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气化效率对IGCC绿色发电的效率有着很大影响。现有气流床煤气化技术存在热回收方案不尽合理之处,华东理工大学提出了回收化学热新型两段组合式气化技术,一段气流床二段固定床组合气化,将一段气化合成气后的高温显热,在二段气化炉内转化为化学热,提高总气化热效率。本文采用基于径向基函数网络(RBF)模型,预测此气化工艺产气的低位热值,并用遗传算法优化网络参数,将模型计算结果与实验比较,两者吻合较好,表明模型预测该流程的低位热值性能稳定,精度高。
Gasification efficiency has a significant impact on the efficiency of IGCC green power generation. Existing entrained bed coal gasification technology heat recovery program is not reasonable, East China University of Science and Technology proposed a new two-stage combined heat recovery chemistry gasification technology, a first-stage fluidized bed combination fixed bed gasification, a gasification synthesis High-temperature gas sensible heat, in the second gasifier into chemical heat, increase the total gasification thermal efficiency. In this paper, based on Radial Basis Function (RBF) model, the low calorific value of gas generation in this gasification process is predicted, and the genetic algorithm is used to optimize the network parameters. The calculated results are in good agreement with the experimental results, The process of low calorie performance stable, high precision.