scholarly journals Development of New Algorithm for Aniline Point Estimation of Petroleum Fraction

Processes ◽  
2019 ◽  
Vol 7 (12) ◽  
pp. 912
Author(s):  
Kaiyue Wang ◽  
Xiaoyan Sun ◽  
Shuguang Xiang ◽  
Yushi Chen
Keyword(s):  
Physical Property ◽  
Point Estimation ◽  
Petroleum Fraction ◽  
Estimation Methods ◽  
Hydrocarbon Mixture ◽  
Ternary Interaction ◽  
Ignition Point ◽  
Hydrocarbon Content ◽  
Petroleum Fractions ◽  
Average Boiling Point

The aniline point (AP) is an important physical property of a petroleum fraction. The AP gives an indication of the aromatic hydrocarbon content in a hydrocarbon mixture and can also be an indicator of the ignition point of a diesel fraction. In this study, common estimation methods were introduced and evaluated, and their limitations were analyzed. Multiple linear regression was used in constructing a quantitative function to solve for the AP using the average boiling point and specific gravity. The iterative modification algorithm of the ternary interaction algorithm was used to obtain the predicted value of the petroleum fraction AP, and the proposed algorithm was tested using 127 actual petroleum fractions. The average estimation deviation of the proposed method was 3.55%; hence, compared to the commonly used estimation methods, the prediction accuracy was significantly improved. This method offers important practical value in the calculation of the petroleum fraction AP and other petroleum fraction properties, thereby providing reference significance.

Comparisons of Point Estimation Methods in the 2-parameter Weibull Distribution

1980 ◽  
Vol R-29 (1) ◽  
pp. 21-21 ◽  
Author(s):  
Naoto Kaio ◽  
Shunji Osaki
Keyword(s):  
Weibull Distribution ◽  
Point Estimation ◽  
Estimation Methods

scholarly journals Inference for the Exponential Distribution under Generalized Progressively Hybrid Censored Data from Partially Accelerated Life Tests with a Time Transformation Function

Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1510
Author(s):  
Alaa H. Abdel-Hamid ◽  
Atef F. Hashem
Keyword(s):  
Exponential Distribution ◽  
Failure Rate ◽  
Censored Data ◽  
Sufficient Conditions ◽  
Transformation Function ◽  
Point Estimation ◽  
Estimation Methods ◽  
Time Transformation ◽  
Monte Carlo Simulation Study ◽  
Accelerated Life

In this article, the tampered failure rate model is used in partially accelerated life testing. A non-decreasing time function, often called a ‘‘time transformation function", is proposed to tamper the failure rate under design conditions. Different types of the proposed function, which have sufficient conditions in order to be accelerating functions, are investigated. A baseline failure rate of the exponential distribution is considered. Some point estimation methods, as well as approximate confidence intervals, for the parameters involved are discussed based on generalized progressively hybrid censored data. The determination of the optimal stress change time is discussed under two different criteria of optimality. A real dataset is employed to explain the theoretical outcomes discussed in this article. Finally, a Monte Carlo simulation study is carried out to examine the performance of the estimation methods and the optimality criteria.

Photocatalytic Degradation of Synthetic Sulfur Pollutants in Petroleum Fractions under Different pH and Photocatalyst

2021 ◽  
Vol 02 (01) ◽  
Author(s):  
Mohamad Alif Hakimi Hamdan ◽  
◽  
Nur Hanis Hayati Hairom ◽  
Nurhafisza Zaiton ◽  
Zawati Harun ◽  
...  
Keyword(s):  
Photocatalytic Degradation ◽  
Zno Nanoparticles ◽  
Low Cost ◽  
Industrial Effluents ◽  
Degradation Process ◽  
Vital Role ◽  
Petroleum Fraction ◽  
Precipitation Method ◽  
Synthesis Process ◽  
Petroleum Fractions

Thiophene is one of the sulfur compounds in the petroleum fraction that can be harmful to living things and lead to a critical effect on the ecosystem. Photocatalytic degradation is one of the promising methods in treating wastewater as it can mineralization of pollutants into carbon dioxide and water. Other than that, this method is non-toxic and relatively low cost. The production of hydroxyl radicals playing a vital role in the degradation of organic pollutants. It has been claimed that the usage of zinc oxide (ZnO) nanoparticles could give an excellent degradation process as this photocatalyst have high photosensitivity, low cost and chemically stable. However, the preparation method of ZnO nanoparticles will affect the agglomeration, particle size, shape and morphology of particles and lead to influence the photocatalytic activity in degrading thiophene. Therefore, this study focused on the effectiveness of ZnO nanoparticles in the presence of fibrous nanosilica (KCC-1) and polyethylene glycol (PEG) as the capping agent to degrade synthetic thiophene. ZnO/KCC-1 had been synthesized via the precipitation method and characterized by using Fourier Transform Infrared (FTIR). The chemical bond and nature of the photocatalyst from the FTIR results proved that the synthesis process to produce the ZnO/KCC-1 was succeed. The large surface area of KCC-1 increases the effectiveness of ZnO which is supported by the experimental data. Accordingly, the optimum condition for photocatalytic degradation of thiophene is under pH 7 by using ZnO/KCC-1 as photocatalyst. Hence, it is believed that this research could be implemented to remove the thiophene in petroleum fraction from the actual industrial effluents and this can preserve nature in the future.

scholarly journals Applying Ideal Point Estimation Methods to the Council of Ministers

2007 ◽  
Vol 8 (2) ◽  
pp. 279-296 ◽  
Author(s):  
Sara Hagemann
Keyword(s):  
Ideal Point ◽  
Point Estimation ◽  
Estimation Methods ◽  
Council Of Ministers ◽  
Ideal Point Estimation

Point Estimation Methods

2012 ◽  
pp. 433-521
Author(s):  
Ron C. Mittelhammer
Keyword(s):  
Point Estimation ◽  
Estimation Methods

Point Estimation Methods

1996 ◽  
pp. 427-508
Author(s):  
Ron C. Mittelhammer
Keyword(s):  
Point Estimation ◽  
Estimation Methods

scholarly journals Linear Regression Estimation Methods for Inferring Standard Values of Snow Load in Small Sample Situations

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Xudong Wang ◽  
Jitao Yao
Keyword(s):  
Linear Regression ◽  
Coefficient Of Variation ◽  
Small Sample ◽  
Point Estimation ◽  
Estimation Methods ◽  
Small Samples ◽  
Unbiased Estimation ◽  
Regression Estimation ◽  
Snow Load ◽  
Standard Values

The aim of this paper is to establish a new method for inferring standard values of snow load in small sample situations. Due to the incomplete meteorological data in some areas, it is often necessary to infer the standard values of snow load in the conditions of small samples in engineering, but the point estimation methods of classical statistics adopted till now do not take into account the influences of statistical uncertainty, and the inference results are always aggressive. In order to overcome the above shortcomings, according to the basic principle of optimal linear unbiased estimation and invariant estimation of the minimum type I distribution parameters and the tantile, using the least square method, the linear regression estimation methods for inferring standard values of snow load in small sample situations are proposed, which can take into account two cases such as parameter-free and known coefficient of variation, and the predicted formulas of snow load standard values are given, respectively. Through numerical integration and Monte Carlo numerical simulation, the numerical table of correlation coefficients is established, which is more convenient for the direct application of inferential formulas. According to the results of theoretical analysis and examples, when using the indirect point estimation methods to infer the standard values of snow load in the conditions of small samples, the inference results are always small. The linear regression estimation method is suitable for inferring standard values of snow load in the conditions of small samples, which can give more reasonable results. When using the linear regression estimation to infer standard values of snow load in practical application, even if the coefficient of variation is unknown, it can set the upper limit value of the coefficient of variation according to the experience; meanwhile, according to the parameter-free and known coefficient of variation, the estimation is carried out, respectively, and the smaller value of the two is taken as the final estimate. The method can be extended to the statistical inference of variable load standard values such as wind load and floor load.

scholarly journals Prediction of the Flash Point of Binary and Ternary Straight-Chain Alkane Mixtures

2014 ◽  
Vol 2014 ◽  
pp. 1-5 ◽  
Author(s):  
X. Li ◽  
P. P. Duan ◽  
K. N. Sun ◽  
X. Yan
Keyword(s):  
Fire Hazard ◽  
Physical Property ◽  
Simple Calculation ◽  
Flash Point ◽  
Ternary Mixtures ◽  
Hydrocarbon Mixture ◽  
Average Absolute Deviation ◽  
Straight Chain ◽  
Absolute Deviation ◽  
Calculation Process

The flash point is an important physical property used to estimate the fire hazard of a flammable liquid. To avoid the occurrence of fire or explosion, many models are used to predict the flash point; however, these models are complex, and the calculation process is cumbersome. For pure flammable substances, the research for predicting the flash point is systematic and comprehensive. For multicomponent mixtures, especially a hydrocarbon mixture, the current research is insufficient to predict the flash point. In this study, a model was developed to predict the flash point of straight-chain alkane mixtures using a simple calculation process. The pressure, activity coefficient, and other associated physicochemical parameters are not required for the calculation in the proposed model. A series of flash points of binary and ternary mixtures of straight-chain alkanes were determined. The results of the model present consistent experimental results with an average absolute deviation for the binary mixtures of 0.7% or lower and an average absolute deviation for the ternary mixtures of 1.03% or lower.

scholarly journals Robust Estimation methods of Generalized Exponential Distribution with Outliers

2020 ◽  
pp. 545-559
Author(s):  
Hisham Mohamed Almongy ◽  
Ehab M. Almetwally
Keyword(s):  
Robust Estimation ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Point Estimation ◽  
Least Square ◽  
Estimation Methods ◽  
Monte Carlo Simulation Study ◽  
Least Absolute Deviations ◽  
Complete Dataset ◽  
M Estimation

This paper discussed robust estimation for point estimation of the shape and scale parameters for generalized exponential (GE) distribution using a complete dataset in the presence of various percentages of outliers. In the case of outliers, it is known that classical methods such as maximum likelihood estimation (MLE), least square (LS) and maximum product spacing (MPS) in case of outliers cannot reach the best estimator. To confirm this fact, these classical methods were applied to the data of this study and compared with non-classical estimation methods. The non-classical (Robust) methods such as least absolute deviations (LAD), and M-estimation (using M. Huber (MH) weight and M. Bisquare (MB) weight) had been introduced to obtain the best estimation method for the parameters of the GE distribution. The comparison was done numerically by using the Monte Carlo simulation study. The two real datasets application confirmed that the M-estimation method is very much suitable for estimating the GE parameters. We concluded that the M-estimation method using Huber object function is a suitable estimation method in estimating the parameters of the GE distribution for a complete dataset in the presence of various percentages of outliers.

Development of State Estimation Filter Simulator Built on an Integrated GUI Framework

2016 ◽  
Vol 20 (5) ◽  
pp. 721-729
Author(s):  
Masaru Morita ◽  
◽  
Takeshi Nishida
Keyword(s):  
Time Series ◽  
User Interface ◽  
State Estimation ◽  
Graphical User Interface ◽  
Kalman Filters ◽  
Point Estimation ◽  
Estimation Methods ◽  
Estimation Accuracy ◽  
System Designer ◽  
The Relationship

We have developed a graphical user interface (GUI)-based state estimation filter simulator (called StefAny) that makes it easy to understand and compare the behaviors of filters such as Kalman filters (KFs) and particle filters (PFs). The key feature of StefAny is to show, when a system designer applies a PF, a detailed graph representing the relationship among the distribution and weights of all particles on any arbitrary timeline through simulation. Moreover, the timeline can be specified on another graph showing an estimated time series for each filter. These features enable system designers to easily check the compatibility between a filter and a target distribution, which determines the state estimation accuracy. In this paper, we present the functions of StefAny and demonstrate in detail how StefAny facilitates understanding of the properties of filters via a compatibility check comparison experiment for PFs, point estimation methods, and distributions.

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