LIFE-TIME PREDICTION

The changing catalyst porous texture is modelled using a Bethe network originating from percolation concepts. Preliminary results indicate that reliable metal deposition profiles and catalyst life-time predictions can be made by the proposed catalyst deactivation model. 

The general approach for modelling catalyst deactivation is schematically organised in Figure 2. The central part are the mass balances of reactants, intermediates, and metal deposits. In these mass balances, coefficients are present to describe reaction kinetics (reaction rate constant), mass transfer (diffusion coefficient), and catalyst porous texture (accessible porosity and effective transport properties). The mass balances together with the initial and boundary conditions define the catalyst deactivation model. The boundary conditions are determined by the axial position in the reactor. Simulations result in metal deposition profiles in catalyst pellets and catalyst life-time predictions. 

An analysis of error propagation demonstrates that either activation energies must be measured extremely precisely or the temperature extrapolation to service conditions must be over an extremely modest range if life-time prediction from accelerated weight-loss experiments is to be meaningful. 

On the view point of the life time prediction, tensile strength is not the appropriate factor, because there are three types of the relationship between the stress (dose) and the response (values of tensile strength) as shown in Figure 4, 5 and 6. 

T. Thompson and P.P. Klemchuck, Light stabilization of bisphenol A polycarbonate, in R. L. Clough, N.C. Billingham, and K.T. Gillen (Eds.), Polymer Durability, Degradation and Life Time Prediction, Advances in Chemistry Series 249, American Chemical Society, Washington, DC, 1996, pp. 303-317. 

Additional Tg-value determinations on foams aged at different temperatures proved subsequently, to supply excellent data for foam life-time predictions. 

The properties required for thermal stress analysis Included Young s modulus, Poisson s ratio, thermal conductivity, thermal expansion coefficient and specific heat. The CARES analysis requires strength data (from which the Weibull parameters are calculated) and Poisson s ratio. Fracture toughness was also measured. Although this parameter is not required for the fast fracture prediction made by CARES, it 1s used in life-time prediction and is related to the properties used in the reliability analysis. Strength measurements, which are the basis of reliability predictions, are controlled by both the size of flaws inherent in ceramic materials and the fracture toughness. Toughness represents the ability of a material to tolerate flaws. 

TG-MS), are applied quite rarely, although they may yield useful information for both fabrication (by thermal processing methods) and life-time predictions of polymeric biocomposites. 

Tochdfiek, J., Vrasnickova, Z. Polymer life-time prediction the role of temperature in UV accelerated ageing of polypropylene and its copolymers. Polym. Test. 36, 82-87 (2014)... 

Life-Time Prediction of Multicomponent Polymeric Materials... 

He is the author of over 3,000 articles and reports in the fields of theory and practice of pol5mier aging and development of new stabilizers for polymers, organization of their industrial production, life-time predictions for use and storage, and the mechanisms of oxidation, hydrolysis, biodegradation, and decreasing of pol5mier flammability. 

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