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The Method to Identify Dynamic Characteristics of Hydro Plant's Supporting Structure Using Machine HaltingProcess

Lian Jijian,Tian Huijing,Qin Liang,Zhang Yongji

Strategic Study of CAE 2006, Volume 8, Issue 4,   Pages 72-75

Abstract:

During running process, the effect of dynamic loads is quite complex.dynamics, applies the test data to analyze the structure vibration and variability of loads during machinehalting process，and further puts forward a new method to identify dynamic characteristics of hydro plantssupporting structure using machine halting process.,when analyzing halting problem.

Keywords： supporting structure of power house     machine halting process     dynamic characteristics     wavelet analysis    

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Hybrid method integrating machine learning and particle swarm optimization for smart chemical process

Haoqin Fang, Jianzhao Zhou, Zhenyu Wang, Ziqi Qiu, Yihua Sun, Yue Lin, Ke Chen, Xiantai Zhou, Ming Pan

Frontiers of Chemical Science and Engineering 2022, Volume 16, Issue 2,   Pages 274-287 doi: 10.1007/s11705-021-2043-0

Abstract: Modeling and optimization is crucial to smart chemical process operations.However, a large number of nonlinearities must be considered in a typical chemical process accordingThus, this paper presents an efficient hybrid framework of integrating machine learning and particleSecondly, four well-known machine learning methods, namely, K-nearest neighbors, decision tree, supportvector machine, and artificial neural network, were compared and used to obtain the prediction models

Keywords： smart chemical process operations     data generation     hybrid method     machine learning     particle swarm optimization    

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Machine learning enabled prediction and process optimization of VFA production from riboflavin-mediated

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 11, doi: 10.1007/s11783-023-1735-8

Abstract:

● Data-driven approach was used to simulate VFA production from WAS fermentation.

Keywords： Machine learning     Volatile fatty acids     Riboflavin     Waste activated sludge     eXtreme Gradient Boosting    

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Two-sided ultrasonic surface rolling process of aeroengine blades based on on-machine noncontact measurement

Shulei YAO, Xian CAO, Shuang LIU, Kaiming ZHANG, Xiancheng ZHANG, Congyang GONG, Chengcheng ZHANG

Frontiers of Mechanical Engineering 2020, Volume 15, Issue 2,   Pages 240-255 doi: 10.1007/s11465-019-0581-7

Abstract: The ultrasonic surface rolling process (USRP) is a novel surface treatment technique that can highlyThis study presents a two-sided USRP (TS-USRP) machining for aeroengine blades on the basis of on-machine

Keywords： aeroengine blades     on-machine noncontact measurement     point cloud processing     path planning     surface strengthening    

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Data Analytics and Machine Learning for Smart Process Manufacturing: Recent Advances and Perspectives Perspective

Chao Shang、 Fengqi You

Engineering 2019, Volume 5, Issue 6,   Pages 1010-1016 doi: 10.1016/j.eng.2019.01.019

Abstract: The burgeoning era of big data is influencing the process industries tremendously, providing unprecedentedTo attain this goal, data analytics and machine learning are indispensable.In this paper, we review recent advances in data analytics and machine learning applied to the monitoringoptimization of industrial processes, paying particular attention to the interpretability and functionality of machine

Keywords： Big data     Machine learning     Smart manufacturing     Process systems engineering    

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New method for computer numerical control machine tool calibration: Relay method

LIU Huanlao, SHI Hanming, LI Bin, ZHOU Huichen

Frontiers of Mechanical Engineering 2007, Volume 2, Issue 3,   Pages 301-304 doi: 10.1007/s11465-007-0053-3

Abstract: measurement system to identify volumetric errors on the planes of computer numerical control (CNC) machineDuring the process, all position errors on the entire plane table are measured by the equipment, whichThe process outlined above is called the relay method.repeatability are high, and the method can be used to calibrate geometric errors on the plane of CNC machine

Keywords： positional     volumetric     information     process     repeatability    

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An integrated approach for machine-learning-based system identification of dynamical systems under control

Frontiers of Chemical Science and Engineering 2022, Volume 16, Issue 2,   Pages 237-250 doi: 10.1007/s11705-021-2058-6

Abstract: ., model predictive control, can offer superior control of key process variables for multiple-input multiple-outputquality of the system model is critical to controller performance and should adequately describe the processfurther possibilities for direct data-driven methodologies for model-based control which, in the face of process

Keywords： nonlinear model predictive control     black-box modeling     continuous-time system identification     machinelearning     industrial applications of process control    

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Automated synthesis of steady-state continuous processes using reinforcement learning

Frontiers of Chemical Science and Engineering 2022, Volume 16, Issue 2,   Pages 288-302 doi: 10.1007/s11705-021-2055-9

Abstract: Automated flowsheet synthesis is an important field in computer-aided process engineering.An agent is trained to take discrete actions and sequentially build up flowsheets that solve a given processThe method is applied successfully to a reaction-distillation process in a quaternary system.

Keywords： automated process synthesis     flowsheet synthesis     artificial intelligence     machine learning     reinforcement    

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in the presence of different random noises and uncertainty: Implementation of generalized Gaussian processregression machine

Nasser L. AZAD,Ahmad MOZAFFARI

Frontiers of Mechanical Engineering 2015, Volume 10, Issue 4,   Pages 405-412 doi: 10.1007/s11465-015-0354-x

Abstract: Then, by using a Gaussian process regression machine (GPRM), a reliable model is used for the sake of

Keywords： automotive engine     calibration     coldstart operation     Gaussian process regression machine (GPRM)     uncertainty    

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Estimation of flexible pavement structural capacity using machine learning techniques

Nader KARBALLAEEZADEH, Hosein GHASEMZADEH TEHRANI, Danial MOHAMMADZADEH SHADMEHRI, Shahaboddin SHAMSHIRBAND

Frontiers of Structural and Civil Engineering 2020, Volume 14, Issue 5,   Pages 1083-1096 doi: 10.1007/s11709-020-0654-z

Abstract: In this study, three machine learning methods entitled Gaussian process regression, M5P model tree, andUsing machine learning methods instead of back-calculation improves the calculation process quality and

Keywords： transportation infrastructure     flexible pavement     structural number prediction     Gaussian process regression    

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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:

The world’s increasing population requires the process industry to produce food, fuels, chemicalsFunctional process materials lie at the heart of this challenge.Due to the strong interaction between material selection and the operation of the process in which thematerial is used, it is essential to perform material and process design simultaneously.This article highlights the significance of hybrid modeling in multiscale material and process design

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

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Machine Learning in Chemical Engineering: Strengths, Weaknesses, Opportunities, and Threats Perspective

Maarten R. Dobbelaere, Pieter P. Plehiers, Ruben Van de Vijver, Christian V. Stevens, Kevin M. Van Geem

Engineering 2021, Volume 7, Issue 9,   Pages 1201-1211 doi: 10.1016/j.eng.2021.03.019

Abstract: Many recent efforts have facilitated the roll-out of machine learning techniques in the research fieldby developing large databases, benchmarks, and representations for chemical applications and new machineMachine learning has significant advantages over traditional modeling techniques, including flexibilityThe greatest opportunities involve using machine learning in time-limited applications such as real-timeNevertheless, machine learning will definitely become a trustworthy element in the modeling toolbox of

Keywords： Artificial intelligence     Machine learning     Reaction engineering     Process engineering    

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Challenges of human–machine collaboration in risky decision-making

Frontiers of Engineering Management 2022, Volume 9, Issue 1,   Pages 89-103 doi: 10.1007/s42524-021-0182-0

Abstract: The purpose of this paper is to delineate the research challenges of human–machine collaboration in riskyTechnological advances in machine intelligence have enabled a growing number of applications in human–machineTherefore, it is desirable to achieve superior performance by fully leveraging human and machine capabilitiesAfterward, we review the literature on human–machine collaboration in a general decision context, fromthe perspectives of human–machine organization, relationship, and collaboration.

Keywords： human–machine collaboration     risky decision-making     human–machine team and interaction     task allocation     human–machine relationship    

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Spatial prediction of soil contamination based on machine learning: a review

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 8, doi: 10.1007/s11783-023-1693-1

Abstract:

● A review of machine learning (ML) for spatial prediction of soil

Keywords： Soil contamination     Machine learning     Prediction     Spatial distribution    

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Predicting the elemental compositions of solid waste using ATR-FTIR and machine learning

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 10, doi: 10.1007/s11783-023-1721-1

Abstract:

● A method based on ATR-FTIR and ML was developed to predict CHNS contents in waste.

Keywords： Elemental composition     Infrared spectroscopy     Machine learning     Moisture interference     Solid waste     Spectral    

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Title Author Date Type Operation

The Method to Identify Dynamic Characteristics of Hydro Plant's Supporting Structure Using Machine HaltingProcess

Lian Jijian,Tian Huijing,Qin Liang,Zhang Yongji

Journal Article

Hybrid method integrating machine learning and particle swarm optimization for smart chemical process

Haoqin Fang, Jianzhao Zhou, Zhenyu Wang, Ziqi Qiu, Yihua Sun, Yue Lin, Ke Chen, Xiantai Zhou, Ming Pan

Journal Article

Machine learning enabled prediction and process optimization of VFA production from riboflavin-mediated

Journal Article

Two-sided ultrasonic surface rolling process of aeroengine blades based on on-machine noncontact measurement

Shulei YAO, Xian CAO, Shuang LIU, Kaiming ZHANG, Xiancheng ZHANG, Congyang GONG, Chengcheng ZHANG

Journal Article

Data Analytics and Machine Learning for Smart Process Manufacturing: Recent Advances and Perspectives

Chao Shang、 Fengqi You

Journal Article

New method for computer numerical control machine tool calibration: Relay method

LIU Huanlao, SHI Hanming, LI Bin, ZHOU Huichen

Journal Article

An integrated approach for machine-learning-based system identification of dynamical systems under control

Journal Article

Automated synthesis of steady-state continuous processes using reinforcement learning

Journal Article

in the presence of different random noises and uncertainty: Implementation of generalized Gaussian processregression machine

Nasser L. AZAD,Ahmad MOZAFFARI

Journal Article

Estimation of flexible pavement structural capacity using machine learning techniques

Nader KARBALLAEEZADEH, Hosein GHASEMZADEH TEHRANI, Danial MOHAMMADZADEH SHADMEHRI, Shahaboddin SHAMSHIRBAND

Journal Article

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

Teng Zhou, Rafiqul Gani, Kai Sundmacher

Journal Article

Machine Learning in Chemical Engineering: Strengths, Weaknesses, Opportunities, and Threats

Maarten R. Dobbelaere, Pieter P. Plehiers, Ruben Van de Vijver, Christian V. Stevens, Kevin M. Van Geem

Journal Article

Challenges of human–machine collaboration in risky decision-making

Journal Article

Spatial prediction of soil contamination based on machine learning: a review

Journal Article

Predicting the elemental compositions of solid waste using ATR-FTIR and machine learning

Journal Article