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.

Sasol, an integrated energy and chemicals company based in South Africa, leads the world in producing liquid fuels from natural gas and coal. Sasol faces many challenges, such as stricter fuel specifications, fluctuating oil and gas prices, and unique developing-world issues. Historically, the petrochemical industry based business decisions on average production limits. Sasol critically needed a better method to understand and include the effect of variability and dynamics in its decisions. The company's modeling operations using stochastic simulation (MOSS) methodology is an application of operations research that has helped to radically improve decision making. Sasol used this methodology to build three discrete-event simulation models spanning its unique coal-to-liquids value chain. The models have repeatedly proven their value by enhancing insights, enabling collaboration, ensuring efficient and effective production, and improving Sasol's bottom line. This work has applications in the wider chemical and fuels industries and represents a major step forward for operations research and chemical engineering.

No abstract

In this paper, different dissimilarity measures are investigated to construct maximin designs for compositional data. Specifically, the effect of different dissimilarity measures on the maximin design criterion for two case studies is presented. Design evaluation criteria are proposed to distinguish between the maximin designs generated. An optimization algorithm is also presented. Divergence is found to be the best dissimilarity measure to use in combination with the maximin design criterion for creating space-filling designs for mixture variables.

No abstract