Factory dispersal

As shown in Table 5-1. only people living near chemical factories or work sites are likely to be exposed to measurable amounts of acrylonitrile in air and water. Dispersion modeling studies have indicated that approximately 2.6 million people living within 30 km of emission sources may be... 

A process additive is an ingredient which is added in a small dosage to a rubber compound solely to influence the performance of the compound in factory processes, or to enhance physical properties by aiding filler dispersion. 

Bayer Technology Services has introduced BayAPS PP in a factory producing about 40 pigment dispersions in 80 different packings on several production lines with a common packing station. 

Public concern about industrial chemical exposures might also be misguided. The EPA typically uses mathematical dispersion models to calculate human exposure to chemicals released into the air by major stationary sources like factories and power plants. There is little evidence that the models are predictive. In one experiment, a tracer gas was released from the Alaska pipeline terminus at Valdez. Actual exposure, as measured by personal exposure badges, was compared with the predictions of the EPA dispersion model. The statistical correlation between them was near zero (— 0.01), meaning the predictions were worthless (Wallace 1993, 137-38). 

In the manufacture of SAN and ABS and polymer dispersions (and also of chemical intermediates) under normal conditions, spot measurements of 5 ppm [11 mg/m ] were found during 1963-74, and it was assumed that higher levels occurred under some conditions. In 1975-77, monthly readings averaged 1.5 ppm [3.3 mg/m ] (Thiess Fleig, 1978). In ABS factories in France, short-tenn (< 2 h) area measurements averaged... 

Processing variables can affect to a very great extent the results obtained on the rubber product or test piece and, in fact, a great number of physical tests are carried out in order to detect the result of these variables, for example state of cure and dispersion. In a great many cases, tests are made on the factory prepared mix or the final product as it is received but, where the experiment involves the laboratory preparation of compounds and their moulding, it is sensible to have standard procedures to help reduce as far a possible sources of variability. Such procedures are provided by ISO 2393 which covers both mills and internal mixers of the Banbury or Intermix type, and also procedures for compression moulding. 

Sahoo, N.R. and Pandalai, H.S. (2000) Secondary geochemical dispersion in the Precambrian auriferous Hutti-Maski schist belt, Raichur district, Karnataka, India. Part II. Application of factorial design in the analysis of secondary dispersion of As. Journal of Geochemical Exploration, 71(3), 291-303. 

The CIPE plan called for a remediation of asbestos-containing materials in the Etemit and in the ILVA steelwork factories. Ninety percent of the buildings, squares, and sites were cleared of asbestos by March 4,2000. During the remediation activities, and in coordination with the local Health Unities (ASL)), a series of control samples were collected. 915 samples were analyzed to evaluate the presence of aerially dispersed asbestos fibers in nearby locations. No values exceeding WHO limits were detected. 1044 samples and analyses from the Etemit site and 56 from the ILVA site were also collected to monitor fiber dispersion inside the area of the operations. 

A wide variety of xenobiotics can act as EDC (Fig. 8.5), bisphenol-A (BPA) and nonylphenol (NP) being the most extensively studied. They are widely dispersed in the environment, but they can mainly be found in wastewater effluents. BPA is used as a raw material for the production of polycarbonates and epoxy resins, and is present in the discharges of BPA producing factories, from installations that incorporate BPA into plastic, from leaching of plastic wastes and landfill sites. 

We now consider factorial experiments in which there is no replication of design factor combinations and no use of noise factors. The idea of identifying dispersion effects in unreplicated factorials again has roots in the work of Taguchi. It was first studied in detail by Box and Meyer (1986) and has since attracted considerable interest and research. 

In many of the descriptions, we refer to statistics for the dispersion effect of factor j . By this we mean either a main effect associated directly with a single factor or an interaction effect associated with some collection of factors. Much of the presentation focuses on two-level factorial designs and +1 and -1 are used to denote the high and low levels of each factor in these designs. 

Box and Meyer also derived a useful result (which is applied in some of the subsequent methods in this chapter) that relates dispersion effects to location effects in regular 2k p designs. We present the result first for 2k designs and then explain how to extend it to fractional factorial designs. First, fit a fully saturated regression model, which includes all main effects and all possible interactions. Let /3, denote the estimated regression coefficient associated with contrast i in the saturated model. Based on the results, determine a location model for the data that is, decide which of the are needed to describe real location effects. We now compute the Box-Meyer statistic associated with contrast j from the coefficients 0, that are not in the location model. Let i o u denote the contrast obtained by elementwise multiplication of the columns of +1 s and—1 s for contrasts i and u. The n regression coefficients from the saturated model can be decomposed into n/2 pairs such that for each pair, the associated contrasts satisfy i o u = j that is, contrast i o u is identical to contrast j . Then Box and Meyer proved that equivalent expressions for the sums of squares SS(j+) and SS(j-) in their dispersion statistic are... 

Brenneman (2000) found that Harvey s method could underestimate the dispersion effect of factor j if that factor was left out of the location model. This result led Brenneman and Nair (2001) to propose a modified version of Harvey s method for two-level factorial experiments that is based on the results of Bergman and Hynen (1997). In the modified version, the dispersion statistic for factor j is computed from residuals from an expanded location model that includes the effect of factor j and all its interactions with other effects in the location model. For two-level designs, the modified Harvey s statistic for factor j is then... 

In Section 5, we introduced the dyestuffs experiment to illustrate the methods for screening for dispersion effects in unreplicated fractional factorial experiments. Typically we anticipate that smaller experiments will be used for screening. So, in this section, we analyze two sets of 16 runs that are extracted from the dyestuffs experiment and which constitute fractional factorials more typical of the actual size of screening experiments. 

The potential importance for dispersion of main effects for E and F and an EF interaction suggests that this would be a good test case for the McGrath-Lin statistic. The appropriate location model includes main effects and all interactions of factors A, E, and F. That model has zero residuals for all four observations with E and F at their low values. (As in the full factorial, our software actually computes these as machine-zero.) Thus, the McGrath-Lin statistic will indicate strong effects for all three terms. However, the presence of the zero residuals casts questions on the validity of any distributional results for the statistic. 

Brenneman, W. A. (2000). Inference for location and dispersion effects in unreplicated factorial experiments. PhD Dissertation, University of Michigan, Ann Arbor. Brenneman, W. A. and Nair, V. N. (2001). Methods for identifying dispersion effects in unreplicated factorial experiments. Technometrics, 43, 388-405. 

Holm, S. and Wiklander, K. (1999). Simultaneous estimation of location and dispersion in two-level fractional factorial designs. Journal of Applied Statistics, 26, 235-242. 

Liao, C. T. (2000). Identification of dispersion effects from unreplicated 2n k fractional factorial designs. Computational Statistics and Data Analysis, 33, 291-298. 

McGrath, R. N. and Lin, D. K. J. (2001). Testing multiple dispersion effects in unreplicated fractional factorial designs. Technometrics, 43, 406-414. 

Nair, V. N. and Pregibon, D. (1988). Analyzing dispersion effects from replicated factorial experiments. Technometrics, 30, 247-257. 

Pan, G. (1999). The impact of unidentified location effects on dispersion effects identification from unreplicated factorial designs. Technometrics, 41, 313-326. 

Pan, G. and Taam, W. (2002). On generalized linear model method for detecting dispersion effects in unreplicated factorial designs. Journal of Statistical Computation and Simulation, 72, 431-450. 

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