Equipment costs may be obtained from equipment vendors or published cost data. Published cost data are usually presented as cost versus capacity charts or expressed as a power law of capacity  [c.416]

The average error is around 30%. This formula applies to pure substances and mixtures. For pure hydrocarbons, it is preferable to refer to solubility charts published by the API if good accuracy is required.  [c.168]

Certain curves, T = f(% distilled), level off at high temperatures due to the change in pressure and to the utilization of charts for converting temperatures under reduced pressure to equivalent temperatures under atmospheric pressure.  [c.332]

Currently the charts used most often for this purpose are those published by the API. (Maxwell and Bonnel charts) from the Technical Data Book (see Chapter 4).  [c.332]

The viscosity of a hydrocarbon mixture, as with all liquids, decreases when the temperature increases. The way in which lubricant viscosities vary with temperature is quite complex and, in fact, charts proposed by ASTM D 341 or by Groff (1961) (Figure 6.1) are used that provide a method to find the viscosity index for any lubricant system. Remember that a high viscosity index corresponds to small variation of viscosity between the low and high  [c.354]

Maxwell, J.B. and L.S. Bonnel (1955), Vapor pressure charts for petroleum engineers. Esso Research and Engineering Co., NJ.  [c.458]

The z-factor must be determined empirically (i.e. by experiment), but this has been done for many hydrocarbon gases, and correlation charts exist for the approximate determination of the z factor at various conditions of pressure and temperature. (Ref. Standing, M.B. and Katz, D.L., Density of natural gases, Trans. AIME, 1942).  [c.106]

The fluid properties of formation water may be looked up on correlation charts, as may most of the properties of oil and gas so far discussed. Many of these correlations are also available as computer programmes. It is always worth checking the range of applicability of the correlations, which are often based on empirical measurements and are grouped into fluid types (e.g. California light gases).  [c.116]

Field analogues should be based on reservoir rock type (e.g. tight sandstone, fractured carbonate), fluid type, and environment of deposition. This technique should not be overlooked, especially where little information is available, such as at the exploration stage. Summary charts such as the one shown in Figure 8.19 may be used in conjunction with estimates of macroscopic sweep efficiency (which will depend upon well density and positioning, reservoir homogeneity, offtake rate and fluid type) and microscopic displacement efficiency (which may be estimated if core measurements of residual oil saturation are available).  [c.207]

If produced gas contains water vapour it may have to be dried (dehydrated). Water condensation in the process facilities can lead to hydrate formation and may cause corrosion (pipelines are particularly vulnerable) in the presence of carbon dioxide and hydrogen sulphide. Hydrates are formed by physical bonding between water and the lighter components in natural gas. They can plug pipes and process equipment. Charts such as the one below are available to predict when hydrate formation may become a problem.  [c.250]

Whilst network analysis is a useful tool for estimating timing and resources, it is not a very good means for displaying schedules. Bar charts are used more commonly to illustrate planning expectations and as a means to determine resource loading.  [c.297]

Firm items such as pipelines are often estimated using charts of cost versus size and length. The total of such items and allowances may form a preliminary project estimate. In addition to allowances some contingency s often made for expected but undefined changes, for example to cover design and construction changes within the project scope. The objective of such an approach is to define an estimate that has as much chance of under running as over running (sometimes termed a 50/50 estimate).  [c.299]

In addition, on the basis of analogous specimens, the accumulation of damage and plastic deformation of metal structure were simulated. These results provide the possibility to obtain the prediction charts of the metal work s residual resource.  [c.29]

Socrates G 1994 Infrared Characteristic Group Frequencies Tables and Charts 2nd edn (Chichester Wiley)  [c.1795]

Drawing-, text-, and structure-input tools are provided that enable easy generation of flow charts, textual annotations or labels, structures, or reaction schemes. It is also possible to select different representation styles for bond types, ring sizes, molecular orbitals, and reaction arrows. The structure diagrams can be verified according to free valences or atom labels. Properties such as molecular  [c.140]

It is often important in practice to know when a process has changed sufficiently so that steps may be taken to remedy the situation. Such problems arise in quality control where one must, often quickly, decide whether observed changes are due to simple chance fluctuations or to actual changes in the amount of a constituent in successive production lots, mistakes of employees, etc. Control charts provide a useful and simple method for dealing with such problems.  [c.211]

The focus of this chapter is on the two principal components of a quality assurance program quality control and quality assessment. In addition, considerable attention is given to the use of control charts for routinely monitoring the quality of analytical data.  [c.705]

Construction of Property Control Charts The simplest form for a property control chart is a sequence of points, each of which represents a single determination of the property being monitored. To construct the control chart, it is first necessary to determine the mean value of the property and the standard deviation for its measurement. These statistical values are determined using a minimum of 7 to 15 samples (although 30 or more samples are desirable), obtained while the system is known to be under statistical control. The center line (CL) of the control chart is determined by the average of these n points  [c.715]

Property control charts can also be constructed using points that are the mean value, Xj, for a set of r replicate determinations on a single sample. The mean for the ith sample is given by  [c.716]

Interpreting Control Charts The purpose of a control chart is to determine if a system is in statistical control. This determination is made by examining the location of individual points in relation to the warning limits and the control limits, and the distribution of the points around the central line. If we assume that the data are normally distributed, then the probability of finding a point at any distance from the mean value can be determined from the normal distribution curve. The upper and lower control limits for a property control chart, for example, are set to +3S, which, if S is a good approximation for O, includes 99.74% of the data. The probability that a point will fall outside the UCL or LCL, therefore, is only 0.26%. The  [c.718]

Example of the use of subrange precision control charts for samples that span a range of analyte concentrations. The precision control charts are used for  [c.719]

Examples of property control charts that show a run of data (highlighted In box) Indicating that the system Is out of statistical control.  [c.720]

The same rules apply to precision control charts with the exception that there are no lower warning and lower control limits.  [c.721]

Using Control Charts for Quality Assurance Control charts play an important role in a performance-based program of quality assurance because they provide an easily interpreted picture of the statistical state of an analytical system. Quality assessment samples such as blanks, standards, and spike recoveries can be monitored with property control charts. A precision control chart can be used to monitor duplicate samples.  [c.721]

Once a control chart is in use, new quality assessment data should be added at a rate sufficient to ensure that the system remains in statistical control. As with prescriptive approaches to quality assurance, when a quality assessment sample is found to be out of statistical control, all samples analyzed since the last successful verification of statistical control must be reanalyzed. The advantage of a performance-based approach to quality assurance is that a laboratory may use its experience, guided by control charts, to determine the frequency for collecting quality assessment samples. When the system is stable, quality assessment samples can be acquired less frequently.  [c.721]

The use of several QA/QC methods is described in this article, including control charts for monitoring the concentration of solutions of thiosulfate that have been prepared and stored with and without proper preservation the use of method blanks and standard samples to determine the presence of determinate error and to establish single-operator characteristics and the use of spiked samples and recoveries to identify the presence of determinate errors associated with collecting and analyzing samples.  [c.722]

Laquer, F. C. Quality Control Charts in the Quantitative Analysis Laboratory Using Conductance Measurement,  [c.722]

This experiment demonstrates how control charts and an analysis of variance can be used to evaluate the quality of results in a quantitative analysis for chlorophyll a and b in plant material.  [c.722]

Additional information about the construction and use of control charts maybe found in the following sources.  [c.724]

Separate chapters on developing a standard method and quality assurance. Two chapters provide coverage of methods used in developing a standard method of analysis, and quality assurance. The chapter on developing a standard method includes topics such as optimizing experimental conditions using response surfaces, verifying the method through the blind analysis of standard samples and ruggedness testing, and collaborative testing using Youden s two-sample approach and ANOVA. The chapter on quality assurance covers quality control and internal and external techniques for quality assessment, including the use of duplicate samples, blanks, spike recoveries, and control charts.  [c.813]

Typical (a) gas and (b) liquid chromatograms. The charts show amounts (y-axis) of substance emerging from a column versus time (x-axis). The time taken (measured at the top of a peak) for a substance to elute is called a retention time.  [c.247]

Pareto chart Pareto charts Parex process  [c.723]

Shewhart control charts  [c.883]

High density charting ablators such as carbon-phenolic contain high density reinforcements to improve shear resistance. In contrast, lower density charring ablators as a rule are used for low shear environments. The ApoUo mission reentry conditions are typical of a relatively low shear environment, so low density ablators consisting of epoxy—novolac resin containing phenolic microbaUoons and silica fiber reinforcement have been used. In order to improve the shear resistance and safety factor of the material for this mission, the ablator was injected into the cavities of a fiberglass-reinforced phenolic honeycomb that was bonded to the substmcture of the craft (48).  [c.6]

They-function is a definite integral of an expression including Jq, the modified Bessel function of the first kind, y-function curves use stoichiometric time and the number of theoretical stages as the two parameters to fit breakthrough curves and extend to other conditions. These curves have been approximated for use on PC microcomputers (108). A phenomenological model requires the deterrnination of two parameters, a transfer coefficient, and a linear isotherm constant, from a complete breakthrough curve. The solution to the model is in an infinite series form, which is calculable by a hand-held calculator or personal computer (109). Another method separates the equiUbrium from the kinetic effects by constmcting effective equiUbrium curves. Because the solution to the model involves nonlinear algebraic or differential equations, graphs called solution charts are used to predict breakthrough fronts (110). Theoretical stages form the essence of the discrete cell model graphical procedures, which are appHed to flat isotherms and incorporate pore diffusivity and axial dispersion (98). Another solution technique is the use of fast Eourier transforms. Linear isotherms are required, but their appHcabiUty for predicting breakthrough curves has been demonstrated for isothermal and nonisothermal adsorbers (111). Another model, with a solution in infinite series form, incorporates separate mass-transfer coefficients for external film, macropore, and micropore resistances (112). Techniques have also been developed to predict breakthrough from fluidized beds. The behavior of organic solvents adsorbed from air on activated carbon was shown to exhibit breakthrough times that can be correlated to the adsorption capacity and the amount of bed expansion (113).  [c.286]

Thermal Requirements. When a temperature-swing cycle is heating limited, the regeneration design is only concerned with transferring energy to the system. Charts for isothermal, linear-isotherm adsorption that were derived by Hougen and Marshall (121) from earlier work on heat transfer from a gas to fixed beds (122) can be reappHed to heat transfer for heating-limited regeneration when the heat of adsorption is negligible (123). This  [c.286]

Psychrometrics. Psychrometrics is the branch of thermodynamics that deals specifically with moist air, a biaary mixture of dry air and water vapor. The properties of moist air are frequentiy presented on psychrometric charts such as that shown ia Figure 2 for the normal air conditioning range at atmospheric pressure. Similar charts exist for temperatures below 0°C and above 50°C as well as for other barometric pressures. AH mass properties ate related to the mass of the dry air.  [c.353]

To set up a control chart, individual observations might be plotted in sequential order and then compared with control limits established from sufficient past experience. Limits of 1.96cr corresponding to a confidence level of 95%, might be set for control limits. The probability of a future observation falling outside these limits, based on chance, is only 1 in 20. A greater proportion of scatter might indicate a nonrandom distribution (a systematic error). It is common practice with some users of control charts to set inner control limits, or warning limits, at 1.96cr and outer control limits of 3.00cr. The outer control limits correspond to a confidence level of 99.8%, or a probability of 0.002 that a point will fall outside the limits. One-half of this probability corresponds to a high result and one-half to a low result. However, other confidence limits can be used as well the choice in each case depends on particular circumstances.  [c.211]

Control charts were originally developed in the 1920s as a quality assurance tool for the control of manufactured products.Two types of control charts are commonly used in quality assurance a property control chart in which results for single measurements, or the means for several replicate measurements, are plotted sequentially and a precision control chart in which ranges or standard deviations are plotted sequentially. In either case, the control chart consists of a line representing the mean value for the measured property or the precision, and two or more boundary lines whose positions are determined by the precision of the measurement process. The position of the data points about the boundary lines determines whether the system is in statistical control.  [c.714]

The precision control chart is strictly valid only for the replicate analysis of identical samples, such as a calibration standard or a standard reference material. Its use for the analysis of nonidentical samples, such as a series of clinical or environmental samples, is complicated by the fact that the range usually is not independent of the magnitude ofXiarge andAsmall- For example. Table 15.3 shows the relationship between R and the concentration of chromium in water. ° Clearly the significant difference in the average range for these concentrations of Cr makes a single precision control chart impossible. One solution to this problem is to prepare separate precision control charts, each of which covers a range of concentrations for which is approximately constant (Figure 15.5).  [c.718]

See pages that mention the term CHARTS : [c.297]    [c.231]    [c.27]    [c.476]    [c.76]    [c.211]    [c.27]    [c.78]   
Chemoinformatics (2003) -- [ c.27 ]

Applied Process Design for Chemical and Petrochemical Plants, Volume 1 (1999) -- [ c.116 , c.142 , c.143 , c.382 ]