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Hybrid model for BOF oxygen blowing time prediction based on oxygen balance mechanism and deep neural network
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作者 Xin Shao Qing Liu +3 位作者 Zicheng Xin Jiangshan Zhang Tao Zhou Shaoshuai Li 《International Journal of Minerals,Metallurgy and Materials》 SCIE EI CSCD 2024年第1期106-117,共12页
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 ... 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. 展开更多
关键词 basic oxygen furnace oxygen consumption oxygen blowing time oxygen balance mechanism deep neural network hybrid model
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Wireless Network Security Using Load Balanced Mobile Sink Technique
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作者 Reem Alkanhel Mohamed Abouhawwash +2 位作者 S.N.Sangeethaa K.Venkatachalam Doaa Sami Khafaga 《Intelligent Automation & Soft Computing》 SCIE 2023年第2期2135-2149,共15页
Real-time applications based on Wireless Sensor Network(WSN)tech-nologies are quickly increasing due to intelligent surroundings.Among the most significant resources in the WSN are battery power and security.Clustering... Real-time applications based on Wireless Sensor Network(WSN)tech-nologies are quickly increasing due to intelligent surroundings.Among the most significant resources in the WSN are battery power and security.Clustering stra-tegies improve the power factor and secure the WSN environment.It takes more electricity to forward data in a WSN.Though numerous clustering methods have been developed to provide energy consumption,there is indeed a risk of unequal load balancing,resulting in a decrease in the network’s lifetime due to network inequalities and less security.These possibilities arise due to the cluster head’s limited life span.These cluster heads(CH)are in charge of all activities and con-trol intra-cluster and inter-cluster interactions.The proposed method uses Lifetime centric load balancing mechanisms(LCLBM)and Cluster-based energy optimiza-tion using a mobile sink algorithm(CEOMS).LCLBM emphasizes the selection of CH,system architectures,and optimal distribution of CH.In addition,the LCLBM was added with an assistant cluster head(ACH)for load balancing.Power consumption,communications latency,the frequency of failing nodes,high security,and one-way delay are essential variables to consider while evaluating LCLBM.CEOMS will choose a cluster leader based on the influence of the fol-lowing parameters on the energy balance of WSNs.According to simulatedfind-ings,the suggested LCLBM-CEOMS method increases cluster head selection self-adaptability,improves the network’s lifetime,decreases data latency,and bal-ances network capacity. 展开更多
关键词 Wireless sensor network load balancing mechanism optimization power consumption network’s lifetime
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INVESTIGATION INTO EFFECT OF SPRING PRESSURE ON PERFORMANCE OF BALANCED MECHANICAL SEALS 被引量:7
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作者 SUN Jianjun GU Boqin 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2007年第3期39-43,共5页
The loads acting on the sealing elements of balanced mechanical seals are analyzed. When the balance factor approaches the back pressure factor, the spring pressure will become main part of the face pressure. The leak... The loads acting on the sealing elements of balanced mechanical seals are analyzed. When the balance factor approaches the back pressure factor, the spring pressure will become main part of the face pressure. The leakage model of balanced mechanical seals is established on the base of M-B model for rough surface. Several GY-70 type balanced mechanical seals are tested. The influences of the spring pressure both on the leakage rate and on the friction characteristic of balanced mechanical seals are investigated. The research results indicate that as spring pressure increases, both the clear-ance between two end faces and the leakage rate will decrease, and the friction will be more serious because lubrication medium between the rotating ring and the stationary ring reduces, though the increase of the spring pressure may not be enough to change the face friction state of mechanical seals. There exists an optimum spring pressure for mechanical seal operation. Under this spring pres-sure, not only leakage rate is small, but also the seal end surfaces have a fine friction characteristic. Under different operating conditions, identical type mechanical seals may possess different spring pressure. Appropriate selection of spring pressure is valuable to realize long-period and small leakage rate operating of balanced mechanical seals. 展开更多
关键词 balanced mechanical seal Spring pressure Leakage rate Friction characteristic Fractal geometry
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A Novel Method for Type Synthesis of Parallel Mechanism Without Parasitic Motion Based on 2R1T Parallel Mechanism with Rotational Bifurcation
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作者 Jinghan Qin Chunzhan Yu +1 位作者 Zhibo Sun Lei Cao 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2022年第4期191-202,共12页
The parasitic motion has been widely recognized as the major drawback of the parallel mechanism.Therefore a class of 2R1T PMs(parallel mechanism)without parasitic motion has been synthesized.However,these PMs can only... The parasitic motion has been widely recognized as the major drawback of the parallel mechanism.Therefore a class of 2R1T PMs(parallel mechanism)without parasitic motion has been synthesized.However,these PMs can only rotate around two axes in sequential order.It decreases the performance of the balancing adjustment of the end-efector.In this paper,a family of 2R1T PMs without parasitic motion was reconstructed by using a novel method based on the remarkable properties of rotational bifurcation mechanisms,which can rotate in sequential order.Furthermore,some PMs rotating around two continuous axes in an arbitrary order are established by adding single joints.Taking the practicability of these structures into consideration,the workspace of 3-PRPS PM was analyzed as an example.Moreover,this study explores the practical application of the PMs without parasitic motion in developing balance mechanisms in rough-terrain fre-fghting robots.During the climbing process,the tank is adjusted to be parallel to the horizontal plane in real-time.It is proved that this kind of structure realizes continuous rotation around two rotation axes on the premise of no parasitic motion. 展开更多
关键词 Parallel mechanism Rotational bifurcation Parasitic motion Type synthesis balance mechanism
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The Massage Therapy of Balancing the Mechanism of the Body
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作者 Li Jun Xin Lu(Kui tun building army teachers training school, Xinjiang) 《中国针灸》 CAS CSCD 北大核心 1995年第S2期357-358,共2页
TheMassageTherapyofBalancingtheMechanismoftheBody¥LiJun;XinLu(Kuitunbuildingarmyteacherstrainingschool,Xinji... TheMassageTherapyofBalancingtheMechanismoftheBody¥LiJun;XinLu(Kuitunbuildingarmyteacherstrainingschool,Xinjiang)Themassagethe... 展开更多
关键词 The Massage Therapy of Balancing the Mechanism of the Body
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