Background

Natural gas is considered clean technology as compared to other fossil fuel resources. However, there is always a concern related to the carbon dioxide (CO₂) removal via the gas sweetening unit. The current research focuses on enhancing the CO₂ removal and reducing operating cost using a hybrid multi-objective optimisation technique, which incorporates (i) Pareto Front optimisation and (ii) Fuzzy optimisation.

Method

Aspen Plus simulation is performed to simulate diethanolamine (DEA) promoted potassium carbonate (K₂CO₃)-based natural gas sweetening unit. A total of 1000 simulation samples are conducted to study the effect of solvent concentration, flowrate, and temperature on CO₂ concentration in treated gas and operating cost. These solutions are fed into Pareto Front optimisation to determine sets of Pareto-optimal solutions. Thereafter, Fuzzy optimisation is performed to identify a single most optimal solution.

Significant findings

The result showed that optimal solution with CO₂ molar concentration in treated gas of 0.00006676 and operating cost of $19,495.08 h⁻¹ can be achieved at solvent concentration of 17.5 wt.%, flowrate of 42,500 kmol h⁻¹, and temperature of 80 °C. The energy requirement per unit of CO₂ removal for the final optimised solution was 19.31% less than that of the actual plant performance.

中文翻译：

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基于 K2CO3 的天然气脱硫工艺的优化：混合帕累托和模糊优化方法

背景

与其他化石燃料资源相比，天然气被认为是清洁技术。然而，始终存在与通过气体脱硫装置去除二氧化碳 (CO ₂ )相关的问题。目前的研究重点是使用混合多目标优化技术提高 CO _{2去除率和降低运营成本，该技术结合了 (i)} Pareto Front 优化和 (ii)模糊优化。

方法

执行 Aspen Plus 模拟以模拟二乙醇胺 (DEA) 促进的碳酸钾 (K ₂ CO ₃ ) 基天然气脱硫装置。总共进行了1000个模拟样品，以研究溶剂浓度、流量和温度对处理气体中CO ₂浓度和运行成本的影响。这些解决方案被输入帕累托前沿优化以确定帕累托最优解决方案集。此后，执行模糊优化以识别单个最优解。

重要发现

结果表明，在溶剂浓度为 17.5 wt.%、流量为 42,500 kmol h ^-1和温度为 80 °C时，可实现处理气体中 CO ₂摩尔浓度为 0.00006676 且运行成本为 $19,495.08 h ^-1的最佳解决方案. 最终优化解决方案的单位 CO ₂去除能耗比实际装置性能低 19.31%。