Gasification of organic waste represents one of the most effective valorization pathways for renewable energy and resources recovery,while this process can be affected by multi-factors like temperature,feedstock,and s...Gasification of organic waste represents one of the most effective valorization pathways for renewable energy and resources recovery,while this process can be affected by multi-factors like temperature,feedstock,and steam content,making the product’s prediction problematic.With the popularization and promotion of artificial intelligence such as machine learning(ML),traditional artificial neural networks have been paid more attention by researchers from the data science field,which provides scientific and engineering communities with flexible and rapid prediction frameworks in the field of organic waste gasification.In this work,critical parameters including temperature,steam ratio,and feedstock during gasification of organic waste were reviewed in three scenarios including steam gasification,air gasification,and oxygen-riched gasification,and the product distribution and involved mechanism were elaborated.Moreover,we presented the details of ML methods like regression analysis,artificial neural networks,decision trees,and related methods,which are expected to revolutionize data analysis and modeling of the gasification of organic waste.Typical outputs including the syngas yield,composition,and HHVs were discussed with a better understanding of the gasification process and ML application.This review focused on the combination of gasification and ML,and it is of immediate significance for the resource and energy utilization of organic waste.展开更多
This article is devoted to the numerical solution of a projected generalized Sylvester equation with relatively small size. Such an equation arises in stability analysis and control problems for descriptor systems inc...This article is devoted to the numerical solution of a projected generalized Sylvester equation with relatively small size. Such an equation arises in stability analysis and control problems for descriptor systems including model reduction based on balanced truncation. The algebraic formula of the solution of the projected generalized continuous-time Sylvester equation is presented. A direct method based on the generalized Schur factorization is proposed. Moreover, its low-rank version for problems with low-rank right-hand sides is also proposed. The computational cost of the direct method is estimated. Numerical simulation show that this direct method has high accurncv展开更多
Lead titanate nanostructures with different phases and morphologies, layered hexagonal PbTiO2（CO3）0.3- （NO3）0.35（OH） nanosheets, pyrochlore Pb2Ti2O6 nanodendites, pre-perovskite PbTiO3 nanofibres and perovskite ...Lead titanate nanostructures with different phases and morphologies, layered hexagonal PbTiO2（CO3）0.3- （NO3）0.35（OH） nanosheets, pyrochlore Pb2Ti2O6 nanodendites, pre-perovskite PbTiO3 nanofibres and perovskite PbTiO3 nanoplates, have been synthesized via a conventional hydrothermal route assisted with different concentra- tions of tetramethylammonium hydroxide （TMAH）. X-ray diffraction （XRD）, scanning electron microscopy （SEM）, transmission electron microscopy （TEM）, and high-resolution TEM （HRTEM） were employed to characterize the phase, morphology and growth behavior of the synthesized samples. The results reveal that at low TMAH concen- tration the obtained samples are mainly of PbTiO2（CO3）o.3（NO3）0.35（OH） nanosheets. With the TMAH concentration increasing, the obtained samples change to pyrochlore Pb2Ti2O6 nanodendites, pre-perovskite PbTiO3 nanofibres and perovskite PbTiO3 nanoplates in turn. With the basis of the experimental results, the phase- and morpholo- gy-evolution mechanism of the lead titanate nanostructures is discussed by combining the analysis of the lattice structure feature and the properties of TMAH.展开更多
基金This work is supported by Sichuan Science and Technology Program(2021JDR0343)the Project Fund of Chengdu Science and Technology Bureau(2019-YF09-00086-SN).
文摘Gasification of organic waste represents one of the most effective valorization pathways for renewable energy and resources recovery,while this process can be affected by multi-factors like temperature,feedstock,and steam content,making the product’s prediction problematic.With the popularization and promotion of artificial intelligence such as machine learning(ML),traditional artificial neural networks have been paid more attention by researchers from the data science field,which provides scientific and engineering communities with flexible and rapid prediction frameworks in the field of organic waste gasification.In this work,critical parameters including temperature,steam ratio,and feedstock during gasification of organic waste were reviewed in three scenarios including steam gasification,air gasification,and oxygen-riched gasification,and the product distribution and involved mechanism were elaborated.Moreover,we presented the details of ML methods like regression analysis,artificial neural networks,decision trees,and related methods,which are expected to revolutionize data analysis and modeling of the gasification of organic waste.Typical outputs including the syngas yield,composition,and HHVs were discussed with a better understanding of the gasification process and ML application.This review focused on the combination of gasification and ML,and it is of immediate significance for the resource and energy utilization of organic waste.
基金supported by the National Natural Science Foundation of China(Nos.10801048,10926150,11101149)the Natural Science Foundation of Hunan Province(No.09JJ6014)+4 种基金the Key Program of the Scientific Research Foundation from Education Bureau of Hunan Province(No.09A033)the Scientific Research Foundation of Education Bureau of Hunan Province for Outstanding Young Scholars in University(No.10B038)the Science and Technology Planning Project of Hunan Province(No.2010JT4042)the Young Core Teacher Foundation of Hunan Province in Universitythe Fundamental Research Funds for the Central Universities
文摘This article is devoted to the numerical solution of a projected generalized Sylvester equation with relatively small size. Such an equation arises in stability analysis and control problems for descriptor systems including model reduction based on balanced truncation. The algebraic formula of the solution of the projected generalized continuous-time Sylvester equation is presented. A direct method based on the generalized Schur factorization is proposed. Moreover, its low-rank version for problems with low-rank right-hand sides is also proposed. The computational cost of the direct method is estimated. Numerical simulation show that this direct method has high accurncv
基金This work is supported by the National Natural Sci- ence Foundation of China （Nos. 61274004, 51232006） and the Zhejiang Natural Science Foundation of China （No. LY 12B07007）.
文摘Lead titanate nanostructures with different phases and morphologies, layered hexagonal PbTiO2（CO3）0.3- （NO3）0.35（OH） nanosheets, pyrochlore Pb2Ti2O6 nanodendites, pre-perovskite PbTiO3 nanofibres and perovskite PbTiO3 nanoplates, have been synthesized via a conventional hydrothermal route assisted with different concentra- tions of tetramethylammonium hydroxide （TMAH）. X-ray diffraction （XRD）, scanning electron microscopy （SEM）, transmission electron microscopy （TEM）, and high-resolution TEM （HRTEM） were employed to characterize the phase, morphology and growth behavior of the synthesized samples. The results reveal that at low TMAH concen- tration the obtained samples are mainly of PbTiO2（CO3）o.3（NO3）0.35（OH） nanosheets. With the TMAH concentration increasing, the obtained samples change to pyrochlore Pb2Ti2O6 nanodendites, pre-perovskite PbTiO3 nanofibres and perovskite PbTiO3 nanoplates in turn. With the basis of the experimental results, the phase- and morpholo- gy-evolution mechanism of the lead titanate nanostructures is discussed by combining the analysis of the lattice structure feature and the properties of TMAH.
