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Xueying Lu, Kirk E. Jordan, Mary F. Wheeler, Edward O. Pyzer-Knapp, Matthew Benatan
《工程(英文)》 2022年 第18卷 第11期 页码 96-104 doi: 10.1016/j.eng.2022.06.011
We present a framework that couples a high-fidelity compositional reservoir simulator with Bayesian optimization (BO) for injection well scheduling optimization in geological carbon sequestration. This work represents one of the first at tempts to apply BO and high-fidelity physics models to geological carbon storage. The implicit parallel accurate reservoir simulator (IPARS) is utilized to accurately capture the underlying physical processes during CO2 sequestration. IPARS provides a framework for several flow and mechanics models and thus supports both stand-alone and coupled simulations. In this work, we use the compositional flow module to simulate the geological carbon storage process. The compositional flow model, which includes a hysteretic three-phase relative permeability model, accounts for three major CO2 trapping mechanisms: structural trapping, residual gas trapping, and solubility trapping. Furthermore, IPARS is coupled to the International Business Machines (IBM) Corporation Bayesian Optimization Accelerator (BOA) for parallel optimizations of CO2 injection strategies during field-scale CO2 sequestration. BO builds a probabilistic surrogate for the objective function using a Bayesian machine learning algorithm—the Gaussian process regression, and then uses an acquisition function that leverages the uncertainty in the surrogate to decide where to sample. The IBM BOA addresses the three weaknesses of standard BO that limits its scalability in that IBM BOA supports parallel (batch) executions, scales better for high-dimensional problems, and is more robust to initializations. We demonstrate these merits by applying the algorithm in the optimization of the CO2 injection schedule in the Cranfield site in Mississippi, USA, using field data. The optimized injection schedule achieves 16% more gas storage volume and 56% less water/surfactant usage compared with the baseline. The performance of BO is compared with that of a genetic algorithm (GA) and a covariance matrix adaptation (CMA)-evolution strategy (ES). The results demonstrate the superior performance of BO, in that it achieves a competitive objective function value with over 60% fewer forward model evaluations.
Evaluation of liquefaction-induced lateral displacement using Bayesian belief networks
Mahmood AHMAD, Xiao-Wei TANG, Jiang-Nan QIU, Feezan AHMAD
《结构与土木工程前沿(英文)》 2021年 第15卷 第1期 页码 80-98 doi: 10.1007/s11709-021-0682-3
关键词: Bayesian belief network seismically induced soil liquefaction interpretive structural modeling lateral displacement
A Bayesian modeling approach to bi-directional pedestrian flows in carnival events
S. Q. XIE, S. C. WONG, William H. K. LAM
《工程管理前沿(英文)》 2017年 第4卷 第4期 页码 483-489 doi: 10.15302/J-FEM-2017023
关键词: pedestrian flow model bi-directional interactions empirical studies Bayesian inference
《能源前沿(英文)》 2022年 第16卷 第2期 页码 277-291 doi: 10.1007/s11708-021-0731-6
关键词: sooting tendency yield sooting index Bayesian multiple kernel learning surrogate assessment surrogate formulation
《环境科学与工程前沿(英文)》 2023年 第17卷 第7期 doi: 10.1007/s11783-023-1685-1
● A hydrodynamic-Bayesian inference model was developed for water pollution tracking.
关键词: Identification of pollution sources Water quality restoration Bayesian inference Hydrodynamic model Inverse problem
A novel multimode process monitoring method integrating LDRSKM with Bayesian inference
Shi-jin REN,Yin LIANG,Xiang-jun ZHAO,Mao-yun YANG
《信息与电子工程前沿(英文)》 2015年 第16卷 第8期 页码 617-633 doi: 10.1631/FITEE.1400263
关键词: Multimode process monitoring Local discriminant regularized soft k-means clustering Kernel support vector data description Bayesian inference Tennessee Eastman process
Weichao Yue, Xiaofang Chen, Weihua Gui, Yongfang Xie, Hongliang Zhang
《化学科学与工程前沿(英文)》 2017年 第11卷 第3期 页码 414-428 doi: 10.1007/s11705-017-1663-x
关键词: abnormal aluminum electrolysis cell condition Fuzzy-Bayesian network multi-source knowledge solidification and reasoning root cause analysis
数据中心设计——一种微结构材料体系设计新方法 Article
Wei Chen, Akshay Iyer, Ramin Bostanabad
《工程(英文)》 2022年 第10卷 第3期 页码 89-98 doi: 10.1016/j.eng.2021.05.022
在高通量计算材料科学时代,材料基因组计划的核心是为计算材料设计建立数据处理、材料结构和材料属性(PSP)之间的关系。近年来,在数据获取和存储,微结构表征和重建(MCR),机器学习(ML),材料建模和仿真,数据处理、材料制造和实验方面取得的技术进步,显著提升了研究人员在PSP关系的建立和逆向材料设计方面的能力。本文将从设计研究的角度审视这些进步。特别介绍了一种数据中心设计方法,并从本质上将该方法分为三个方面:设计表征、设计评估和设计合成。每个方面的发展都由领域知识指导并从中受益。因此,针对每个方面,提出了一种应用广泛的计算方法,这些方法的集成实现了以数据为中心的材料发现和设计。
裂缝性储层数据驱动模型证伪与不确定性量化 Article
方军龄, 龚斌, Jef Caers
《工程(英文)》 2022年 第18卷 第11期 页码 116-128 doi: 10.1016/j.eng.2022.04.015
天然裂缝的许多特性是不确定的,如裂缝的空间分布、岩石物理特性和流体流动性能。贝叶斯定理提供了一个框架来量化地质建模和流动模拟的不确定性,从而支持储层物性预测。贝叶斯方法在裂缝性储层中的应用大多局限于合成案例。然而,在现场应用中,一个主要问题是贝叶斯先验是被证伪的,因为它不能预测油气藏的生产历史。在本文中,我们展示了如何利用全局敏感性分析(GSA)来确定先验被证伪的原因。然后,我们采用近似贝叶斯计算(ABC)方法,结合基于决策树的代理模型来拟合生产历史。我们将这两种方法应用于一个复杂的裂缝性油气藏,其中综合考虑了所有不确定因素,包括油层物理特性、岩石物理特性、流体特性、离散裂缝参数以及压力和渗透率的动态变化。我们成功地找出了证伪的几个原因。结果表明,我们提出的方法可以有效地量化裂缝性储层建模和流动模拟的不确定性。此外,关键参数的不确定性,如裂缝开度和断层传导率,得到了降低。
基于变分贝叶斯多稀疏成分提取的空间碎片超高速撞击损伤重构方法研究 Research Article
黄雪刚,石安华,罗庆,罗锦阳
《信息与电子工程前沿(英文)》 2022年 第23卷 第4期 页码 530-541 doi: 10.1631/FITEE.2000575
Zheng LI,Rong QI,Wei AN,Takashi MINO,Tadashi SHOJI,Willy VERSTRAETE,Jian GU,Shengtao LI,Shiwei XU,Min YANG
《环境科学与工程前沿(英文)》 2015年 第9卷 第3期 页码 534-544 doi: 10.1007/s11783-014-0660-2
关键词: activated sludge model Bayesian inference biological nutrient removal closed-loop bioreactor oxidation ditch denitrifying polyphosphate accumulating organisms
Ke GUO, Xia-bi LIU, Lun-hao GUO, Zong-jie LI, Zeng-min GENG
《信息与电子工程前沿(英文)》 2018年 第19卷 第5期 页码 639-650 doi: 10.1631/FITEE.1700007
化学工程师的主动机器学习 Perspective
Yannick Ureel, Maarten R. Dobbelaere, Yi Ouyang, Kevin De Ras, Maarten K. Sabbe, Guy B. Marin, Kevin M. Van Geem
《工程(英文)》 2023年 第27卷 第8期 页码 23-30 doi: 10.1016/j.eng.2023.02.019
By combining machine learning with the design of experiments, thereby achieving so-called active machine learning, more efficient and cheaper research can be conducted. Machine learning algorithms are more flexible and are better than traditional design of experiment algorithms at investigating processes spanning all length scales of chemical engineering. While active machine learning algorithms are maturing, their applications are falling behind. In this article, three types of challenges presented by active machine learning—namely, convincing the experimental researcher, the flexibility of data creation, and the robustness of active machine learning algorithms—are identified, and ways to overcome them are discussed. A bright future lies ahead for active machine learning in chemical engineering, thanks to increasing automation and more efficient algorithms that can drive novel discoveries.
关键词: Active machine learning Active learning Bayesian optimization Chemical engineering Design of experiments
IN2CLOUD: A novel concept for collaborative management of big railway data
Jing LIN, Uday KUMAR
《工程管理前沿(英文)》 2017年 第4卷 第4期 页码 428-436 doi: 10.15302/J-FEM-2017048
关键词: railway intelligent asset management collaborative learning big data hybrid cloud Bayesian
王良民,马建峰
《中国工程科学》 2008年 第10卷 第8期 页码 87-96
标题 作者 时间 类型 操作
基于样地调查的地质碳储量的贝叶斯优化
Xueying Lu, Kirk E. Jordan, Mary F. Wheeler, Edward O. Pyzer-Knapp, Matthew Benatan
期刊论文
Evaluation of liquefaction-induced lateral displacement using Bayesian belief networks
Mahmood AHMAD, Xiao-Wei TANG, Jiang-Nan QIU, Feezan AHMAD
期刊论文
A Bayesian modeling approach to bi-directional pedestrian flows in carnival events
S. Q. XIE, S. C. WONG, William H. K. LAM
期刊论文
An assessment of surrogate fuel using Bayesian multiple kernel learning model in sight of sooting tendency
期刊论文
Identification of pollution sources in rivers using a hydrodynamic diffusion wave model and improved Bayesian-Markov
期刊论文
A novel multimode process monitoring method integrating LDRSKM with Bayesian inference
Shi-jin REN,Yin LIANG,Xiang-jun ZHAO,Mao-yun YANG
期刊论文
A knowledge reasoning Fuzzy-Bayesian network for root cause analysis of abnormal aluminum electrolysis
Weichao Yue, Xiaofang Chen, Weihua Gui, Yongfang Xie, Hongliang Zhang
期刊论文
long-term nutrient removal in a full-scale closed-loop bioreactor for sewage treatment: an example of Bayesian
Zheng LI,Rong QI,Wei AN,Takashi MINO,Tadashi SHOJI,Willy VERSTRAETE,Jian GU,Shengtao LI,Shiwei XU,Min YANG
期刊论文
化学工程师的主动机器学习
Yannick Ureel, Maarten R. Dobbelaere, Yi Ouyang, Kevin De Ras, Maarten K. Sabbe, Guy B. Marin, Kevin M. Van Geem
期刊论文
IN2CLOUD: A novel concept for collaborative management of big railway data
Jing LIN, Uday KUMAR
期刊论文