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Multi-stage optimization in a pilot scale gasification plant

机译:中试气化厂的多阶段优化

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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. (C) 2017 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All, rights reserved.
机译:二维多相CFD模型与先进的统计方法相结合,以找到最佳操作条件,以最大化一组选定的响应,这些响应是表征运行市政固体废物的中试流化床气化炉正常运行的特征。在使用CFD仿真计算27个不同运行条件下的7个响应之后,基于响应面方法进行了一次响应优化,以识别最佳运行条件。然后,可取性概念被有利地用于进行多重优化,其中所有响应均以正常工业条件为目标。设置最佳响应的操作条件并不总是与最稳定的过程一致。为了同时针对优化条件和鲁棒条件,采用了响应面和误差传播方法相结合的多重优化方法。最后,减少公差间隔以增加过程Cpk和六个sigma标准约20%。确定了进一步提高过程性能的新措施,并计算了输入因素对响应的传递变化。 (C)2017氢能出版物有限公司。由Elsevier Ltd.发行。保留所有权利。

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