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hybrid model for BOF oxygen blowing time prediction based on oxygen balance mechanism and deep neural network

International Journal of Minerals,Metallurgy and Materials 2024年第1期31卷 106-117页

作者： Xin Shao Qing Liu Zicheng Xin Jiangshan Zhang Tao Zhou Shaoshuai Li State Key Laboratory of Advanced Metallurgy University of Science and Technology BeijingBeijing 100083China Engineering Research Center of MES Technology for Iron&Steel Production Ministry of EducationBeijing 100083China Laiwu Iron and Steel Group Yinshan Section Steel Co. Ltd.Jinan 271104China

The amount of oxygen blown into the converter is one of the key parameters for the control of the converter blowing process,which directly affects the tap-to-tap time of converter. In this study, a hybrid model based on oxygen balance mechanism (OBM) and deep neural network (DNN) was established for predicting oxygen blowing time in converter. A three-step method was utilized in the hybrid model. First, the oxygen consumption volume was predicted by the OBM model and DNN model, respectively. Second, a more accurate oxygen consumption volume was obtained by integrating the OBM model and DNN model. Finally, the converter oxygen blowing time was calculated according to the oxygen consumption volume and the oxygen supply intensity of each heat. The proposed hybrid model was verified using the actual data collected from an integrated steel plant in China, and compared with multiple linear regression model, OBM model, and neural network model including extreme learning machine, back propagation neural network, and DNN. The test results indicate that the hybrid model with a network structure of 3 hidden layer layers, 32-16-8 neurons per hidden layer, and 0.1 learning rate has the best prediction accuracy and stronger generalization ability compared with other models. The predicted hit ratio of oxygen consumption volume within the error±300 m^(3)is 96.67%;determination coefficient (R^(2)) and root mean square error (RMSE) are0.6984 and 150.03 m^(3), respectively. The oxygen blow time prediction hit ratio within the error±0.6 min is 89.50%;R2and RMSE are0.9486 and 0.3592 min, respectively. As a result, the proposed model can effectively predict the oxygen consumption volume and oxygen blowing time in the converter.

关键词： basic oxygen furnace oxygen consumption oxygen blowing time oxygen balance mechanism deep neural network hybrid model

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Enhanced wear prediction of tunnel boring machine disc cutters for accurate remaining useful life estimation using a hybrid model

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Frontiers of Structural and Civil Engineering 2024年第4期18卷 642-662页

作者： Xinghai ZHOU Yakun ZHANG Guofang GONG Huayong YANG Qiaosong CHEN Yuxi CHEN Zhixue SU The State Key Laboratory of Fluid Power&Mechatronic Systems Zhejiang UniversityHangzhou 310027China Guangzhou Rail Transit Construction Engineering Consulting Co. Ltd.Guangzhou 510000China China Railway Engineering Equipment Group Co. Ltd.Zhengzhou 450016China

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 *** paper introduces a novel hybrid model,integrating fundamental and data-driven approaches,to enhance wear prediction of TBM disc cutters and enable accurate RUL *** fundamental model is improved by incorporating composite wear mechanisms and load estimation techniques,showcasing superior prediction accuracy compared to single-mechanism ***,the hybrid model innovatively incorporates a data-driven supplementary residual term into the improved fundamental model,leading to a high-performance wear prediction *** 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 *** 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 *** model’s generalization capability is also improved by expanding and updating the data *** 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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Enhancing Software Effort Estimation:A hybrid model Combining LSTM and Random Forest

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Journal of Harbin Institute of Technology(New Series) 2024年第4期31卷 42-51页

作者： Badana Mahesh Mandava Kranthi Kiran Department of Computer Science and Engineering GITAM Deemed to be UniversityVishakhapatnam 561203India Department of Computer Science and Engineering Anil Neerukonda Institute of Technology&SciencesVishakhapatnam 531162India

Effort estimation plays a crucial role in software development projects,aiding in resource allocation,project planning,and risk *** estimation techniques often struggle to provide accurate estimates due to the complex nature of software *** recent years,machine learning approaches have shown promise in improving the accuracy of effort estimation *** study proposes a hybrid model that combines Long Short-Term Memory(LSTM)and Random Forest(RF)algorithms to enhance software effort *** proposed hybrid model takes advantage of the strengths of both LSTM and RF *** evaluate the performance of the hybrid model,an extensive set of software development projects is used as the experimental *** experimental results demonstrate that the proposed hybrid model outperforms traditional estimation techniques in terms of accuracy and *** integration of LSTM and RF enables the model to efficiently capture temporal dependencies and non-linear interactions in the software development *** hybrid model enhances estimation accuracy,enabling project managers and stakeholders to make more precise predictions of effort needed for upcoming software projects.

关键词： software effort estimation hybrid model ensemble learning LSTM temporal dependencies non⁃linear relationships

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hybrid model of Molten Steel Temperature Prediction Based on Ladle Heat Status and Artificial Neural Network

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Journal of Iron and Steel Research International 2014年第2期21卷 181-190页

作者： Fei HE Dong-feng HE An-jun XU Hong-bing WANG Nai-yuan TIAN School of Metallurgical and Ecological Engineering University of Science and Technology Beijing State Key Laboratory of Advanced Metallurgy University of Science and Technology Beijing School of Computer and Communication Engineering University of Science and Technology Beijing

Aiming at the characteristics of the practical steelmaking process, a hybrid model based on ladle heat sta- tus and artificial neural network has been proposed to predict molten steel temperature. The hybrid model could over- come the difficulty of accurate prediction using a single mathematical model, and solve the problem of lacking the consideration of the influence of ladle heat status on the steel temperature in an intelligent model. By using the hybrid model method, forward and backward prediction models for molten steel temperature in steelmaking process are es- tablished and are used in a steelmaking plant. The forward model, starting from the end-point of BOF, predicts the temperature in argon-blowing station, starting temperature in LF, end temperature in LF and tundish temperature forwards, with the production process evolving. The backward model, starting from the required tundish tempera- ture, calculates target end temperature in LF, target starting temperature in LF, target temperature in argon-blo- wiag station and target BOF end-point temperature backwards. Actual application results show that the models have better prediction accuracy and are satisfying for the process of practical production.

关键词： steelmaking process hybrid model ladle heat status neural network molten steel temperature prediction

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hybrid model based on K-means++ algorithm, optimal similar day approach, and long short-term memory neural network for short-term photovoltaic power prediction

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Global Energy Interconnection 2023年第2期6卷 184-196页

作者： Ruxue Bai Yuetao Shi Meng Yue Xiaonan Du Shandong Engineering Laboratory for High-efficiency Energy Conservation and Energy Storage Technology&Equipment School of Energy and Power EngineeringShandong UniversityJinan Shandong 250061P.R.China

Photovoltaic(PV) power generation is characterized by randomness and intermittency due to weather ***, large-scale PV power connections to the grid can threaten the stable operation of the power system. An effective method to resolve this problem is to accurately predict PV power. In this study, an innovative short-term hybrid prediction model(i.e., HKSL) of PV power is established. The model combines K-means++, optimal similar day approach,and long short-term memory(LSTM) network. Historical power data and meteorological factors are utilized. This model searches for the best similar day based on the results of classifying weather types. Then, the data of similar day are inputted into the LSTM network to predict PV power. The validity of the hybrid model is verified based on the datasets from a PV power station in Shandong Province, China. Four evaluation indices, mean absolute error, root mean square error(RMSE),normalized RMSE, and mean absolute deviation, are employed to assess the performance of the HKSL model. The RMSE of the proposed model compared with those of Elman, LSTM, HSE(hybrid model combining similar day approach and Elman), HSL(hybrid model combining similar day approach and LSTM), and HKSE(hybrid model combining K-means++,similar day approach, and LSTM) decreases by 66.73%, 70.22%, 65.59%, 70.51%, and 18.40%, respectively. This proves the reliability and excellent performance of the proposed hybrid model in predicting power.

关键词： PV power prediction hybrid model K-means++ optimal similar day LSTM

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A hybrid model of a subminiature helicopter in horizontal turn

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High Technology Letters 2007年第2期13卷 113-118页

作者：陈丽 Gong Zhenbang Liu Liang Institute of Aerospace Science ＆ Technology Shanghai Jiaotong University Shanghai200030P.R. China School of Mechatronics Engineering and Automation Shanghai University Shanghai 200072 P.R. China）

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.

关键词： subminiature helicopter horizontal turn mechanism model compensated neural net-work hybrid model

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hybrid model for prediction of welding distortions in large structures

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Frontiers of Materials Science 2011年第2期5卷 209-215页

作者： Vesselin MICHAILOV Nikolay DOYNOV Christoph STAPELFELD Ralf OSSENBRINK Chair of Joining and Welding Technology Brandenburg Technical University of Cottbus Konrad-Wachsmann-Allee 17 03046 Cottbus Germany

The paper presents a short overview of the contemporary approaches for calculating welding distortions. In order to meet the existing challenges, an advanced hybrid model for prediction of welding distortions in large structures is described. For the purpose of illustrating the capability of this model, a simulation case is put into discussion. The results are validated by comparison with experimental data, as well as with common simulation technique. Analysis of the calculation costs is also presented. The directions for development of calculation technique, based on the presented model, are also suggested.

关键词： hybrid model welding distortion calculation

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hybrid model enabling highly efficient follicular segmentation in thyroid cytopathological whole slide image

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Intelligent Medicine 2021年第2期1卷 70-79页

作者： Chuang Zhu Siyan Tao Huang Chen Minzhen Li Ying Wang Jun Liu Mulan Jin Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and ConvergenceSchool of Artificial IntelligenceBeijing University of Posts and TelecommunicationsBeijing 100876China Department of Pathology China-Japan Friendship HospitalBeijing 100029China Department of Pathology Beijing Chao-Yang HospitalCapital Medical UniversityBeijing 100020China

Background The prevalence of thyroid cancer is growing *** and precise diagnosis is critical in thy-roid cancer *** automatic thyroid cancer diagnostic tool can be valuable to achieve early detection and diagnostic *** the follicular areas in the sample contain useful information to the thyroid cancer diagnosis based on fine needle aspiration(FNA).This study aimed to develop a highly efficient accurate method for follicular cell areas segmentation(FCAS)of thyroid cytopathological whole slide images(WSIs).Methods A total of 96 cell samples from July 2017 to July 2018 were collected in one hospital in Beijing,***-three WSIs were selected and manually labeled,including 17 cases of papillary thyroid carci-noma sample and 26 cases of benign *** thousand and nine hundred cropped typical image patches(available on https://***/bupt-ai-cz/hybrid-model-Enabling-Highly-Efficient-Follicular-Segmentation)of 1024×1024 pixels from 13 large WSIs were used for patch-level model training and testing and all of the 13 large WSIs were papillary thyroid carcinoma *** testing WSIs with an average size 36,217×29,400(from 10,240×10,240 to 81,920×61,440)were used to test the effectiveness of the hybrid *** on the traditional semantic segmentation model deeplabv3,we constructed a hybrid segmentation architecture by adding a classification branch into the segmentation scheme to improve *** was used to measure the performance of the classification model;pixel accuracy(pAcc),mean accuracy(mAcc),mean intersection over union(mIoU),and frequency weighted intersection over union(fwIoU)were used to measure the performance of the segmentation model,*** Using this method,up to 93%WSI segmentation time was reduced by skipping the colloidal areas and the blank background *** average processing time of 30 WSI was 49.49 *** the patch dataset,this hybrid model might reach pAcc=98.65%,mAcc=85.60%,mI

关键词： Thyroid cancer hybrid model Follicular cell areas segmentation Whole slide image

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A LINEAR hybrid model OF MSE AND BEM FOR FLOATING STRUCTURES IN COASTAL ZONES

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Journal of Hydrodynamics 2006年第6期18卷 649-658页

作者： ZHANG Jun MIAO Guo-ping School of Naval Architecture Ocean and Civil Engineering Shanghai Jiaotong University Shanghai 200030China The First Engineers Scientific Research Institute of the General Armaments Wuxi 214035 China

A linear hybrid model of Mild Slope Equation （MSE） and Boundary Element Method （BEM） is developed to study the wave propagation around floating structures in coastal zones. Both the wave refraction under the influence of topography and the wave diffraction by floating structures are considered. Hence, the model provides wave properties around the coastal floating structures of arbitrary shape but also the wave forces on and the hydrodynamic characteristics of the structures. Different approaches are compared to demonstrate the validity of the present hybrid model. Several numerical tests are carried out for the cases of pontoons under different circumstances. The results show that the influence of topography on the hydrodynamic characteristics of floating structures in coastal regions is important and must not be ignored in the most wave period range with practical interests.

关键词： hybrid model Mild Slope Equation (MSE),Boundary Element Method (BEM) marine floating structures,coastal regions wave propagation wave force

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Predictive control for mechanical system with backlash based on hybrid model

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Journal of Systems Engineering and Electronics 2009年第6期20卷 1301-1308页

作者： Dou Lihua Dong Lingxun Chen Jie Xia Yuanqing Dept. of Information Science and Technology Beijing Inst. of Technology Beijing 100081 P. R. China

The mechanical system with backlash is distinguished between a＂backlash mode＂and a＂contact mode＂.The inherent switching between the two operating modes makes the system a prime example of hybrid *** eliminating the bad effect of backlash, a piecewise affine（PWA） model of the mechanical servo system with backlash is *** optimal control of constrained PWA system is obtained by taking advantage of model predictive control（MPC） method, and the explicit solution of MPC in a look-up table form is figured out by combining the dynamic programming and multi-parametric quadratic programming, thereby establishing an explicit hybrid model predictive ***, a piecewise quadratic（PWQ） function for guaranteeing the stability of closed-loop control is found by formulating the search of PWQ function as a semi-definite programming *** the tracking experiments, it is demonstrated that the explicit hybrid model predictive controller has a good traction control effect on the mechanical system with *** error meets the demands of real ***, compared to the direct on-line computation, the computation burden is reduced by the explicit solution, thereby being suitable for real-time control of system with short sampling time.

关键词： predictive controller hybrid model hybrid control backlash mode.

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