Self-optimization

Design and develop self-optimizing chemical systems. 

Building on the approach that allows optimization of biological systems through evolution, this would let a system produce the optimal new substance, and produce it as a single product rather than as a mixture from which the desired component must be isolated and identified. Self-optimizing systems would allow visionary chemical scientists to use this approach to make new medicines, catalysts, and other important chemical products—in part by combining new approaches to informatics with rapid experimental screening methods. 

B. EIGENSTRUCTURE. The plant-wide control problem is still a very active area of research. In recent years there has been more emphasis on finding control structures for individual unit operations and for entire plants that are inherently simple, self-regulatory, and self-optimizing. This approach has been called a search for the eigemtructure of the plant. 

Fig. 8. Scheme of a sequential self-optimizing Analytical Instrument... 

Fig. 9. A self optimizing Flow Injection Analyzer (FIA). From J. Betteridge, Lecture presented at Analysis 84, London. Reproduced by permission of the author...

Closed-loop response to process disturbances and step changes in setpoint is simulated with the model of Kiparissides extended to predict the behavior of downstream reactors. Additionally, a self-optimizing control loop is simulated for conversion control of downstream reactors when the first reactor of the train is operating under closed-loop control with dead-time compensation. 

Larsson, T., Hestetun, K., Hovland, E., and Skogestad, S. (2001). Self-optimizing control of a large-scale plant the Tennessee Eastman process. Ind. Eng. Chem. Res., 40(22), 4889M901. 

Skogestad, S. (2000). Plantwide control the search for the self-optimizing control structure. J. Proc. Contr., 10, 487-507. 

On the other hand, in the formation of guanine quartet-based hydrogels, it was shown that the system selected those components that generated the most stable gel [50] (see Fig. 5 in [50]). Such a self-optimization behavior may be of much deeper general significance, namely for prebiotic chemical evolution, whereby selection is driven by phase cohesion, the entity selected being that giving the most stable... 

In this context the supramolecular crystalline [15-22] or hybrid materials [23-35] can be prepared and constitutionally self-sorted by using an irreversible kinetic process like crystallization or sol-gel polymerization. The self-selection is based on constitutional internal interactions of library components, resulting in the dynamic amplification of self-optimized architectures, during the phase change process. With all this in mind, the second part will be devoted to sol-gel resolution of dynamic molecular supramolecular libraries, emphasizing recent developments, especially as pursued in our laboratory. 

Legrand YM, van der Lee A, Barboiu M (2007) Self-optimizing charge transfer energy phenomena in metallosupramolecular complexes by dynamic constitutional self-sorting. Inorg Chem 46 9540-9547... 

Boal AK, Rotello VM (2000) Fabrication and self-optimization of multivalent receptors on nanoparticle scaffolds. J Am Chem Soc 122 734—735... 

This method of selecting catalytic sites significantly depends on spontaneous processes, in contrast to the development of artificial enzymes and catalytic antibodies. The selection process is based on self-assembly, selforganization and self-optimization. Therefore, this selection approach bears the characteristics of supramolecular chemistry. A similar concept is used in natural evolution processes, resulting in the complicated life forms we see around us today. Therefore, it is clear that we can design the self-organizational processes used in supramolecular chemistry to proceed according to the concepts followed by this natural evolutionary process. 

NCO tracking - " , self-optimizing control - , extremum seeking - ... 

Model-based approaches allow fast derivative computation by relying on a process model, yet only approximate derivatives are obtained. In self-optimizing control [12,21], the idea is to use a plant model to select linear combinations of outputs, the tracking of which results in optimal performance, also in the presence of uncertainty in other words, these linear combinations of outputs approximate the process derivatives. Also, a way of calculating the gradient based on the theory of neighbouring extremals has been presented in [13] however, an important limitation of this approach is that it provides only a first-order approximation and that the accuracy of the derivatives depends strongly on the reliability of the plant model. 

Below we will use a simple example to explain the principle of the standard, non-self-optimizing method. Say that we have five data pairs x,y such that the x-values are equidistant, with a nearest-neighbor distance S. For any odd-numbered set of equidistant data (such as the five considered here), the x- value in the middle of the set is the average x of the x-values in the set. We now start by subtracting x from all five x-values, so that the new x- values will be-25, -5, 0, 5, and 25. 

Below we show a derivative version, called ELS for equidistant least squares, that uses the same algorithm but with easier-to-make input boxes. It provides two options a fixed or a variable (self-optimizing) order, selected by the macros ELSfixed and ELSoptimized respectively. The main program is the subroutine ELS, which itself calls on several functions and on a subroutine, ConvolutionFactors. The latter need not be a separate subroutine, since it is only called once in the program. However, by placing it in a separate subroutine it becomes available for use in other programs, such as Interpolation, that could benefit from it. 

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