先进设计与优化,有效提高船载二氧化碳捕获能力

IF 3.8 3区 工程技术 Q2 ENGINEERING, CHEMICAL
Dat-Nguyen Vo, Xuewen Zhang, Kuniadi Wandy Huang, Xunyuan Yin
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引用次数: 0

摘要

舰载二氧化碳捕集(SCC)工艺面临着巨大的挑战,包括高成本和需要额外的加热能源才能捕集 90% 的二氧化碳。因此,本研究利用相关性分析和基于机器学习的优化,提出了先进的设计和集成框架,以实现节能且经济高效的 SCC 工艺。具体而言,我们开发了二氧化碳捕获和船舶发动机模拟器,并对其进行了验证,然后将其应用于开发 SCC 工艺的传统设计和四种先进设计。接下来,我们开发了第一个深度神经网络(DNN)模型作为替代模型,以较低的计算成本精确预测传统设计的性能,并以此为基础提出了两个优化问题。优化结果表明,使用传统设计捕获 90% 的 CO2 需要额外消耗 1.369 兆瓦的热能,成本为 108.583 美元/tCO2。然后,对四种先进设计进行了分析,以展示其降低二氧化碳捕集成本和供热能耗的潜力,并通过相关方法确定使用精蒸汽压缩的 SCC(LVC-SCC)设计是最可行的设计。最后,为 LVC-SCC 设计开发了第二个基于 DNN 的代用模型,然后用于制定第三个优化问题。优化结果证实,LVC-SCC 设计充分利用了现有的加热能源,以 53.54 美元/吨 CO2 的价格捕获了 90% 的二氧化碳(约 8.89 吨 CO2/小时),仅排放 0.46 ppm 的一乙醇胺。此外,与传统设计相比,LVC-SCC 设计大大降低了成本、供热能耗和制冷能耗,降幅分别约为 49.8%、15% 和 12%。建议的设计、基于机器学习的优化方法以及由此得出的结论为推动国际航运业到 2050 年实现温室气体净零排放提供了有价值的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Advanced Designs and Optimization for Efficiently Enhancing Shipboard CO2 Capture

Advanced Designs and Optimization for Efficiently Enhancing Shipboard CO2 Capture
Shipboard CO2 capture (SCC) processes face significant challenges, including high costs and the need for extra heating energy to capture 90% of the CO2. Therefore, this study proposes advanced designs and an integration framework using correlation analysis and machine learning-based optimization to achieve the energy- and cost-effective SCC process. Specifically, we develop CO2 capture and ship engine simulators, which are validated and then applied to develop conventional and four advanced designs for the SCC process. Next, a first deep neural network (DNN) model is developed as a surrogate model to precisely predict the performance of the conventional design at low computation cost, serving as the basis for formulating two optimization problems. The optimization results reveal that capturing 90% of CO2 by using the conventional design requires an additional 1.369 MW of heating energy, costing 108.583 $/tCO2. Then, the four advanced designs are analyzed to exhibit their potential for reducing the CO2 capture cost and heating energy, with correlation methods identifying SCC using lean vapor compression (LVC-SCC) design as the most feasible design. Finally, a second DNN-based surrogate model is developed for the LVC-SCC design before being used to formulate the third optimization problem. The optimization results confirm that the LVC-SCC design leverages available heating energy sources to capture 90% of CO2 (approximately 8.89 tCO2/h) at 53.54 $/tCO2, emitting only 0.46 ppm monoethanolamine. Moreover, compared to the conventional design, the LVC-SCC design significantly reduces the cost, heating energy, and cooling energy by approximately 49.8%, 15%, and 12%, respectively. The proposed designs, the machine learning-based optimization approach, and the resulting findings provide valuable solutions for driving the international shipping industry toward achieving net-zero greenhouse gas emissions by 2050.
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来源期刊
Industrial & Engineering Chemistry Research
Industrial & Engineering Chemistry Research 工程技术-工程:化工
CiteScore
7.40
自引率
7.10%
发文量
1467
审稿时长
2.8 months
期刊介绍: ndustrial & Engineering Chemistry, with variations in title and format, has been published since 1909 by the American Chemical Society. Industrial & Engineering Chemistry Research is a weekly publication that reports industrial and academic research in the broad fields of applied chemistry and chemical engineering with special focus on fundamentals, processes, and products.
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