Metal-Substituted Microporous Aluminophosphates

This chapter aims to present the zeotypes aluminophosphates (AlPOs) as a complementary alternative to zeolites in the isomorphic incorporation of metal ions within all-inorganic microporous frameworks as well as to discuss didactically the catalytic consequences derived from the distinctive features of both frameworks. It does not intend to be a compilation of either all or the most significant publications involving metal-substituted microporous aluminophosphates. Families of AlPOs and zeolites, which include metal ion-substituted variants, are the dominant microporous materials. Both these systems are widely used as catalysts, in particular through aliovalent metal ions substitution. Here, some general description of the synthesis procedures and characterization techniques of the MeAPOs (metal-contained aluminophosphates) is given along with catalytic properties. Next, some illustrative examples of the catalytic possibilities of MeAPOs as catalysts in the transformation of the organic molecules are given. The oxidation of the hardly activated hydrocarbons has probably been the most successful use of AlPOs doped with the divalent transition metal ions Co2+, Mn2+, and Fe2+, whose incorporation in zeolites is disfavoured. The catalytic role of these MeAPOs is rationalized based on the knowledge acquired from a combination of the most advanced characterization techniques. Finally, the importance of the high specificity of the structure-directing agents employed in the preparation of MeAPOs is discussed taking N,N-methyldicyclohexylamine in the synthesis of AFI-structured materials as a driving force. It is shown how such a high specificity could be predicted and how it can open great possibilities in the control of parameters as critical in catalysis as crystal size, inter-and intracrystalline mesoporosity, acidity, redox properties, incorporation of a great variety of heteroatom ions or final environment of the metal site (surrounding it by either P or Al)

Optimal experiment design in closed loop with unknown, nonlinear and implicit controllers using stealth identification

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A unified experiment design framework for detection and identification in closed-loop performance diagnosis

This paper presents a least-costly experiment design framework for closed-loop performance diagnosis using prediction error identification. The performance diagnosis methodology consists in verifying whether an identified model of the true system lies in a performance-related region of interest. The experiment design framework minimizes the overall excitation cost incurred for detecting the cause of the performance drop and re-identifying the system dynamics when the degraded performance is due to control-relevant system changes. The optimal design of excitation signals is performed for a desired detection rate and a pre-specified level of accuracy required for the re-identified model