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2022 SPE Improved Oil Recovery conference, IOR 2022

作者： Bordeaux-Rego, Fabio Mehrabi, Mehran Eltahan, Esmail Sepehrnoori, Kamy University of Texas Austin United States

ISBN: (纸本)9781613998502

This paper presents a new wettability alteration model based on surface complexation theory and anextensive experimental dataset. The objective is to provide a general correlation for contact angle calculationthat (1) captures the main mechanisms that impact rock-brine-oil wettability and (2) minimizes the numberof parameters used to tune with experimental data. We compile a set of 141 zeta-potential and contact-angle measurements from the literature. We study theoil/rock surface-complexation reactions and model the electrostatic behavior of each data point. We developa new wettability model that estimates the contact angle and consists of five terms based on the Young-Laplace equation. We use the Nelder-Mead optimization algorithm to determine the model-parameter valuesthat produce the best fit of experimental observations. The contact angle estimates produced by our modelare also verified against those calculated by Extended-Derjaguin-Landau-Verwey-Overbeekand (EDLVO)theory and are validated using UTCOMP-IPhreeqc to simulate five limestone Amott tests from the literature. The Blind-testing test reveals that our model is predictive of the experimental data (R2 = 0.81, RMSE =12.5). While reducing the tuning parameters by half, our model is comparable to and-in some cases-evensuperior to the EDLVO modeling in predicting the contact angle measurements. We argue that EDLVOmodeling has 10+ parameters, and the individual errors associated with each parameter could lead to wrongpredictions. Amott-test simulations show excellent agreement between the proposed wettability-alterationmodel and experimental data. The rock's initial wettability was measured to be strongly oil-wet, with anegative Amott index and recovery factor around 5%, corroborating the calculated contact angle of 160degrees. The recovery factor increases to about 20-35% as the rock becomes more water-wet after interactionwith engineered water (contact angle changes to 90-64 degrees). Further analysis indicat

关键词： Wetting

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International conference on Intelligent and Fuzzy Systems, INFUS 2020

International Conference on Intelligent and Fuzzy Systems, I...

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International conference on Intelligent and Fuzzy Systems, INFUS 2020

ISBN: (纸本)9783030511555

The proceedings contain 197 papers. The special focus in this conference is on Intelligent and Fuzzy Systems. The topics include: Basket Patterns in Turkey: A Clustering of FMCG Baskets Using Consumer Panel Data;modeling Urban Traffic by Means of Traffic Density Data: Istanbul Case;realising Newspaper Sales by Using Statistic Methods;extensions of Fuzzy Sets in Big Data Applications: A Literature Review;a Big Data Semantic driven Context Aware Recommendation Method;Blended Environment of Naive Bayes and Support Vector Machine (SVM) for Designing simulation Based E-Learning Respiratory System;interCriteria Analysis of Public Health Data in Bulgaria;a Case Study on Vehicle Battery Manufacturing Using Fuzzy Analysis of Variance;new Suggestion for Fuzzy Random Variable and Its Statistical simulation;fuzzy Centrality Analysis for Smart City Trajectories;improving Customer Experience for an Internet Service Provider: A Neural Networks Approach;journey Segmentation of Turkish Tobacco Users Using Sequence Clustering Techniques;segmentation of Larynx Histopathology Images via Convolutional Neural Networks;Glioma Brain Tumor Grade Classification from MRI Using Convolutional Neural Networks Designed by Modified FA;dynamic System Control Using Z-number Based Fuzzy Neural Network;comprehensive Research of the Commodity Market: Theoretical, Methodological and Modern Approaches Using Neural Networks with Fuzzy Logic;the Maximum Lexicographic Contraflow Finding in a Fuzzy Dynamic Network;An Enhanced Particle Swarm optimization with Levy Flight for RBF Neural Network in Typical Karst Area, South China;a Method for Forecasting the Demand for Pharmaceutical Products in a Distributed Pharmacy Network Based on an Integrated Approach Using Fuzzy Logic and Neural Networks.

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Innovation and Exploration of Practical Education Mode of New Engineering Subject driven by BIM Technology 2

Innovation and Exploration of Practical Education Mode of Ne...

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2nd International conference on Education, Knowledge and Information Management, ICEKIM 2021

作者： Gao, Zhangwang Wu, Bei Shi, Yansiyi He, Yarong Tianjin University Renai College Department of Management Tianjin China

ISBN: (纸本)9781728168340

BIM is the abbreviation of Building Information modeling, which refers to the technology of creating and maintaining digital information of construction products and its engineering application in the whole life cycle of construction projects. It has five characteristics: visualization, simulation, coordination, plotting and optimization. All engineering majors are independent in teaching and practice, which is easy to form an information island. According to the requirements of new engineering construction, the upgrading and transformation of BIM based engineering multi specialty collaborative new engineering industry education integration talent training mode has been put on the agenda. To build a multi professional collaborative BIM new engineering practice education platform of Tianjin University Ren'ai College - intelligent construction innovation college, and form the '433' BIM new engineering practice education mode, change the simple confirmatory practice teaching form, solve the problem that practice teaching is not closely linked with theory, and form a benign 'interactive mechanism' with theoretical teaching, so as to improve the quality of applied talents training. © 2021 IEEE.

关键词： Engineering education

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Data driven NARMAX modeling for PEMFC air compressor

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2020年第39期45卷 20321-20328页

作者： Deng, Zhihua Chen, Qihong Zhang, Liyan Fu, Zhichao Wuhan Univ Technol Sch Automat Wuhan 430070 Peoples R China

Air compressor of proton exchange membrane fuel cell (PEMFC) system is usually nonlinear and strong coupled. It is difficult to establish a online optimization oriented model. In order to solve this problem, this paper proposed a nonlinear autoregressive moving average with exogenous inputs (NARMAX) model for air compressor of PEMFC system. The NARMAX model is an equivalent time-varying linear model, and the time-varying parameters are identified by recurrent neural network (RNN). simulation results show that the proposed method has small fitting error, the error of air flow and pressure ratio approximate zero, while the mean square error (MSE) of air flow and pressure ratio are 1.5171e-07 and 6.3767e-05, respectively. Therefore, the established air compressor model is accurate and effective. (C) 2019 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： Proton exchange membrane fuel cell (PEMFC) Nonlinear autoregressive moving average with exogenous inputs (NARMAX) Recurrent neural network (RNN) Data-driven Air compressor

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DATA-driven APPROACHES TO LEARN HYCHEM MODELS

DATA-DRIVEN APPROACHES TO LEARN HYCHEM MODELS

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ASME-Internal-Combustion-Engine-Division Fall Technical conference (ICEF)

作者： Ji, Weiqi Zanders, Julian Park, Ji-Woong Deng, Sili MIT Dept Mech Engn Cambridge MA 02139 USA MIT Dept Elect Engn & Comp Sci Cambridge MA 02139 USA Argonne Natl Lab Argonne IL 60439 USA

ISBN: (纸本)9780791885512

The HyChem (Hybrid Chemistry) approach has recently been proposed for modeling high-temperature combustion of real, multi-component fuels. The approach combines lumped reaction steps for fuel thermal and oxidative pyrolysis with detailed chemistry for the oxidation of the resulting pyrolysis products. Determining the pyrolysis submodel requires extensive experimentation on speciation measurements. Recent work has been directed to learn HyChem from an existing HyChem model for a similar fuel, which requires less data. However, the approach usually shows substantial discrepancies with experimental data within the Negative Temperature Coefficient (NTC) regime, as the low-temperature chemistry is more fuel-specific than high-temperature chemistry. This paper proposes a machine learning approach to learn the HyChem models that can cover both high-temperature and low-temperature regimes. Specifically, we develop a HyChem model using the experimental datasets of ignition delay times covering a wide range of temperatures and equivalence ratios. The chemical kinetic model is treated as a neural network model, and we then employ stochastic gradient descent (SGD), a technique that was developed for deep learning, for the training. We demonstrate the approach in learning the HyChem model for F-24, which is a Jet-A derived fuel, and compare the results with previous work employing genetic algorithms. The results show that the SGD approach can achieve comparable model performance with genetic algorithms but the computational cost is reduced by 1000 times. In addition, with regularization in SGD, the SGD approach changes the kinetic parameters from their original values much less than genetic algorithm and is thus more likely to retrain mechanistic meanings. Finally, our approach is built upon open-source packages and can be applied to the development and optimization of chemical kinetic models for internal combustion engine simulations.

关键词： Combustion simulation Internal combustion engines modeling Chemistry Neural network models Pyrolysis Ignition delay

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1st International Scientific conference on Telecommunications, Computing and Control, TELECCON 2019

1st International Scientific Conference on Telecommunication...

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1st International Scientific conference on Telecommunications, Computing and Control, TELECCON 2019

ISBN: (纸本)9789813366312

The proceedings contain 46 papers. The special focus in this conference is on Telecommunications, Computing and Control. The topics include: Solving Multicriteria optimization Problem for an Oil Refinery Plant;methods and Techniques for Increasing the Accuracy of Continuous Non-invasive Blood Pressure Measurement Under Dynamic Loads;computer modeling of Robust Control of Vibrationless Movement of Multi-mode Flexible Structures;feature-Based Plant Seedlings Classification;medical Training simulation in Virtual Reality;application of the Hybrid Model to Numerical modeling of the Urban Transport Network Topology;Synchronization Scheme for UWB Wireless Sensor Network System;the Deep Survival Forest and Elastic-Net-Cox Cascade Models as Extensions of the Deep Forest;an Explanation Method for Siamese Neural Networks;detection and Prediction of Safety Faults in Inter-Device Networks Applying a Set of Data-driven Methods;Hierarchical Multi-agent System for Production Control Using KPI Reconciliation;semi-supervised Learning for Medical Image Segmentation;developing a New Generation of Reconfigurable Heterogeneous Distributed High Performance Computing System;Usage of a BART Algorithm and Cognitive Services to Research Collaboration Platforms;a Computer-Aided Diagnosis System in the Diagnosis of Multiple Sclerosis;Predicting Students’ Performance on MOOC Using Data Mining Algorithms;on the Implementation of the Planar3D Model Using the Explicit Time Integration Scheme and the Statistical Front Tracking Method;fast Fourier Transform in Planar3D Model Using an Explicit Numerical Integration Scheme;employee Performance Analytics Approach Based on Anomaly Detection in User Activity;deep Predictive Control;development of an Algorithm for Determining the Railway Tracks on Video Image;on the Computational Complexity of Deep Learning Algorithms.

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Quantifying Occupant Behavior Uncertainty in Spatio-Temporal Visual Comfort Assessment of National Fitness Halls: A Machine Learning-Based Co-simulation Framework 20th

Quantifying Occupant Behavior Uncertainty in Spatio-Temporal...

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20th International conference on Computer-Aided Architectural Design Futures, CAAD Futures 2023

作者： Li, Yu Li, Lingling Shen, Pengyuan Cui, Xue School of Architecture Harbin Institute of Technology Harbin150001 China Key Laboratory of Cold Region Urban and Rural Human Settlement Environment Science and Technology Ministry of Industry and Information Technology Harbin150001 China School of Architecture Harbin Institute of Technology Shenzhen518055 China Department of Building and Real Estate Faculty of Construction and Environment The Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9783031371882

Occupant behavior has been recognized as the main factor influencing visual comfort gaps between simulated and actual conditions. However, existing daylight and glare simulation methods mostly fail to deal with occupant behavior uncertainty. This study proposes a machine learning-based co-simulation framework to better quantify sports behavior uncertainty in the visual comfort assessment of national fitness halls. The framework improves upon existing approaches by formulating an artificial neural network (ANN)-based multi-objective optimization (MOO) workflow to improve overall visual comfort in large sports spaces. Additionally, a data-driven prediction model is developed to quantify the player’s occupancy probability in all possible view scenes during sports. Moreover, the new term, ‘probabilistic visual comfort’ is introduced to evaluate visual comfort accounting for sports behavior uncertainty. To verify the effectiveness of the proposed framework, a case study is conducted using tennis sports as the research scenario. The results indicate that the ANN-based MOO significantly improves the efficiency of searching for the final optimum solution(s) with validated accuracy. Furthermore, probabilistic visual comfort fits better than conventional metrics in evaluating occupant’s actual visual comfort perception in sports scenarios. The proposed framework provides new insights into incorporating occupant behavior modeling with visual comfort assessment. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Uncertainty analysis

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MPGCN-OPF: A Message Passing Graph Convolution Approach for Optimal Power Flow for Distribution Network

MPGCN-OPF: A Message Passing Graph Convolution Approach for ...

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International conference on Power Electronic Drives

作者： Dinesh Kumar Mahto Vikash Kumar Saini Akhilesh Mathur Rajesh Kumar Seema Verma Department of Electrical Engineering MNIT Jaipur India Centre of Energy & Environment MNIT Jaipur India Department of Physical Sciences Banasthali Vidyapith India

Optimal power flow (OPF) i s t he nonlinear, non convex optimization setpoint problem of power and voltage due to the complex relation between system operating status and the OPF solutions with satisfying load demand. The recent advancement in machine learning computation models with unforeseen data availability witnessed a significant transition into data-driven approaches due to information and a feature mapping property. In this paper, we proposed the Message passing graph convolution (MPGCN) model into a unified framework for OPF solutions. The proposed methodology is based on graph convolution property and message passing interface to take advantage of both techniques. The proposed model is an effective alternative to the existing popular DNN technique in terms of model loss function & performance evalu-ation indices with the IEEE-33 bus power distribution network. The simulation results validate that the proposed MPGCN-OPF model outperforms the DNN model. The performance evaluation indices of the proposed model include MSE, RMSE, and MAE are 0.0664, 0.2576, and 0.0719 respectively.

关键词： Training Convolution Computational modeling Message passing simulation Distribution networks Voltage

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Antenna modeling by Nested Kriging with Automated Domain Thickness Determination

Antenna Modeling by Nested Kriging with Automated Domain Thi...

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IEEE-MTT-S International conference on Numerical Electromagnetic and Multiphysics modeling and optimization (NEMO)

作者： Koziel, Slawomir Pietrenko-Dabrowska, Anna Cheng, Qingsha S. Reykjavik Univ Engn Opt & Modeling Ctr Reykjavik Iceland Gdansk Univ Technol Fac Elect Telecom & Inf Gdansk Poland Southern Univ Sci & Tech Dept Elect & Elect Eng Shenzhen Peoples R China

ISBN: (纸本)9781728169668

Fast surrogate models may alleviate the difficulties pertinent to high computational cost of electromagnetic (EM)-driven design procedures. Approximation surrogates are by far the most popular but their applicability to antenna modeling is severely limited by the curse of dimensionality. Domain confinement, as in the recently proposed nested kriging approach, offers a viable workaround this issue, in particular, enables the construction of reliable surrogates over wide ranges of antenna operating conditions and geometry parameters. Unfortunately, the original nested kriging method requires the user to set up the domain thickness (the ratio of its lateral to tangential size). The value of this parameter is critical for achieving a proper balance between the model predictive power and the cost of training data acquisition. This paper proposes a procedure for automated a priori determination of the domain thickness and highlights the computational benefits associated with the employment of the presented approach using a dual-band dipole antenna example.

关键词： Antenna modeling data-driven modeling EM simulation nested kriging domain confinement

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Application of Multi-objective optimization on Ergonomics in Production - A Case Study

Application of Multi-objective Optimization on Ergonomics in...

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AHFE International conference on Additive Manufacturing, modeling Systems and 3D Prototyping

作者： Pascual, Aitor Iriondo Hogberg, Dan Syberfeldt, Anna Brolin, Erik Hanson, Lars Univ Skovde Sch Engn Sci Skovde Sweden Scania CV Sodertalje Sweden

ISBN: (纸本)9783030202163;9783030202156

Taking a holistic perspective is central in production development, aiming to optimize ergonomics and overall production system performance. Hence, there is a need for tools and methods that can support production companies to identify the production system alternatives that are optimal regarding both ergonomics and production efficiency. The paper presents a devised case study where multi-objective optimization is applied, as a step to towards the development of such an optimization tool. The overall objective in the case study is to find the best order in which an operator performs manual tasks during a workday, considering ergonomics and production system efficiency simultaneously. More specifically, reducing the risk of injury from lifting tasks and improving the throughput are selected as the two optimization objectives. An optimization tool is developed, which communicates with a digital human modelling tool to simulate work tasks and assess ergonomics.

关键词： Ergonomics optimization simulation Digital human modelling Productivity

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