Experimental design and statistical evaluation of a full-scale gasification project

Coetzer, R.L.J.; Keyser, M.J.

doi:10.1016/s0378-3820(02)00251-5

Cited by 20 publications

(30 citation statements)

References 8 publications

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“…There are of course many other variables that might influence the gasification process but considerable effort was made to control those variables during the test runs. The experimental conditions of the process variables that were investigated were deliberately changed according to a planned experimental programme (Coetzer and Keyser, 2003).…”

Section: The Methodology Of Process Robustness Studiesmentioning

confidence: 99%

“…Although statistical response surface models were developed previously in Coetzer and Keyser (2003, 2004) to study specific objectives of optimization and robustness, they did not consider the effect of the coal composition, and the variability thereof, on sustainable gasifier performance. The aim of this paper is to demonstrate the effect of the process variables on gasifier performance when the assumption of fully controllable variables cannot be made.…”

Section: The Methodology Of Process Robustness Studiesmentioning

confidence: 99%

“…Therefore, three size fractions were selected to define the coal PSD, and, for the purposes of this paper, will be referred to as the coarse, medium and fine fractions. For the factorial design, Coetzer and Keyser (2003) developed statistical response surface models and evaluated the effect of the process parameters on gasifier performance in terms of carbon utilization and utility consumption.…”

Section: Introductionmentioning

confidence: 99%

“…However, the development of the models in Coetzer and Keyser (2003) was based on the assumption that all the test parameters are equally well controllable during normal operation. In practice the run‐of‐mine coal can be highly variable in certain coal seams, such as the stone or ash content in the coal.…”

Section: Introductionmentioning

confidence: 99%

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Dual Response Surface Optimization with Hard-to-Control Variables for Sustainable Gasifier Performance

Coetzer

Rossouw

Lin

2008

Journal of the Royal Statistical Society Series C: Applied Statistics

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Dual response surface optimization of the Sasol-Lurgi fixed bed dry bottom gasification process was carried out by performing response surface modelling and robustness studies on the process variables of interest from a specially equipped full-scale test gasifier. Coal particle size distribution and coal composition are considered as hard-to-control variables during normal operation. The paper discusses the application of statistical robustness studies as a method for determining the optimal settings of process variables that might be hard to control during normal operation. Several dual response surface strategies are evaluated for determining the optimal process variable conditions. It is shown that a narrower particle size distribution is optimal for maximizing gasification performance which is robust against the variability in coal composition. Copyright Journal compilation (c) 2008 Royal Statistical Society.

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Section: The Methodology Of Process Robustness Studiesmentioning

confidence: 99%

Section: The Methodology Of Process Robustness Studiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Dual Response Surface Optimization with Hard-to-Control Variables for Sustainable Gasifier Performance

Coetzer

Rossouw

Lin

2008

Journal of the Royal Statistical Society Series C: Applied Statistics

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“…However, the blending is constrained by the capacity of the stockpile yards and the rate of production at the different mines. It was shown previously that the quality of the coal feedstock, and the stability thereof, has a significant effect on the amount of gas produced [1,2,3]. Therefore, to ensure stable feed at desired qualities being delivered to the factory an efficient coal stacking process is required.…”

Section: Introductionmentioning

confidence: 99%

Simulation of a coal stacking process using an online X-Ray Fluorescence analyser

Rossouw

Coetzer

Roux

2018

ORiON

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The Sasol Coal Value Chain is a complex system consisting of blending, stacking and reclaiming of no fewer than six different coal sources with vastly different coal qualities. The amount and quality of the gas produced from coal depend crucially on the quality of the coal reclaimed from the coal stacking yards. In this paper the development of a real time coal quality simulation model using information from an online X-Ray Fluorescence analyser, integrated with various data sources from the Coal Supply Facility, is presented. The integration of different data sources is discussed to create a centralised and standardised data framework for input to the simulation model. The simulation of a heap profile of the coal quality for each heap stacked, together with the quality of the reclaimed coal, is discussed in detail. It is shown how the generated information from the model is utilised in the development of a reclaiming strategy.

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Optimal Designs for Multiple-Mixture by Process Variable Experiments

Coetzer

Haines

2013

Contributions to Statistics

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Experimental design and statistical evaluation of a full-scale gasification project

Cited by 20 publications

References 8 publications

Dual Response Surface Optimization with Hard-to-Control Variables for Sustainable Gasifier Performance

Dual Response Surface Optimization with Hard-to-Control Variables for Sustainable Gasifier Performance

Simulation of a coal stacking process using an online X-Ray Fluorescence analyser

Optimal Designs for Multiple-Mixture by Process Variable Experiments

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