Ash Aerosol and Deposit Formation from Combustion of Coal and Its Blend with Woody Biomass at Two Combustion Scales: Part 2─Tests on a 471 MWe Full-Scale Boiler

When moving towards CO2 neutral biofuels, fluidized bed combustion represents a good and flexible combustion technique. Biofuels typically have a high volatile content and varying moisture content. Fluidized bed combustion can provide even combustion conditions regardless of big variations in the fuel quality and fuel properties. However, compared to conventional fuels, biofuels often contain high amounts of chlorine and alkali metals, which set certain challenges for the boiler design. The problems that might occur due to high alkali and chlorine levels in the fuels are mainly slagging, fouling, corrosion and bed sintering. Since the variations in fuel properties between different fuels are big, it is of outmost importance from the boiler manufacturer’s point of view, to be able to predict the behavior of a specific fuel or fuel mixture in a very early stage of boiler design. For this purpose different kinds of calculation and prediction tools are needed. For prediction of slagging and fouling an ash behavior prediction tool has been developed. The prediction routine is based on advanced multi-phase multi-component equilibrium calculations, using the fuel composition and combustion conditions as input. Based on the calculations, the rate of deposit formation, the composition of the deposits and the corrosivity of the deposits at different locations in the boiler can be estimated. The prediction tool can be used in boiler design for defining the optimum arrangement of the superheaters, maximum flue gas temperature in the superheater area and maximum steam temperature. It can also be used for specification of maximum limits of troublesome high alkali, high chlorine fuels in fuel mixtures. In this study the prediction routine has been performed for three biofuels / biofuel mixtures. The calculated results have been evaluated with full scale and pilot scale probe measurements as well as with full scale long term operational experience.

Download Full-text

Development of a new steady state zero-dimensional simulation model for woody biomass gasification in a full scale plant

Energy Conversion and Management ◽

10.1016/j.enconman.2016.05.009 ◽

2016 ◽

Vol 120 ◽

pp. 358-369 ◽

Cited By ~ 32

Author(s):

Marco Formica ◽

Stefano Frigo ◽

Roberto Gabbrielli

Keyword(s):

Steady State ◽

Simulation Model ◽

Woody Biomass ◽

Biomass Gasification ◽

Full Scale ◽

Dimensional Simulation

Download Full-text

Relation of Statistically Significant, Abnormal, and Typical WAIS-R VIQ-PIQ Discrepancies to Full Scale IQs

European Journal of Psychological Assessment ◽

10.1027//1015-5759.16.2.107 ◽

2000 ◽

Vol 16 (2) ◽

pp. 107-114 ◽

Cited By ~ 3

Author(s):

Louis M. Hsu ◽

Judy Hayman ◽

Judith Koch ◽

Debbie Mandell

Keyword(s):

Graphical Method ◽

The United States ◽

Full Scale ◽

True Score ◽

Absolute Value ◽

Normative Population ◽

The Us ◽

The Mean ◽

And Performance ◽

Quadratic Models

Summary: In the United States' normative population for the WAIS-R, differences (Ds) between persons' verbal and performance IQs (VIQs and PIQs) tend to increase with an increase in full scale IQs (FSIQs). This suggests that norm-referenced interpretations of Ds should take FSIQs into account. Two new graphs are presented to facilitate this type of interpretation. One of these graphs estimates the mean of absolute values of D (called typical D) at each FSIQ level of the US normative population. The other graph estimates the absolute value of D that is exceeded only 5% of the time (called abnormal D) at each FSIQ level of this population. A graph for the identification of conventional “statistically significant Ds” (also called “reliable Ds”) is also presented. A reliable D is defined in the context of classical true score theory as an absolute D that is unlikely (p < .05) to be exceeded by a person whose true VIQ and PIQ are equal. As conventionally defined reliable Ds do not depend on the FSIQ. The graphs of typical and abnormal Ds are based on quadratic models of the relation of sizes of Ds to FSIQs. These models are generalizations of models described in Hsu (1996) . The new graphical method of identifying Abnormal Ds is compared to the conventional Payne-Jones method of identifying these Ds. Implications of the three juxtaposed graphs for the interpretation of VIQ-PIQ differences are discussed.

Download Full-text

A Modification of the Payne-Jones Method of Identifying Abnormal Differences in WISC-R Performance and Verbal IQ's

European Journal of Psychological Assessment ◽

10.1027/1015-5759.12.1.27 ◽

1996 ◽

Vol 12 (1) ◽

pp. 27-32 ◽

Cited By ~ 1

Author(s):

Louis M. Hsu

Keyword(s):

Full Scale ◽

True Score ◽

Verbal Iq ◽

Score Difference ◽

The Difference ◽

And Performance

The difference (D) between a person's Verbal IQ (VIQ) and Performance IQ (PIQ) has for some time been considered clinically meaningful ( Kaufman, 1976 , 1979 ; Matarazzo, 1990 , 1991 ; Matarazzo & Herman, 1985 ; Sattler, 1982 ; Wechsler, 1984 ). Particularly useful is information about the degree to which a difference (D) between scores is “abnormal” (i.e., deviant in a standardization group) as opposed to simply “reliable” (i.e., indicative of a true score difference) ( Mittenberg, Thompson, & Schwartz, 1991 ; Silverstein, 1981 ; Payne & Jones, 1957 ). Payne and Jones (1957) proposed a formula to identify “abnormal” differences, which has been used extensively in the literature, and which has generally yielded good approximations to empirically determined “abnormal” differences ( Silverstein, 1985 ; Matarazzo & Herman, 1985 ). However applications of this formula have not taken into account the dependence (demonstrated by Kaufman, 1976 , 1979 , and Matarazzo & Herman, 1985 ) of Ds on Full Scale IQs (FSIQs). This has led to overestimation of “abnormality” of Ds of high FSIQ children, and underestimation of “abnormality” of Ds of low FSIQ children. This article presents a formula for identification of abnormal WISC-R Ds, which overcomes these problems, by explicitly taking into account the dependence of Ds on FSIQs.

Download Full-text