Although industrial processes often perform perfectly under design conditions, they may deviate from the optimal operating point owing to parameters drift, environmental disturbances, etc. Thus, it is necessary to dev...Although industrial processes often perform perfectly under design conditions, they may deviate from the optimal operating point owing to parameters drift, environmental disturbances, etc. Thus, it is necessary to develop efficacious strategies or procedure to assess the process performance online. In this paper, we explore the issue of operating optimality assessment for complex industrial processes based on performance-similarity considering nonlinearities and outliers simultaneously, and a general enforced online performance assessment framework is proposed. In the offline part, a new and modified total robust kernel projection to latent structures algorithm,T-KPRM, is proposed and used to evaluate the complex nonlinear industrial process, which can effectively extract the optimal-index-related process variation information from process data and establish assessment models for each performance grades overcoming the effects of outlier. In the online part, the online assessment results can be obtained by calculating the similarity between the online data from a sliding window and each of the performance grades. Furthermore, in order to improve the accuracy of online assessment, we propose an online assessment strategy taking account of the effects of noise and process uncertainties. The Euclidean distance between the sliding data window and the optimal evaluation level is employed to measure the contribution rates of variables, which indicate the possible reason for the non-optimal operating performance. The proposed framework is tested on a real industrial case: dense medium coal preparation process, and the results shows the efficiency of the proposed method comparing to the existing method.展开更多
The sub-Saharan Africa(SSA)region has experienced substantial population growth over the past decades,accompanied by severe hunger and environmental degradation.Underperforming governance is a major driver of unsustai...The sub-Saharan Africa(SSA)region has experienced substantial population growth over the past decades,accompanied by severe hunger and environmental degradation.Underperforming governance is a major driver of unsustainable agricultural production and land use in SSA.The impacts of governance performance on food security and the environment in SSA require better understanding by considering socioeconomic and biophysical dynamics.Using an agro-economic dynamic optimization model,we investigate the impacts of governance performance on land use,greenhouse gas(GHG)emissions,and food security in the SSA region by 2050.Our findings indicate that strong governance could lead to less deforestation,thus reducing GHG emissions in the agriculture,forestry,and other land use(AFOLU)sector.Strong governance could also improve food security,with higher agricultural productivity,lower food prices and food expenditure share,as well as higher self-sufficiency.These findings highlight that those efforts should extend beyond specific agricultural and environmental measures and promote integrated governance to achieve long-term synergies between food and environmental security in SSA.展开更多
基金Supported by the National Natural Science Foundation of China(61503384,61603393)Natural Science Foundation of Jiangsu(BK20150199,BK20160275)+1 种基金the Foundation Research Funds for the Central Universities(2015QNA65)the Postdoctoral Foundation of Jiangsu Province(1501081B)
文摘Although industrial processes often perform perfectly under design conditions, they may deviate from the optimal operating point owing to parameters drift, environmental disturbances, etc. Thus, it is necessary to develop efficacious strategies or procedure to assess the process performance online. In this paper, we explore the issue of operating optimality assessment for complex industrial processes based on performance-similarity considering nonlinearities and outliers simultaneously, and a general enforced online performance assessment framework is proposed. In the offline part, a new and modified total robust kernel projection to latent structures algorithm,T-KPRM, is proposed and used to evaluate the complex nonlinear industrial process, which can effectively extract the optimal-index-related process variation information from process data and establish assessment models for each performance grades overcoming the effects of outlier. In the online part, the online assessment results can be obtained by calculating the similarity between the online data from a sliding window and each of the performance grades. Furthermore, in order to improve the accuracy of online assessment, we propose an online assessment strategy taking account of the effects of noise and process uncertainties. The Euclidean distance between the sliding data window and the optimal evaluation level is employed to measure the contribution rates of variables, which indicate the possible reason for the non-optimal operating performance. The proposed framework is tested on a real industrial case: dense medium coal preparation process, and the results shows the efficiency of the proposed method comparing to the existing method.
基金supported by the National Science Foundation of China(grant nos.72273126,72104213,and 72134006)the National Key Research and Development Program of China(grant no.2020YFA0608604)the Fundamental Research Funds for the Central Universities(grant no.S20230139)
文摘The sub-Saharan Africa(SSA)region has experienced substantial population growth over the past decades,accompanied by severe hunger and environmental degradation.Underperforming governance is a major driver of unsustainable agricultural production and land use in SSA.The impacts of governance performance on food security and the environment in SSA require better understanding by considering socioeconomic and biophysical dynamics.Using an agro-economic dynamic optimization model,we investigate the impacts of governance performance on land use,greenhouse gas(GHG)emissions,and food security in the SSA region by 2050.Our findings indicate that strong governance could lead to less deforestation,thus reducing GHG emissions in the agriculture,forestry,and other land use(AFOLU)sector.Strong governance could also improve food security,with higher agricultural productivity,lower food prices and food expenditure share,as well as higher self-sufficiency.These findings highlight that those efforts should extend beyond specific agricultural and environmental measures and promote integrated governance to achieve long-term synergies between food and environmental security in SSA.
