We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpr...We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpreting these parameters is crucial for effectively exploring and developing oil and gas.However,with the increasing complexity of geological conditions in this industry,there is a growing demand for improved accuracy in reservoir parameter prediction,leading to higher costs associated with manual interpretation.The conventional logging interpretation methods rely on empirical relationships between logging data and reservoir parameters,which suffer from low interpretation efficiency,intense subjectivity,and suitability for ideal conditions.The application of artificial intelligence in the interpretation of logging data provides a new solution to the problems existing in traditional methods.It is expected to improve the accuracy and efficiency of the interpretation.If large and high-quality datasets exist,data-driven models can reveal relationships of arbitrary complexity.Nevertheless,constructing sufficiently large logging datasets with reliable labels remains challenging,making it difficult to apply data-driven models effectively in logging data interpretation.Furthermore,data-driven models often act as“black boxes”without explaining their predictions or ensuring compliance with primary physical constraints.This paper proposes a machine learning method with strong physical constraints by integrating mechanism and data-driven models.Prior knowledge of logging data interpretation is embedded into machine learning regarding network structure,loss function,and optimization algorithm.We employ the Physically Informed Auto-Encoder(PIAE)to predict porosity and water saturation,which can be trained without labeled reservoir parameters using self-supervised learning techniques.This approach effectively achieves automated interpretation and facilitates generalization across diverse datasets.展开更多
Accurate prediction of strip width is a key factor related to the quality of hot rolling manufacture.Firstly,based on strip width formation mechanism model within strip rolling process,an improved width mechanism calc...Accurate prediction of strip width is a key factor related to the quality of hot rolling manufacture.Firstly,based on strip width formation mechanism model within strip rolling process,an improved width mechanism calculation model is delineated for the optimization of process parameters via the particle swarm optimization algorithm.Subsequently,a hybrid strip width prediction model is proposed by effectively combining the respective advantages of the improved mechanism model and the data-driven model.In acknowledgment of prerequisite for positive error in strip width prediction,an adaptive width error compensation algorithm is proposed.Finally,comparative simulation experiments are designed on the actual rolling dataset after completing data cleaning and feature engineering.The experimental results show that the hybrid prediction model proposed has superior precision and robustness compared with the improved mechanism model and the other eight common data-driven models and satisfies the needs of practical applications.Moreover,the hybrid model can realize the complementary advantages of the mechanism model and the data-driven model,effectively alleviating the problems of difficult to improve the accuracy of the mechanism model and poor interpretability of the data-driven model,which bears significant practical implications for the research of strip width control.展开更多
Data-driven techniques are reshaping blast furnace iron-making process(BFIP)modeling,but their“black-box”nature often obscures interpretability and accuracy.To overcome these limitations,our mechanism and data co-dr...Data-driven techniques are reshaping blast furnace iron-making process(BFIP)modeling,but their“black-box”nature often obscures interpretability and accuracy.To overcome these limitations,our mechanism and data co-driven strategy(MDCDS)enhances model transparency and molten iron quality(MIQ)prediction.By zoning the furnace and applying mechanism-based features for material and thermal trends,coupled with a novel stationary broad feature learning system(StaBFLS),interference caused by nonstationary process characteristics are mitigated and the intrinsic information embedded in BFIP is mined.Subsequently,by integrating stationary feature representation with mechanism features,our temporal matching broad learning system(TMBLS)aligns process and quality variables using MIQ as the target.This integration allows us to establish process monitoring statistics using both mechanism and data-driven features,as well as detect modeling deviations.Validated against real-world BFIP data,our MDCDS model demonstrates consistent process alignment,robust feature extraction,and improved MIQ modeling—Yielding better fault detection.Additionally,we offer detailed insights into the validation process,including parameter baselining and optimization.展开更多
The aerostatic spindle is a key component of ultra-precision machine tools,and its error motion is crucial to machining accuracy and reliability.Spindle error motion is unavoidable,and its online monitoring and predic...The aerostatic spindle is a key component of ultra-precision machine tools,and its error motion is crucial to machining accuracy and reliability.Spindle error motion is unavoidable,and its online monitoring and prediction are quite important.Currently,there are relatively few studies on the online monitoring and prediction methods for the aerostatic spindle,and the level of intelligence is relatively low.To address this problem,an error motion monitoring system based on digital twin(DT)technology was established for the aerostatic spindle.A spindle error motion prediction method based on a mechanism and data fusion model(MDFM)was proposed.Additionally,a highly available and interactive aerostatic spindle DT service platform was developed.Experimental results have verified the good performance of this platform.The platform facilitates interaction between the physical and virtual entities of the aerostatic spindle,enabling three-dimensional visualization,monitoring,prediction,and simulation of spindle error motion,and shows good potential for engineering applications.展开更多
Drying （conditioning） is an important procedure to prevent hydrate formation during gas pipeline gas-up and to protect pipelines against corrosion. The air-drying method is preferred in offshore gas pipelines pre-co...Drying （conditioning） is an important procedure to prevent hydrate formation during gas pipeline gas-up and to protect pipelines against corrosion. The air-drying method is preferred in offshore gas pipelines pre-commissioning. The air-drying process of gas pipelines commonly includes two steps, air purging and soak test. The mass conservation and the phase equilibrium theory are applied to setting up the mathematical models of air purging, which can be used to simulate dry airflow rate and drying time. Fick diffusion law is applied to setting up the mathematical model of soak test, which can predict the water vapor concentration distribution. The results calculated from the purging model and the soak test model are in good agreement with the experimental data in the DF 1-1 offshore production pipeline conditioning. The models are verified to be available for the air-drying project design of offshore gas pipelines. Some proposals for airdrying engineering and operational procedures are put forward by analyzing the air-drying process of DFI-1 gasexporting pipelines.展开更多
Continent subduction is one of the hot research problems in geoscience. New models presented here have been set up and two-dimensional numerical modeling research on the possibility of continental subduction has been ...Continent subduction is one of the hot research problems in geoscience. New models presented here have been set up and two-dimensional numerical modeling research on the possibility of continental subduction has been made with the finite element software, ANSYS, based on documentary evidence and reasonable assumptions that the subduction of oceanic crust has occurred, the subduction of continental crust can take place and the process can be simplified to a discontinuous plane strain theory model. The modeling results show that it is completely possible for continental crust to be subducted to a depth of 120 km under certain circumstances and conditions. At the same time, the simulations of continental subduction under a single dynamical factor have also been made, including the pull force of the subducted oceanic lithosphere, the drag force connected with mantle convection and the push force of the mid-ocean ridge. These experiments show that the drag force connected with mantle convection is critical for continent subduction.展开更多
The test selection and optimization （TSO） can improve the abilities of fault diagnosis, prognosis and health-state evalua- tion for prognostics and health management （PHM） systems. Traditionally, TSO mainly focuse...The test selection and optimization （TSO） can improve the abilities of fault diagnosis, prognosis and health-state evalua- tion for prognostics and health management （PHM） systems. Traditionally, TSO mainly focuses on fault detection and isolation, but they cannot provide an effective guide for the design for testability （DFT） to improve the PHM performance level. To solve the problem, a model of TSO for PHM systems is proposed. Firstly, through integrating the characteristics of fault severity and propa- gation time, and analyzing the test timing and sensitivity, a testability model based on failure evolution mechanism model （FEMM） for PHM systems is built up. This model describes the fault evolution- test dependency using the fault-symptom parameter matrix and symptom parameter-test matrix. Secondly, a novel method of in- herent testability analysis for PHM systems is developed based on the above information. Having completed the analysis, a TSO model, whose objective is to maximize fault trackability and mini- mize the test cost, is proposed through inherent testability analysis results, and an adaptive simulated annealing genetic algorithm （ASAGA） is introduced to solve the TSO problem. Finally, a case of a centrifugal pump system is used to verify the feasibility and effectiveness of the proposed models and methods. The results show that the proposed technology is important for PHM systems to select and optimize the test set in order to improve their performance level.展开更多
Firstly, the research progress of grey model GM （1,1） is summarized, which is divided into three development stages： assimilation, alienation and melting stages. Then, the matrix analysis theory is used to study th...Firstly, the research progress of grey model GM （1,1） is summarized, which is divided into three development stages： assimilation, alienation and melting stages. Then, the matrix analysis theory is used to study the modeling mechanism of GM （1,1）, which decomposes the modeling data matrix into raw data transformation matrix, accumulated generating operation matrix and background value selection matrix. The changes of these three matrices are the essential reasons affecting the modeling and the accuracy of GM （1,1）. Finally, the paper proposes a generalization grey model GGM （1,1）, which is a extended form of GM （1,1） and also a unified form of model GM （1,1）, model GM （1,1,α）, stage grey model, hopping grey model, generalized accumulated model, strengthening operator model, weakening operator model and unequal interval model. And the theory and practical significance of the extended model is analyzed.展开更多
The trajectory related and Direct Current(DC)Electromagnetic Interference(EMI)of lithium battery,fuel cell and photovoltaic modules has a great influence on the small-scale Unmanned Aerial Vehicle(UAV)airborne magneto...The trajectory related and Direct Current(DC)Electromagnetic Interference(EMI)of lithium battery,fuel cell and photovoltaic modules has a great influence on the small-scale Unmanned Aerial Vehicle(UAV)airborne magnetometer and is hard to be shielded,calibrated or filtered.Besides,the mechanisms underlying the DC EMI have been rarely investigated yet.To cope with this problem,this paper systematically studies the EMI models,and proposes an online 3-layer EMI reduction scheme.First,EMI coupled with UAV motion model and hybrid power system is established.Second,the mechanism EMI models of hybrid power system are established and verified based on the proposed concept“equivalent current”.Third,an online 3-layer EMI reduction scheme is proposed,including battery layer,trajectory planning layer and energy management layer.In the first main layer,EMI self-cancellation is realized by rotating battery inclinations and symmetrical circuits.In response to errors,the trajectory planning layer reduces the EMI intensity by optimizing an optimal trajectory,while the energy management layer prioritizes power allocation to power sources that can produce small and stable EMI.Simulation results of climb,level flight and descent illustrate the efficaciousness and applicability of the proposed online 3-layer EMI reduction scheme.展开更多
Motion simulation and performance analysis of mechanism are important methods for analyzing assembly quality after finishing assembly simulation in virtual assembly environment. However, most simulation systems have n...Motion simulation and performance analysis of mechanism are important methods for analyzing assembly quality after finishing assembly simulation in virtual assembly environment. However, most simulation systems have no function of mechanism motion simulation due to the randomicity of mechanism and lack of universal mechanism modeling method. In order to realize the simulation of any mechanism after finishing assembly simulation in a virtual environment, a new universal mechanism modeling method is presented. Two main models are contained in the mechanism model： information model and mathematical model. Firstly, the information model of mechanism is proposed to describe the data structure of mechanism which contains bottom geometry data, information of constraint, link, kinematic pair and physical data. Because the object of mechanism simulation is the assembly, which is assembled during the process of assembly simulation, the information of mechanism can be obtained automatically through mechanism automatic search method. Secondly, mathematical model of mechanism is presented. The mathematical model uses mathematical method to express the mechanism. In order to realize the automatic expression of any random mechanism, basic constraint library is presented, consequently random mechanism can be described based on the basic constraint library. Finally, two examples are introduced to validate the method in the prototype system named VAPP（Virtual Assembly Process Planning）. The validation result shows that the mechanism modeling provides a universal modeling method for mechanism motion simulation in virtual assembly environment. This research has important effect on the development both of mechanism motion simulation and virtual assembly.展开更多
Initial residual stress is the main reason causing machining deformation of the workpiece,which has been deemed as one of the most important aspects of machining quality issues.The inference of the distribution of ini...Initial residual stress is the main reason causing machining deformation of the workpiece,which has been deemed as one of the most important aspects of machining quality issues.The inference of the distribution of initial residual stress inside the blank has significant meaning for machining deformation control.Due to the principle error of existing residual stress detection methods,there are still challenges in practical applications.Aiming at the detection problem of the initial residual stress field,an initial residual stress inference method by incorporating monitoring data and mechanism model is proposed in this paper.Monitoring data during machining process is used to represent the macroscopic characterization of the unbalanced residual stress,and the finite element numerical model is used as the mechanism model so as to solve the problem that the analytic mechanism model is difficult to establish;the policy gradient approach is introduced to solve the gradient descent problem of the combination of learning model and mechanism model.Finally,the initial residual stress field is obtained through iterative calculation based on the fusing method of monitoring data and mechanism model.Verification results show that the proposed inference method of initial residual stress field can accurately and effectively reflect the machining deformation in the actual machining process.展开更多
A suitable pH value of the slurry is a key to efficient mineral flotation. Considering the control delay problem of pH value caused by offline pH measurement, an integrated prediction model for pH value in bauxite fro...A suitable pH value of the slurry is a key to efficient mineral flotation. Considering the control delay problem of pH value caused by offline pH measurement, an integrated prediction model for pH value in bauxite froth flotation is proposed, which considers the effect of ore compositions on pH value. Firstly, a regression model is obtained for alkali(Na_2CO_3) consumed by the reaction between ore and alkali. According to the first-order hydrolysis of the remaining alkali, a mechanism-based prediction model is presented for the pH value. Then, considering the complexity of the flotation mechanism, an error prediction model which uses time series of the error of the mechanism model as inputs is presented based on autoregressive moving average(ARMA) method to compensate the mechanism model. Finally, expert rules are established to correct the error compensation direction, which could reflect the dynamic changes during the process accurately and effectively. Simulation results using industrial data show that the presented model meets the needs of the industrial process, which laid the foundation for predictive control of pH regulator.展开更多
To fully display the modeling mechanism of the novelfractional order grey model （FGM （q,1））, this paper decomposesthe data matrix of the model into the mean generation matrix, theaccumulative generation matrix and...To fully display the modeling mechanism of the novelfractional order grey model （FGM （q,1））, this paper decomposesthe data matrix of the model into the mean generation matrix, theaccumulative generation matrix and the raw data matrix, whichare consistent with the fractional order accumulative grey model（FAGM （1,1））. Following this, this paper decomposes the accumulativedata difference matrix into the accumulative generationmatrix, the q-order reductive accumulative matrix and the rawdata matrix, and then combines the least square method, findingthat the differential order affects the model parameters only byaffecting the formation of differential sequences. This paper thensummarizes matrix decomposition of some special sequences,such as the sequence generated by the strengthening and weakeningoperators, the jumping sequence, and the non-equidistancesequence. Finally, this paper expresses the influences of the rawdata transformation, the accumulation sequence transformation,and the differential matrix transformation on the model parametersas matrices, and takes the non-equidistance sequence as an exampleto show the modeling mechanism.展开更多
The landing buffer is an important problem in the research on bionic locust jumping robots, and the different modes of landing and buffering can affect the dynamic performance of the buffering process significantly. B...The landing buffer is an important problem in the research on bionic locust jumping robots, and the different modes of landing and buffering can affect the dynamic performance of the buffering process significantly. Based on an experimental observation, the different modes of landing and buffering are determined, which include the different numbers of landing legs and different motion modes of legs in the buffering process. Then a bionic locust mechanism is established, and the springs are used to replace the leg muscles to achieve a buffering effect. To reveal the dynamic performance in the buffering process of the bionic locust mechanism, a dynamic model is established with different modes of landing and buffering. In particular, to analyze the buffering process conveniently, an equivalent vibration dynamic model of the bionic locust mechanism is proposed.Given the support forces of the ground to the leg links, which can be obtained from the dynamic model, the spring forces of the legs and the impact resistance of each leg are the important parameters affecting buffering performance, and evaluation principles for buffering performance are proposed according to the aforementioned parameters. Based on the dynamic model and these evaluation principles, the buffering performances are analyzed and compared in different modes of landing and buffering on a horizontal plane and an inclined plane. The results show that the mechanism with the ends of the legs sliding can obtain a better dynamic performance. This study offers primary theories for buffering dynamics and an evaluation of landing buffer performance,and it establishes a theoretical basis for studies and engineering applications.展开更多
A FCC mechanism model was used to predict the effects of propylene promoter in a 3.0 Mt/a FCCU. The FCC mechanism model was developed based on one set of commercial FCC data without using the promoter, and was modifie...A FCC mechanism model was used to predict the effects of propylene promoter in a 3.0 Mt/a FCCU. The FCC mechanism model was developed based on one set of commercial FCC data without using the promoter, and was modified by using another set of commercial FCC data with 3m% promoter in the catalyst inventory, and the calculations by means of this simulation model were performed to predict the data of the FCC unit containing 4m% promoter in the catalyst inventory. The test results showed that the calculated values agreed well with the data obtained from the commercial FCC unit, in which the deviations of calculated product yields versus the actual product yields at the commercial FCC unit were equal to 1.74 percentage points for gasoline, 2.59 percentage points for diesel, 1.50 percentage points for dry gas and LPG, and 0.28 percentage points for coke. The proposed method regarding the development of a simulation model and modifications to the model for commercial FCC unit was feasible.展开更多
Based on the Residual Oil Hydrodesulfurization Treatment Unit(S-RHT),the n-order reaction kinetic model for residual oil HDS reactions and artificial neural network(ANN)model were developed to determine the sulfur con...Based on the Residual Oil Hydrodesulfurization Treatment Unit(S-RHT),the n-order reaction kinetic model for residual oil HDS reactions and artificial neural network(ANN)model were developed to determine the sulfur content of hydrogenated residual oil.The established ANN model covered 4 input variables,1 output variable and 1 hidden layer with 15 neurons.The comparison between the results of two models was listed.The results showed that the predicted mean relative errors of the two models with three different sample data were less than 5%and both the two models had good predictive precision and extrapolative feature for the HDS process.The mean relative error of 5 sets of testing data of the ANN model was 1.62%—3.23%,all of which were smaller than that of the common mechanism model(3.47%—4.13%).It showed that the ANN model was better than the mechanism model both in terms of fitting results and fitting difficulty.The models could be easily applied in practice and could also provide a reference for the further research of residual oil HDS process.展开更多
A hybrid model of a subminiature helicopter in horizontal turn is presented. This model is based on a mechanism model and its compensated neural network （NN）. First, the nonlinear dynamics of a sub-miniature helicop...A hybrid model of a subminiature helicopter in horizontal turn is presented. This model is based on a mechanism model and its compensated neural network （NN）. First, the nonlinear dynamics of a sub-miniature helicopter is established. Through the linearization of the nonlinear dynamics on a trim point, the linear time-invariant mechanism model in horizontal turn is obtained. Then a diagonal recursive neural network is used to compensate the model error between the mechanism model and the nonlinear model, thus the hybrid model of a subminiature helicopter in horizontal turn is achieved. Simulation results show that the hybrid model has higher accuracy than the mechanism model and the obtained compensated-NN has good generalization capability.展开更多
An online model was proposed to identify the reasons behind changes in the energy consumption of the reheating furnace of a steel processing plant.The heat conversion of the furnace was analyzed and integrated with th...An online model was proposed to identify the reasons behind changes in the energy consumption of the reheating furnace of a steel processing plant.The heat conversion of the furnace was analyzed and integrated with the fuel consumption of the furnace to obtain a model of the energy consumption.Combined with the mechanism analysis,the basic parameters affecting energy consumption were determined,and four key influencing factors were obtained:furnace output,furnace charging temperature,furnace tapping temperature,and steel type.The specific calculation method of the contribution of each influencing factor was derived to define the conditions of the baseline energy consumption,while the online data were used to calculate the energy value and the actual performance value of the baseline energy consumption.The contribution of each influencing factor was determined through normalization.The cloud platform was used for database reconstruction and programming to realize the online intelligent evaluation of the energy consumption of the reheating furnace.Finally,a case study of the evaluation of the practical energy consumption of a steel rolling furnace in a steel plant was presented.The intelligent evaluation results were quantified and displayed online,and the performance of the system in reducing production line energy consumption was demonstrated.展开更多
A mathematical model has been developed to describe the dynamic behaviours of NO+CO reaction on supported Pt MO catalyst. The ignited state kinetics can be fit quantitatively using directly a Langmuir Henshelwood bimo...A mathematical model has been developed to describe the dynamic behaviours of NO+CO reaction on supported Pt MO catalyst. The ignited state kinetics can be fit quantitatively using directly a Langmuir Henshelwood bimolecular rate expression with a heat of adsorption of NO of 32 4 kJ/mol and of CO of 106 7 kJ/mol, respectively.展开更多
Dune riocks are aeolian sands cemented ty calcium carbonate under subaerial conditions. They have been found in many of the coastal belts of Fujian, Guangdong and Hainan Provinces in South China. The grain composition...Dune riocks are aeolian sands cemented ty calcium carbonate under subaerial conditions. They have been found in many of the coastal belts of Fujian, Guangdong and Hainan Provinces in South China. The grain composition of the dune rocks is mainly quartz sands and shell fragments. The quartz sands are medium and fine sized, relatively well sorted and positively skewed. Their surface texture formed in aeolian environments is characterized ty dishshaped depressions, meniscus depressions and V-shaped depressions with rounded edges. The most common bedding type of the rocks is larg (thickness>1.5m), steeply dipping (32--40°) with cross strata tolaner and convex upward). Mg and Sr contents are very low in the rock chemical composition which is classified into low Mg and low Sr category. The typical species of microfossils in the dune rocks are mainly freshwater ones and lack of typical saltwaer or semi-saltwater ones with incomplete assemblage of marine species. The cement minerals in the rocks are mainly low-Mg calcite and the common cement fabrics are meniscus cement and gravitational cement in response to impermanent water in vadose zones. Therefore, the dune rocks may be apparently distinguished from the beach rocks.展开更多
基金supported by National Key Research and Development Program (2019YFA0708301)National Natural Science Foundation of China (51974337)+2 种基金the Strategic Cooperation Projects of CNPC and CUPB (ZLZX2020-03)Science and Technology Innovation Fund of CNPC (2021DQ02-0403)Open Fund of Petroleum Exploration and Development Research Institute of CNPC (2022-KFKT-09)
文摘We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpreting these parameters is crucial for effectively exploring and developing oil and gas.However,with the increasing complexity of geological conditions in this industry,there is a growing demand for improved accuracy in reservoir parameter prediction,leading to higher costs associated with manual interpretation.The conventional logging interpretation methods rely on empirical relationships between logging data and reservoir parameters,which suffer from low interpretation efficiency,intense subjectivity,and suitability for ideal conditions.The application of artificial intelligence in the interpretation of logging data provides a new solution to the problems existing in traditional methods.It is expected to improve the accuracy and efficiency of the interpretation.If large and high-quality datasets exist,data-driven models can reveal relationships of arbitrary complexity.Nevertheless,constructing sufficiently large logging datasets with reliable labels remains challenging,making it difficult to apply data-driven models effectively in logging data interpretation.Furthermore,data-driven models often act as“black boxes”without explaining their predictions or ensuring compliance with primary physical constraints.This paper proposes a machine learning method with strong physical constraints by integrating mechanism and data-driven models.Prior knowledge of logging data interpretation is embedded into machine learning regarding network structure,loss function,and optimization algorithm.We employ the Physically Informed Auto-Encoder(PIAE)to predict porosity and water saturation,which can be trained without labeled reservoir parameters using self-supervised learning techniques.This approach effectively achieves automated interpretation and facilitates generalization across diverse datasets.
基金supported by the National Natural Science Foundation of China(No.62273234)Key Research and Development Program of Shaanxi(Program No.2022GY-306)Technology Innovation Leading Program of Shaanxi(Program No.2022QFY01-16).
文摘Accurate prediction of strip width is a key factor related to the quality of hot rolling manufacture.Firstly,based on strip width formation mechanism model within strip rolling process,an improved width mechanism calculation model is delineated for the optimization of process parameters via the particle swarm optimization algorithm.Subsequently,a hybrid strip width prediction model is proposed by effectively combining the respective advantages of the improved mechanism model and the data-driven model.In acknowledgment of prerequisite for positive error in strip width prediction,an adaptive width error compensation algorithm is proposed.Finally,comparative simulation experiments are designed on the actual rolling dataset after completing data cleaning and feature engineering.The experimental results show that the hybrid prediction model proposed has superior precision and robustness compared with the improved mechanism model and the other eight common data-driven models and satisfies the needs of practical applications.Moreover,the hybrid model can realize the complementary advantages of the mechanism model and the data-driven model,effectively alleviating the problems of difficult to improve the accuracy of the mechanism model and poor interpretability of the data-driven model,which bears significant practical implications for the research of strip width control.
基金supported in part by the National Natural Science Foundation of China(61933015,61703371,62273030)the Central University Basic Research Fund of China(K20200002)(for NGICS Platform,Zhejiang University)the Social Development Project of Zhejiang Provincial Public Technology Research(LGF19F030004,LGG21F030015).
文摘Data-driven techniques are reshaping blast furnace iron-making process(BFIP)modeling,but their“black-box”nature often obscures interpretability and accuracy.To overcome these limitations,our mechanism and data co-driven strategy(MDCDS)enhances model transparency and molten iron quality(MIQ)prediction.By zoning the furnace and applying mechanism-based features for material and thermal trends,coupled with a novel stationary broad feature learning system(StaBFLS),interference caused by nonstationary process characteristics are mitigated and the intrinsic information embedded in BFIP is mined.Subsequently,by integrating stationary feature representation with mechanism features,our temporal matching broad learning system(TMBLS)aligns process and quality variables using MIQ as the target.This integration allows us to establish process monitoring statistics using both mechanism and data-driven features,as well as detect modeling deviations.Validated against real-world BFIP data,our MDCDS model demonstrates consistent process alignment,robust feature extraction,and improved MIQ modeling—Yielding better fault detection.Additionally,we offer detailed insights into the validation process,including parameter baselining and optimization.
基金supported by the National Natural Science Foundation of China(Grant No.52475494),the Zhejiang Provincial Natural Science Foundation of China(Grant No.LY22E050003),the Fundamental Research Funds for the Provincial Universities of Zhejiang(Grant No.RF-A2020005).
文摘The aerostatic spindle is a key component of ultra-precision machine tools,and its error motion is crucial to machining accuracy and reliability.Spindle error motion is unavoidable,and its online monitoring and prediction are quite important.Currently,there are relatively few studies on the online monitoring and prediction methods for the aerostatic spindle,and the level of intelligence is relatively low.To address this problem,an error motion monitoring system based on digital twin(DT)technology was established for the aerostatic spindle.A spindle error motion prediction method based on a mechanism and data fusion model(MDFM)was proposed.Additionally,a highly available and interactive aerostatic spindle DT service platform was developed.Experimental results have verified the good performance of this platform.The platform facilitates interaction between the physical and virtual entities of the aerostatic spindle,enabling three-dimensional visualization,monitoring,prediction,and simulation of spindle error motion,and shows good potential for engineering applications.
文摘Drying （conditioning） is an important procedure to prevent hydrate formation during gas pipeline gas-up and to protect pipelines against corrosion. The air-drying method is preferred in offshore gas pipelines pre-commissioning. The air-drying process of gas pipelines commonly includes two steps, air purging and soak test. The mass conservation and the phase equilibrium theory are applied to setting up the mathematical models of air purging, which can be used to simulate dry airflow rate and drying time. Fick diffusion law is applied to setting up the mathematical model of soak test, which can predict the water vapor concentration distribution. The results calculated from the purging model and the soak test model are in good agreement with the experimental data in the DF 1-1 offshore production pipeline conditioning. The models are verified to be available for the air-drying project design of offshore gas pipelines. Some proposals for airdrying engineering and operational procedures are put forward by analyzing the air-drying process of DFI-1 gasexporting pipelines.
文摘Continent subduction is one of the hot research problems in geoscience. New models presented here have been set up and two-dimensional numerical modeling research on the possibility of continental subduction has been made with the finite element software, ANSYS, based on documentary evidence and reasonable assumptions that the subduction of oceanic crust has occurred, the subduction of continental crust can take place and the process can be simplified to a discontinuous plane strain theory model. The modeling results show that it is completely possible for continental crust to be subducted to a depth of 120 km under certain circumstances and conditions. At the same time, the simulations of continental subduction under a single dynamical factor have also been made, including the pull force of the subducted oceanic lithosphere, the drag force connected with mantle convection and the push force of the mid-ocean ridge. These experiments show that the drag force connected with mantle convection is critical for continent subduction.
基金supported by the National Natural Science Foundation of China(51175502)
文摘The test selection and optimization （TSO） can improve the abilities of fault diagnosis, prognosis and health-state evalua- tion for prognostics and health management （PHM） systems. Traditionally, TSO mainly focuses on fault detection and isolation, but they cannot provide an effective guide for the design for testability （DFT） to improve the PHM performance level. To solve the problem, a model of TSO for PHM systems is proposed. Firstly, through integrating the characteristics of fault severity and propa- gation time, and analyzing the test timing and sensitivity, a testability model based on failure evolution mechanism model （FEMM） for PHM systems is built up. This model describes the fault evolution- test dependency using the fault-symptom parameter matrix and symptom parameter-test matrix. Secondly, a novel method of in- herent testability analysis for PHM systems is developed based on the above information. Having completed the analysis, a TSO model, whose objective is to maximize fault trackability and mini- mize the test cost, is proposed through inherent testability analysis results, and an adaptive simulated annealing genetic algorithm （ASAGA） is introduced to solve the TSO problem. Finally, a case of a centrifugal pump system is used to verify the feasibility and effectiveness of the proposed models and methods. The results show that the proposed technology is important for PHM systems to select and optimize the test set in order to improve their performance level.
基金supported by the National Natural Science Foundation of China(70971103)the Specialized Research Fund for the Doctora Program of Higher Education(20120143110001)
文摘Firstly, the research progress of grey model GM （1,1） is summarized, which is divided into three development stages： assimilation, alienation and melting stages. Then, the matrix analysis theory is used to study the modeling mechanism of GM （1,1）, which decomposes the modeling data matrix into raw data transformation matrix, accumulated generating operation matrix and background value selection matrix. The changes of these three matrices are the essential reasons affecting the modeling and the accuracy of GM （1,1）. Finally, the paper proposes a generalization grey model GGM （1,1）, which is a extended form of GM （1,1） and also a unified form of model GM （1,1）, model GM （1,1,α）, stage grey model, hopping grey model, generalized accumulated model, strengthening operator model, weakening operator model and unequal interval model. And the theory and practical significance of the extended model is analyzed.
文摘The trajectory related and Direct Current(DC)Electromagnetic Interference(EMI)of lithium battery,fuel cell and photovoltaic modules has a great influence on the small-scale Unmanned Aerial Vehicle(UAV)airborne magnetometer and is hard to be shielded,calibrated or filtered.Besides,the mechanisms underlying the DC EMI have been rarely investigated yet.To cope with this problem,this paper systematically studies the EMI models,and proposes an online 3-layer EMI reduction scheme.First,EMI coupled with UAV motion model and hybrid power system is established.Second,the mechanism EMI models of hybrid power system are established and verified based on the proposed concept“equivalent current”.Third,an online 3-layer EMI reduction scheme is proposed,including battery layer,trajectory planning layer and energy management layer.In the first main layer,EMI self-cancellation is realized by rotating battery inclinations and symmetrical circuits.In response to errors,the trajectory planning layer reduces the EMI intensity by optimizing an optimal trajectory,while the energy management layer prioritizes power allocation to power sources that can produce small and stable EMI.Simulation results of climb,level flight and descent illustrate the efficaciousness and applicability of the proposed online 3-layer EMI reduction scheme.
基金supported by National Natural Science Foundation of China(Grant No. 51275047)
文摘Motion simulation and performance analysis of mechanism are important methods for analyzing assembly quality after finishing assembly simulation in virtual assembly environment. However, most simulation systems have no function of mechanism motion simulation due to the randomicity of mechanism and lack of universal mechanism modeling method. In order to realize the simulation of any mechanism after finishing assembly simulation in a virtual environment, a new universal mechanism modeling method is presented. Two main models are contained in the mechanism model： information model and mathematical model. Firstly, the information model of mechanism is proposed to describe the data structure of mechanism which contains bottom geometry data, information of constraint, link, kinematic pair and physical data. Because the object of mechanism simulation is the assembly, which is assembled during the process of assembly simulation, the information of mechanism can be obtained automatically through mechanism automatic search method. Secondly, mathematical model of mechanism is presented. The mathematical model uses mathematical method to express the mechanism. In order to realize the automatic expression of any random mechanism, basic constraint library is presented, consequently random mechanism can be described based on the basic constraint library. Finally, two examples are introduced to validate the method in the prototype system named VAPP（Virtual Assembly Process Planning）. The validation result shows that the mechanism modeling provides a universal modeling method for mechanism motion simulation in virtual assembly environment. This research has important effect on the development both of mechanism motion simulation and virtual assembly.
基金National Natural Science Foundation of China(Grant No.51775278)National Science Fund of China for Distinguished Young Scholars(Grant No.51925505).
文摘Initial residual stress is the main reason causing machining deformation of the workpiece,which has been deemed as one of the most important aspects of machining quality issues.The inference of the distribution of initial residual stress inside the blank has significant meaning for machining deformation control.Due to the principle error of existing residual stress detection methods,there are still challenges in practical applications.Aiming at the detection problem of the initial residual stress field,an initial residual stress inference method by incorporating monitoring data and mechanism model is proposed in this paper.Monitoring data during machining process is used to represent the macroscopic characterization of the unbalanced residual stress,and the finite element numerical model is used as the mechanism model so as to solve the problem that the analytic mechanism model is difficult to establish;the policy gradient approach is introduced to solve the gradient descent problem of the combination of learning model and mechanism model.Finally,the initial residual stress field is obtained through iterative calculation based on the fusing method of monitoring data and mechanism model.Verification results show that the proposed inference method of initial residual stress field can accurately and effectively reflect the machining deformation in the actual machining process.
基金Supported by the National Natural Science Foundation of China(61673401)the Foundation for Innovative Research Groups of the National Natural Science Foundation of China(61621062)the Fundamental Research Funds for the Central Universities of Central South University(2016zzts343)
文摘A suitable pH value of the slurry is a key to efficient mineral flotation. Considering the control delay problem of pH value caused by offline pH measurement, an integrated prediction model for pH value in bauxite froth flotation is proposed, which considers the effect of ore compositions on pH value. Firstly, a regression model is obtained for alkali(Na_2CO_3) consumed by the reaction between ore and alkali. According to the first-order hydrolysis of the remaining alkali, a mechanism-based prediction model is presented for the pH value. Then, considering the complexity of the flotation mechanism, an error prediction model which uses time series of the error of the mechanism model as inputs is presented based on autoregressive moving average(ARMA) method to compensate the mechanism model. Finally, expert rules are established to correct the error compensation direction, which could reflect the dynamic changes during the process accurately and effectively. Simulation results using industrial data show that the presented model meets the needs of the industrial process, which laid the foundation for predictive control of pH regulator.
基金supported by the National Natural Science Foundation of China(5147915151279149+2 种基金71540027)the China Postdoctoral Science Foundation Special Foundation Project(2013T607552012M521487)
文摘To fully display the modeling mechanism of the novelfractional order grey model （FGM （q,1））, this paper decomposesthe data matrix of the model into the mean generation matrix, theaccumulative generation matrix and the raw data matrix, whichare consistent with the fractional order accumulative grey model（FAGM （1,1））. Following this, this paper decomposes the accumulativedata difference matrix into the accumulative generationmatrix, the q-order reductive accumulative matrix and the rawdata matrix, and then combines the least square method, findingthat the differential order affects the model parameters only byaffecting the formation of differential sequences. This paper thensummarizes matrix decomposition of some special sequences,such as the sequence generated by the strengthening and weakeningoperators, the jumping sequence, and the non-equidistancesequence. Finally, this paper expresses the influences of the rawdata transformation, the accumulation sequence transformation,and the differential matrix transformation on the model parametersas matrices, and takes the non-equidistance sequence as an exampleto show the modeling mechanism.
基金supported by the National Natural Science Foundation of China (Grant 51375035)the Specialized Research Fund for the Doctoral Program of Higher Education of China (Grant 20121102110021)
文摘The landing buffer is an important problem in the research on bionic locust jumping robots, and the different modes of landing and buffering can affect the dynamic performance of the buffering process significantly. Based on an experimental observation, the different modes of landing and buffering are determined, which include the different numbers of landing legs and different motion modes of legs in the buffering process. Then a bionic locust mechanism is established, and the springs are used to replace the leg muscles to achieve a buffering effect. To reveal the dynamic performance in the buffering process of the bionic locust mechanism, a dynamic model is established with different modes of landing and buffering. In particular, to analyze the buffering process conveniently, an equivalent vibration dynamic model of the bionic locust mechanism is proposed.Given the support forces of the ground to the leg links, which can be obtained from the dynamic model, the spring forces of the legs and the impact resistance of each leg are the important parameters affecting buffering performance, and evaluation principles for buffering performance are proposed according to the aforementioned parameters. Based on the dynamic model and these evaluation principles, the buffering performances are analyzed and compared in different modes of landing and buffering on a horizontal plane and an inclined plane. The results show that the mechanism with the ends of the legs sliding can obtain a better dynamic performance. This study offers primary theories for buffering dynamics and an evaluation of landing buffer performance,and it establishes a theoretical basis for studies and engineering applications.
文摘A FCC mechanism model was used to predict the effects of propylene promoter in a 3.0 Mt/a FCCU. The FCC mechanism model was developed based on one set of commercial FCC data without using the promoter, and was modified by using another set of commercial FCC data with 3m% promoter in the catalyst inventory, and the calculations by means of this simulation model were performed to predict the data of the FCC unit containing 4m% promoter in the catalyst inventory. The test results showed that the calculated values agreed well with the data obtained from the commercial FCC unit, in which the deviations of calculated product yields versus the actual product yields at the commercial FCC unit were equal to 1.74 percentage points for gasoline, 2.59 percentage points for diesel, 1.50 percentage points for dry gas and LPG, and 0.28 percentage points for coke. The proposed method regarding the development of a simulation model and modifications to the model for commercial FCC unit was feasible.
文摘Based on the Residual Oil Hydrodesulfurization Treatment Unit(S-RHT),the n-order reaction kinetic model for residual oil HDS reactions and artificial neural network(ANN)model were developed to determine the sulfur content of hydrogenated residual oil.The established ANN model covered 4 input variables,1 output variable and 1 hidden layer with 15 neurons.The comparison between the results of two models was listed.The results showed that the predicted mean relative errors of the two models with three different sample data were less than 5%and both the two models had good predictive precision and extrapolative feature for the HDS process.The mean relative error of 5 sets of testing data of the ANN model was 1.62%—3.23%,all of which were smaller than that of the common mechanism model(3.47%—4.13%).It showed that the ANN model was better than the mechanism model both in terms of fitting results and fitting difficulty.The models could be easily applied in practice and could also provide a reference for the further research of residual oil HDS process.
文摘A hybrid model of a subminiature helicopter in horizontal turn is presented. This model is based on a mechanism model and its compensated neural network （NN）. First, the nonlinear dynamics of a sub-miniature helicopter is established. Through the linearization of the nonlinear dynamics on a trim point, the linear time-invariant mechanism model in horizontal turn is obtained. Then a diagonal recursive neural network is used to compensate the model error between the mechanism model and the nonlinear model, thus the hybrid model of a subminiature helicopter in horizontal turn is achieved. Simulation results show that the hybrid model has higher accuracy than the mechanism model and the obtained compensated-NN has good generalization capability.
基金supported by the National Key Researchand Development Programof China (Grant No.2020YFB1711101)the Anhui Provincial University Natural Science Foundation Key Project (Grant No.KJ2019A127).
文摘An online model was proposed to identify the reasons behind changes in the energy consumption of the reheating furnace of a steel processing plant.The heat conversion of the furnace was analyzed and integrated with the fuel consumption of the furnace to obtain a model of the energy consumption.Combined with the mechanism analysis,the basic parameters affecting energy consumption were determined,and four key influencing factors were obtained:furnace output,furnace charging temperature,furnace tapping temperature,and steel type.The specific calculation method of the contribution of each influencing factor was derived to define the conditions of the baseline energy consumption,while the online data were used to calculate the energy value and the actual performance value of the baseline energy consumption.The contribution of each influencing factor was determined through normalization.The cloud platform was used for database reconstruction and programming to realize the online intelligent evaluation of the energy consumption of the reheating furnace.Finally,a case study of the evaluation of the practical energy consumption of a steel rolling furnace in a steel plant was presented.The intelligent evaluation results were quantified and displayed online,and the performance of the system in reducing production line energy consumption was demonstrated.
文摘A mathematical model has been developed to describe the dynamic behaviours of NO+CO reaction on supported Pt MO catalyst. The ignited state kinetics can be fit quantitatively using directly a Langmuir Henshelwood bimolecular rate expression with a heat of adsorption of NO of 32 4 kJ/mol and of CO of 106 7 kJ/mol, respectively.
文摘Dune riocks are aeolian sands cemented ty calcium carbonate under subaerial conditions. They have been found in many of the coastal belts of Fujian, Guangdong and Hainan Provinces in South China. The grain composition of the dune rocks is mainly quartz sands and shell fragments. The quartz sands are medium and fine sized, relatively well sorted and positively skewed. Their surface texture formed in aeolian environments is characterized ty dishshaped depressions, meniscus depressions and V-shaped depressions with rounded edges. The most common bedding type of the rocks is larg (thickness>1.5m), steeply dipping (32--40°) with cross strata tolaner and convex upward). Mg and Sr contents are very low in the rock chemical composition which is classified into low Mg and low Sr category. The typical species of microfossils in the dune rocks are mainly freshwater ones and lack of typical saltwaer or semi-saltwater ones with incomplete assemblage of marine species. The cement minerals in the rocks are mainly low-Mg calcite and the common cement fabrics are meniscus cement and gravitational cement in response to impermanent water in vadose zones. Therefore, the dune rocks may be apparently distinguished from the beach rocks.
