A novel approach named aligned mixture probabilistic principal component analysis(AMPPCA) is proposed in this study for fault detection of multimode chemical processes. In order to exploit within-mode correlations,the...A novel approach named aligned mixture probabilistic principal component analysis(AMPPCA) is proposed in this study for fault detection of multimode chemical processes. In order to exploit within-mode correlations,the AMPPCA algorithm first estimates a statistical description for each operating mode by applying mixture probabilistic principal component analysis(MPPCA). As a comparison, the combined MPPCA is employed where monitoring results are softly integrated according to posterior probabilities of the test sample in each local model. For exploiting the cross-mode correlations, which may be useful but are inadvertently neglected due to separately held monitoring approaches, a global monitoring model is constructed by aligning all local models together. In this way, both within-mode and cross-mode correlations are preserved in this integrated space. Finally, the utility and feasibility of AMPPCA are demonstrated through a non-isothermal continuous stirred tank reactor and the TE benchmark process.展开更多
Recent research has underscored the critical need for high-level global products to support comprehensive environmental monitoring and management[1],aligning closely with the objectives of the United Nations'Susta...Recent research has underscored the critical need for high-level global products to support comprehensive environmental monitoring and management[1],aligning closely with the objectives of the United Nations'Sustainable Development Goals(SDGs),particularly SDG 11.This goal focuses on transforming cities into inclusive,safe,resilient,and sustainable spaces.Understanding the relationship between urban expansion and population growth is essential for advancing sustainable urban development.However,the SDG11.3.1 indicator,despite having a clear evaluation method(the Ratio of Land Consumption Rate to Population Growth Rate(LCRPGR)),currently faces a significant challenge because of the lack of available data[2].This data gap hinders the comprehensive and effective measurement of urban land use efficiency,impacting the sustainable management of urban expansion.Therefore,there is an urgent need to acquire precise global-scale information on urban spatial distributions to address the data shortfall for the SDG 11.3.1 indicator and to facilitate improvements in the methods for expanding and monitoring this indicator.This process will assist in comprehensively assessing and managing the economic,social,and environmental impacts of urbanization,thereby supporting the objectives involved in sustainable urban development[3].展开更多
基金Supported by the National Natural Science Foundation of China(61374140)Shanghai Pujiang Program(12PJ1402200)
文摘A novel approach named aligned mixture probabilistic principal component analysis(AMPPCA) is proposed in this study for fault detection of multimode chemical processes. In order to exploit within-mode correlations,the AMPPCA algorithm first estimates a statistical description for each operating mode by applying mixture probabilistic principal component analysis(MPPCA). As a comparison, the combined MPPCA is employed where monitoring results are softly integrated according to posterior probabilities of the test sample in each local model. For exploiting the cross-mode correlations, which may be useful but are inadvertently neglected due to separately held monitoring approaches, a global monitoring model is constructed by aligning all local models together. In this way, both within-mode and cross-mode correlations are preserved in this integrated space. Finally, the utility and feasibility of AMPPCA are demonstrated through a non-isothermal continuous stirred tank reactor and the TE benchmark process.
基金supported by the National Natural Science Foundation of China(42171291 and 42361144884)the Innovative Research Program of the International Research Center of Big Data for Sustainable Development Goals(CBAS2022IRP04)Joint HKU-CAS Laboratory for iEarth(313GJHZ2022074MI,E4F3050300)。
文摘Recent research has underscored the critical need for high-level global products to support comprehensive environmental monitoring and management[1],aligning closely with the objectives of the United Nations'Sustainable Development Goals(SDGs),particularly SDG 11.This goal focuses on transforming cities into inclusive,safe,resilient,and sustainable spaces.Understanding the relationship between urban expansion and population growth is essential for advancing sustainable urban development.However,the SDG11.3.1 indicator,despite having a clear evaluation method(the Ratio of Land Consumption Rate to Population Growth Rate(LCRPGR)),currently faces a significant challenge because of the lack of available data[2].This data gap hinders the comprehensive and effective measurement of urban land use efficiency,impacting the sustainable management of urban expansion.Therefore,there is an urgent need to acquire precise global-scale information on urban spatial distributions to address the data shortfall for the SDG 11.3.1 indicator and to facilitate improvements in the methods for expanding and monitoring this indicator.This process will assist in comprehensively assessing and managing the economic,social,and environmental impacts of urbanization,thereby supporting the objectives involved in sustainable urban development[3].
