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Bayesian Optimization for Field-Scale Geological Carbon Storage

Xueying Lu, Kirk E. Jordan, Mary F. Wheeler, Edward O. Pyzer-Knapp, Matthew Benatan

Engineering 2022, Volume 18, Issue 11,   Pages 96-104 doi: 10.1016/j.eng.2022.06.011

Abstract:

We present a framework that couples a high-fidelity compositional reservoir simulator with Bayesian optimization (BO) for injection well scheduling optimization in geological carbon sequestrationFurthermore, IPARS is coupled to the International Business Machines (IBM) Corporation BayesianBO builds a probabilistic surrogate for the objective function using a Bayesian machine learning algorithmWe demonstrate these merits by applying the algorithm in the optimization of the CO2 injection schedule

Keywords： Compositional flow     Bayesian optimization     Geological carbon storage     CCUS     Machine learning     AI for    

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Evaluation of liquefaction-induced lateral displacement using Bayesian belief networks

Mahmood AHMAD, Xiao-Wei TANG, Jiang-Nan QIU, Feezan AHMAD

Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 1,   Pages 80-98 doi: 10.1007/s11709-021-0682-3

Abstract: a novel probabilistic framework for evaluating liquefaction-induced lateral displacement using the Bayesian

Keywords： Bayesian belief network     seismically induced soil liquefaction     interpretive structural modeling     lateral    

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A Bayesian modeling approach to bi-directional pedestrian flows in carnival events

S. Q. XIE, S. C. WONG, William H. K. LAM

Frontiers of Engineering Management 2017, Volume 4, Issue 4,   Pages 483-489 doi: 10.15302/J-FEM-2017023

Abstract: Bayesian inference was employed to calibrate the model by using prior data obtained from a previous controlled

Keywords： pedestrian flow model     bi-directional interactions     empirical studies     Bayesian inference    

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An assessment of surrogate fuel using Bayesian multiple kernel learning model in sight of sooting tendency

Frontiers in Energy 2022, Volume 16, Issue 2,   Pages 277-291 doi: 10.1007/s11708-021-0731-6

Abstract: values for surrogate fuels was proposed with the application of a machine learning method, named the Bayesian

Keywords： sooting tendency     yield sooting index     Bayesian multiple kernel learning     surrogate assessment     surrogate    

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Identification of pollution sources in rivers using a hydrodynamic diffusion wave model and improved Bayesian-Markov

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 7, doi: 10.1007/s11783-023-1685-1

Abstract:

● A hydrodynamic-Bayesian inference model was developed for water

Keywords： Identification of pollution sources     Water quality restoration     Bayesian inference     Hydrodynamic model    

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A novel multimode process monitoring method integrating LDRSKM with Bayesian inference

Shi-jin REN,Yin LIANG,Xiang-jun ZHAO,Mao-yun YANG

Frontiers of Information Technology & Electronic Engineering 2015, Volume 16, Issue 8,   Pages 617-633 doi: 10.1631/FITEE.1400263

Abstract: A local discriminant regularized soft -means (LDRSKM) method with Bayesian inference is proposed forTwo Bayesian inference based global fault detection indicators are then developed using the local monitoringBased on clustering analysis, Bayesian inference and manifold learning methods, the within and cross-mode

Keywords： monitoring     Local discriminant regularized soft k-means clustering     Kernel support vector data description     Bayesian    

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A knowledge reasoning Fuzzy-Bayesian network for root cause analysis of abnormal aluminum electrolysis

Weichao Yue, Xiaofang Chen, Weihua Gui, Yongfang Xie, Hongliang Zhang

Frontiers of Chemical Science and Engineering 2017, Volume 11, Issue 3,   Pages 414-428 doi: 10.1007/s11705-017-1663-x

Abstract: In view of this, a method based on Fuzzy-Bayesian network to construct multi-source knowledge solidificationcause analysis by finding the abnormal state of root node, which has a maximum posterior probability by Bayesian

Keywords： abnormal aluminum electrolysis cell condition     Fuzzy-Bayesian network     multi-source knowledge solidification    

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Data Centric Design: A New Approach to Design of Microstructural Material Systems Article

Wei Chen, Akshay Iyer, Ramin Bostanabad

Engineering 2022, Volume 10, Issue 3,   Pages 89-98 doi: 10.1016/j.eng.2021.05.022

Abstract:

Building processing, structure, and property (PSP) relations for computational materials design is at the heart of the Materials Genome Initiative in the era of high-throughput computational materials science. Recent technological advancements in data acquisition and storage, microstructure characterization and reconstruction (MCR), machine learning (ML), materials modeling and simulation, data processing, manufacturing, and experimentation have significantly advanced researchers’ abilities in building PSP relations and inverse material design. In this article, we examine these advancements from the perspective of design research. In particular, we introduce a data-centric approach whose fundamental aspects fall into three categories: design representation, design evaluation, and design synthesis. Developments in each of these aspects are guided by and benefit from domain knowledge. Hence, for each aspect, we present a wide range of computational methods whose integration realizes data-centric materials discovery and design.

Keywords： Materials informatics     Machine learning     Microstructure     Reconstruction     Bayesian optimization     Mixed-variable    

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Data-Driven Model Falsification and Uncertainty Quantification for Fractured Reservoirs Article

Junling Fang, Bin Gong, Jef Caers

Engineering 2022, Volume 18, Issue 11,   Pages 116-128 doi: 10.1016/j.eng.2022.04.015

Abstract: Bayesian theorem provides a framework to quantify the uncertainty in geological modeling and flow simulationThe application of Bayesian methods to fractured reservoirs has mostly been limited to synthetic casesIn field applications, however, one of the main problems is that the Bayesian prior is falsified, becauseWe then employ an approximate Bayesian computation (ABC) method combined with a tree-based surrogate

Keywords： Bayesian evidential learning     Falsification     Fractured reservoir     Random forest     Approximate Bayesian computation    

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Variational Bayesian multi-sparse component extraction for damage reconstruction of space debris hypervelocity Research Article

Xuegang HUANG, Anhua SHI, Qing LUO, Jinyang LUO,emei-126@126.com

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 4,   Pages 530-541 doi: 10.1631/FITEE.2000575

Abstract: To improve the survivability of orbiting spacecraft against space debris impacts, we propose an impact method. First, a multi-area damage mining model, which can describe damages in different spatial layers, is built based on an infrared thermal image sequence. Subsequently, to identify different impact damage types from infrared image data effectively, the inference is used to solve for the parameters in the model. Then, an image-processing framework is proposed to eliminate errors and compare locations of different damage types. It includes an image segmentation algorithm with an energy function and an image fusion method with . In the experiment, the proposed method is used to evaluate the complex damages caused by the impact of the secondary debris cloud on the rear wall of the typical Whipple shield configuration. Experimental results show that it can effectively identify and evaluate the complex damage caused by , including surface and internal defects.

Keywords： Hypervelocity impact     Variational Bayesian     Sparse representation     Damage assessment    

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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

Frontiers of Environmental Science & Engineering 2015, Volume 9, Issue 3,   Pages 534-544 doi: 10.1007/s11783-014-0660-2

Abstract: To overcome the identifiability problem, the classic Bayesian inference approach was utilized for parameterlong-term trend of nutrient removal and short-term variations of the process performance, showing that the Bayesian

Keywords： activated sludge model     Bayesian inference     biological nutrient removal     closed-loop bioreactor     oxidation    

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A new constrained maximum margin approach to discriminative learning of Bayesian classifiers None

Ke GUO, Xia-bi LIU, Lun-hao GUO, Zong-jie LI, Zeng-min GENG

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 5,   Pages 639-650 doi: 10.1631/FITEE.1700007

Abstract: We propose a novel discriminative learning approach for Bayesian pattern classification, called ‘constrainedWe applied the proposed CMM approach to learn Bayesian classifiers based on Gaussian mixture models,

Keywords： Discriminative learning     Statistical modeling     Bayesian pattern classifiers     Gaussian mixture models     UCI    

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Active Machine Learning for Chemical Engineers: A Bright Future Lies Ahead! Perspective

Yannick Ureel, Maarten R. Dobbelaere, Yi Ouyang, Kevin De Ras, Maarten K. Sabbe, Guy B. Marin, Kevin M. Van Geem

Engineering 2023, Volume 27, Issue 8,   Pages 23-30 doi: 10.1016/j.eng.2023.02.019

Abstract:

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. 

Keywords： Active machine learning     Active learning     Bayesian optimization     Chemical engineering     Design of experiments    

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IN2CLOUD: A novel concept for collaborative management of big railway data

Jing LIN, Uday KUMAR

Frontiers of Engineering Management 2017, Volume 4, Issue 4,   Pages 428-436 doi: 10.15302/J-FEM-2017048

Abstract: IN2CLOUD will help the movement of railway industry systems from “local” to “global” optimization in

Keywords： railway     intelligent asset management     collaborative learning     big data     hybrid cloud     Bayesian    

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Hybrid Bayesian Network Method for Predicting Intrusion

Wang Liangmin, Ma Jianfeng

Strategic Study of CAE 2008, Volume 10, Issue 8,   Pages 87-96

Abstract: >To solve the open problem of predicting intrusion in Reactive Intrusion Tolerance System, a hybrid BayesianSecondly, a hybrid Bayesian network model based on this intrusion model is presented to show the casualIn this hybrid Bayesian network model, the connections of the same layer are continuous, but that ofThe algorithm for computing the joint probability distribution of the hybrid Bayesian network is presentedIn the end, the efficiency of the intrusion model and hybrid Bayesian network in predicting intrusion

Keywords： intrusion tolerance     alert correlation     intrusion model     intrusion prediction    

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Title Author Date Type Operation

Bayesian Optimization for Field-Scale Geological Carbon Storage

Xueying Lu, Kirk E. Jordan, Mary F. Wheeler, Edward O. Pyzer-Knapp, Matthew Benatan

Journal Article

Evaluation of liquefaction-induced lateral displacement using Bayesian belief networks

Mahmood AHMAD, Xiao-Wei TANG, Jiang-Nan QIU, Feezan AHMAD

Journal Article

A Bayesian modeling approach to bi-directional pedestrian flows in carnival events

S. Q. XIE, S. C. WONG, William H. K. LAM

Journal Article

An assessment of surrogate fuel using Bayesian multiple kernel learning model in sight of sooting tendency

Journal Article

Identification of pollution sources in rivers using a hydrodynamic diffusion wave model and improved Bayesian-Markov

Journal Article

A novel multimode process monitoring method integrating LDRSKM with Bayesian inference

Shi-jin REN,Yin LIANG,Xiang-jun ZHAO,Mao-yun YANG

Journal Article

A knowledge reasoning Fuzzy-Bayesian network for root cause analysis of abnormal aluminum electrolysis

Weichao Yue, Xiaofang Chen, Weihua Gui, Yongfang Xie, Hongliang Zhang

Journal Article

Data Centric Design: A New Approach to Design of Microstructural Material Systems

Wei Chen, Akshay Iyer, Ramin Bostanabad

Journal Article

Data-Driven Model Falsification and Uncertainty Quantification for Fractured Reservoirs

Junling Fang, Bin Gong, Jef Caers

Journal Article

Variational Bayesian multi-sparse component extraction for damage reconstruction of space debris hypervelocity

Xuegang HUANG, Anhua SHI, Qing LUO, Jinyang LUO,emei-126@126.com

Journal Article

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

Journal Article

A new constrained maximum margin approach to discriminative learning of Bayesian classifiers

Ke GUO, Xia-bi LIU, Lun-hao GUO, Zong-jie LI, Zeng-min GENG

Journal Article

Active Machine Learning for Chemical Engineers: A Bright Future Lies Ahead!

Yannick Ureel, Maarten R. Dobbelaere, Yi Ouyang, Kevin De Ras, Maarten K. Sabbe, Guy B. Marin, Kevin M. Van Geem

Journal Article

IN2CLOUD: A novel concept for collaborative management of big railway data

Jing LIN, Uday KUMAR

Journal Article

Hybrid Bayesian Network Method for Predicting Intrusion

Wang Liangmin, Ma Jianfeng

Journal Article