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Smart Manufacturing for the Oil Refining and Petrochemical Industry

Zhihong Yuan,Weizhong Qin,Jinsong Zhao

Engineering 2017, Volume 3, Issue 2, Pages 179-182 doi: 10.1016/J.ENG.2017.02.012

Abstract:

Smart manufacturing will transform the oil refining and petrochemical sector into a connected, information-drivenenvironment.the petrochemical sector are demonstrated, such as the fault detection of a catalytic cracking unit drivenby big data, advanced optimization for the planning and scheduling of oil refinery sites, and more.

Keywords： Smart manufacturing Petrochemical Data-/information-driven environment

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Data-driven distribution network topology identification considering correlated generation power of distributed

Frontiers in Energy 2022, Volume 16, Issue 1, Pages 121-129 doi: 10.1007/s11708-021-0780-x

Abstract: This paper proposes a data-driven topology identification method for distribution systems with distributedSecond, a maximal information coefficient-based maximum spanning tree algorithm is developed to obtain

Keywords： power distribution network data-driven topology identification distributed energy resource maximalinformation coefficient

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Framework based on building information modeling, mixed reality, and a cloud platform to support information

Berardo NATICCHIA, Alessandra CORNELI, Alessandro CARBONARI

Frontiers of Engineering Management 2020, Volume 7, Issue 1, Pages 131-141 doi: 10.1007/s42524-019-0071-y

Abstract: The quality of information flow management has a remarkable effect on the entire life cycle of buildingsmeans of improving the organization and exchange of information.BIM tools integrate multiple levels of information within a single digital model of a building.Technicians can benefit from real-time communication with the data repository whenever the need for gatheringcontextual information and/or updating any data in the digital model arises.

Keywords： information flow management BIM mixed reality common data environment facility management

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Data driven models for compressive strength prediction of concrete at high temperatures

Mahmood AKBARI, Vahid JAFARI DELIGANI

Frontiers of Structural and Civil Engineering 2020, Volume 14, Issue 2, Pages 311-321 doi: 10.1007/s11709-019-0593-8

Abstract: The use of data driven models has been shown to be useful for simulating complex engineering processes, when the only information available consists of the data of the process.In this study, four data-driven models, namely multiple linear regression, artificial neural network,driven models to predict the compressive strength at high temperature.driven models to make satisfactory results.

Keywords： data driven model compressive strength concrete high temperature

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Prediction of hydro-suction dredging depth using data-driven methods

Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 3, Pages 652-664 doi: 10.1007/s11709-021-0719-7

Abstract: In this study, data-driven methods (DDMs) including different kinds of group method of data handlingAlso, 33 data samples from three previous studies were used.Data-driven simulation results indicated that the HGSO algorithm accurately trains the GMDH methods better

Keywords： sedimentation water resources dam engineering machine learning heuristic

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Digital twin for healthy indoor environment: A vision for the post-pandemic era

Frontiers of Engineering Management 2023, Volume 10, Issue 2, Pages 300-318 doi: 10.1007/s42524-022-0244-y

Abstract: Indoor environment has significant impacts on human health as people spend 90% of their time indoors.health awareness have further elevated the urgency for cultivating and maintaining a healthy indoor environmentThe advancement in emerging digital twin technologies including building information modeling (BIM),Internet of Things (IoT), data analytics, and smart control have led to new opportunities for buildingSpecifically, the current applications of BIM, IoT sensing, data analytics, and smart building control

Keywords： digital twin healthy indoor environment building information modeling occupant–building interaction

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Physics-Informed Deep Learning-Based Real-Time Structural Response Prediction Method

Ying Zhou,Shiqiao Meng,Yujie Lou,Qingzhao Kong,

Engineering doi: 10.1016/j.eng.2023.08.011

Abstract: structural response prediction method that can predict a large number of nodes in a structure through a data-drivenThe proposed method includes a Phy-Seisformer model that incorporates the physical information of the

Keywords： Structural seismic response prediction Physics information informed Real-time prediction Earthquake engineering Data-driven machine learning

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Optimal Antibody Purification Strategies Using Data-Driven Models Article

Songsong Liu, Lazaros G. Papageorgiou

Engineering 2019, Volume 5, Issue 6, Pages 1077-1092 doi: 10.1016/j.eng.2019.10.011

Abstract: Data-driven models of chromatography throughput are developed considering loaded mass, flow velocity,height as the inputs, using manufacturing-scale simulated datasets based on microscale experimental datato minimize the total cost of goods per gram of the antibody purification process, incorporating the data-driven

Keywords： Antibody purification Multiscale optimization Antigen-binding fragment Mixed-integer programming Data-driven

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Connecting Information to Promote Public Health

Xue-mei Su,Jia Zhao

Frontiers of Engineering Management 2016, Volume 3, Issue 4, Pages 384-389 doi: 10.15302/J-FEM-2016054

Abstract: With the development of information technology in the past 12 years, China has established the specializedor vertical web-based information systems for data collection of disease and related risk factor.These information systems are described as public health information systems (PHIS) in China.with multiple and time-consuming reporting requirements cannot deliver timely, accurate and complete dataoccurs under the policy and investment of health since 2009 in China, there should be a connection and data

Keywords： public health information system data exchange China

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Data-Driven Anomaly Diagnosis for Machining Processes Article

Y.C. Liang, S. Wang, W.D. Li, X. Lu

Engineering 2019, Volume 5, Issue 4, Pages 646-652 doi: 10.1016/j.eng.2019.03.012

Abstract: To address this issue, this paper presents a novel data-driven diagnosis system for anomalies.In this system, power data for condition monitoring are continuously collected during dynamic machininganalysis, preprocessing mechanisms have been designed to denoise, normalize, and align the monitored dataImportant features are extracted from the monitored data and thresholds are defined to identify anomaliesBased on historical data, the values of thresholds are optimized using a fruit fly optimization (FFO)

Keywords： Computer numerical control machining Anomaly detection Fruit fly optimization algorithm Data-driven

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Machine Learning and Data-Driven Techniques for the Control of Smart Power Generation Systems: An Uncertainty Review

Li Sun, Fengqi You

Engineering 2021, Volume 7, Issue 9, Pages 1239-1247 doi: 10.1016/j.eng.2021.04.020

Abstract: The burgeoning era of machine learning (ML) and data-driven control (DDC) techniques promises an improved

Keywords： Smart power generation Machine learning Data-driven control Systems engineering

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Information Sunshine: the Collisionless Information Sharing Architecture

Li Youping

Strategic Study of CAE 2000, Volume 2, Issue 1, Pages 24-27

Abstract:

The novel information sharing architecture, Information Sunshine, is presented in the paper.Two core concepts of the Information Sunshine idea are Data Stream Environment (DSE) and Personal Demandingpush .capacity of DVB, and then the nationwide flow of the high speed data stream forms a digital environmentAfter storing, users can locally interact with the information without remote connection requirement.Only single-way broadcasting network required by the Information Sunshine architecture, so it is low

Keywords： Data broadcasting DVB network collisionless

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An adaptive data-driven method for accurate prediction of remaining useful life of rolling bearings

Yanfeng PENG, Junsheng CHENG, Yanfei LIU, Xuejun LI, Zhihua PENG

Frontiers of Mechanical Engineering 2018, Volume 13, Issue 2, Pages 301-310 doi: 10.1007/s11465-017-0449-7

Abstract:

A novel data-driven method based on Gaussian mixture model (GMM) and distance evaluation techniqueThe data sets are clustered by GMM to divide all data sets into several health states adaptively andThus, either the health state of the data sets or the number of the states is obtained automatically.training data sets.sets into several health states and remove the abnormal data sets.

Keywords： Gaussian mixture model distance evaluation technique health state remaining useful life rolling bearing

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Hybrid Data-Driven and Mechanistic Modeling Approaches for Multiscale Material and Process Design Perspective

Teng Zhou, Rafiqul Gani, Kai Sundmacher

Engineering 2021, Volume 7, Issue 9, Pages 1231-1238 doi: 10.1016/j.eng.2020.12.022

Abstract: modeling, the material properties, which are computationally expensive to obtain, are described by data-driven

Keywords： Data-driven Surrogate model Machine learning Hybrid modeling Material design Process optimization

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A hierarchical system to predict behavior of soil and cantilever sheet wall by data-driven models

Nang Duc BUI; Hieu Chi PHAN; Tiep Duc PHAM; Ashutosh Sutra DHAR

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 6, Pages 667-684 doi: 10.1007/s11709-022-0822-4

Abstract: The uncertainty of this data-driven system is partially investigated by developing 1000 RFC models, basedon the application of random sampling technique in the data splitting process.Investigation on the distribution of the evaluation metrics reveals negative skewed data toward the 1.0000

Keywords： finite element analysis cantilever sheet wall machine learning artificial neural network random forest