Novel design of lubricant-type vacuum distillation process for lube base oils production from hydrocracking tail oil

Dividing-wall columns(DWCs)are widely used in the separation of ternary mixtures,but rarely seen in the separation of petroleum fractions.This work develops two novel and energy-efficient designs of lubricant-type vacuum distillation process(LVDP)for the separation of hydroisomerization fractions(HIF)of a hydrocracking tail oil(HTO).First,the HTO hydroisomerization reaction is investigated in an experimental fixed-bed reactor to achieve the optimum liquid HIF by analyzing the impact of the operating conditions.A LVDP used for HIF separation is proposed and optimized.Subsequently,two thermal coupling intensified technologies,including side-stream(SC)and dividing-wall column(DWC),are combined with the LVDP to develop side-stream vacuum distillation process(SC-LVDP)and dividing-wall column vacuum distillation process(DWC-LVDP).The performance of LVDP,SC-LVDP,and DWC-LVDP are evaluated in terms of energy consumption,capital cost,total annual cost,product yields,and stripping steam consumption.The results demonstrates that the intensified processes,SC-LVDP and DWC-LVDP significantly decreases the energy consumption and capital cost compared with LVDP.DWC-LVDP further decreases in capital cost due to the removal of the side stripper and narrows the overlap between the third lube oils and fourth lube oils.This study attempts to combine DWC structure into the separation of petroleum fractions,and the proposed approach and the results presented provide an incentive for the industrial implementation of high-quality utilization of HTO through intensified LVDP.

A large forage gap in forage availability in traditional pastoral regions in China

Forage supply has been stressed due to the rapid increase in China's livestock consumption.However,the long-term dynamics of the relationships between forage demand and multi-sourced supply are not understood.Here,we examine the annual forage demand,or practical carrying capacity(PCC),and supply,or theoretical carrying capacity(TCC)from 2000 to 2019 in China.We construct a forage supply-demand index(FSDI)to represent the forage supply pressure using MODIS-derived net primary productivity products and provincial statistical datasets,and we consider two scenarios.First,natural grasslands are the sole source of forage.Second,natural grassland forage supply is supplemented with straw crops.We find an increase in PCC in northwestern China's major pastoral and agropastoral provincial regions,including Inner Mongolia,Gansu,Ningxia and Qinghai,at rate of 0.24-3.59 million sheep units(SU)a year.As the primary source of forage,the theoretical carrying capacity of natural grasslands(TCCgrass)expanded at a rate of 3 million SU/yr nationally.Crop straws fed 126.58 million SU nationally in 2019,which accounted for 11.3%of the total practical carrying capacity and alleviated the forage supply pressure by reducing FSDI by 26.56%.During 2000–2019,the theoretical carrying capacity of straw crops(TCCcrop)increased rapidly from 76.5 million SU to 126.6 million SU,which accounted for 10%-15%of the total forage supply at the national scale.We also discovered large carrying capacity gaps(TCCgap)in the northwestern pastoral provincial regions of Inner Mongolia,Xinjiang,Gansu,and some agricultural provinces such as Shandong and Henan,when we considered forage supply from both natural grasslands and straw crops.Our findings showed a large forage gap in the traditional pastoral regions,and we also discussed green fodder as a potential solution for balancing the supply of and demand for forage,which may shed light on crop and forage planning.

草原健康、退化与恢复一体化评估:概念与方法

草原是我国面积最大的陆地生态系统,具有重要的水库、钱库、粮库、碳库功能,在生物多样性保护、气候和水的调节以及全球生物地球化学循环中发挥着关键作用,为人类提供了广泛的物质和非物质利益,特别是食物生产、水供应和调节、碳储存和气候减缓、授粉和文化服务[1~3].

Prediction of Commuter Vehicle Demand Torque Based on Historical Speed Information

The development of vehicle-to-everything and cloud computing has brought new opportunities and challenges to the automobile industry.In this paper,a commuter vehicle demand torque prediction method based on historical vehicle speed information is proposed,which uses machine learning to predict and analyze vehicle demand torque.Firstly,the big data of vehicle driving is collected,and the driving data is cleaned and features extracted based on road information.Then,the vehicle longitudinal driving dynamics model is established.Next,the vehicle simulation simulator is established based on the longitudinal driving dynamics model of the vehicle,and the driving torque of the vehicle is obtained.Finally,the travel is divided into several accelerationcruise-deceleration road pairs for analysis,and the vehicle demand torque is predicted by BP neural network and Gaussian process regression.

A multi-colloidal gold immunochromatography assay for rapid and simultaneous detection of 13 herbicides

Herbicide residues in agricultural products can have adverse effects on the environment and human health,therefore,there is an urgent need to establish a sensitive,rapid,and wide-ranging detection method.In this study,haptens of phenylurea herbicides(PUs)and sulfonylurea herbicides(SUs)were analyzed and designed based on computational simulation techniques,and two high-performance broad-spectrum monoclonal antibodies against PUs and SUs were prepared.On this basis,a multi-colloidal gold immunochromatography assay(multi-CGIA)was developed to simultaneously detect 13 herbicides in wheat.The visual limit of detection(vLOD)for PUs including diuron,chlortoluron,neburon,chlorbromuron,and linuron was 1-2μg/kg.The vLOD for SUs including metsulfuron methyl,ethametsulfuron-methyl,sulfometuron-methyl,tribenuron methyl,cinosulfuron,triasulfuron,chlorimuron-ethyl,and chlorsulfuron was 2-10μg/kg.The results of real sample determination indicated that the multi-CGIA is accurate,stable,and reliable,and adaptable to on-site preliminary screening of actual samples.

Distributed state estimation for linear timeinvariant dynamical systems:A review of theories and algorithms

Distributed state estimation is of paramount importance in many applications involving the large-scale complex systems over spatially deployed networked sensors.This paper provides an overview for analysis of distributed state estimation algorithms for linear time invariant systems.A number of previous works are reviewed and a clear classification of the main approaches in this field are presented,i.e.,Kalman-filter-type methods and Luenberger-observer-type methods.The design and the stability analysis of these methods are discussed.Moreover,a comprehensive comparison of the existing results is provided in terms of some standard metrics including the graph connectivity,system observability,optimality,time scale and so on.Finally,several important and challenging future research directions are discussed.