A SLon full-scale continuous centrifugal concentrator was used to reconcentrate hematite from a high gradient magnetic separation concentrate to study the effect of impact angle, concentrate mass and drum rotation spe...A SLon full-scale continuous centrifugal concentrator was used to reconcentrate hematite from a high gradient magnetic separation concentrate to study the effect of impact angle, concentrate mass and drum rotation speed on the impact energy of turbulent water sprays for continuous centrifugal concentration, under conditions of feed volume flow rate around 9 m3/h, feed solid concentration of 25%-35% and reciprocating velocity of water sprays at 0.05 m/s. The results indicate that a minimal critical impact energy is required in the water sprays for achieving continuous concentration of the concentrator; an unfitted impact angle reduces the impact efficiency, and the highest impact efficiency of 0.6416 is found at the mpact angle of 60°; the increase in concentrate mass leads to an increase in impact energy, and the highest impact efficiency is maintained when the concentrate mass varies in the range of 0.44-0.59 kg/s; when the concentrate mass and the pressure of water sprays are kept at around 0.45 kg/s and in the range of 0.4-0.6 MPa respectively, the impact energy increases proportionally with the increase of drum rotation speed.展开更多
Quantized kernel least mean square(QKLMS) algorithm is an effective nonlinear adaptive online learning algorithm with good performance in constraining the growth of network size through the use of quantization for inp...Quantized kernel least mean square(QKLMS) algorithm is an effective nonlinear adaptive online learning algorithm with good performance in constraining the growth of network size through the use of quantization for input space. It can serve as a powerful tool to perform complex computing for network service and application. With the purpose of compressing the input to further improve learning performance, this article proposes a novel QKLMS with entropy-guided learning, called EQ-KLMS. Under the consecutive square entropy learning framework, the basic idea of entropy-guided learning technique is to measure the uncertainty of the input vectors used for QKLMS, and delete those data with larger uncertainty, which are insignificant or easy to cause learning errors. Then, the dataset is compressed. Consequently, by using square entropy, the learning performance of proposed EQ-KLMS is improved with high precision and low computational cost. The proposed EQ-KLMS is validated using a weather-related dataset, and the results demonstrate the desirable performance of our scheme.展开更多
This paper studied a supervisory control system for a hybrid off-highway electric vehicle under the chargesustaining(CS)condition.A new predictive double Q-learning with backup models(PDQL)scheme is proposed to optimi...This paper studied a supervisory control system for a hybrid off-highway electric vehicle under the chargesustaining(CS)condition.A new predictive double Q-learning with backup models(PDQL)scheme is proposed to optimize the engine fuel in real-world driving and improve energy efficiency with a faster and more robust learning process.Unlike the existing“model-free”methods,which solely follow on-policy and off-policy to update knowledge bases(Q-tables),the PDQL is developed with the capability to merge both on-policy and off-policy learning by introducing a backup model(Q-table).Experimental evaluations are conducted based on software-in-the-loop(SiL)and hardware-in-the-loop(HiL)test platforms based on real-time modelling of the studied vehicle.Compared to the standard double Q-learning(SDQL),the PDQL only needs half of the learning iterations to achieve better energy efficiency than the SDQL at the end learning process.In the SiL under 35 rounds of learning,the results show that the PDQL can improve the vehicle energy efficiency by 1.75%higher than SDQL.By implementing the PDQL in HiL under four predefined real-world conditions,the PDQL can robustly save more than 5.03%energy than the SDQL scheme.展开更多
This article outlines the theoretical and experimental performance studies of a cylindro-parabolic solar collector. The theoretical study consists on the establishment, through mass and energy balances, of a mathemati...This article outlines the theoretical and experimental performance studies of a cylindro-parabolic solar collector. The theoretical study consists on the establishment, through mass and energy balances, of a mathematical model to control the exiting temperature of the heating fluid as well as the temperatures of the absorber and the glass. The experimental level investigates the influence of the solar absorber tube diameter on the performances of the driving device. Several experiments were made in order to know the possibility to reach temperatures being able to ensure for example the ammonia vaporization in the generator of a solar absorption refrigeration system. These experiments were carried out under various operating and climatic conditions. The results are presented and discussed.展开更多
基金Sponsored by the National Natural Science Foundation of China (Grant No. 50638020)
文摘A SLon full-scale continuous centrifugal concentrator was used to reconcentrate hematite from a high gradient magnetic separation concentrate to study the effect of impact angle, concentrate mass and drum rotation speed on the impact energy of turbulent water sprays for continuous centrifugal concentration, under conditions of feed volume flow rate around 9 m3/h, feed solid concentration of 25%-35% and reciprocating velocity of water sprays at 0.05 m/s. The results indicate that a minimal critical impact energy is required in the water sprays for achieving continuous concentration of the concentrator; an unfitted impact angle reduces the impact efficiency, and the highest impact efficiency of 0.6416 is found at the mpact angle of 60°; the increase in concentrate mass leads to an increase in impact energy, and the highest impact efficiency is maintained when the concentrate mass varies in the range of 0.44-0.59 kg/s; when the concentrate mass and the pressure of water sprays are kept at around 0.45 kg/s and in the range of 0.4-0.6 MPa respectively, the impact energy increases proportionally with the increase of drum rotation speed.
基金supported by the National Key Technologies R&D Program of China under Grant No. 2015BAK38B01the National Natural Science Foundation of China under Grant Nos. 61174103 and 61603032+4 种基金the National Key Research and Development Program of China under Grant Nos. 2016YFB0700502, 2016YFB1001404, and 2017YFB0702300the China Postdoctoral Science Foundation under Grant No. 2016M590048the Fundamental Research Funds for the Central Universities under Grant No. 06500025the University of Science and Technology Beijing - Taipei University of Technology Joint Research Program under Grant No. TW201610the Foundation from the Taipei University of Technology of Taiwan under Grant No. NTUT-USTB-105-4
文摘Quantized kernel least mean square(QKLMS) algorithm is an effective nonlinear adaptive online learning algorithm with good performance in constraining the growth of network size through the use of quantization for input space. It can serve as a powerful tool to perform complex computing for network service and application. With the purpose of compressing the input to further improve learning performance, this article proposes a novel QKLMS with entropy-guided learning, called EQ-KLMS. Under the consecutive square entropy learning framework, the basic idea of entropy-guided learning technique is to measure the uncertainty of the input vectors used for QKLMS, and delete those data with larger uncertainty, which are insignificant or easy to cause learning errors. Then, the dataset is compressed. Consequently, by using square entropy, the learning performance of proposed EQ-KLMS is improved with high precision and low computational cost. The proposed EQ-KLMS is validated using a weather-related dataset, and the results demonstrate the desirable performance of our scheme.
基金Project(KF2029)supported by the State Key Laboratory of Automotive Safety and Energy(Tsinghua University),ChinaProject(102253)supported partially by the Innovate UK。
文摘This paper studied a supervisory control system for a hybrid off-highway electric vehicle under the chargesustaining(CS)condition.A new predictive double Q-learning with backup models(PDQL)scheme is proposed to optimize the engine fuel in real-world driving and improve energy efficiency with a faster and more robust learning process.Unlike the existing“model-free”methods,which solely follow on-policy and off-policy to update knowledge bases(Q-tables),the PDQL is developed with the capability to merge both on-policy and off-policy learning by introducing a backup model(Q-table).Experimental evaluations are conducted based on software-in-the-loop(SiL)and hardware-in-the-loop(HiL)test platforms based on real-time modelling of the studied vehicle.Compared to the standard double Q-learning(SDQL),the PDQL only needs half of the learning iterations to achieve better energy efficiency than the SDQL at the end learning process.In the SiL under 35 rounds of learning,the results show that the PDQL can improve the vehicle energy efficiency by 1.75%higher than SDQL.By implementing the PDQL in HiL under four predefined real-world conditions,the PDQL can robustly save more than 5.03%energy than the SDQL scheme.
文摘This article outlines the theoretical and experimental performance studies of a cylindro-parabolic solar collector. The theoretical study consists on the establishment, through mass and energy balances, of a mathematical model to control the exiting temperature of the heating fluid as well as the temperatures of the absorber and the glass. The experimental level investigates the influence of the solar absorber tube diameter on the performances of the driving device. Several experiments were made in order to know the possibility to reach temperatures being able to ensure for example the ammonia vaporization in the generator of a solar absorption refrigeration system. These experiments were carried out under various operating and climatic conditions. The results are presented and discussed.
