Model-based risk assessment on dynamic control of twin-column continuous capture under feedstock variations

Dynamic control is essential to guarantee the stable performance of continuous chromatography.AutoMAb dynamic control strategy has been developed to ensure a consistent protein load in twincolumn CaptureSMB continuous capture by integrating the UV signal of breakthrough.In this study,the process risk of CaptureSMB continuous capture under AutoMAb control towards the feedstock variations was assessed by a mechanistic model developed by us.The effects of target protein and impurities under the variation range of±10 mAU·min^(-1) on load amount,protein loss,process productivity,and resin capacity utilization were investigated.The results showed that the CaptureSMB process could be successfully controlled by AutoMAb towards increased or slightly decreased concentration of feedstock.However,the load process would be out of control with drastically decreased target protein or impurities,and the decreased impurities would lead to protein loss.It was found that AutoMAb control would cause 44.7%non-operational areas and 18.3%protein loss areas in the variation range of±10 mAU·min^(-1).To improve the stability of the CaptureSMB process,a modified AutoMAb control that would stop the load procedure when the absolute value of the integral area reached the preset value,was proposed to reduce the risk of protein loss and the non-operational area.

Electromagnetic modeling of interference,confocal,and focus variation microscopy

We present a unified electromagnetic modeling of coherence scanning interferometry,confocal microscopy,and focus variation microscopy as the most common techniques for surface topography inspection with micro-and nanometer resolution.The model aims at analyzing the instrument response and predicting systematic deviations.Since the main focus lies on the modeling of the microscopes,the light–surface interaction is considered,based on the Kirchhoff approximation extended to vectorial imaging theory.However,it can be replaced by rigorous methods without changing the microscope model.We demonstrate that all of the measuring instruments mentioned above can be modeled using the same theory with some adaption to the respective instrument.For validation,simulated results are confirmed by comparison with measurement results.

Application of Projection Pursuit Evaluation Model Based on Real-Coded Accelerating Genetic Algorithm in Evaluating Wetland Soil Quality Variations in the Sanjiang Plain, China

A new technique of dimension reduction named projection pursuit is applied to model and evaluatewetland soil quality variations in the Sanjiang Plain, Helongjiang Province, China. By adopting the im-proved real-coded accelerating genetic algorithm (RAGA), the projection direction is optimized and multi-dimensional indexes are converted into low-dimensional space. Classification of wetland soils and evaluationof wetland soil quality variations are realized by pursuing optimum projection direction and projection func-tion value. Therefore, by adopting this new method, any possible human interference can be avoided andsound results can be achieved in researching quality changes and classification of wetland soils.

Research on runoff variations based on wavelet analysis and wavelet neural network model： A case study of the Heihe River drainage basin （1944-2005）

The Heihe River drainage basin is one of the endangered ecological regions of China. The shortage of water resources is the bottleneck, which constrains the sustainable development of the region. Many scholars in China have done researches concerning this problem. Based on previous researches, this paper analyzed characteristics, tendencies, and causes of annual runoff variations in the Yingluo Gorge （1944-2005） and the Zhengyi Gorge （1954-2005）, which are the boundaries of the upper reaches, the middle reaches, and the lower reaches of the Heihe River drainage basin, by wavelet analysis, wavelet neural network model, and GIS spatial analysis. The results show that： （1） annual runoff variations of the Yingluo Gorge have principal periods of 7 years and 25 years, and its increasing rate is 1.04 m^3/s.10y; （2） annual runoff variations of the Zhengyi Gorge have principal periods of 6 years and 27 years, and its decreasing rate is 2.25 m^3/s.10y; （3） prediction results show that： during 2006-2015, annual runoff variations of the Yingluo and Zhengyi gorges have ascending tendencies, and the increasing rates are respectively 2.04 m^3/s.10y and 1.61 m^3/s.10y; （4） the increase of annual runoff in the Yingluo Gorge has causal relationship with increased temperature and precipitation in the upper reaches, and the decrease of annual runoff in the Zhengyi Gorge in the past decades was mainly caused by the increased human consumption of water resources in the middle researches. The study results will provide scientific basis for making rational use and allocation schemes of water resources in the Heihe River drainage basin.

Direct Climatic Effect of Aerosols and Interdecadal Variations over East Asia Investigated by a Regional Coupled Climate-Chemistry/Aerosol Model

The direct climatic effect of aerosols for the 1980-2000 period over East Asia was numerically investigated by a regional scale coupled climate-chemistry/ aerosol model, which includes major anthropogenic aerosols （sulfate, black carbon, and organic carbon） and natural aerosols （soil dust and sea salt）. Anthropogenic emissions used in model simulation are from a global emission inventory prepared for the Intergovernmental Panel on Climate Change Fifth Assessment Report （IPCC AR5）, whereas natural aerosols are calculated online in the model. The simulated 20-year average direct solar radiative effect due to aerosols at the surface was estimated to be in a range of-9- -33 W m-2 over most areas of China, with maxima over the Gobi desert of West China, and-12 W m-2 to -24 W m-2 over the Sichuan Basin, the middle and lower reaches of the Yellow River and the Yangtze River. Aerosols caused surface cooling in most areas of East Asia, with maxima of-0.8℃ to -1.6℃ over the deserts of West China, the Sichuan Basin, portions of central China, and the middle reaches of the Yangtze River. Aerosols induced a precipitation decrease over almost the entire East China, with maxima of-90 mm/year to -150 mm/year over the Sichuan Basin, the middle reaches of the Yangtze River and the lower reaches of the Yellow River. Interdecadal variation of the climate response to the aerosol direct radiative effect is evident, indicating larger decrease in surface air temperature and stronger per- turbation to precipitation in the 1990s than that in the 1980s, which could be due to the interdecadal variation of anthropogenic emissions.

Flashover Characteristics of Flat Plate Model Under DC Voltage in Wind-sand Condition

The influence of sand dust on discharge of external insulation has caused widespread concern.At present,the research results show wind-sand electricity has a remarkable effect on the discharge characteristics of insulator and has little influence on the discharge characteristics of air gap.The flashover of insulator strings occurs along the insulator surface and air gaps,and the sand dust deposited on the insulator surface may affect the flashover characteristics of insulator strings.This paper studies the flashover characteristics of flat plate model under DC voltage in wind-sand condition.The experimental results show that under positive polarity voltage,the flashover voltage of the flat plate model has a maximum value,while under negative polarity voltage,the flashover voltage of the flat plate model has a minimum value with a certain degree of sand dust deposition.The wind or sand in sand-dust weather has an important effect on the flashover characteristics of the flat plate model.In certain variation range of electric charge,electric charge of sand dust has little effect on the flashover voltage of flat plate model under DC voltage.The deposition of sand has significant influence on the flashover process of flat plate model,which is related to the deposition density and moisture content of sand particle.

Multi-field dynamic modeling and numerical simulation of aluminum alloy resistance spot welding

In order to explore the influence of welding parameters and to investigate the Al alloy (AA) nugget formation process, a comprehensive model involving electrical-thermal-mechanical and metallurgical analysis was established to numerically display the resistance spot welding (RSW) process within multiple fields and understand the AA-RSW physics. A multi-disciplinary finite element method (FEM) framework and a empirical sub-model were built to analyze the affecting factors on weld nugget and the underlying nature of welding physics with dynamic simulation procedure. Specifically, a counter-intuitive phenomenon of the resistance time-variation caused by the transient inverse virtual variation (TIVV) effect was highlighted and analyzed on the basis of welding current and temperature distribution simulation. The empirical model describing the TIVV phenomenon was used for modifying the dynamic resistance simulation during the AA spot welding process. The numerical and experimental results show that the proposed multi-field FEM model agrees with the measured AA welding feature, and the modified dynamic resistance model captures the physics of nugget growth and the electrical-thermal behavior under varying welding current and fluctuating heat input.

Study of monthly variations in primary production and their relationships with environmental factors in the Daya Bay based on a general additive model

In this study, the horizontal and vertical distribution of primary production(PP) and its monthly variations were described based on field data collected from the Daya Bay in January–December of 2016. The relationships between PP and environmental factors were analyzed using a general additive model(GAM). Significant seasonal differences were observed in the horizontal distribution of PP, while vertical distribution showed a relatively consistent unimodal pattern. The monthly average PP(calculated by carbon) ranged from 48.03 to 390.56 mg/(m~2·h),with an annual average of 182.77 mg/(m~2·h). The highest PP was observed in May and the lowest in November.Additionally, the overall trend in PP was spring>summer>winter>autumn, and spring PP was approximately three times that of autumn PP. GAM analysis revealed that temperature, bottom salinity, phytoplankton, and photosynthetically active radiation(PAR) had no significant relationships with PP, while longitude, depth, surface salinity, chlorophyll a(Chl a) and transparency were significantly correlated with PP. Overall, the results presented herein indicate that monsoonal changes and terrestrial and offshore water systems have crucial effects on environmental factors that are associated with PP changes.

Experimental evaluation on well pattern adaptability of ultra-low permeability reservoir using sandstone flat model

As for ultra-low permeability reservoir,the adaptability of common nine-spot well pattern is studied through large-scale flat models made by micro-fractured natural sandstone outcrops.Combined with non-linear porous flow characteristics,the concept of dimensionless pressure sweep efficiency and deliverability index are put forward to evaluate the physical models' well pattern adaptability.Through experiments,the models' pressure distribution is measured and on which basis,the pressure gradient fields are drawn and the porous flow regions of these models are divided into dead oil region,non-linear porous flow region,and quasi-linear porous flow region with the help of twin-core non-linear porous flow curve.The results indicate that rectangular well pattern in fracture reservoirs has the best adaptability,while the worst is inverted nine-spot equilateral well pattern.With the increase of drawdown pressure,dead oil region decreases,pressure sweep efficiency and deliverability index increase; meantime,the deliverability index of rectangular well pattern has much more rational increase.Under the same drawdown pressure,the rectangular well pattern has the largest pressure sweep efficiency.

The electronic structure of Nb＿3Al/Nb＿3Sn, a new test case for flat/steep band model of superconductivity

In this work, we choose Nb3Al/Nb3Sn as a new test case for flat/steep band model of superconductivity. Based on the density functional theory in the generalized gradient approximation, the electronic structure of Nb3Al/ Nb3Sn has been studied. The obtained results agree well with those of the earlier studies and show clearly fiat bands around the Fermi level. The steep bands as characterized in this work locate around the M point in the first Brillouin zone. The obtained results reveal that Nb3Al/Nb3Sn fits more to the ＂Flat/steep＂ band model than to the van-Hove singularity scenario. The fiat/steep band condition for superconductivity implies a different thermodynamic behavior of superconductors other than that predicted from the conventional BCS theory. This observation sets up an indicator for selecting a suitable superconductor when its large-scale industrial use is needed, for example, in superconducting maglev system or ITER project.

Statistical Modeling of Gate Capacitance Variations Induced by Random Dopants in Nanometer MOSFETs Reserving Correlations

We consider intrinsic gate capacitance variations due to random dopants in the nanometer metal oxide semi- conductor field effect transistor （MOSFET） channel. The variations of total gate capacitance and gate transcapacitances are investigated and the strong correlations between the trans-capacitance variations are discovered. A simple statistical model is proposed for accurately capturing total gate capacitance variability based on the correlations. The model fits very well with the Monte Carlo simulations and the average errors are -0.033% for n-type metal-oxide semiconductor and -0.012% for p-type metal-oxide semiconductor, respectively. Our simulation studies also indicate that, owing to these correlations, the total gate capacitance variability will not dominate in gate capacitance variations.

Prediction of Salinity Variations in a Tidal Estuary Using Artificial Neural Network and Three-Dimensional Hydrodynamic Models

The simulation of salinity at different locations of a tidal river using physically-based hydrodynamic models is quite cumbersome because it requires many types of data, such as hydrological and hydraulic time series at boundaries, river geometry, and adjusted coefficients. Therefore, an artificial neural network (ANN) technique using a back-propagation neural network (BPNN) and a radial basis function neural network (RBFNN) is adopted as an effective alternative in salinity simulation studies. The present study focuses on comparing the performance of BPNN, RBFNN, and three-dimensional hydrodynamic models as applied to a tidal estuarine system. The observed salinity data sets collected from 18 to 22 May, 16 to 22 October, and 26 to 30 October 2002 (totaling 4320 data points) were used for BPNN and RBFNN model training and for hydrodynamic model calibration. The data sets collected from 30 May to 2 June and 11 to 15 November 2002 (totaling 2592 data points) were adopted for BPNN and RBFNN model verification and for hydrodynamic model verification. The results revealed that the ANN (BPNN and RBFNN) models were capable of predicting the nonlinear time series behavior of salinity to the multiple forcing signals of water stages at different stations and freshwater input at upstream boundaries. The salinity predicted by the ANN models was better than that predicted by the physically based hydrodynamic model. This study suggests that BPNN and RBFNN models are easy-to-use modeling tools for simulating the salinity variation in a tidal estuarine system.

NEAR-OPTIMALITY OF A STOCHASTIC ECONOMIC-ENVIRONMENT MODEL WITH POLLUTION CONTROL STRATEGIES

Economic development has caused a lot of environmental problems,in turn,environmental pollution restricts economic development.Considering the influence of wind direction and speed,temperature and humidity on pollutants,as well as the influence of epidemic,war and exchange rate on economic development.In this paper,we develop a stochastic economic-environment model with pollution control strategies.Furthermore,sufficient and necessary conditions for the near-optimality are established.Finally,we perform some numerical simulations to demonstrate the correctness of the theoretical results,which shows that some control strategies could decrease the environmental pollution,and therefore,could alleviate economic losses caused by environmental pollution.

Changes of coastline and tidal flat and its implication for ecological protection under human activities: Take China’s Bohai Bay as an example

The change processes and trends of shoreline and tidal flat forced by human activities are essential issues for the sustainability of coastal area,which is also of great significance for understanding coastal ecological environment changes and even global changes.Based on field measurements,combined with Linear Regression(LR)model and Inverse Distance Weighing(IDW)method,this paper presents detailed analysis on the change history and trend of the shoreline and tidal flat in Bohai Bay.The shoreline faces a high erosion chance under the action of natural factors,while the tidal flat faces a different erosion and deposition patterns in Bohai Bay due to the impact of human activities.The implication of change rule for ecological protection and recovery is also discussed.Measures should be taken to protect the coastal ecological environment.The models used in this paper show a high correlation coefficient between observed and modeling data,which means that this method can be used to predict the changing trend of shoreline and tidal flat.The research results of present study can provide scientific supports for future coastal protection and management.

Effects of Spark Ignition Engine Operating Parameters on Its Cyclic Variation ——Modeling and Simulation

An engine cyclic variation model has been built by using the residual gas temperature for the n th cycle as the input of the model, through constant pressure intake process, adiabatic compression process, constant volume combustion process, adiabatic expansion process, adiabatic blow down process and constant pressure exhaust process to approximate the thermodynamic processes in the cylinder, finally the residual gas temperature for the ( n+1) th cycle can be estimated. Because of the adding of engine operating parameters such as engine speed, spark advance, equivalence ratio, intake air pressure, intake air temperature to the model, effects of these parameters on cyclic variation can be estimated quantitatively. Since residual gas temperature fluctuation between cycles reflects the circumstances of engine cyclic variation, parameters to which residual gas temperature is sensitive are most likely used as the means to control cyclic variation. Model simulation shows that for the nearly stiochiometric mixture, cyclic variation is not obvious or even quite stable, but for the lean mixture, under the circumstances of partial load and larger spark advances, engine cyclic variations occur chaotically or with bifurcation.

The Impact of Model Based Offset Scaling Technique on the Amplitude Variation with Offset Responses from 3D Seismic Data Acquired from the Tano Basin, Offshore Ghana

Amplitudes have been found to be a function of incident angle and offset. Hence data required to test for amplitude variation with angle or offset needs to have its amplitudes for all offsets preserved and not stacked. Amplitude Variation with Offset (AVO)/Amplitude Variation with Angle (AVA) is necessary to account for information in the offset/angle parameter (mode converted S-wave and P-wave velocities). Since amplitudes are a function of the converted S- and P-waves, it is important to investigate the dependence of amplitudes on the elastic (P- and S-waves) parameters from the seismic data. By modelling these effects for different reservoir fluids via fluid substitution, various AVO geobody classes present along the well and in the entire seismic cube can be observed. AVO analysis was performed on one test well (Well_1) and 3D pre-stack angle gathers from the Tano Basin. The analysis involves creating a synthetic model to infer the effect of offset scaling techniques on amplitude responses in the Tano basin as compared to the effect of unscaled seismic data. The spectral balance process was performed to match the amplitude spectra of all angle stacks to that of the mid (26°) stack on the test lines. The process had an effect primarily on the far (34° - 40°) stacks. The frequency content of these stacks slightly increased to match that of the near and mid stacks. In offset scaling process, the root mean square (RMS) amplitude comparison between the synthetic and seismic suggests that the amplitude of the far traces should be reduced relative to the nears by up to 16%. However, the exact scaler values depend on the time window considered. This suggests that the amplitude scaling with offset delivered from seismic processing is only approximately correct and needs to be checked with well synthetics and adjusted accordingly prior to use for AVO studies. The AVO attribute volumes generated were better at resolving anomalies on spectrally balanced and offset scaled data than data delivered from conventional processing. A typical class II AVO anomaly is seen along the test well from the cross-plot analysis and AVO attribute cube which indicates an oil filled reservoir.

Prediction of the Asian-Australian Monsoon Interannual Variations with the Grid-Point Atmospheric Model of IAP LASG(GAMIL)

Seasonal prediction of Asian-Australian monsoon （A-AM） precipitation is one of the most important and challenging tasks in climate prediction. In this paper, we evaluate the performance of Grid Atmospheric Model of IAP LASG （GAMIL） on retrospective prediction of the A-AM interannual variation （IAV）, and determine to what extent GAMIL can capture the two major observed modes of A-AM rainfall IAV for the period 1979-2003. The first mode is associated with the turnabout of warming （cooling） in the Nifio 3.4 region, whereas the second mode leads the warming/cooling by about one year, signaling precursory conditions for ENSO. We show that the GAMIL one-month lead prediction of the seasonal precipitation anomalies is primarily able to capture major features of the two observed leading modes of the IAV, with the first mode better predicted than the second. It also depicts the relationship between the first mode and ENSO rather well. On the other hand, the GAMIL has deficiencies in capturing the relationship between the second mode and ENSO. We conclude： （1） successful reproduction of the E1 Nifio-excited monsoon-ocean interaction and E1 Nifio forcing may be critical for the seasonal prediction of the A-AM rainfall IAV with the GAMIL; （2） more efforts are needed to improve the simulation not only in the Nifio 3.4 region but also in the joining area of Asia and the Indian-Pacific Ocean; （3） the selection of a one-tier system may improve the ultimate prediction of the A-AM rainfall IAV. These results offer some references for improvement of the GAMIL and associated seasonal prediction skill.

Monitoring models for base flow effect and daily variation of dam seepage elements considering time lag effect

Affected by external environmental factors and evolution of dam performance, dam seepage behavior shows nonlinear time-varying characteristics. In this study, to predict and evaluate the long-term development trend and short-term fluctuation of the dam seepage behavior, two monitoring models were developed, one for the base flow effect and one for daily variation of dam seepage elements. In the first model, to avoid the influence of the time lag effect on the evaluation of seepage variation with the time effect component of seepage elements, the base values of the seepage element and the reservoir water level were extracted using the wavelet multi-resolution analysis method, and the time effect component was separated by the established base flow effect monitoring model. For the development of the daily variation monitoring model for dam seepage elements, all the previous factors, of which the measured time series prior to the dam seepage element monitoring time may have certain influence on the monitored results, were considered. Those factors that were positively correlated with the analyzed seepage element were initially considered to be the support vector machine(SVM) model input factors, and then the SVM kernel function-based sensitivity analysis was performed to optimize the input factor set and establish the optimized daily variation SVM model. The efficiency and rationality of the two models were verified by case studies of the water level of two piezometric tubes buried under the slope of a concrete gravity dam.Sensitivity analysis of the optimized SVM model shows that the influences of the daily variation of the upstream reservoir water level and rainfall on the daily variation of piezometric tube water level are processes subject to normal distribution.

Variation and dispersal of PM_(10) and PM_(2.5) during COVID-19 lockdown over Kolkata metropolitan city,India investigated through HYSPLIT model

The higher concentration of PM_(10) and PM_(2.5) in the lower atmosphere is severely harmful for human health and it also makes visibility diminution along with weather and climate modifications.The main objective is to find out the spatiotemporal variation and dispersal of PM_(10) and PM_(2.5) along with COVID-19 infection in the dusty city Kolkata.The consecutive two years PM_(10) and PM_(2.5) data of different stations have been obtained from State Pollution Control Board,Govt.of West Bengal.Forward trajectory analysis has been done through HYSPLIT(Hybrid Single Particle Lagrangian Integrated Trajectory)model to find the path and direction of air particles.The result showed that the various meteorological or environmental factors(such as temperature,relative humidity,wind,wind speed,pressure and gusty wind)and geographical location regulate the spatiotemporal variation of PM_(10) and PM_(2.5).These factors like high temperature with relative humidity and strong wind influence to disperse the particulate matters from north to south direction from city to outside during summer in Kolkata metropolitan city.During summer(both pre and lockdown years),the height of particles is extended up to 1000 m owing to active atmospheric ventilation whereas in winter it is confined within 100 m.The HYSPLIT model clearly specified that the particles dispersed from south,south-west to north and north east direction due to strong wind.The constant magnification of PM_(10) and PM_(2.5) in the lower atmosphere leads to greater frequency of COVID-19 infections and deaths.In Kolkata,the one of the crucial reasons of high infection and deaths(COVID-19)is co-morbidity of people.

Modeling Seasonal Variations of Subsurface Chlorophyll Maximum in South China Sea

In the South China Sea(SCS), the subsurface chlorophyll maximum(SCM) is frequently observed while the mechanisms of SCM occurrence have not been well understood. In this study, a 1-D physical-biochemical coupled model was used to study the seasonal variations of vertical profiles of chlorophyll-a(Chl-a) in the SCS. Three parameters(i.e., SCM layer(SCML) depth, thickness, and intensity) were defined to characterize the vertical distribution of Chl-a in SCML and were obtained by fitting the vertical profile of Chl-a in the subsurface layer using a Gaussian function. The seasonal variations of SCMs are reproduced reasonably well compared to the observations. The annual averages of SCML depth, thickness, and intensity are 75 ± 10 m, 31 ± 6.7 m, and 0.37 ± 0.11 mg m-3, respectively. A thick, close to surface SCML together with a higher intensity occurs during the northeastern monsoon. Both the SCML thickness and intensity are sensitive to the changes of surface wind speed in winter and summer, but the surface wind speed exerts a minor influence on the SCML depth; for example, double strengthening of the southwestern monsoon in summer can lead to the thickening of SCML by 46%, the intensity decreasing by 30%, and the shoaling by 6%. This is because part of nutrients are pumped from the upper nutricline to the surface mixed layer by strong vertical mixing. Increasing initial nutrient concentrations by two times will increase the intensity of SCML by over 80% in winter and spring. The sensitivity analysis indicates that light attenuation is critical to the three parameters of SCM. Decreasing background light attenuation by 20% extends the euphotic zone, makes SCML deeper(~20%) and thicker(12% – 41%), and increases the intensity by over 16%. Overall, the depth of SCML is mainly controlled by light attenuation, and the SCML thickness and intensity are closely associated with wind and initial nitrate concentration in the SCS.