Experimental Design Optimization of the Determination of Total Halogens in Coal by Combustion–Ion Chromatography

Abstract

Determination of total halogens in coal was investigated with oxygen bomb combustion followed by ion chromatography. Experimental parameters were optimized by fractional factorial design Downloaded by [University of Otago] at 10:25 05 July 20152 and response surface methodology. Fractional factorial design was employed in screening experiments to evaluate the influence of the oxygen pressure, catalyst, absorption solution, reduction reagent, bomb cooling time, and a combustion aid on the combustion of coal in an oxygen bomb. Response surface methodology was conducted to further refine the results obtained by fractional factorial design and to define parameters for the procedure. The accuracy and precision of combustion with ion chromatography were evaluated by the use of two certified reference materials and by fortified in-house coal standards. The limits of detection and quantification for total halogens were 0.84 and 2.80 milligrams per kilogram, respectively, for 500 milligrams samples. Ion chromatography provided relative standard deviations less than 4 percent, recoveries exceeding 95 percent, and was convenient. This method is recommended for routine determination of total halogens in coal because of its reliability.

Resource use efficiency of warm‐water fish culture upon different pond sizes

“…The Pareto chart determines the magnitude and the importance of the impacts of each input. On the other hand, the comparison between absolute values and the significance of the impact of each input can be presented by the chart 41 . According to Table 4 and Figure 4, the impact of fingerling energy input was significant ( P ≤ 0.05) on the yield of warm‐water fish farming in Guilan province.…”

Pushing Digital Automation of Configure-to-Order Services in Small and Medium Enterprises of the Construction Equipment Industry: A Design Science Research Approach

“…Given this data set, we performed statistical analyses based on a response surface methodology (RSM) approach. RSM is a collection of statistical and mathematical methods to investigate the impact of several predicting variables (predictors) on one or more responding variables (response) [33]. Depending on the dominant container size, the statistical analyses led to different regression equations providing for a response, which is the effort of panel modifications in days.…”

Application of two-level full factorial design and response surface methodology in the optimization of inductively coupled plasma-optical emission spectrometry (ICPOES) instrumental parameters for the determination of platinum

“…Statistical evaluation of these effects is done using analysis of variance (ANOVA) (Ferreira et al, 2003;Ignjatovic et al, 2015;Hanrahan et al, 2005;Mutihac and Mutihac, 2008). The determination of which factors and interactions have the most prominent effects on the response is achieved by comparing the statistical p-value for the estimated effects with the critical level, which is usually 0.05 (Ferreira et al, 2003).…”

“…The response surface equation is similar to the factorial design equation, but contains additional quadratic terms that allow curvature in the response, thus making response surfaces ideal for: (i) understanding how input factors influence the response, (ii) finding input factor levels that optimize the response (Hanrahan et al, 2005(Hanrahan et al, , 2006Ghaseni et al, 2011;Sredovic Ignjatovic et al, 2015). The study was carried out by measuring the intensity of the Pt(1) 265.945 nm atomic line.…”

“…Response surface methodology employs mathematical and statistical models to generate response surfaces from experimental data obtained in relation to experimental designs (Bezerra et al, 2008;Sredovic Ignjatovic et al, 2015). The response surface equation is similar to the factorial design equation, but contains additional quadratic terms that allow curvature in the response, thus making response surfaces ideal for: (i) understanding how input factors influence the response, (ii) finding input factor levels that optimize the response (Hanrahan et al, 2005(Hanrahan et al, , 2006Ghaseni et al, 2011;Sredovic Ignjatovic et al, 2015).…”