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Pluripotent stem (PS) cells have the ability to replicate themselves (self-renew) and to generate virtually any given cell type in the adult body (pluripotency). Human PS (hPS) cells are therefore considered promising sources for future cell replacement therapy. Embryonic stem (ES) cells are the major type of PS cells that are derived from blastocyst embryos. Germ cells from testis can also become PS cells when cultured for a long period with a combination of growth factors. Alternatively, differentiated somatic cells can also be converted to PS cells by the method called nuclear reprogramming. This includes somatic cell nuclear transfer where a somatic cell nucleus is injected into an enucleated oocyte giving rise to a reprogrammed PS cell, as well as the recently developed technique of reprogramming differentiated somatic cells into induced pluripotent stem (iPS) cells by introducing defined transcription factors. Regardless of the sources and generation methods, PS cells share common epithelial structures and maintain tight cellular interactions. Although the molecular mechanisms that regulate self-renewal and pluripotency of PS cells have been extensively studied, the basic cellular interactions that govern how PS cells control cell-cell and cell-matrix adhesions are still not fully understood. In addition, there are several obstacles in the current culture methods for hPS cells that need to be overcome in order to achieve the highest safety and consistency required for clinical applications. A Rho-mediated signaling axis has recently been determined to be the core machinery that integrates cellular interactions between PS cells. By chemically engineering this axis, hPS cells are able to self-renew under completely defined conditions while maintaining their multi-differentiation capacities. When combined with the rapid progress in research focusing on iPS cells, these studies on cell-cell and cell-matrix adhesion in PS cells may not only contribute to further understanding PS cell biology, but also lead to the development of novel technologies enabling the derivation and growth of clinically relevant hPS cells for regenerative therapies.

The obvious current reversion to micro-scale investigations in basic chemical engineering, combined with the need, of a quite different nature, in the rapid growth of high added-value and small-lot functional materials, have been pointing to an area not yet sufficiently covered by the unit operations, transport phenomena and chemical reaction engineering. Although it is difficult to define accurately this area, a cursory scan of the activities already in progress has revealed a few common attributes: multi-phased (structured), multi-scaled, multi-disciplined, nonlinear, needs for resolution to reductionism-solvable subsystems, and pervasive in the process industry. From these activities, the present paper drafts a tentative scheme for studying the related problems: first to dissect a problem into various scales — spatial, temporal or otherwise as best suits the case in hand — in order to identify pertinent parameters which are then organized into model formulations. Together with inter-scale model formulations, a zoom-in/zoom-out process is carried out between the scales, by trial-and-error and through reasoning, to arrive at a global formulation of a quantitative solution, in order to derive, eventually, the general from the particular.

In this chapter, we provide a general overview of evolutionary multi-objective optimization, with particular emphasis on algorithms in current use. Several applications of these algorithms in chemical engineering are also discussed and analyzed. We also provide some additional information about public-domain resources available for those interested in pursuing research in this area. In the final part of the chapter, some potential areas for future research are briefly described.

Problems in chemical engineering, like most real-world optimization problems, typically, have several conflicting performance criteria or objectives and they often are computationally demanding, which sets special requirements on the optimization methods used. In this paper, we point out some shortcomings of some widely used basic methods of multi-objective optimization. As an alternative, we suggest using interactive approaches where the role of a decision maker or a designer is emphasized. Interactive multi-objective optimization has been shown to suit well for chemical process design problems because it takes the preferences of the decision maker into account in an iterative manner that enables a focused search for the best Pareto optimal solution, that is, the best compromise between the conflicting objectives. For this reason, only those solutions that are of interest to the decision maker need to be generated making this kind of an approach computationally efficient. Besides, the decision maker does not have to compare many solutions at a time which makes interactive approaches more usable from the cognitive point of view. Furthermore, during the interactive solution process the decision maker can learn about the interrelationships among the objectives. In addition to describing the general philosophy of interactive approaches, we discuss the possibilities of interactive multi-objective optimization in chemical process design and give some examples of interactive methods to illustrate the ideas. Finally, we demonstrate the usefulness of interactive approaches in chemical process design by summarizing some reported studies related to, for example, paper making and sugar industries. Let us emphasize that the approaches described are appropriate for problems with more than two objective functions.

In this chapter, we provide a general overview of evolutionary multiobjective optimization, with particular emphasis on algorithms in current use. Several applications of these algorithms in chemical engineering are also discussed and analyzed. We also provide some additional information about public-domain resources available for those interested in pursuing research in this area. In the final part of the chapter, some potential areas for future research are briefly described.

Problems in chemical engineering, like most real-world optimization problems, typically, have several conflicting performance criteria or objectives and they often are computationally demanding, which sets special requirements on the optimization methods used. In this chapter, we point out some shortcomings of some widely used basic methods of multi-objective optimization. As an alternative, we suggest using interactive approaches where the role of a decision maker or a designer is emphasized. Interactive multi-objective optimization has been shown to suit well for chemical process design problems because it takes the preferences of the decision maker into account in an iterative manner that enables a focused search for the best Pareto optimal solution, that is, the best compromise between the conflicting objectives. For this reason, only those solutions that are of interest to the decision maker need to be generated making this kind of an approach computationally efficient. Besides, the decision maker does not have to compare many solutions at a time which makes interactive approaches more usable from the cognitive point of view. Furthermore, during the interactive solution process the decision maker can learn about the interrelationships among the objectives. In addition to describing the general philosophy of interactive approaches, we discuss the possibilities of interactive multi-objective optimization in chemical process design and give some examples of interactive methods to illustrate the ideas. Finally, we demonstrate the usefulness of interactive approaches in chemical process design by summarizing some reported studies related to, for example, paper making and sugar industries. Let us emphasize that the approaches described are appropriate for problems with more than two objective functions.