass and energy balances, thermodynamics, chemical reaction kinefics) are frequently employed in optimization apphcations. These models are conceptually attractive because a gener model for any system size can be developed before the system is constructed. On the other hand, an empirical model can be devised that simply correlates input-output data without any physiochemical analysis of the process. For... 

The thermodynamic point of view developed in this review and in our original works with regard to the behavior of SAH in laboratory experiments and in soil models can pave, in our opinion, the most rational way for achieving the optimal results. Based on the existing theory of network polymers, this concept is undoubtedly open to further improvement that would expand its prognostic potentialities. 

If the sequence of a protein has more than 90% identity to a protein with known experimental 3D-stmcture, then it is an optimal case to build a homologous structural model based on that structural template. The margins of error for the model and for the experimental method are in similar ranges. The different amino acids have to be mutated virtually. The conformations of the new side chains can be derived either from residues of structurally characterized amino acids in a similar spatial environment or from side chain rotamer libraries for each amino acid type which are stored for different structural environments like beta-strands or alpha-helices. 

OASIS (optimized approach based on structural indices set) has been developed by Mekenyan and co-workers [87]. Given the activities or toxicities of a set of compounds, it generates large numbers of structural indices for each and develops QSAR correlations. The approach has been used to model the acute toxicity of industrial chemicals [88]. It is claimed [89] that the method can be of use in elucidating mechanisms of action. 

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

Based on the experimental data kinetic parameters (reaction orders, activation energies, and preexponential factors) as well as heats of reaction can be estimated. As the kinetic models might not be strictly related to the true reaction mechanism, an optimum found will probably not be the same as the real optimum. Therefore, an iterative procedure, i.e. optimization-model updating-optimization, is used, which lets us approach the real process optimum reasonably well. To provide the initial set of data, two-level factorial design can be used. 

In fact, no model can represent every aspect of an actual production process. Accordingly, the. scheduler must have some flexibility to modify the schedule proposed by the optimization algorithm, based on experience that is gained al.so at the realization of the optimal schedule. This leads to evolutionary improvement strategies starting from approximate optimization techniques. An interactive graphical presentation of the plant should enable quick intervention. 

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