Volume 04 Issue 12-2024
135
International Journal of Advance Scientific Research
(ISSN
–
2750-1396)
VOLUME
04
ISSUE
12
Pages:
135-142
OCLC
–
1368736135
A
BSTRACT
The article discusses the main sources of uncertainty in measurements of physicochemical quantities,
which are an integral part of any experimental research in physics and chemistry. Various factors affecting
the accuracy and reliability of measurement results are analyzed, including errors in instrumental systems,
the influence of external conditions, and the human factor. Particular attention is paid to methods for
estimating and minimizing uncertainty, such as the use of standards, instrument calibration, and statistical
data processing. The work also provides examples of practical measurements in chemical analytics and
materials physics, where uncertainty is critical to the results obtained. Recommendations for uncertainty
management can be useful for both researchers and specialists working in the field of quality control and
product certification.
K
EYWORDS
Measurement uncertainty, physicochemical quantities, experimental research, instrumental errors,
external conditions, human factor, standards, instrument calibration, statistical data processing, chemical
analytics.
Journal
Website:
http://sciencebring.co
m/index.php/ijasr
Copyright:
Original
content from this work
may be used under the
terms of the creative
commons
attributes
4.0 licence.
Research Article
SOURCES OF UNCERTAINTY IN MEASUREMENTS OF
PHYSICOCHEMICAL QUANTITIES
Submission Date:
December 10,
2024,
Accepted Date:
December 15, 2024,
Published Date:
December 20, 2024
Crossref doi:
https://doi.org/10.37547/ijasr-04-12-21
Atamirzaev Nodirbek Bekmirzayevich
Doctoral Student, Department of Metrology and Standardization, Namangan Institute of Engineering
Technology, Namangan, Uzbekistan
Khamidov Doniyor Bakhodirovich
Fergana Branch of National Metrology Institute of Uzbekistan
Volume 04 Issue 12-2024
136
International Journal of Advance Scientific Research
(ISSN
–
2750-1396)
VOLUME
04
ISSUE
12
Pages:
135-142
OCLC
–
1368736135
I
NTRODUCTION
Estimation
x
i
of
the input value
X
i
may be the
reading of a measuring device in the case of a
single measurement, the arithmetic mean value in
the case of multiple measurements, or taken from
regulatory documents, a certificate, testimonials,
reference book, manufacturer's labels, etc.
Before attempting to estimate measurement
uncertainty, a first step is to list the possible
sources of uncertainty. At this stage, there is no
need to consider quantitative aspects; the aim is
only to ensure that there is complete clarity as to
what exactly needs to be considered.
When compiling a list of uncertainty sources, it is
usually convenient to start with the main
expression used to calculate the result from
intermediate quantities, i.e. the mathematical
model of the measurement. All parameters in this
expression may have their own uncertainties and
are therefore potential sources of uncertainty. In
addition, there may be other parameters that are
not explicitly included in the expression used to
find the value of the measure and but which
nevertheless influence the result (e.g. extraction
time or temperature). There may also be hidden
sources of uncertainty. All these sources should
be included in the list. The main sources of
uncertainty are the specification, modelling,
method, measuring instrument, environment,
operator and measured object.
The types of uncertainty components are divided
according to the sources of their occurrence into
uncertainties of the specification of the measured
quantity,
modeling,
method,
measuring
instruments
(instrumental),
environment,
operator (person) and the measured object.
Volume 04 Issue 12-2024
137
International Journal of Advance Scientific Research
(ISSN
–
2750-1396)
VOLUME
04
ISSUE
12
Pages:
135-142
OCLC
–
1368736135
To describe quantitatively the individual
components of uncertainty, some of the
uncertainty sources almost always have to be
considered separately. In some cases this is
necessary only for a very small number of
sources; in others, especially when there are few
or no data on the performance of the method,
each source may require separate consideration.
There are several general techniques for
identifying individual uncertainty components:
–
experimental variation of input variables;
–
use
of
information
from
technical
documentation, such as measurement and
calibration certificates;
–
modeling based on theoretical principles;
–
the use of judgments based on previous
experience or simulation modeling.
The individual components of uncertainty are
discussed below.
The size of the measured quantity initially
depends on the parameters of external influences
affecting the object of measurement. Therefore, a
correct approach to measurement requires a
complete preliminary description (specification)
of
the
measured
quantity.
Incomplete
specification of the measured quantity leads to
the emergence of a corresponding uncertainty.
It is known that the purpose of measurement is to
determine the (numerical) value of the measured
quantity. The description (specification) of the
TYPES OF MEASUREMENT UNCERTAINTY
Uncertainty
specifications
Uncertainties
measured object
Instrumental
uncertainty
Uncertainty
modeling
Methodical
uncertainty
Uncertainties
measurement conditions
Uncertainty
operator
Volume 04 Issue 12-2024
138
International Journal of Advance Scientific Research
(ISSN
–
2750-1396)
VOLUME
04
ISSUE
12
Pages:
135-142
OCLC
–
1368736135
measured quantity includes indications of the
time of measurement and the conditions of their
implementation. The conditions of measurement
are specified as a set of influencing quantities, i.e.
quantities that are not the subject of
measurement, but affect their result, for example,
the temperature of the measuring instruments.
The dependence of the measured physical
quantity y on the parameters of external
influences is described by means of the influence
function. The influence function can be
determined experimentally or exist only as an
algorithm that must be implemented numerically.
Inadequate determination of influence quantities
is a cause of specification uncertainty and can
lead to inconsistency between measurements of
the same quantity carried out in different
laboratories.
In the example given, additional input quantities
may be required to improve measurement
accuracy, taking into account the known non-
uniform temperature distribution across the
resistor, the possible non-linear temperature
coefficient of resistance, or the possible
dependence of resistance on atmospheric
pressure.
In practice, the specification of the measure and
depends on the required accuracy of
measurement. The measure and should be
defined with sufficient completeness in relation
to the required accuracy so that for all practical
purposes related to the measurement its value is
unique.
A person's idea of the object of measurement is
reflected in his consciousness in the form of a
certain model, described by a set of parameters.
The measured quantities determined by models
always differ from the properties of real objects,
since a model can never be an absolute copy of the
original. This difference is expressed by
uncertainty, caused by the inadequacy of the
model to the measured quantity.
In many cases, the developed physical theory
allows us to construct fairly good models
describing the influence of various factors on the
measurement result. For example, the influence
of temperature on volume and density is well
studied. In such cases, the uncertainty can be
calculated or estimated directly from the existing
relationship using uncertainty propagation
methods.
In other situations it may be necessary to use
approximate theoretical models combined with
experimental data. For example, if the result of an
analytical measurement depends on some
reaction to produce a derivative that takes some
time to occur, then it may be necessary to
estimate the uncertainty associated with the time.
This can be done by simply varying the time taken
for the reaction to occur.
When measuring the concentration of a solution
by titration, sources of uncertainty may include
errors in measuring the volume of the titrant,
inaccuracies in the preparation of standard
solutions, and the effect of temperature on the
reaction rate.
Volume 04 Issue 12-2024
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International Journal of Advance Scientific Research
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2750-1396)
VOLUME
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135-142
OCLC
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1368736135
Temperature measurement in thermodynamic
experiments: The use of thermocouples to
measure temperature can be subject to
uncertainty
due
to
calibration
errors,
environmental influences (e.g. radiation from
external sources), or faults in the measuring
equipment.
Determination of the density of a substance: In
density measurement experiments using a
hydrometer, sources of uncertainty include
errors in volume measurement (e.g. due to air
bubbles in the liquid) and temperature variations
affecting the volume of the liquid.
The inadequacy of the model to the real object
gives rise to uncertainty even before
measurements (a priori), called modeling
(recognition) uncertainty.
The complexity of the model and the degree of its
adequacy to the real object depends on the
following factors:
a) the type and properties of the measurement
object;
b) the purpose and required accuracy of the
measurement;
c) the amount of a priori information about the
object, the qualifications of the metrologist
performing the measurements.
In the process of creating a model, a paradoxical
situation arises. In order to measure the desired
value, it is necessary to have a priori information
about its properties, according to which the
measurement model is established. And these
properties can be determined (measured) only in
the process of experimental study of the object.
It should be noted that the absence of differences
in the measurement results does not always
guarantee the correctness of the selected model
[11]. Experimental verification of the selected
model will be reliable only if a properly planned
measurement methodology is used.
A measurement method is a logical sequence of
operations described in general form and used
when performing measurements [1, 10, 12].
Imperfections in a measurement method result in
methodological errors. Their distinctive feature is
that they can only be determined by creating a
mathematical model or by simulating the
measured object. After creating such a model and
determining its parameters, it is possible to
estimate
the
methodological
error
of
measurement, which is systematic in nature. The
estimate of the methodological error can be used
as a correction to the measurement result. The
corrected measurement result is burdened with
an unexcluded residual systematic error (RESI),
caused by errors in determining the model
parameters. The standard deviation of the REI is
an estimate of the methodological uncertainty.
Let us consider some examples of methodological
uncertainties.
Uncertainty in assessing the impact of a
measuring instrument on the object of
measurement
. We will study this uncertainty
using the example of a voltmeter connected to a
voltage source with internal resistance
R
i
. The
voltmeter itself has an input resistance
R
in
.
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International Journal of Advance Scientific Research
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OCLC
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Measurement results. The measurement method
may include computational operations -
determination of the mean, root mean square or
mean absolute value of a series of observations of
a changing parameter of the measured quantity,
numerical
integration
or
differentiation,
calculation of the value of an elementary function
by expansion into a series, etc. Depending on the
selected processing algorithm, the measurement
results may be burdened with corresponding
errors. The standard deviation of these errors is
an estimate of the uncertainty of the processing
algorithm used.
Uncertainties arising from approximation and
simplification
. Such uncertainties include
uncertainties of indirect measurements caused by
the simplification of the relationship between the
measured quantity and its arguments measured
using direct measurements.
For example, the result of measuring the power
P
n
generator using a microwave absorption
wattmeter, which is the load of the transmission
line, depends on the parameters of their
mismatch with the transmission line, expressed
through the complex reflection coefficients of the
generator and wattmeter [13].
Methodological uncertainty also includes
uncertainties caused by the number of
observations, duration of measurement, choice of
methodology and measurement instruments, etc.
C
ONCLUSION
In the course of studying the sources of
uncertainty in measurements of physical and
chemical quantities, it was shown that the
accuracy and reliability of measurements depend
on many factors, including the characteristics of
measuring
instruments,
their
calibration
methods, the influence of external conditions, and
errors associated with the human factor. Analysis
of these factors allows us to identify the main
causes of errors and propose ways to minimize
them, which is critically important for ensuring
the reliability of experimental data.
Methods for assessing and accounting for
uncertainty, such as statistical processing of
results and the use of international standards, are
essential tools for improving the accuracy of
measurements. The practical implementation of
these methods not only improves the quality of
scientific research, but also increases the level of
confidence in the data obtained in industry,
analytical chemistry and other applied areas.
It is therefore important to continue improving
uncertainty control methods to improve the
quality of measurements and ensure the accuracy
of scientific conclusions, as well as compliance
with international standards and requirements
for the results of product examination and
certification.
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International Journal of Advance Scientific Research
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VOLUME
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Pages:
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OCLC
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1368736135
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