Multi-stage optimization in a pilot scale gasification plant

Abstract

A 2-D multiphase CFD model was coupled with advanced statistical methods to find the best operating conditions to maximize a set of selected responses that characterize the normal operation of a pilot scale fluidized bed gasifier running Municipal Solid Waste. After using CFD simulations to compute 7 responses at 27 different operating conditions, a single response optimization based on the response surface method was carried out to identify the best operating conditions. Then, the desirability concept was advantageously used to proceed with a multiple optimization where all the responses were targeted under normal industrial conditions. The operating conditions that set the optimized responses not always coincide with the most stable process. To target both optimized and robust conditions a multiple optimization combining the response surface and the propagation of error methods were employed. Finally, the tolerance intervals were reduced to increase the process Cpk and six sigma standards about 20%. New measures to further increase the process performance were identified and the transmitted variation to the response from input factors was computed.