Application of Bayesian Approach in the Parameter Estimation of Continuous Lumping Kinetic Model of Hydrocracking Process

Hydrocracking is a catalytic reaction process in the petroleum refineries for converting the higher boiling temperature residue of crude oil into a lighter fraction of hydrocarbons such as gasoline and diesel. In this study, a modified continuous lumping kinetic approach is applied to model the hydro-cracking of vacuum gas oil. The model is modified to take into consideration the reactor temperature on the reaction yield distribution. The model is calibrated by maximizing the likelihood function between the modeled and measured data at four different reactor temperatures. Bayesian approach parameter estimation is also applied to obtain the confidence interval of model parameters by considering the uncertainty associated with the measured errors and the model structural errors. Then Monte Carlo simulation is applied to the posterior range of the model parameters to obtain the 95% confidence interval of the model outputs for each individual fraction of the hydrocracking products. A good agreement is observed between the output of the calibrated model and the measured data points. The Bayesian approach based on the Markov Chain Monte Carlo simulation is shown to be efficient to quantify the uncertainty associated with the parameter values of the continuous lumping model.

Optimal Parameter and Uncertainty Estimation of a Land Surface Model: Sensitivity to Parameter Ranges and Model Complexities

Most previous land-surface model calibration studies have defined globalranges for their parameters to search for optimal parameter sets. Little work has been conducted tostudy the impacts of realistic versus global ranges as well as model complexities on the calibrationand uncertainty estimates. The primary purpose of this paper is to investigate these impacts byemploying Bayesian Stochastic Inversion (BSI) to the Chameleon Surface Model (CHASM). The CHASM wasdesigned to explore the general aspects of land-surface energy balance representation within acommon modeling framework that can be run from a simple energy balance formulation to a complexmosaic type structure. The BSI is an uncertainty estimation technique based on Bayes theorem,importance sampling, and very fast simulated annealing. The model forcing data and surface flux datawere collected at seven sites representing a wide range of climate and vegetation conditions. Foreach site, four experiments were performed with simple and complex CHASM formulations as well asrealistic and global parameter ranges. Twenty eight experiments were conducted and 50 000 parametersets were used for each run. The results show that the use of global and realistic ranges givessimilar simulations for both modes for most sites, but the global ranges tend to produce someunreasonable optimal parameter values. Comparison of simple and complex modes shows that the simplemode has more parameters with unreasonable optimal values. Use of parameter ranges and modelcomplexities have significant impacts on frequency distribution of parameters, marginal posteriorprobability density functions, and estimates of uncertainty of simulated sensible and latent heatfluxes. Comparison between model complexity and parameter ranges shows that the former has moresignificant impacts on parameter and uncertainty estimations.

A Bayesian model calibration framework for stochastic compartmental models with both time-varying and timeinvariant parameters

We consider state and parameter estimation for compartmental models having both timevarying and time-invariant parameters.In this manuscript,we first detail a general Bayesian computational framework as a continuation of our previous work.Subsequently,this framework is specifically tailored to the susceptible-infectious-removed(SIR)model which describes a basic mechanism for the spread of infectious diseases through a system of coupled nonlinear differential equations.The SIR model consists of three states,namely,the susceptible,infectious,and removed compartments.The coupling among these states is controlled by two parameters,the infection rate and the recovery rate.The simplicity of the SIR model and similar compartmental models make them applicable to many classes of infectious diseases.However,the combined assumption of a deterministic model and time-invariance among the model parameters are two significant impediments which critically limit their use for long-term predictions.The tendency of certain model parameters to vary in time due to seasonal trends,non-pharmaceutical interventions,and other random effects necessitates a model that structurally permits the incorporation of such time-varying effects.Complementary to this,is the need for a robust mechanism for the estimation of the parameters of the resulting model from data.To this end,we consider an augmented state vector,which appends the time-varying parameters to the original system states whereby the time evolution of the time-varying parameters are driven by an artificial noise process in a standard manner.Distinguishing between time-varying and time-invariant parameters in this fashion limits the introduction of artificial dynamics into the system,and provides a robust,fully Bayesian approach for estimating the timeinvariant system parameters as well as the elements of the process noise covariance matrix.This computational framework is implemented by leveraging the robustness of the Markov chain Monte Carlo algorithm permits the estimation of time-invariant parameters while nested nonlinear filters concurrently perform the joint estimation of the system states and time-varying parameters.We demonstrate performance of the framework by first considering a series of examples using synthetic data,followed by an exposition on public health data collected in the province of Ontario.

Enhanced Vibrating Particles System Algorithm for Parameters Estimation of Photovoltaic System

To evaluate the performance of a photovoltaic panel, several parameters must be extracted from the photovoltaic. These parameters are very important for the evaluation, monitoring and optimization of photovoltaic. Among the methods developed to extract photovoltaic parameters from current-voltage (I-V) characteristic curve, metaheuristic algorithms are the most used nowadays. A new metaheuristic algorithm namely enhanced vibrating particles system algorithm is presented here to extract the best values of parameters of a photovoltaic cell. Five recent algorithms (grey wolf optimization (GWO), moth-flame optimization algorithm (MFOA), multi-verse optimizer (MVO), whale optimization algorithm (WAO), salp swarm-inspired algorithm (SSA)) are also implemented on the same computer. Enhanced vibrating particles system is inspired by the free vibration of the single degree of freedom systems with viscous damping. To extract the photovoltaic parameters using enhanced vibrating particles system algorithm, the problem can be set as an optimization problem with the objective to minimize the difference between measured and estimated current. Four case studies have been implemented here. The results and comparison with other methods exhibit high accuracy and validity of the proposed enhanced vibrating particles system algorithm to extract parameters of a photovoltaic cell and module.

Differences in parameter estimates derived from various methods for the ORYZA(v3) Model

Parameter estimation is always a difficult issue for crop model users, and inaccurate parameter values will result in deceptive model predictions. Parameter values may vary with different inversion methods due to equifinality and differences in the estimating processes. Therefore, it is of great importance to evaluate the factors which may influence parameter estimates and to make a comparison of the current widely-used methods. In this study, three popular frequentist methods(SCE-UA, GA and PEST) and two Bayesian-based methods(GLUE and MCMC-AM) were applied to estimate nine cultivar parameters using the ORYZA(v3) Model. The results showed that there were substantial differences between the parameter estimates derived by the different methods, and they had strong effects on model predictions. The parameter estimates given by the frequentist methods were obviously sensitive to initial values, and the extent of the sensitivity varied with algorithms and objective functions. Among the frequentist methods, the SCE-UA was recommended due to the balance between stable convergence and high efficiency. All the parameter estimates remarkably improved the goodness of model-fit, and the parameter estimates derived from the Bayesian-based methods had relatively worse performance compared to the frequentist methods. In particular, the parameter estimates with the highest probability density of posterior distributions derived from the MCMC-AM method(MCMC_P_(max)) led to results equivalent to those derived from the frequentist methods, and even better in some situations. Additionally, model accuracy was greatly influenced by the values of phenology parameters in validation.

A Fully Bayesian Sparse Probit Model for Text Categorization

Nowadays a common problem when processing data sets with the large number of covariates compared to small sample sizes (fat data sets) is to estimate the parameters associated with each covariate. When the number of covariates far exceeds the number of samples, the parameter estimation becomes very difficult. Researchers in many fields such as text categorization deal with the burden of finding and estimating important covariates without overfitting the model. In this study, we developed a Sparse Probit Bayesian Model (SPBM) based on Gibbs sampling which utilizes double exponentials prior to induce shrinkage and reduce the number of covariates in the model. The method was evaluated using ten domains such as mathematics, the corpuses of which were downloaded from Wikipedia. From the downloaded corpuses, we created the TFIDF matrix corresponding to all domains and divided the whole data set randomly into training and testing groups of size 300. To make the model more robust we performed 50 re-samplings on selection of training and test groups. The model was implemented in R and the Gibbs sampler ran for 60 k iterations and the first 20 k was discarded as burn in. We performed classification on training and test groups by calculating P (yi = 1) and according to [1] [2] the threshold of 0.5 was used as decision rule. Our model’s performance was compared to Support Vector Machines (SVM) using average sensitivity and specificity across 50 runs. The SPBM achieved high classification accuracy and outperformed SVM in almost all domains analyzed.

基于贝叶斯分析的干热河谷区橙子林冠层蒸腾耗水模拟

需耗水机制是进行农田/果园水分管理和调控的基础。本文聚焦蒸腾耗水机制,基于贝叶斯参数估计方法对比了不同Jarvis-Stewart模型配置对干热河谷区橙子林蒸腾耗水量的模拟效果,探索了Jarvis-Stewart模型在影响因子交互效应较强条件下蒸腾耗水模拟中的适用性。结果表明,考虑不同影响因子及其限制函数会对模拟效果产生较大影响,其中考虑土壤含水率和叶面积指数对模拟效果改善作用明显,而引入饱和水汽压差和气温则不同程度地降低模拟精度;考虑的影响因子越多,模型结构越复杂,模拟效果不一定越好;筛选出的最佳模型结构基本实现了橙子林蒸腾耗水的可靠模拟,但模拟效果仍有明显改进空间,因此,应综合考虑模型复杂程度、模拟精度及不确定性等,进一步探究适宜的模型结构。研究可为果园节水灌溉技术体系建立和水分管理优化提供科学依据,也能为耗水模型的进一步发展和完善提供理论支撑。

悬臂结构地震响应分析的广义单自由度模型研究

针对有限元方法存在计算时间过长的缺点,不适用于需要多次响应分析的结构抗震性能评估问题。进行了悬臂结构线性地震响应分析的广义单自由度模型研究,提出了多项式形函数和基于多项式形函数的单自由度模型,并通过与有限元分析结果的对比分析,调查了基于多项式形函数的单自由度模型对典型地震波激励下结构响应分析的有效性。结果表明:提出的基于多项式形函数的广义单自由度模型平均误差为7.20%,为提高建模准确性,建议采用水平集中荷载施加方法进行多项式形函数参数估计。

On ionosphere-delay processing methods for single-frequency precise-point positioning

In single-frequency precise-point positioning of a satellite,ionosphere delay is one of the most important factors impacting the accuracy. Because of the instability of the ionosphere and uncertainty of its physical properties, the positioning accuracy is seriously limited when using a precision-limited model for correction. In order to reduce the error, we propose to introduce some ionosphere parameter for real-time ionosphere-delay estimation by applying various mapping functions. Through calculation with data from the IGS（ International GPS Service） tracking station and comparison among results of using several different models and mapping functions, the feasibility and effectiveness of the new method are verified.

控制大田和自然大田稻纵卷叶螟危害水稻生理生态参数估算模型

为了准确监测和客观评估稻纵卷叶螟对水稻生长发育和产量形成的危害,利用ASD Field Spec3地物波谱仪和SPAD-502叶绿素仪分别采集控制大田试验(2015年和2019年)和自然大田试验(2020年)在各生育期(拔节期、孕穗期、灌浆期、成熟期)水稻的冠层高光谱数据和SPAD值,调查采集样点的虫量和水稻卷叶率,对比分析两种试验中稻纵卷叶螟的虫害发生特征、水稻冠层光谱特征和水稻生理生态参数特征,建立基于高光谱参数的水稻受稻纵卷叶螟危害的生理生态参数估算模型。结果表明,(1)两种试验的水稻SPAD值和冠层的红边至近红外波段的反射率均随着稻纵卷叶螟虫害程度的加重而降低,而可见光波段的反射率则相反;(2)自然大田试验的SPAD值和红光至近红外波段的冠层反射率在水稻生长发育前期要显著低于控制大田试验,而到了后期则反而要略高于控制大田试验;(3)综合分析筛选出自然大田试验和控制大田试验中的多个虫害特征参数和植被指数分别构建出了SPAD的单因子和多因子估算模型,各模型均达到了较好的估算效果,在单因子模型中EVI的二项式函数模拟效果最好,而多因子线性回归估测模型的模拟效果优于所有的单因子模型;(4)通过2021年对这些模型的应用检验发现:这些模型中基于虫量、卷叶率、OSAVI、EVI和DVI的单因子估算模型的SPAD估算值与实测值拟合度很高,其Rv 2均超过了0.8,达到了比较理想的估算效果,这为稻纵卷叶螟危害下的水稻SPAD值估测提供了一种精度较高且可行的估算方法。

Poisson截断δ冲击模型失效参数的Bayes估计

针对Poisson截断δ冲击模型失效参数的估计问题,利用Bayes估计方法,在最小均方误差原则下,基于寿命终止时总冲击次数及冲击到达时间这两类样本数据,在不同的先验假设下,给出Poisson截断δ冲击模型失效参数δ的Bayes估计量.

基于等值单机非线性模型的多换流器并联直流系统暂态稳定性分析及控制参数整定方法

针对“状态变量多、阶数高”给多换流器并联直流系统暂态稳定性分析带来的困难,提出计及多换流器动态交互的等值单机非线性模型,简化大扰动稳定性分析及控制参数设计复杂度。首先,对每台换流器下垂双环控制中的所有状态变量做等效变换,建立直流系统的等值单机非线性模型;其次,基于等值单机的Takagi-Sugeno(TS)模糊模型刻画出最大估计吸引域,分析系统控制参数设计不合理导致的“小扰动稳定、大扰动失稳”问题;然后,提出基于等值单机的直流系统控制参数整定方法,通过等值单机设计推导系统中每台换流器的控制参数,降低了直流系统控制参数设计的难度,最后,RT-BOX硬件在环实验平台验证等值单机非线性模型及理论分析的准确性。

臭氧胁迫冬小麦叶片高光谱特征和叶绿素含量估算

为无损、快速监测臭氧胁迫下冬小麦叶片叶绿素含量,建立叶绿素含量与光谱指标的定量关系,基于自由式臭氧浓度增加系统平台观测了臭氧浓度升高下拔节期、开花期及灌浆期冬小麦叶片的叶绿素含量和光谱特征。通过线性回归、人工神经网络(ANN)以及偏最小二乘回归(PLSR)模型对臭氧胁迫下叶片高光谱特征进行了叶绿素含量的估算。结果表明:臭氧胁迫冬小麦叶片的光谱曲线特征出现绿峰“红移”和红边位置“蓝移”现象。相比于拔节期和开花期,小麦叶片在灌浆期受到臭氧的影响更大。臭氧胁迫下叶绿素含量与部分光谱特征参数及遥感植被指数存在显著相关关系,所有模型均取得了较高的估算精度(R^(2)>0.8),其中以光谱特征参数为建模参量的偏最小二乘回归模型精度最高。该方法可用于臭氧胁迫下冬小麦叶片叶绿素含量的估测,动态监测作物的臭氧胁迫。

多维度食品安全评估指数体系模型的构建及应用

目的:通过食品安全评价指数体系来评价地区食品安全状况。方法:在构建的指数体系中,综合考虑影响食品安全的各种因素,如地市规模、食品种类、检测项目危害、生产地等,并采用多种数据来源对模型进行支撑。结果:利用2021年广西食品安全评价抽检数据进行实证,分别使用经典大样本估计法和经验贝叶斯估计法进行模型计算后,发现基于食品种类的安全指数的评估结果与简单合格率评估结果存在一定差异,但两种算法均显示餐饮食品的安全情况显著低于其他食品大类。此外,使用贝叶斯估计方法有效解决了合格率为100%或数值普遍接近时的计算问题。结论:该模型可以实现从地区、食品种类、地区和食品种类等多角度、不同层次计算得到食品安全指数结果。

基于MCMC方法的单指标众数模型Bayes估计

将单指标模型和众数回归模型相结合,构造一种新的模型——单指标众数模型,详细给出了单指标众数模型的定义,为了对模型的参数进行Bayes估计,设定了该模型的参数先验分布,然后利用MCMC方法模拟参数的后验分布,并用参数的后验均值作为参数的估计值,将参数的估计值和真值进行比较后,发现估计的效果非常好。最后,随机模拟和实际例子的分析表明所提出的模型和方法是行之有效的。

纵向数据下存在测量误差的单指标模型的估计及应用

纵向数据是一类在社会学、经济学、生物医学、传染病学等领域有着广泛应用的重要的数据类型.然而在实际问题中,人们会经常遇到变量维数很高且关心的变量不能直接观测也即存在测量误差的情形.为了解决此类问题,本文研究存在测量误差的纵向数据下单指标模型的估计问题.基于局部线性光滑法和模拟外推(SIMEX)法,本文构造了估计单指标参数和非参连接函数的新方法.通过蒙特卡罗数值模拟验证所提估计方法的有效性,与忽略测量误差的Naive估计以及忽略个体内部相关性的估计相比,本文所构造的估计具有更小的均方误差.最后,我们将本文方法应用到上市公司投资需求的实际数据分析中,结果表明在实际问题中测量误差对参数估计影响显著.

单指标众数模型的Bayes局部影响分析

近年来,单指标众数模型基于数据删除模型和众数漂移模型的统计诊断与局部影响分析深受广大学者的青睐。这些都是从频率的角度基于该模型利用一些诊断统计量来进行数据分析。从Bayes的角度基于MCMC方法研究单指标众数模型的局部影响分析,得到因变量和自变量的扰动结果,并根据扰动结果判断异常值。

油菜红边特征及其叶面积指数的高光谱估算模型

在2003～2004年油菜生长季，选用6个油菜品种，设置3个氮素水平的小区试验。在不同发育期同步测定油菜冠层的光谱反射率及对应叶片的叶面积指数。利用油菜冠层的光谱反射率数据提取红边参数，分析其变化规律，油菜叶面积指数与红边参数的相关性，估算结果表明：油菜冠层红边一阶导数光谱具有“双峰”现象，红边位置λred位于690～720nm之间，在油菜生长旺盛期间出现“红边平台”，前期有“红移”，后期有“蓝移”现象；叶面积指数与冠层光谱红边参数λred、Dλred、Sred之间在开花前存在显著相关，但开花后相关性不显著；利用开花前的红边参数可以估算油菜的叶面积指数，开花后的红边参数不能用于估算油菜的叶面积指数；最后建立了不同时期和开花前油菜叶面积指数的估算模型。

基于三维矿化域模型的泥河铁矿床动态储量估算

基于泥河铁矿床矿体地质特征的详细研究,本文结合边界品位指标以及样条曲线方法对矿体边界进行简化平滑处理,并通过对各剖面的矿体解译边界进行圆滑渐变处理建立控制矿化边界的矿化域模型。基于矿化域模型,用于储量估算的样品分析数据具有更好的连续性和全面性,避免了双指标圈矿带来的矿体形态过度复杂和在三维空间内不可避免的空间占位现象。基于矿化域模型进行的储量估算,可以更快速、合理地获取矿体品位的空间分布特征,从而提高金属矿床储量估算结果的准确性和合理性。

具有杠杆效应SV模型的贝叶斯分析及其应用

对具有杠杆效应的 SV模型进行了的贝叶斯分析 ,使用基于 Gibbs取样的 BUGS软件对模型的参数进行了估计。用上海和深圳股市的指数收益时间序列对杠杆效应 SV模型进行检验 ,指出沪。