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    基于PB设计和响应面法的净化厂尾气处理装置用能优化

    Energy consumption optimization of tail gas treatment unit based on Plackett-Burman design and response surface methodology

    • 摘要: 针对天然气净化厂尾气处理装置能耗较高的问题,本研究提出了一种结合工艺过程模拟和数据统计分析的优化方法。首先利用Plackett-Burman(PB)试验设计筛选出对尾气处理装置能耗影响显著的关键因素,再通过Box-Behnken Design(BBD)响应面试验对尾气处理工艺进行优化,最后通过模拟分析验证了BBD响应面优化方法的有效性。研究结果表明,吸收塔塔板数、贫胺液循环量、富胺液入塔温度和再生塔回流比对尾气处理装置能耗的影响最为显著,各因素的影响显著程度由大到小依次为贫胺液循环量>再生塔回流比>富胺液入塔温度>吸收塔塔板数。确定的最佳工艺条件:吸收塔塔板数为10块,贫胺液循环量为90 000 kg·h−1,富胺液入塔温度为100 ℃,再生塔回流比为3.0,在此优化条件下,尾气处理装置的能耗可降低31.63%。Aspen软件模拟值与模型预测值的相对误差仅为0.07%,所建立的尾气处理装置能耗模型准确可靠。研究结论可为实际天然气净化厂尾气处理装置的用能优化提供参考。

       

      Abstract: To address the issue of high energy consumption in tail gas treatment units of natural gas purification plants, this research proposes an optimization method that combines process simulation with statistical data analysis. Initially, the Plackett-Burman (PB) experimental design was employed to identify the key factors that significantly affect the energy consumption of tail gas treatment unit. Subsequently, the Box-Behnken Design (BBD) response surface methodology was used to optimize the tail gas treatment process. Finally, the effectiveness of the BBD optimization method was validated through simulation analysis. The results indicated that number of absorber stages, lean amine circulation rate, rich amine temperature to regenerator, and regenerator reflux ratio were the most significant factors affecting the energy consumption of tail gas treatment unit. The order of significance of these factors was as follows: lean amine circulation rate > regenerator reflux ratio > rich amine temperature to regenerator > number of absorber stages. The optimal process conditions determined were: number of absorber stages was 10, lean amine circulation rate was 90 000 kg·h−1, rich amine temperature to regenerator was 100 °C, and regenerator reflux ratio was 3.0. Under the optimized conditions, the energy consumption of the tail gas treatment unit could be reduced by 31.63%. The relative error between the software simulation value and the model prediction value was only 0.07%, confirming the accuracy and reliability of the established energy consumption model for the tail gas treatment unit. The research conclusions can provide a reference for energy optimization of tail gas treatment units in actual natural gas purification plants.

       

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