Sub-surface geospatial intelligence in carbon capture, utilization and storage: A machine learning approach for offshore storage site selection

选址 地理空间分析 海底管道 选择(遗传算法) 碳纤维 环境科学 计算机科学 工程类 人工智能 地质学 遥感 物理 算法 复合数 核物理学 岩土工程
作者
Mehdi Nassabeh,Zhenjiang You,Alireza Keshavarz,Stefan Iglauer
出处
期刊:Energy [Elsevier BV]
卷期号:305: 132086-132086 被引量:5
标识
DOI:10.1016/j.energy.2024.132086
摘要

This study introduces an innovative data-driven and machine-learning framework designed to accurately predict site scores in the site screening study for specific offshore CO2 storage sites. The framework seamlessly integrates diverse sub-surface geospatial data sources with human aided expert-weighted criteria, thereby providing a high-resolution screening tool. Tailored to accommodate varying data accessibility and the significance of criteria, this approach considers both technical and non-technical factors. Its purpose is to facilitate the identification of priority locations for projects associated with Carbon Capture, Utilization, and Storage (CCUS). Through aggregating and analyzing geospatial datasets, the study employs machine learning algorithms and an expert-weighted model to identify suitable geologic CCUS regions. This process adheres to stringent safety, risk control, and environmental guidelines, addressing situations where human analysis may fail to recognize patterns and provide detailed insights in suitable site screening techniques. The primary emphasis of this research is to bridge the gap between scientific inquiry and practical application, facilitating informed decision-making in the implementation of CCUS projects. Rigorous assessments encompassing geological, oceanographic, and eco-sensitivity metrics contribute valuable insights for policymakers and industry leaders. To ensure the accuracy, efficiency, and scalability of the established offshore CO2 storage facilities, the proposed machine learning approach undergoes benchmarking. This comprehensive evaluation includes the utilization of machine learning algorithms such as Extreme Gradient Boosting (XGBoost), Random Forest (RF), Multilayer Extreme Learning Machine (MLELM), and Deep Neural Network (DNN) for predicting more suitable site scores. Among these algorithms, the DNN algorithm emerges as the most effective in site score prediction. The strengths of the DNN algorithm encompass nonlinear modeling, feature learning, scale invariance, handling high-dimensional data, end-to-end learning, transfer learning, representation learning, and parallel processing. The evaluation results of the DNN algorithm demonstrate high accuracy in the testing subset, with values of AAPD (Average Absolute Percentage Difference) = 1.486%, WAAPD (Weighted Average Absolute Percentage Difference) = 0.0149%, VAF (Variance Accounted For) = 0.9937, RMSE (Root Mean Square Error) = 0.9279, RSR (Root Sum of Squares Residuals) = 0.0068, and R2 (Coefficient of Determination) = 0.9937.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
薄雪草完成签到,获得积分10
2秒前
大橙子发布了新的文献求助10
2秒前
快乐学习每一天完成签到 ,获得积分10
2秒前
薄荷味完成签到 ,获得积分0
4秒前
科研通AI2S应助笑林采纳,获得10
4秒前
无心的雅旋完成签到,获得积分10
5秒前
量子星尘发布了新的文献求助10
6秒前
Horizon完成签到 ,获得积分10
10秒前
Oliver完成签到 ,获得积分10
12秒前
Superman完成签到 ,获得积分10
15秒前
Tina酱完成签到 ,获得积分10
15秒前
琪琪完成签到,获得积分10
15秒前
双碳小王子完成签到,获得积分10
17秒前
smottom应助科研通管家采纳,获得10
19秒前
19秒前
明时完成签到,获得积分10
20秒前
杨瑞东完成签到 ,获得积分10
23秒前
yyyy完成签到,获得积分10
31秒前
缥缈的平卉完成签到 ,获得积分10
32秒前
量子星尘发布了新的文献求助10
43秒前
李爱国应助大橙子采纳,获得10
44秒前
magictoo发布了新的文献求助30
50秒前
52秒前
yang完成签到,获得积分10
52秒前
Minicoper发布了新的文献求助10
53秒前
快乐丸子完成签到,获得积分10
54秒前
简单而复杂完成签到,获得积分10
54秒前
大橙子发布了新的文献求助10
58秒前
张牧之完成签到 ,获得积分10
1分钟前
冷冷暴力完成签到,获得积分10
1分钟前
YYY完成签到,获得积分10
1分钟前
1分钟前
gujian完成签到 ,获得积分10
1分钟前
帅气的秘密完成签到 ,获得积分10
1分钟前
自然函发布了新的文献求助10
1分钟前
冰冰双双完成签到,获得积分10
1分钟前
开心夏旋完成签到 ,获得积分0
1分钟前
我要读博士完成签到 ,获得积分10
1分钟前
活泼的大船完成签到,获得积分10
1分钟前
AFF完成签到,获得积分10
1分钟前
高分求助中
【提示信息,请勿应助】关于scihub 10000
Les Mantodea de Guyane: Insecta, Polyneoptera [The Mantids of French Guiana] 3000
徐淮辽南地区新元古代叠层石及生物地层 3000
The Mother of All Tableaux: Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 3000
Handbook of Industrial Diamonds.Vol2 1100
Global Eyelash Assessment scale (GEA) 1000
Picture Books with Same-sex Parented Families: Unintentional Censorship 550
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 4038128
求助须知:如何正确求助?哪些是违规求助? 3575831
关于积分的说明 11373827
捐赠科研通 3305610
什么是DOI,文献DOI怎么找? 1819255
邀请新用户注册赠送积分活动 892655
科研通“疑难数据库(出版商)”最低求助积分说明 815022