Machine learning based techno-economic process optimisation for CO2 capture via enhanced weathering
Machine learning based techno-economic process optimisation for CO2 capture via enhanced weathering
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DOI：
10.1016/j.egyai.2023.100234
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发表时间：
2023-01
期刊：
影响因子：
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通讯作者：
Hai-feng Jiang;Shuo Wang;Lei Xing;V. Pinfield;Jin Xuan
中科院分区：
文献类型：
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作者：
Hai-feng Jiang;Shuo Wang;Lei Xing;V. Pinfield;Jin Xuan
本研究评估了在不同工艺配置和条件下，在串联填充气泡塔（S-PBC）接触器中基于增强风化（EW）的co2捕集的实用性和经济性。S-PBC接触器通过多物理场、机器学习、多变量、多目标优化的整体m4模型，充分利用了丰富海水和高效淡水的优势。然后进行经济分析，以调查不同的S-PBC配置的成本。基于新型机器学习算法扩展自适应混合函数（E-AHF）的数据驱动代理模型通过物理模型生成的数据实现和训练。应用遗传算法和NSGA-II进行单目标和多目标优化，以8个设计变量的最优值实现最大co2捕获率（CR）和最小能耗（EC）。预测CR和EC的r2均大于0.96，相对误差均小于5%。m4模型已被证明是执行多变量和多目标优化的有效方法，在保持高预测精度的同时显着减少了计算时间和资源。最优CR值为0.1014 kg h−1，最优EC值为6.1855 MJ kg−1。最具前景的S-PBC配置的计算净成本约为400美元t - 1CO2，比当前直接空气捕获（DAC）的净成本低100美元t - 1CO2，但由于二氧化碳捕获速度较慢而受到损害。
This work evaluated the practicability and economy of the enhanced weathering (EW)-based CO2capture in series packed bubble column (S-PBC) contactors operated with different process configurations and conditions. The S-PBC contactors are designed to fully use the advantages of abundant seawater and highly efficient freshwater through a holistic M4model, including multi-physics, machine learning, multi-variable and multi-objective optimisation. An economic analysis is then performed to investigate the cost of different S-PBC configurations. A data-driven surrogate model based on a novel machine learning algorithm, extended adaptive hybrid functions (E-AHF), is implemented and trained by the data generated by the physics-based models. GA and NSGA-II are applied to perform single- and multi-objective optimisation to achieve maximum CO2capture rate (CR) and minimum energy consumption (EC) with the optimal values of eight design variables. The R2for the prediction of CR and EC is higher than 0.96 and the relative errors are lower than 5%. The M4model has proven to be an efficient way to perform multi-variable and multi-objective optimisation, that significantly reduces computational time and resources while maintaining high prediction accuracy. The trade-off of the maximum CR and minimum EC is presented by the Pareto front, with the optimal values of 0.1014 kg h−1for CR and 6.1855 MJ kg−1CO2for EC.The calculated net cost of the most promising S-PBC configuration is around 400 $ t−1CO2, which is about 100 $ t−1CO2lower than the net cost of current direct air capture (DAC), but compromised by slower CO2capture rate.