Polyethylene is the type of waste plastic that accounts for the most significant proportion of municipal solid waste.Waste polyethylene can be valorized via pyrolysis and produce value-added oil,gas,and char.On the ot...Polyethylene is the type of waste plastic that accounts for the most significant proportion of municipal solid waste.Waste polyethylene can be valorized via pyrolysis and produce value-added oil,gas,and char.On the other hand,self-sustaining smoldering is an emerging technical means to deal with sand/soil contaminated by organic matter.The high-temperature heat generated by smoldering can be used as a heat source for pyrolyzing waste polyethylene.Therefore,this study investigates numerically the pyrolysis of waste polyethylene driven by self-sustaining smoldering.A novel 4-step lumped kinetic model is proposed for simulating the pyrolysis of waste polyethylene.The results indicate that the operating parameters can determine the pyrolysis product yields by regulating the pyrolysis temperature and the volatile residence time.Note that higher temperatures and longer residence times favor the generation of shorter-chain pyrolysis products because of the intensified volatiles’secondary cracking.It can be concluded that a high interface-wall heat transfer coefficient(400 W m^(-2)K^(-1)),a low PE content(0.20),a high char concentration(2.4%),and a moderate air velocity(0.040 m s^(-1))are beneficial to oil yield.To some extent,this study may broaden the boundaries for the application of self-sustained smoldering-driven pyrolysis.展开更多
A decomposition clustering ensemble(DCE)learning approach is proposed for forecasting foreign exchange rates by integrating the variational mode decomposition(VMD),the selforganizing map(SOM)network,and the kemel extr...A decomposition clustering ensemble(DCE)learning approach is proposed for forecasting foreign exchange rates by integrating the variational mode decomposition(VMD),the selforganizing map(SOM)network,and the kemel extreme leaming machine(KELM).First,the exchange rate time series is decomposed into N subcomponents by the VMD method.Second,each subcomponent series is modeled by the KELM.Third,the SOM neural network is introduced to cluster the subcomponent forecasting results of the in-sample dataset to obtain cluster centers.Finally,each cluster's ensemble weight is estimated by another KELM,and the final forecasting results are obtained by the corresponding clusters'ensemble weights.The empirical results illustrate that our proposed DCE learning approach can significantly improve forecasting performance,and statistically outperform some other benchmark models in directional and level forecasting accuracy.展开更多
In this article, we study numerically a Helmholtz decomposition methodology, based on a formulation of the mathematical model as a saddle-point problem. We use a preconditioned conjugate gradient algorithm, applied to...In this article, we study numerically a Helmholtz decomposition methodology, based on a formulation of the mathematical model as a saddle-point problem. We use a preconditioned conjugate gradient algorithm, applied to an associated operator equation of elliptic type, to solve the problem. To solve the elliptic partial differential equations, we use a second order mixed finite element approximation for discretization. We show, using 2-D synthetic vector fields, that this approach, yields very accurate solutions at a low computational cost compared to traditional methods with the same order of approximation.展开更多
In this study,the problem of bundle adjustment was revisited,and a novel algorithm based on block matrix Cholesky decomposition was proposed to solve the thorny problem of self-calibration bundle adjustment.The innova...In this study,the problem of bundle adjustment was revisited,and a novel algorithm based on block matrix Cholesky decomposition was proposed to solve the thorny problem of self-calibration bundle adjustment.The innovation points are reflected in the following aspects:①The proposed algorithm is not dependent on the Schur complement,and the calculation process is simple and clear;②The complexities of time and space tend to O(n)in the context of world point number is far greater than that of images and cameras,so the calculation magnitude and memory consumption can be reduced significantly;③The proposed algorithm can carry out self-calibration bundle adjustment in single-camera,multi-camera,and variable-camera modes;④Some measures are employed to improve the optimization effects.Experimental tests showed that the proposed algorithm has the ability to achieve state-of-the-art performance in accuracy and robustness,and it has a strong adaptability as well,because the optimized results are accurate and robust even if the initial values have large deviations from the truth.This study could provide theoretical guidance and technical support for the image-based positioning and 3D reconstruction in the fields of photogrammetry,computer vision and robotics.展开更多
基金supported by the China National Key Research and Development Plan Project(Grant No.2018YFA0702300)the National Natural Science Foundation of China(Grant Nos.51950410590 and52227813)。
文摘Polyethylene is the type of waste plastic that accounts for the most significant proportion of municipal solid waste.Waste polyethylene can be valorized via pyrolysis and produce value-added oil,gas,and char.On the other hand,self-sustaining smoldering is an emerging technical means to deal with sand/soil contaminated by organic matter.The high-temperature heat generated by smoldering can be used as a heat source for pyrolyzing waste polyethylene.Therefore,this study investigates numerically the pyrolysis of waste polyethylene driven by self-sustaining smoldering.A novel 4-step lumped kinetic model is proposed for simulating the pyrolysis of waste polyethylene.The results indicate that the operating parameters can determine the pyrolysis product yields by regulating the pyrolysis temperature and the volatile residence time.Note that higher temperatures and longer residence times favor the generation of shorter-chain pyrolysis products because of the intensified volatiles’secondary cracking.It can be concluded that a high interface-wall heat transfer coefficient(400 W m^(-2)K^(-1)),a low PE content(0.20),a high char concentration(2.4%),and a moderate air velocity(0.040 m s^(-1))are beneficial to oil yield.To some extent,this study may broaden the boundaries for the application of self-sustained smoldering-driven pyrolysis.
基金supported by the National Natural Science Foundation of China under Project No.71801213 and No.71642006the Hong Kong R&D Projects under Project No.7004715the Research Grant Council of Hong Kong under Project No.2016-3-56.
文摘A decomposition clustering ensemble(DCE)learning approach is proposed for forecasting foreign exchange rates by integrating the variational mode decomposition(VMD),the selforganizing map(SOM)network,and the kemel extreme leaming machine(KELM).First,the exchange rate time series is decomposed into N subcomponents by the VMD method.Second,each subcomponent series is modeled by the KELM.Third,the SOM neural network is introduced to cluster the subcomponent forecasting results of the in-sample dataset to obtain cluster centers.Finally,each cluster's ensemble weight is estimated by another KELM,and the final forecasting results are obtained by the corresponding clusters'ensemble weights.The empirical results illustrate that our proposed DCE learning approach can significantly improve forecasting performance,and statistically outperform some other benchmark models in directional and level forecasting accuracy.
文摘In this article, we study numerically a Helmholtz decomposition methodology, based on a formulation of the mathematical model as a saddle-point problem. We use a preconditioned conjugate gradient algorithm, applied to an associated operator equation of elliptic type, to solve the problem. To solve the elliptic partial differential equations, we use a second order mixed finite element approximation for discretization. We show, using 2-D synthetic vector fields, that this approach, yields very accurate solutions at a low computational cost compared to traditional methods with the same order of approximation.
基金National Natural Science Foundation of China(Nos.41571410,41977067,42171422)。
文摘In this study,the problem of bundle adjustment was revisited,and a novel algorithm based on block matrix Cholesky decomposition was proposed to solve the thorny problem of self-calibration bundle adjustment.The innovation points are reflected in the following aspects:①The proposed algorithm is not dependent on the Schur complement,and the calculation process is simple and clear;②The complexities of time and space tend to O(n)in the context of world point number is far greater than that of images and cameras,so the calculation magnitude and memory consumption can be reduced significantly;③The proposed algorithm can carry out self-calibration bundle adjustment in single-camera,multi-camera,and variable-camera modes;④Some measures are employed to improve the optimization effects.Experimental tests showed that the proposed algorithm has the ability to achieve state-of-the-art performance in accuracy and robustness,and it has a strong adaptability as well,because the optimized results are accurate and robust even if the initial values have large deviations from the truth.This study could provide theoretical guidance and technical support for the image-based positioning and 3D reconstruction in the fields of photogrammetry,computer vision and robotics.
