Download Combinatorial Development of Solid Catalytic Materials: by Manfred Baerns, Martin Holena PDF

By Manfred Baerns, Martin Holena

The e-book offers a entire therapy of combinatorial improvement of heterogeneous catalysts. particularly, computer-aided techniques that experience performed a key function in combinatorial catalysis and high-throughput experimentation over the past decade - evolutionary optimization and synthetic neural networks - are defined. The booklet is exclusive in that it describes evolutionary optimization in a broader context of equipment of attempting to find optimum catalytic fabrics, together with statistical layout of experiments, in addition to offers neural networks in a broader context of knowledge analysis.It is the 1st e-book that demystifies the recognition of synthetic neural networks, explaining its rational primary - their common approximation potential. even as, it exhibits the constraints of that potential and describes tools for a way it may be more desirable. The ebook is additionally the 1st that offers different very important subject matters concerning evolutionary optimization and synthetic neural networks: computerized producing of problem-tailored genetic algorithms, and tuning evolutionary algorithms with neural networks. either aren't in basic terms theoretically defined, but additionally good illustrated via unique case reports.

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Extra info for Combinatorial Development of Solid Catalytic Materials: Design of High-Throughput Experiments, Data Analysis, Data Mining (Catalytic Science

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Ii) To modify the objective function by superposing a penalty for unfeasibility. Such a penalty can be constant (the offspring is penalised for being unfeasible), or it can reflect either the amount of constraint violation or the effort needed to correct the unfeasibility. This approach works well if theoretical considerations allow an appropriate choice of penalty function. If, on the other hand, some heuristic penalty function is used, then its values typically turn out to be either too low, which can allow the optimisation paths to remain permanently outside the feasibility area, or too high, thus suppressing information on the value of the objective function for unfeasible offsprings.

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And Lauterbach, J. (2004). Development and optimization of NOx storage and reduction catalysts using statistically guided high-throughput experimentation, Catal. Today, 98, 375– 385. , Zanthoff, H. and Mirodatos, C. (2001). Combinatorial approaches to heterogeneous catalysis: strategies and perspectives for academic research, Catal. Today, 67, 309–318. Q. W. (2003). Catalyst design for methane oxidative coupling by using artificial neural network and hybrid genetic algorithm, Chem. Eng. , 58, 81–87.

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