煤气洗脱苯双吸收剂流程模拟优化
Simulation and optimization of dual-absorbent eluting benzene from coal gas process
提出了煤气洗脱苯的新工艺,使用2种不同的吸收剂来回收粗苯和粗萘。采用遗传算法对简化模型中的吸收剂组成进行优化,基于遗传算法优化的结果,新工艺以二甲苯和洗油为吸收剂。通过对后续再生工艺流程和参数的优化,得到当二甲苯贫溶剂流量为715 kg/h,半贫溶剂流量为11 900 kg/h时再生能耗最小。该方案与仅使用洗油的传统工艺相比,减少了24%的吸收剂循环量,降低了25%的再生能耗,并实现了粗苯的初步BTX分离。
A new process is proposed for eluting benzene from coal gas,which uses two different absorbents to recover crude benzene and crude naphthalene,respectively.The composition of the absorbent in the simplified model is optimized by means of genetic algorithm.Based on the results from genetic algorithm optimization,xylene and washing oil are selected as the absorbents in the new process.Through optimizing the subsequent regeneration process and parameters,it is concluded that the regeneration energy consumption is the smallest when the flow rate of xylene lean solvent is 715 kg·h-1 and the flow rate of semi-lean solvent is 11 900 kg·h-1.Compared with the conventional process using washing oil alone,this new scheme reduces the cycling amount of absorbent by 24%,reduces the regeneration energy consumption by 25%,and achieves a preliminary BTX separation for crude benzene.
煤气 / 能耗 / 流程模拟 / 双吸收剂 / 遗传算法 / 吸收
coal gas / energy consumption / process simulation / dual-absorbent / genetic algorithms / absorb
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国家自然科学基金项目(21276039)
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