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contain a systematic error whose size increases with increasing range of
E
ʱ
vari-
ability. The error can be eliminated by using a flexible integral method. All it takes
is to perform either time or temperature integration by segments that correspond to
small intervals of conversion, ∆
ʱ
. Vyazovkin has proposed a method [
36
] that ef-
fectively eliminates this error by performing integration over small time segments.
The method is based on Eq. 2.18, whereas Eq. 2.19 is modified as follows:
t
E
RT t
−
a
∫
JE Tt
[, ( ]
=
exp
α
d
t
.
(2.20)
α
α
()
t
a
−
∆
α
When integration is carried out over small segments, the
E
ʱ
value is assumed to
be constant only within a small interval of ∆α that is typically taken to be 0.01-
0.02. The efficiency of integration by segments can be demonstrated by comparing
the resulting
E
ʱ
versus
ʱ
dependence with that estimated by the Friedman method
(Eq. 2.5). Because the latter is a differential method, it does not introduce the afore-
mentioned integration error into the
E
ʱ
values. The application of the integral meth-
od with integration by segments and the differential Friedman method to the same
set of simulated data demonstrates [
36
] that the resulting
E
ʱ
versus
ʱ
dependencies
are virtually identical.
As already mentioned, in the integral methods, the systematic error introduced
by non-constancy of
E
ʱ
is proportional to the magnitude of
E
ʱ
variation. In the case
of significant variation of
E
ʱ
with
ʱ,
the relative error in
E
ʱ
can be as large as 20-
30 %. A process that is well known [
2
] to demonstrate significant variability of
E
ʱ
is
the thermal dehydration of calcium oxalate monohydrate. The
E
ʱ
dependencies ob-
tained by respectively neglecting and accounting for variability of
E
ʱ
are displayed
in Fig.
2.7
which also shows the size of the error introduced when the variability is
neglected. It is generally recommended [
37
] that one should account for variability
Fig. 2.7
E
ʱ
dependen-
cies estimated for thermal
dehydration of CaC
2
O
4
ᄋH
2
O
by isoconversional method
(Eq. 2.18).
Squares
represent
the
E
ʱ
values obtained when
using regular integration
(Eq. 2.19).
Circles
represent
the
E
ʱ
values estimated via
integration by segments
(Eq. 2.20). The
dotted line
shows the size of the relative
error in
E
ʱ
caused by integra-
tion that does not account for
variability of
E
ʱ
. (Repro-
duced from Vyazovkin [
36
]
with permission of Wiley)
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