[Objectives] To optimize volatile oil extraction from Ocimum basilicum with the water vapor distillation. [Methods] An orthogonal design was carried out to determine the volume of volatile oil by 3 factors: the immers...[Objectives] To optimize volatile oil extraction from Ocimum basilicum with the water vapor distillation. [Methods] An orthogonal design was carried out to determine the volume of volatile oil by 3 factors: the immersion time, distillation time, amount of water. The ratio of the oil in inclusion complex was used to evaluate the technology based on the orthogonal design. [Results] The best volatile oil extraction condition was to add 400 mL of water into the mixture of crude drugs, and to extract the herbal medicine for 2 h with advanced soaking for 6 h with water. [Conclusions] The process is stable, reasonable, and feasible.展开更多
This paper discusses some crucial problems arising in the implementation of the inelli-gent control of the vacuum distillation,such as the analysis of the plant characteristics,con-trol strategy selection,knowledge re...This paper discusses some crucial problems arising in the implementation of the inelli-gent control of the vacuum distillation,such as the analysis of the plant characteristics,con-trol strategy selection,knowledge representation,specification of maintenance strategy forthe knowledge base and accomplishment of the control system,etc.At the same time,wegive a concrete system along with its results and evaluations.展开更多
The electric power industry is the key to achieving the goals of carbon peak and neutrality.Accurate forecasting of carbon emissions in the electric power industry can aid in the prompt adjustment of power generation ...The electric power industry is the key to achieving the goals of carbon peak and neutrality.Accurate forecasting of carbon emissions in the electric power industry can aid in the prompt adjustment of power generation policies and the early achievement of carbon reduction targets.This study proposes a new approach that combines the decomposition-ensemble paradigm with knowledge distillation to forecast daily carbon emissions.First,seasonal and trend decomposition using locally weighted scatterplot smoothing(STL)is used to decompose the data into three subcomponents.Second,two heterogeneous deep neural network models are jointly trained to predict each subcomponent based on online knowledge distillation.During training,the two models learn and provide feedback to each other.The first model-ensemble stage is performed by synthesizing the predictions for each subcomponent of the two models.Finally,the second model-ensemble stage is performed.The predictions for each subcomponent are integrated using linear addition to obtain the final results.In addition,to avoid leakage of test data caused by decomposing the entire time series,a recursive forecasting strategy is applied.Multistep predictions are obtained by forecasting 7,15,and 30 days in the future.Experimental results using metaheuristic algorithms to optimize hyperparameters show that the proposed method evaluated on the daily carbon emissions dataset has better forecasting performance than all baselines.展开更多
Traditional distillation(TD)is generally an energy-intensive and inefficient process for separation and purification of liquids in chemical industries.Herein,we developed an interface-enhanced distillation(IED)by empl...Traditional distillation(TD)is generally an energy-intensive and inefficient process for separation and purification of liquids in chemical industries.Herein,we developed an interface-enhanced distillation(IED)by employing a well-arranged membrane of reduced graphene oxide(rGO)sheet arrays embedded with silicon dioxide nanofibres(rGO/SiO2)as the evaporation intermediate layer on the liquid surface.This IED enlarges the evaporation surfaces and weakens the intermolecular forces on the liquid/solid/gas interfaces,realizing the fast and even low temperature fraction collection with less energy consumption.The IED delivers evaporation rates 200%–300%times that of TD,meanwhile having an energy saving of 40%–60%and a time saving of 50%–70%for diverse liquid feeds.In atmospheric IED manner,high boiling point and perishable organics can be collected with high quality at a temperature lower than their boiling points.This IED provides an innovative strategy for highly efficient distillation in chemical industries.展开更多
建立了采用全二维气相色谱-飞行时间质谱(GC×GC-TO FM S)分析烟草的中性化学成分的方法。以DB-Petro(50m×200μm×0.5μm)为第一维色谱柱,DB-1701(2.3m×100μm×0.1μm)为第二维色谱柱;调制周期为8s;柱头压力为5...建立了采用全二维气相色谱-飞行时间质谱(GC×GC-TO FM S)分析烟草的中性化学成分的方法。以DB-Petro(50m×200μm×0.5μm)为第一维色谱柱,DB-1701(2.3m×100μm×0.1μm)为第二维色谱柱;调制周期为8s;柱头压力为550kPa;采用程序升温方式,初始温度分别为80℃和85℃。采用所建立的方法对不同部位的烟叶、不同品种烟草中的25种中性香味成分含量进行了测定和对比。结果表明:云南楚雄产云烟85的中性香味成分(不包括新植二烯)的总量以中部叶最高,其次是上部叶,下部叶最少;国内外不同品种的烤烟中中性香味成分的含量高低顺序为:巴西烤烟最高,其次是津巴布韦烤烟、云烟85、中烟101、NC89、K326;4类烟草中中性香味成分含量最高的是香料烟,其次是白肋烟、烤烟、马里兰烟。展开更多
基金Supported by Key Natural Science Research Project of Higher Learning Institutions in Anhui Province in 2018(KJ2018A0884)the Natural Science Research Program of Anhui Colleges and Universities in 2017(KJ2017A772)
文摘[Objectives] To optimize volatile oil extraction from Ocimum basilicum with the water vapor distillation. [Methods] An orthogonal design was carried out to determine the volume of volatile oil by 3 factors: the immersion time, distillation time, amount of water. The ratio of the oil in inclusion complex was used to evaluate the technology based on the orthogonal design. [Results] The best volatile oil extraction condition was to add 400 mL of water into the mixture of crude drugs, and to extract the herbal medicine for 2 h with advanced soaking for 6 h with water. [Conclusions] The process is stable, reasonable, and feasible.
基金Supported by the High Technology Research and Development Programme of ChinaNational Natural Science Foundation of China and China Petro-chemical Corporation.
文摘This paper discusses some crucial problems arising in the implementation of the inelli-gent control of the vacuum distillation,such as the analysis of the plant characteristics,con-trol strategy selection,knowledge representation,specification of maintenance strategy forthe knowledge base and accomplishment of the control system,etc.At the same time,wegive a concrete system along with its results and evaluations.
基金This work is supported by the National Natural Science Foundation of China(Grant Nos.:71971089 and 72001083)the Natural Science Foundation of Guangdong Province(Grant No.:2022A1515011612).
文摘The electric power industry is the key to achieving the goals of carbon peak and neutrality.Accurate forecasting of carbon emissions in the electric power industry can aid in the prompt adjustment of power generation policies and the early achievement of carbon reduction targets.This study proposes a new approach that combines the decomposition-ensemble paradigm with knowledge distillation to forecast daily carbon emissions.First,seasonal and trend decomposition using locally weighted scatterplot smoothing(STL)is used to decompose the data into three subcomponents.Second,two heterogeneous deep neural network models are jointly trained to predict each subcomponent based on online knowledge distillation.During training,the two models learn and provide feedback to each other.The first model-ensemble stage is performed by synthesizing the predictions for each subcomponent of the two models.Finally,the second model-ensemble stage is performed.The predictions for each subcomponent are integrated using linear addition to obtain the final results.In addition,to avoid leakage of test data caused by decomposing the entire time series,a recursive forecasting strategy is applied.Multistep predictions are obtained by forecasting 7,15,and 30 days in the future.Experimental results using metaheuristic algorithms to optimize hyperparameters show that the proposed method evaluated on the daily carbon emissions dataset has better forecasting performance than all baselines.
基金This work was supported by the Ministry of Science and Technology of China(2016YFA0200200 and 2017YFB1104300)the National Science Foundation of China(51673026,51433005 and 21805160)+1 种基金NSFC-MAECI(51861135202),NSFC-STINT(21911530143)and Beijing Natural Science Foundation(2152028).Computations were carried out on the“Explorer 100”cluster system of Tsinghua National Laboratory for Information Science and Technology.
文摘Traditional distillation(TD)is generally an energy-intensive and inefficient process for separation and purification of liquids in chemical industries.Herein,we developed an interface-enhanced distillation(IED)by employing a well-arranged membrane of reduced graphene oxide(rGO)sheet arrays embedded with silicon dioxide nanofibres(rGO/SiO2)as the evaporation intermediate layer on the liquid surface.This IED enlarges the evaporation surfaces and weakens the intermolecular forces on the liquid/solid/gas interfaces,realizing the fast and even low temperature fraction collection with less energy consumption.The IED delivers evaporation rates 200%–300%times that of TD,meanwhile having an energy saving of 40%–60%and a time saving of 50%–70%for diverse liquid feeds.In atmospheric IED manner,high boiling point and perishable organics can be collected with high quality at a temperature lower than their boiling points.This IED provides an innovative strategy for highly efficient distillation in chemical industries.
文摘建立了采用全二维气相色谱-飞行时间质谱(GC×GC-TO FM S)分析烟草的中性化学成分的方法。以DB-Petro(50m×200μm×0.5μm)为第一维色谱柱,DB-1701(2.3m×100μm×0.1μm)为第二维色谱柱;调制周期为8s;柱头压力为550kPa;采用程序升温方式,初始温度分别为80℃和85℃。采用所建立的方法对不同部位的烟叶、不同品种烟草中的25种中性香味成分含量进行了测定和对比。结果表明:云南楚雄产云烟85的中性香味成分(不包括新植二烯)的总量以中部叶最高,其次是上部叶,下部叶最少;国内外不同品种的烤烟中中性香味成分的含量高低顺序为:巴西烤烟最高,其次是津巴布韦烤烟、云烟85、中烟101、NC89、K326;4类烟草中中性香味成分含量最高的是香料烟,其次是白肋烟、烤烟、马里兰烟。
