A New Iterative Method of Construction of the Hammett Acidity Function

1997 ◽  
Vol 62 (4) ◽  
pp. 645-655 ◽  
Author(s):  
Oldřich Pytela
Keyword(s):  
Acid Concentration ◽  
Moving Average ◽  
Acidity Function ◽  
Hammett Acidity Function ◽  
Fourth Step ◽  
The Mean ◽  
The Given ◽  
Gradual Transformation ◽  
New Iterative Method ◽  
Weighted Moving Average

A new iterative algorithm has been devised for construction of the H acidity function. The procedure is based on gradual transformation of the dependence of log I vs acid concentration into the dependence of H vs acid concentration, and it involves four steps. In the first step a continuous acidity function is obtained by taking the average of log I values for the same acid concentrations. The second step of the algorithm is smoothing out of the acidity function by means of smoothing the H value by weighted moving average. In the third step, the mean distances between the log I values and the corresponding values of the smoothed acidity function H are calculated for the given indicator, and in the fourth step these distances are used together with log I values for calculating a new acidity function. The procedure designed was converted into a programme in the Delphi 2 language for PC Pentium and was successfully validated on literature data.

IONIZATION OF ORGANIC COMPOUNDS: III. BASICITIES OF PROPIONIC ACID AND PROPIONAMIDE

1962 ◽  
Vol 40 (5) ◽  
pp. 966-975 ◽  
Author(s):  
J. T. Edward ◽  
I. C. Wang
Keyword(s):  
Sulphuric Acid ◽  
Propionic Acid ◽  
Organic Compounds ◽  
Acid Concentration ◽  
Ultraviolet Absorption ◽  
Protonation Constants ◽  
Acidity Function ◽  
Hammett Acidity Function ◽  
Ionization Ratio

Protonation constants (pKBH+) of −6.8 and −0.9 have been determined for propionic acid and propionamide, respectively, from measurements of their ultraviolet absorption in various concentrations of sulphuric acid. The ionization ratio of propionamide and of other amides increases more slowly than the Hammett acidity function, h0, with increase in acid concentration. This may be explained by assuming that in a given concentration of sulphuric acid the protonated amide is more heavily hydrated than the protonated Hammett indicator used to establish the h0 scale for this region of acid concentrations.

A Directional Multivariate Control Chart for Monitoring Univariate Autocorrelated Processes

2012 ◽  
Vol 217-219 ◽  
pp. 2607-2613
Author(s):  
Wen Wan Yang ◽  
Xue Min Zi ◽  
Chang Liang Zou
Keyword(s):  
Control Chart ◽  
Moving Average ◽  
Sign Test ◽  
Directional Information ◽  
Autocorrelated Process ◽  
Autocorrelated Processes ◽  
Multivariate Control Chart ◽  
The Mean ◽  
Multivariate Control ◽  
Weighted Moving Average

A new nonparametric multivariate control chart, based on a spatial-sign test and integrating the directional information from processes with the exponentially weighted moving average (EWMA) scheme, is developed for monitoring the mean of a univariate autocorrelated process. Simulation studies show that it has robustness in in-control (IC) performance, and it is more sensitive to the small and moderate mean shifts for non-normality underlying process than other existing multivariate chart methods.

scholarly journals Exponentially Weighted Moving Average Control Charts for the Process Mean Using Exponential Ratio Type Estimator

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Hina Khan ◽  
Saleh Farooq ◽  
Muhammad Aslam ◽  
Masood Amjad Khan
Keyword(s):  
Control Charts ◽  
Sampling Design ◽  
Control Structure ◽  
Moving Average ◽  
Auxiliary Information ◽  
Ranked Set Sampling ◽  
Process Mean ◽  
Average Control ◽  
The Mean ◽  
Weighted Moving Average

This study proposes EWMA-type control charts by considering some auxiliary information. The ratio estimation technique for the mean with ranked set sampling design is used in designing the control structure of the proposed charts. We have developed EWMA control charts using two exponential ratio-type estimators based on ranked set sampling for the process mean to obtain specific ARLs, being suitable when small process shifts are of interest.

Progressive mean exponentially weighted moving average control chart for monitoring the process location

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Nurudeen Ayobami Ajadi ◽  
Osebekwin Asiribo ◽  
Ganiyu Dawodu
Keyword(s):  
Control Chart ◽  
Moving Average ◽  
Performance Measure ◽  
Exponentially Weighted Moving Average ◽  
Average Scheme ◽  
Content Type ◽  
The Mean ◽  
Memory Type ◽  
Weighted Moving Average ◽  
Exponentially Weighted

PurposeThis study aims to focus on proposing a new memory-type chart called progressive mean exponentially weighted moving average (PMEWMA) control chart. This memory-type chart is an improvement on the existing progressive mean control chart, to detect small and moderate shifts in a process.Design/methodology/approachThe PMEWMA control chart is developed by using a cumulative average of the exponentially weighted moving average scheme known as the progressive approach. This scheme is designed based on the assumption that data follow a normal distribution. In addition, the authors investigate the robustness of the proposed chart to the normality assumption.FindingsThe variance and the mean of the scheme are computed, and the mean is found to be an unbiased estimator of the population mean. The proposed chart's performance is compared with the existing charts in the literature by using the average run-length as the performance measure. Application examples from the petroleum and bottling industry are also presented for practical considerations. The comparison shows that the PMEWMA chart is quicker in detecting small shifts in the process than the other memory-type charts covered in this study. The authors also notice that the PMEWMA chart is affected by higher kurtosis and skewness.Originality/valueA new memory-type scheme is developed in this research, which is efficient in detecting small and medium shifts of a process mean.

On designing a robust double exponentially weighted moving average control chart for process monitoring

2018 ◽  
Vol 40 (15) ◽  
pp. 4253-4265 ◽  
Author(s):  
Ishaq Adeyanju Raji ◽  
Nasir Abbas ◽  
Muhammad Riaz
Keyword(s):  
Moving Average ◽  
Real Life ◽  
Exponentially Weighted Moving Average ◽  
Data Set ◽  
Parameter Estimations ◽  
Real Life Data ◽  
Monitoring Process ◽  
The Mean ◽  
Weighted Moving Average ◽  
Exponentially Weighted

A double exponentially weighted moving average chart has been proven more efficient for monitoring process mean in comparison to the classical exponentially weighted moving average chart. We, in this article, made a careful investigation on how well this scheme performs with the presence of disturbances in the process under consideration. This investigation was motivated in exploring the scheme with some robust statistic, as the mean estimator performs woefully. We also evaluated the effects of parameter estimation on the phase II assuming the parameters are unknown. Adopting a 20% trimmed mean of trimeans (robust) reveals the effect of parameter estimations. We substantiated these claims by applying the scheme on a real-life data set. The findings of the study pronounced the trimean estimator to be the best of all the five estimators used, including the mean.

scholarly journals On Efficient Monitoring of Weibull Lifetimes Using Censored Median Hybrid DEWMA Chart

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Syed Muhammad Muslim Raza ◽  
Sajid Ali ◽  
Ismail Shah ◽  
Lichen Wang ◽  
Zhen Yue
Keyword(s):  
Censored Data ◽  
Control Chart ◽  
Scale Parameter ◽  
Average Run Length ◽  
Moving Average ◽  
Type I ◽  
Conditional Median ◽  
The Mean ◽  
Censored Observations ◽  
Weighted Moving Average

A control chart named as the hybrid double exponentially weighted moving average (HDEWMA) to monitor the mean of Weibull distribution in the presence of type-I censored data is proposed in this study. In particular, the focus of this study is to use the conditional median (CM) for the imputation of censored observations. The control chart performance is assessed by the average run length (ARL). A comparison between CM-DEWMA control chart and CM-based HDEWMA control chart is also presented in this article. Assuming different shift sizes and censoring rates, it is observed that the proposed control chart outperforms the CM-DEWMA chart. The effect of estimation, particularly the scale parameter estimation, on ARL is also a part of this study. Finally, a practical example is provided to understand the application and to investigate the performance of the proposal in practical scenarios.

A SYNTHETIC CONTROL CHART FOR MONITORING THE PROCESS MEAN OF SKEWED POPULATIONS BASED ON THE WEIGHTED VARIANCE METHOD

2008 ◽  
Vol 15 (03) ◽  
pp. 217-245 ◽  
Author(s):  
MICHAEL B. C. KHOO ◽  
ZHANG WU ◽  
ABDU M. A. ATTA
Keyword(s):  
Control Chart ◽  
Moving Average ◽  
Synthetic Control ◽  
Exponentially Weighted Moving Average ◽  
Process Mean ◽  
Variance Method ◽  
The Mean ◽  
Synthetic Chart ◽  
Weighted Moving Average ◽  
Exponentially Weighted

A synthetic control chart for detecting shifts in the process mean integrates the Shewhart [Formula: see text] chart and the conforming run length chart. It is known to outperform the Shewhart [Formula: see text] chart for all magnitudes of shifts and is also superior to the exponentially weighted moving average chart and the joint [Formula: see text]-exponentially weighted moving average charts for shifts of greater than 0.8σ in the mean. A synthetic chart for the mean assumes that the underlying process follows a normal distribution. In many real situations, the normality assumption may not hold. This paper proposes a synthetic control chart to monitor the process mean of skewed populations. The proposed synthetic chart uses a method based on a weighted variance approach of setting up the control limits of the [Formula: see text] sub-chart for skewed populations when process parameters are known and unknown. For symmetric populations, however, the limits of the new [Formula: see text] sub-chart are equivalent to that of the existing [Formula: see text] sub-chart which assumes a normal underlying distribution. The proposed synthetic chart based on the weighted variance method is compared by Monte Carlo simulation with many existing control charts for skewed populations when the underlying populations are Weibull, lognormal, gamma and normal and it is generally shown to give the most favourable results in terms of false alarm and mean shift detection rates.

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