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Estimation of composite load model with aggregate induction motor dynamic load for an isolated hybrid

Nitin Kumar SAXENA,Ashwani Kumar SHARMA

《能源前沿（英文）》 2015年 第9卷 第4期   页码 472-485 doi: 10.1007/s11708-015-0373-7

摘要： It is well recognized that the voltage stability of a power system is affected by the load model and hence, to effectively analyze the reactive power compensation of an isolated hybrid wind-diesel based power system, the loads need to be considered along with the generators in a transient analysis. This paper gives a detailed mathematical modeling to compute the reactive power response with small voltage perturbation for composite load. The composite load is a combination of the static and dynamic load model. To develop this composite load model, the exponential load is used as a static load model and induction motors (IMs) are used as a dynamic load model. To analyze the dynamics of IM load, the fifth, third and first order model of IM are formulated and compared using differential equations solver in Matlab coding. Since the decentralized areas have many small consumers which may consist large numbers of IMs of small rating, it is not realistic to model either a single large rating unit or all small rating IMs together that are placed in the system. In place of using a single large rating IM, a group of motors are considered and then the aggregate model of IM is developed using the law of energy conservation. This aggregate model is used as a dynamic load model. For different simulation studies, especially in the area of voltage stability with reactive power compensation of an isolated hybrid power system, the transfer function of the composite load is required. The transfer function of the composite load is derived in this paper by successive derivation for the exponential model of static load and for the fifth and third order IM dynamic load model using state space model.

关键词： isolated hybrid power system (IHPS)     composite load model     static load     dynamic load     induction motor load model     aggregate load    

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Diffusion process in enzyme–metal hybrid catalysts

《化学科学与工程前沿（英文）》 2022年 第16卷 第6期   页码 921-929 doi: 10.1007/s11705-022-2144-4

摘要： Enzyme–metal hybrid catalysts bridge the gap between enzymatic and heterogeneous catalysis, which is significant for expanding biocatalysis to a broader scope. Previous studies have demonstrated that the enzyme–metal hybrid catalysts exhibited considerably higher catalytic efficiency in cascade reactions, compared with that of the combination of separated enzyme and metal catalysts. However, the precise mechanism of this phenomenon remains unclear. Here, we investigated the diffusion process in enzyme–metal hybrid catalysts using Pd/lipase-Pluronic conjugates and the combination of immobilized lipase (Novozyme 435) and Pd/C as models. With reference to experimental data in previous studies, the Weisz–Prater parameter and efficiency factor of internal diffusion were calculated to evaluate the internal diffusion limitations in these catalysts. Thereafter, a kinetic model was developed and fitted to describe the proximity effect in hybrid catalysts. Results indicated that the enhanced catalytic efficiency of hybrid catalysts may arise from the decreased internal diffusion limitation, size effect of Pd clusters and proximity of the enzyme and metal active sites, which provides a theoretical foundation for the rational design of enzyme–metal hybrid catalysts.

关键词： enzyme–metal hybrid catalyst     internal diffusion     proximity effect     kinetic model    

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Statics of levitated vehicle model with hybrid magnets

Desheng LI, Zhiyuan LU, Tianwu DONG

《机械工程前沿（英文）》 2009年 第4卷 第1期   页码 35-39 doi: 10.1007/s11465-009-0002-4

摘要： By studying the special characteristics of permanent and electronic magnets, a levitated vehicle model with hybrid magnets is established. The mathematical model of the vehicle is built based on its dynamics equation by studying its machine structure and working principle. Based on the model, the basic characteristics and the effect between the excluding forces from permanent magnets in three different spatial directions are analyzed, statics characteristics of the interference forces in three different spatial directions are studied, and self-adjusting equilibrium characteristics and stabilization are analyzed. Based on the structure above, the vehicle can levitate steadily by control system adjustment.

关键词： magnetic levitation     permanent magnet     modeling     equilibrium    

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prediction of tunnel boring machine disc cutters for accurate remaining useful life estimation using a hybridmodel

《结构与土木工程前沿（英文）》 2024年 第18卷 第4期   页码 642-662 doi: 10.1007/s11709-024-1058-2

摘要： In tunnel construction with tunnel boring machines (TBMs), accurate prediction of the remaining useful life (RUL) of disc cutters is critical for timely maintenance and replacement to avoid delays and cost overruns. This paper introduces a novel hybrid model, integrating fundamental and data-driven approaches, to enhance wear prediction of TBM disc cutters and enable accurate RUL estimation. The fundamental model is improved by incorporating composite wear mechanisms and load estimation techniques, showcasing superior prediction accuracy compared to single-mechanism models. Additionally, the hybrid model innovatively incorporates a data-driven supplementary residual term into the improved fundamental model, leading to a high-performance wear prediction model. Using actual field data from a highway tunnel project in Shenzhen, the performance of the hybrid model is rigorously tested and compared with pure fundamental and data-driven models. The hybrid model outperforms the other models, achieving the highest accuracy in predicting TBM disc cutter wear (mean absolute error (MAE) = 0.53, root mean square error (RMSE) = 0.64). Furthermore, this study thoroughly analyzes the hybrid model’s generalization capability, revealing significant impacts of geological conditions on prediction accuracy. The model’s generalization capability is also improved by expanding and updating the data sets. The RUL estimation results provided by the hybrid model are straightforward and effective, making it a valuable tool by which construction staff can monitor TBM disc cutters.

关键词： tunnel boring machine     disc cutter     wear prediction     remaining useful life     field data     hybrid model    

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Online soft measurement for wastewater treatment system based on hybrid deep learning

《环境科学与工程前沿（英文）》 2024年 第18卷 第2期 doi: 10.1007/s11783-024-1780-y

摘要：

● A hybrid model is proposed to overcome limitations of single model with time series.

关键词： Prediction model     Soft measurement     CNN-BNLSTM-AM model     TPE optimization algorithm    

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Prediction of falling weight deflectometer parameters using hybrid model of genetic algorithm and adaptive

《结构与土木工程前沿（英文）》 2023年 第17卷 第5期   页码 812-826 doi: 10.1007/s11709-023-0940-7

摘要： A falling weight deflectometer is a testing device used in civil engineering to measure and evaluate the physical properties of pavements, such as the modulus of the subgrade reaction (Y1) and the elastic modulus of the slab (Y2), which are crucial for assessing the structural strength of pavements. In this study, we developed a novel hybrid artificial intelligence model, i.e., a genetic algorithm (GA)-optimized adaptive neuro-fuzzy inference system (ANFIS-GA), to predict Y1 and Y2 based on easily determined 13 parameters of rigid pavements. The performance of the novel ANFIS-GA model was compared to that of other benchmark models, namely logistic regression (LR) and radial basis function regression (RBFR) algorithms. These models were validated using standard statistical measures, namely, the coefficient of correlation (R), mean absolute error (MAE), and root mean square error (RMSE). The results indicated that the ANFIS-GA model was the best at predicting Y1 (R = 0.945) and Y2 (R = 0.887) compared to the LR and RBFR models. Therefore, the ANFIS-GA model can be used to accurately predict Y1 and Y2 based on easily measured parameters for the appropriate and rapid assessment of the quality and strength of pavements.

关键词： falling weight deflectometer     modulus of subgrade reaction     elastic modulus     metaheuristic algorithms    

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A novel hybrid model for water quality prediction based on VMD and IGOA optimized for LSTM

《环境科学与工程前沿（英文）》 2023年 第17卷 第7期 doi: 10.1007/s11783-023-1688-y

摘要：

● A novel VMD-IGOA-LSTM model has proposed for the prediction of water quality.

关键词： Water quality prediction     Grasshopper optimization algorithm     Variational mode decomposition     Long short-term memory neural network    

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A hybrid deep learning model for robust prediction of the dimensional accuracy in precision milling of

《机械工程前沿（英文）》 2022年 第17卷 第3期 doi: 10.1007/s11465-022-0688-0

摘要： The use of artificial intelligence to process sensor data and predict the dimensional accuracy of machined parts is of great interest to the manufacturing community and can facilitate the intelligent production of many key engineering components. In this study, we develop a predictive model of the dimensional accuracy for precision milling of thin-walled structural components. The aim is to classify three typical features of a structural component—squares, slots, and holes—into various categories based on their dimensional errors (i.e., “high precision,” “pass,” and “unqualified”). Two different types of classification schemes have been considered in this study: those that perform feature extraction by using the convolutional neural networks and those based on an explicit feature extraction procedure. The classification accuracy of the popular machine learning methods has been evaluated in comparison with the proposed deep learning model. Based on the experimental data collected during the milling experiments, the proposed model proved to be capable of predicting dimensional accuracy using cutting parameters (i.e., “static features”) and cutting-force data (i.e., “dynamic features”). The average classification accuracy obtained using the proposed deep learning model was 9.55% higher than the best machine learning algorithm considered in this paper. Moreover, the robustness of the hybrid model has been studied by considering the white Gaussian and coherent noises. Hence, the proposed hybrid model provides an efficient way of fusing different sources of process data and can be adopted for prediction of the machining quality in noisy environments.

关键词： precision milling     dimensional accuracy     cutting force     convolutional neural networks     coherent noise    

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A hybrid spatial-temporal deep learning prediction model of industrial methanol-to-olefins process

《化学科学与工程前沿（英文）》 2024年 第18卷 第4期 doi: 10.1007/s11705-024-2403-7

摘要： Methanol-to-olefins, as a promising non-oil pathway for the synthesis of light olefins, has been successfully industrialized. The accurate prediction of process variables can yield significant benefits for advanced process control and optimization. The challenge of this task is underscored by the failure of traditional methods in capturing the complex characteristics of industrial processes, such as high nonlinearities, dynamics, and data distribution shift caused by diverse operating conditions. In this paper, we propose a novel hybrid spatial-temporal deep learning prediction model to address these issues. Firstly, a unique data normalization technique called reversible instance normalization is employed to solve the problem of different data distributions. Subsequently, convolutional neural network integrated with the self-attention mechanism are utilized to extract the temporal patterns. Meanwhile, a multi-graph convolutional network is leveraged to model the spatial interactions. Afterward, the extracted temporal and spatial features are fused as input into a fully connected neural network to complete the prediction. Finally, the outputs are denormalized to obtain the ultimate results. The monitoring results of the dynamic trends of process variables in an actual industrial methanol-to-olefins process demonstrate that our model not only achieves superior prediction performance but also can reveal complex spatial-temporal relationships using the learned attention matrices and adjacency matrices, making the model more interpretable. Lastly, this model is deployed onto an end-to-end Industrial Internet Platform, which achieves effective practical results.

关键词： methanol-to-olefins     process variables prediction     spatial-temporal     self-attention mechanism     graph convolutional network    

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The study of hybrid model identification, computation analysis and fault location for nonlinear dynamic

XIE Hong, HE Yi-gang, ZENG Guan-da

《机械工程前沿（英文）》 2006年 第1卷 第2期   页码 233-237 doi: 10.1007/s11465-006-0003-5

摘要： This paper presents the hybrid model identification for a class of nonlinear circuits and systems via a combination of the block-pulse function transform with the Volterra series. After discussing the method to establish the hybrid model and introducing the hybrid model identification, a set of relative formulas are derived for calculating the hybrid model and computing the Volterra series solution of nonlinear dynamic circuits and systems. In order to significantly reduce the computation cost for fault location, the paper presents a new fault diagnosis method based on multiple preset models that can be realized online. An example of identification simulation and fault diagnosis are given. Results show that the method has high accuracy and efficiency for fault location of nonlinear dynamic circuits and systems.

关键词： block-pulse function     nonlinear     multiple     diagnosis     combination    

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Convergence performance comparisons of PID, MRAC, and PID+MRAC hybrid controller

Dan ZHANG,Bin WEI

《机械工程前沿（英文）》 2016年 第11卷 第2期   页码 213-217 doi: 10.1007/s11465-016-0386-x

摘要：

This study proposes a hybrid controller by combining a proportional-integral-derivative (PID) control and a model reference adaptive control (MRAC), which named as PID+MRAC controller. The convergence performances of the PID control, MRAC, and hybrid PID+MRAC are also compared. Through the simulation in Matlab, the results show that the convergence speed and performance of the MRAC and the PID+MRAC controller are better than those of the PID controller. In addition, the convergence performance of the hybrid control is better than that of the MRAC control.

关键词： proportional-integral-derivative (PID) control     model reference adaptive control     hybrid control     convergence speed     comparison    

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An artificial neural network model on tensile behavior of hybrid steel-PVA fiber reinforced concrete

Fangyu LIU, Wenqi DING, Yafei QIAO, Linbing WANG

《结构与土木工程前沿（英文）》 2020年 第14卷 第6期   页码 1299-1315 doi: 10.1007/s11709-020-0712-6

摘要： The tensile behavior of hybrid fiber reinforced concrete (HFRC) is important to the design of HFRC and HFRC structure. This study used an artificial neural network (ANN) model to describe the tensile behavior of HFRC. This ANN model can describe well the tensile stress-strain curve of HFRC with the consideration of 23 features of HFRC. In the model, three methods to process output features (no-processed, mid-processed, and processed) are discussed and the mid-processed method is recommended to achieve a better reproduction of the experimental data. This means the strain should be normalized while the stress doesn’t need normalization. To prepare the database of the model, both many direct tensile test results and the relevant literature data are collected. Moreover, a traditional equation-based model is also established and compared with the ANN model. The results show that the ANN model has a better prediction than the equation-based model in terms of the tensile stress-strain curve, tensile strength, and strain corresponding to tensile strength of HFRC. Finally, the sensitivity analysis of the ANN model is also performed to analyze the contribution of each input feature to the tensile strength and strain corresponding to tensile strength. The mechanical properties of plain concrete make the main contribution to the tensile strength and strain corresponding to tensile strength, while steel fibers tend to make more contributions to these two items than PVA fibers.

关键词： artificial neural network     hybrid fiber reinforced concrete     tensile behavior     sensitivity analysis     stress-strain curve    

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Evaluation of factors influencing soluble microbial product in submerged MBR through hybrid ASM model

Fangyue LI, Joachim BEHRENDT, Knut WICHMANN, Ralf OTTERPOHL

《环境科学与工程前沿（英文）》 2009年 第3卷 第2期   页码 226-235 doi: 10.1007/s11783-009-0008-5

摘要： In this study, a mathematical model was established to predict the formation of the soluble microbial product (SMP) in a submerged membrane bioreactor. The developed model was calibrated under the reference condition. Simulation results were in good agreement with the measured results under the reference condition. The calibrated model was then used in the scenario studies to evaluate the effect of three chosen operating parameters: hydraulic retention time (HRT), dissolved oxygen concentration, and sludge retention time (SRT). Simulation results revealed that the SMP dominated the soluble organic substances in the supernatant. The scenario studies also revealed that the HRT can be decreased to 1 h without deteriorating the effluent quality; dissolved oxygen concentration in the reactor can be kept at 2-3 mg/L to maintain the effluent quality, reduce the content of SMP, and minimize operating costs; the optimal SRT can be controlled to 10-15 d to achieve complete nitrification process, less membrane fouling potential, and acceptable organic removal efficiency.

关键词： hybrid activated sludge model (ASM)     membrane bioreactor (MBR)     soluble microbial product (SMP)    

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Vibration-based crack prediction on a beam model using hybrid butterfly optimization algorithm with artificial

Abdelwahhab KHATIR; Roberto CAPOZUCCA; Samir KHATIR; Erica MAGAGNINI

《结构与土木工程前沿（英文）》 2022年 第16卷 第8期   页码 976-989 doi: 10.1007/s11709-022-0840-2

摘要： Vibration-based damage detection methods have become widely used because of their advantages over traditional methods. This paper presents a new approach to identify the crack depth in steel beam structures based on vibration analysis using the Finite Element Method (FEM) and Artificial Neural Network (ANN) combined with Butterfly Optimization Algorithm (BOA). ANN is quite successful in such identification issues, but it has some limitations, such as reduction of error after system training is complete, which means the output does not provide optimal results. This paper improves ANN training after introducing BOA as a hybrid model (BOA-ANN). Natural frequencies are used as input parameters and crack depth as output. The data are collected from improved FEM using simulation tools (ABAQUS) based on different crack depths and locations as the first stage. Next, data are collected from experimental analysis of cracked beams based on different crack depths and locations to test the reliability of the presented technique. The proposed approach, compared to other methods, can predict crack depth with improved accuracy.

关键词： damage prediction     ANN     BOA     FEM     experimental modal analysis    

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Hybrid deep learning model for risk prediction of fracture in patients with diabetes and osteoporosis

《医学前沿（英文）》 2022年 第16卷 第3期   页码 496-506 doi: 10.1007/s11684-021-0828-7

摘要： The fracture risk of patients with diabetes is higher than those of patients without diabetes due to hyperglycemia, usage of diabetes drugs, changes in insulin levels, and excretion, and this risk begins as early as adolescence. Many factors including demographic data (such as age, height, weight, and gender), medical history (such as smoking, drinking, and menopause), and examination (such as bone mineral density, blood routine, and urine routine) may be related to bone metabolism in patients with diabetes. However, most of the existing methods are qualitative assessments and do not consider the interactions of the physiological factors of humans. In addition, the fracture risk of patients with diabetes and osteoporosis has not been further studied previously. In this paper, a hybrid model combining XGBoost with deep neural network is used to predict the fracture risk of patients with diabetes and osteoporosis, and investigate the effect of patients’ physiological factors on fracture risk. A total of 147 raw input features are considered in our model. The presented model is compared with several benchmarks based on various metrics to prove its effectiveness. Moreover, the top 18 influencing factors of fracture risks of patients with diabetes are determined.

关键词： XGBoost     deep neural network     healthcare     risk prediction    

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标题 作者 时间 类型 操作

Estimation of composite load model with aggregate induction motor dynamic load for an isolated hybrid

Nitin Kumar SAXENA,Ashwani Kumar SHARMA

期刊论文

Diffusion process in enzyme–metal hybrid catalysts

期刊论文

Statics of levitated vehicle model with hybrid magnets

Desheng LI, Zhiyuan LU, Tianwu DONG

期刊论文

prediction of tunnel boring machine disc cutters for accurate remaining useful life estimation using a hybridmodel

期刊论文

Online soft measurement for wastewater treatment system based on hybrid deep learning

期刊论文

Prediction of falling weight deflectometer parameters using hybrid model of genetic algorithm and adaptive

期刊论文

A novel hybrid model for water quality prediction based on VMD and IGOA optimized for LSTM

期刊论文

A hybrid deep learning model for robust prediction of the dimensional accuracy in precision milling of

期刊论文

A hybrid spatial-temporal deep learning prediction model of industrial methanol-to-olefins process

期刊论文

The study of hybrid model identification, computation analysis and fault location for nonlinear dynamic

XIE Hong, HE Yi-gang, ZENG Guan-da

期刊论文

Convergence performance comparisons of PID, MRAC, and PID+MRAC hybrid controller

Dan ZHANG,Bin WEI

期刊论文

An artificial neural network model on tensile behavior of hybrid steel-PVA fiber reinforced concrete

Fangyu LIU, Wenqi DING, Yafei QIAO, Linbing WANG

期刊论文

Evaluation of factors influencing soluble microbial product in submerged MBR through hybrid ASM model

Fangyue LI, Joachim BEHRENDT, Knut WICHMANN, Ralf OTTERPOHL

期刊论文

Vibration-based crack prediction on a beam model using hybrid butterfly optimization algorithm with artificial

Abdelwahhab KHATIR; Roberto CAPOZUCCA; Samir KHATIR; Erica MAGAGNINI

期刊论文

Hybrid deep learning model for risk prediction of fracture in patients with diabetes and osteoporosis

期刊论文