Fluid Catalytic Cracking Unit Emissions and Their Impact

This article was published in the journal, Water Air and Soil Pollution [© Springer].The original publication is available at www.springerlink.comFluid catalytic cracking unit is of great importance in petroleum refining industries as it treats heavy fractions from various process units to produce light ends (valuable products). FCC unit feedstock consists of heavy hydrocarbon with high sulfur contents, and the catalyst in use is zeolite impregnated with rare earth metals, i.e., lanthanum and cerium. Catalytic cracking reaction takes place at elevated temperature in fluidized bed reactor generating sulfur-contaminated coke on the catalyst with large quantity of attrited catalyst fines. In the regenerator, coke is completely burnt producing SO2, PM emissions. The impact of the FCC unit is assessed in the immediate neighborhood of the refinery. Year-long emission inventories for both SO2 and PM have been prepared for one of the major petroleum refining industry in Kuwait. The corresponding comprehensive meteorological data are obtained and preprocessed using Aermet (Aermod preprocessor). US EPA approved dispersion model, Aermod, is used to predict ground level concentrations of both pollutants in the selected study area. Model output is validated with measured values at discrete receptors, and an extensive parametric study has been conducted using three scenarios, stack diameter, stack height, and emission rate. It is noticed that stack diameter has no effect on ground level concentration, as stack exit velocity is a function of stack diameter. With the increase in stack height, the predicted concentrations decrease showing an inverse relation. The influence of the emission rate is linearly related to the computed ground level concentrations

Monitoring stabilizing procedures of archaeological iron using electrochemical impedance spectroscopy

A methodology for monitoring washing procedures applied to stabilize archaeological iron is described. It is based on the combination of voltammetry of microparticles (VMP) with electrochemical impedance spectroscopy (EIS). A semi-empirical approach is used where the impedances at low and high frequencies were related with the fraction areas of passive and corrosion layers generated during the stabilizing treatment, the thickness, and the porosity of the corrosion layer. The variation of such parameters with the time of washing was determined from EIS data for four types of desalination procedures using concentrated NaOH and/or Na2SO3 aqueous solutions on archaeological iron artifacts. After 2 months of treatment, EIS data indicate that an essentially identical “stable” state was attained in all cases, as confirmed by the formation of a passive magnetite layer identified in VMP measurements while the rate of variation of corroded surface and porosity at short washing times varied significantly from one stabilization procedure to another.Peer Reviewe

A Critical Assessment of Kriging Model Variants for High-Fidelity Uncertainty Quantification in Dynamics of composite Shells

This paper presents a critical comparative assessment of Kriging model variants for surrogate based uncertainty propagation considering stochastic natural frequencies of composite doubly curved shells. The five Kriging model variants studied here are: Ordinary Kriging, Universal Kriging based on pseudo-likelihood estimator, Blind Kriging, Co-Kriging and Universal Kriging based on marginal likelihood estimator. First three stochastic natural frequencies of the composite shell are analysed by using a finite element model that includes the effects of transverse shear deformation based on Mindlin’s theory in conjunction with a layer-wise random variable approach. The comparative assessment is carried out to address the accuracy and computational efficiency of five Kriging model variants. Comparative performance of different covariance functions is also studied. Subsequently the effect of noise in uncertainty propagation is addressed by using the Stochastic Kriging. Representative results are presented for both individual and combined stochasticity in layer-wise input parameters to address performance of various Kriging variants for low dimensional and relatively higher dimensional input parameter spaces. The error estimation and convergence studies are conducted with respect to original Monte Carlo Simulation to justify merit of the present investigation. The study reveals that Universal Kriging coupled with marginal likelihood estimate yields the most accurate results, followed by Co-Kriging and Blind Kriging. As far as computational efficiency of the Kriging models is concerned, it is observed that for high-dimensional problems, CPU time required for building the Co-Kriging model is significantly less as compared to other Kriging variants