
Research Article


Modelling the Performance of Sodium Nitrite and Aniline as Inhibitors in the Corrosion of Steelreinforced Concrete 

J.O. Okeniyi,
O.A. Omotosho,
O.O. Ajayi,
O.O. James
and
C.A. Loto



ABSTRACT

The performances of sodium nitrite and aniline inhibitors on the corrosion of concrete steel rebar partially immersed in sodium chloride and sulphuric acid media were investigated in this study. The open circuit potential corrosion monitoring technique was employed for the marine and acidic simulating environments and potential readings were taken in accordance with ASTM C 876. Inhibiting quality and uniformity of the inhibitors were then analyzed using the Weibull probability density distribution as an extreme value statistical modelling approach to study performance effectiveness and predict the most efficient inhibitor in each media. In the statistically analyzed experimental results for each of the inhibitor concentrations employed, (0.679 M) sodium nitrite is identified as exhibiting the best inhibiting quality in sodium chloride while (0.137 M) aniline was predicted as showing the lowest probability of corrosion risk in sulphuric acid medium. The synergetic admixtures employed in the study performed poorly in inhibiting effectiveness compared to the control specimens in the two media considered. The overall probabilistic results predicted preferences of sodium nitrite as inhibitors in the sodium chloride medium simulating saline environments and aniline in sulphuric acidic medium simulating sewage or underground microbial environments.





Received: August 05, 2011;
Accepted: November 30, 2011;
Published: January 31, 2012


INTRODUCTION
Steel reinforced concrete is a very important item of construction, used for
major portion of infrastructural developments that is vital to people’s
standard of living globally (Duffo et al., 2009;
Ha et al., 2007), hence, its durability, safe
functioning, serviceability and maintainability is essential for sustainable
developments. However, the durability of reinforced concrete structures could
be curtailed by premature or unexpected corrosion failure in the structures
(Duffo et al., 2009) with debilitating costs
of repair or replacement, which constitute major part of current spending on
infrastructure (Duffo et al., 2009; Song
and Saraswathy, 2007; Smith and Virmani, 2000).
Normally, due to the presence of sodium and potassium oxides, as well as calcium
hydroxide produced in the hydration reactions of cement components, high alkaline
solution within the pore structure of cement paste matrix enables formation
of a passive film on steel rebar surface to protect it from further corroding
(Smith and Virmani, 2000; Liu, 1996).
However, this protection can be completely depleted by aging of the structure
(Bhargava et al., 2007), the ingress of aggressive
electrochemical agents of corrosion in the form of carbonation, chloride contamination
(Bertolini et al., 2004; Richard,
2002; NEA/CSNI, 2002; Schiegg
et al., 2000) and the progressive Biogenic Sulphuric Acid (BSA) attack
on concrete in sewage environments (Hewayde et al.,
2007). Carbonation, not only hardens the concrete surface but also reduces
the passifying alkalinity of the concrete rendering the embedded steel reinforcement
unprotected (Bertolini et al., 2004). Concentration
of chlorides sufficient for corrosion attack in concrete, is obtained from sodium
chloride naturally present in sea water and marine atmospheres, or artificially
introduced as deicing salts in highways and parking facilities in temperate
countries (Bertolini et al., 2004; Broomfield,
2003; US Army Corps of Engineers, 1995). This actively
breaks down the protective passive oxide layer, which was originally produced
by the passivating alkaline pore water, on the embedded steel surface. BSA attack
on concrete revert hydration products of concrete matrix to gypsum (CaSO_{4})
and induce the formation of ettringite (3CaO.Al_{2}O_{3}.3CaSO_{4}.31H_{2}O
or 3CaO.Al_{2}O_{3}.CaSO_{4}.12H_{2}O) through
the combined mechanisms of sulphate reducing bacterial (e.g., Desulfovibrio)
and sulphur oxidizing bacterial (e.g., Thiobacillus thiooxidans) on
concrete (Vollertsen et al., 2008; Hewayde
et al., 2007; Parande et al., 2006).
Rust products from carbonation and chloride attack and gypsum and ettringite
from BSA attack are all weak in structural strength even as they are also expansive
within the restricted space of the concrete. As a result, expansive stresses
are set up within the concrete which lead to cracking, delamination and subsequently
spalling of the concrete cover. This leads to progressive exposure of the steel
rebar to further corrosion, degradation of structural integrity and reduction
of load bearing capacity, culminating eventually in catastrophic collapse of
the concrete structure (Song and Saraswathy, 2007; Hewayde
et al., 2007; Parande et al., 2006;
Gaal, 2004). This therefore makes the need to combat
this mode of concrete degradation essential.
The use of corrosion inhibitor, amongst others has been recognised in studies
as a viable means of protecting steel reinforced concrete from corrosion in
potentially corrosive environments (Song and Saraswathy,
2007; Hewayde et al., 2007; Parande
et al., 2006; Smith and Virmani, 2000; Schiegg
et al., 2000). Several authors have worked on the effectiveness of
inhibitors in various environments many of which studied the inhibitors individually
and in synergies (Burubai and Dagogo, 2007; Afolabi,
2007; Saraswathy and HaWon, 2007; Hewayde
et al., 2007; Loto, 1992), employing the
OCP technique. This technique which only determines inhibitor effectiveness
is found to have a major shortcoming in the form of fluctuations of potential
readings during experiments (Burubai and Dagogo, 2007).
These fluctuations make data interpretation difficult and sometimes impossible.
Apart from the fact that, there is a dearth of studies that employed the statistical
approach of the extreme value distribution, the use of sodium nitrite (NaNO_{2})
and aniline in synergy have not been reported elsewhere, though they have been
used individually and in synergy with other inhibitors (Burubai
and Dagogo, 2007; Afolabi, 2007; Saraswathy
and HaWon, 2007; Loto, 1992). This study therefore
focuses on the evaluation of the performance of sodium nitrite and aniline inhibitors
on the corrosion of steel reinforced concrete using statistical method of the
extreme value distribution. The synergetic effects of the combination of the
two inhibitors in comparison with their individual effects were also studied.
The study specifically employed the Weibull Probability Distribution Function
(PDF), as an extreme value statistical modelling tool (Roberge
and Klassen, 2003), to analyse the quality, reliability and uniformity of
inhibitor performance in investigating the comparative effectiveness of the
inhibitors.
MATERIALS AND METHODS
Concrete blocks used for the experiment were made, in accordance with literatures
(Ha et al., 2007; Burubai
and Dagogo, 2007), using Portland cement, sand and gravel mixed with water
at a mix ratio of 1:2:4. The formulation used for the reinforced concrete specimens
for cement, water, sand and gravel, respectively was 320, 140, 700 and 1150
all in kg m^{3}. The water/cement (w/c) ratio was 0.44. Thirty concrete
blocks, in twosets of fifteen block specimens were used in the study. Each
of the specimens was admixed with different concentrations of inhibitors and
with a fixed amount of 0.1 M sodium chloride. The first sets of fifteen specimens
were immersed in 3.5% sodium chloride (NaCl) medium to simulate marine/saline
environments while the second sets were immersed in 0.5 M dilute sulphuric acid
(H_{2}SO_{4}) medium to simulate microbial (sewage) environment.
All chemicals used were of AnalaR reagent grade. Concrete samples were premixed
with NaNO_{2} and aniline inhibitors concentration that include (1.5
g) 0.136 M, (3.0 g) 0.272 M, (4.5 g) 0.408 M, (6.0 g) 0.544 M, (7.5 g) 0.679
M, (9.0 g) 0.815 M and (1 mL) 0.069 M, (2 mL) 0.137 M, (3 mL) 0.206 M, (4 mL)
0.274 M, (5 mL) 0.343 M, (6 mL) 0.411 M, respectively. The synergetic combinations
include concrete specimen admixed with 0.136 M NaNO_{2} and 0.274 M
aniline and 0.272 M NaNO_{2} and 0.069 M aniline, respectively.
DINST 60 mm type of steel rebar was used for the reinforcement. The steel
was obtained from Oshogbo Steel Rolling Mill, Nigeria and its chemical composition
include: 0.3% C, 0.25% Si, 1.5% Mn, 0.04% P, 0.64% S, 0.25% Cu, 0.1% Cr, 0.11%
Ni and the remainder Fe. The rebar were cut into several pieces each with a
length of 160 and 10 mm diameter and embedded in the concrete. Abrasive grinder
was used to remove the mill scales and rust stains on the steel specimens before
each was placed in its concrete block. The protruded end of the block was painted
to prevent atmospheric corrosion (Burubai and Dagogo, 2007).
Each concrete block was partially immersed in their respective test medium
such that the liquid level was just below the exposed steel reinforcement but
not making contact with it. Open Circuit Potential (OCP) readings were then
obtained, by placing a Copper/copper Sulphate Electrode (CSE) firmly on the
concrete block. One of the two lead terminals of a high impedance multimeter
was connected to the CSE and the other to the exposed part of the embedded steel
reinforcement to make a complete electrical circuit. OCP for all the specimens
were monitored over an exposure period of 32 days. The readings were taken at
three different points on each concrete block directly over the embedded steel
reinforcement (Saraswathy and HaWon, 2007; Burubai
and Dagogo, 2007) in 2day intervals for the exposure period. All the experiments
were performed under free corrosion potential and at ambient temperature. The
average of the three readings was computed and this was subjected to data analysis
and interpretation based on the standards of American Society for Testing and
Materials, ASTM C87691 R99 (Ha et al., 2007;
Song and Saraswathy, 2007; ASTM,
2007). Curves of mean corrosion potential readings against exposure time,
obtained during the experiment are presented in Fig. 1 for
the first set of specimens with inhibitor admixtures in NaCl medium, while the
curves for the second set of specimen with inhibitor admixtures in H_{2}SO_{4}
medium are presented in Fig. 2.
Data analysis: The twoparameter Weibull distribution function used
is given by Haynie (2005), Murthy et
al. (2004) and Montgomery and Runger (2003):
where, k is the shape parameter and c is the characteristic value or
the scale parameter. Equation can be expressed in linear form (Haynie,
2005; Murthy et al., 2004) to obtain:

Fig. 1: 
Curves of mean potential vs. time for concrete sample admixed
with varying concentrations of sodium nitrite and aniline in NaCl medium 

Fig. 2: 
Curves of mean potential vs. time for concrete sample admixed
with varying concentrations of sodium nitrite and aniline in H_{2}SO_{4}
medium 
A threshold value x_{0} = 0 had been assumed by using the twoparameter
Weibull (Murthy et al., 2004). For this, the consistencies
of the negative OCP values with the logarithmic nature of Eq.
2 had been ensured by taking x values to be in negative millivolts versus
copper/copper sulphate electrode i.e., mV (CSE). This approach finds similarity
with the data presentation approach of Burubai and Dagogo
(2007). Hence, positive values of x are used in the equation. For measurement
of quality for the data, a Weibull prediction of the mean μ is given by
Haynie (2005) and Murthy et al.
(2004):
where, Γ ( ) is the gamma function of ( ).
Goodness of fit test: To study how well the OCP data follow the Weibull
distribution, the KolmogorovSmirnov (KS) goodness of fit test (GoF) (Izquierdo
et al., 2004; Murthy et al., 2004; Roberge,
2003; Roberge, 1999) was employed. This GoF criteria
measures the absolute difference between the empirical distribution function
F*(x) and the theoretical distribution function F(x) (Okeniyi
and Okeniyi, 2011; Polyanin and Manzhirov, 2007; Soong,
2004) using the expression:
where, n is the sample size. The value of d from Eq. 4 is
useful for ascertaining region of the level of significance (α) of the
KS goodnessoffit from tables, using the condition d_{computed}<c_{n,α(tabulated)}
(Soong, 2004; Gibbons and Chakraborti,
2003). In this study, however, the pvalue of the KS goodnessoffit test
was computed directly from the d value and sample size n in Microsoft^{®}
Excel^{®} using the method described in (Okeniyi
and Okeniyi, 2011). Thus, based on α = 0.05, level of significance,
the computed pvalue, could be subjected to test the hypotheses:
Where:
H_{0} 
= 
Null hypothesis that the measured gas emission data follow
the Weibull distribution 
H_{A} 
= 
Alternative hypothesis that the measured gas emission data does not follow
the Weibull distribution 
RESULTS AND DISCUSSION
Observation of the plots of the potential readings in Fig. 1
and 2 show that these readings fluctuate from the beginning
to the end of the experiment, for each concentration of inhibitors presented.
These fluctuations occur in the form of spikes of varying amplitudes and make
interpretation of observed data difficult. Although, the concrete admixed with
0.679 M NaNO_{2} could be deduced from Fig. 1 to monotonically
lead the stack in inhibition effectiveness in the NaCl medium, in spite of the
fluctuations in the OCP plots, it is almost impossible however to identify which
concentration exhibited such optimal effectiveness of inhibition in the sulphuric
acid medium from the fluctuating curves in Fig. 2. These fluctuations
could be due to reactions between the inhibitor, alkaline pore solution, steel
rebar and the medium resulting in complex formations which accounted for sharp
oscillatory drifts between the active and passive regions. For these fluctuations
in the OCP readings for each admixed inhibitor, the Weibull distribution modelling
tool was employed to study the underlying characteristics, especially of inhibiting
quality and effectiveness, with subsequent GoF analysis using the KS GoF criteria.
The results from these analyses are presented in Table 1 and
2 for admixed inhibitors in NaCl and H_{2}SO_{4}
media, respectively.
From Table 1, the pvalues from the KS goodness of fit test shows that the measured OCP data for all the reinforced concrete samples immersed in NaCl medium followed the twoparameter Weibull distribution. For the statistical modelling for these inhibitors, using the Weibull PDF, the pvalues is greater than the significant values of α (i.e., p>0.05) thus satisfying the null hypothesis in Eq. 5. Also, the large values of shape parameters (k) of the Weibull modelling of the admixed inhibitors imply small scatters of OCP data and these translate to good uniformity of the measured data.
Table 2 shows that the pvalues of the KS GoF for the reinforced
concrete admixed inhibitors in H_{2}SO_{4} medium are not only
lower than that obtained in NaCl medium but also that p<0.05 for seven of
these fifteen specimens.
Table 1: 
Results of Weibull distribution modelling of aniline and NaNO_{2}
admixtures in reinforced concrete samples immersed in NaCl medium 

Table 2: 
Results of Weibull distribution modelling of aniline and NaNO_{2}
admixtures in reinforced concrete samples immersed in H_{2}SO_{4}
medium 

By this, while we fail to reject the null hypothesis that the measured OCP
data follow the twoparameter Weibull PDF for the remaining eight admixed inhibitors
having p>0.05, there is need to reject the null hypothesis for the other
seven concrete admixed inhibitors. The reason for this kind of result could
be due to the large scatter of OCP data, derivable from the smallness of the
shape parameter (k), which is a measure of the uniformity of the measured data,
predominant with these inhibitors in the highly corroding H_{2}SO_{4}
medium. Such large scatter in measured OCP data populations, in corrosion sense,
might have resulted from initiations of pitting corrosion occurring when sulphate
ions in ample concentration hit the steel rebar surface (Hausmann,
2004).
To further investigate performance prediction of the admixed inhibitors in reinforced concrete, the mean values obtained from the Weibull model were subjected to the corrosion classification standard of ASTM C 876 with reference to CSE. Corrosion classification conditions obtained based on the standard are presented in Table 3 and 4 for admixed inhibitors in NaCl and H_{2}SO_{4} media, respectively.
Table 3: 
Corrosion condition prediction for admixed inhibitor in NaCl
medium 

Table 4: 
Corrosion condition prediction for admixed inhibitor in H_{2}SO_{4}
medium 

The performance ranking of inhibiting quality based on Weibull model prediction is presented in Fig. 3 and 4 for admixed inhibitors in NaCl and H_{2}SO_{4} media, respectively. Figure 3 identified 7.5 g NaNO_{2} admixture in steel reinforced concrete as exhibiting optimum inhibiting effectiveness in NaCl medium, according to the performance ranking of the Weibull model prediction. This inhibiting effectiveness is valued at a quality of 270.10 mV (CSE), from Table 1, representing a slight overprediction compared to its actual mean OCP of 268.88 mV (CSE) from measured data. The reliability of this modelled quality stands at a probability of 47.80% bearing good comparison with the probability of observing its measured actual mean OCP data valued at 47.01%. This optimal inhibiting quality predicted for 7.5 g NaNO_{2} admixture moderated the corrosion condition to the intermediate corrosion risk range from high (i.e., greater than 90% probability that corrosion will occur) modelled for the control specimen (Table 3) according to ASTM C87691 R99.
Also identified by the Weibull prediction modelling presented in Fig.
3 as exhibiting good inhibiting effectiveness in NaCl medium compared to
the control specimen is 4.5 g NaNO_{2} admixture followed, in order
of predicted effectiveness, by 4 mL aniline admixture in steel reinforced concrete.
The quality of inhibition predicted for 4.5 g NaNO_{2} admixture in
Table 1 is valued at386.53 mV (CSE) at a reliability of 48.24%
probability, also representing good model prediction compared to a measured
actual mean value of 386.75 mV (CSE) at 48.33% probability of observation.
Predicted for 4 mL aniline admixture, from Table 1, include
the modelled quality of 447.12 mV (CSE) at a reliability of 45.57% probability
from measured OCP data with the mean of 448.00 mV (CSE) observed at a probability
of 46.21%.
Compared to the control specimen having no admixed inhibitor, all other concentrations
of admixed inhibitors studied in this work are predicted as exhibiting poor
corrosion inhibition in NaCl medium. This model prediction of the Weibull probability
density distribution, though giving more perceptible interpretations than that
from the OCP data curves in Fig. 1, finds good agreement with
performance rankings deducible.

Fig. 3: 
Performance ranking of inhibiting quality of admixed inhibitors
in NaCl medium based on Weibull model prediction 

Fig. 4: 
Performance ranking of inhibiting quality of admixed inhibitors
in H_{2}SO_{4} medium based on Weibull model prediction 
In Fig. 1, for instance, 7.5 g NaNO_{2} had been
observed as leading the stack even as 1.5 g NaNO_{2} could be identified
as exhibiting lowest mean corrosion potential performance as confirmed by the
Weibull rankings of performance prediction presented in Fig. 3.
In H_{2}SO_{4} medium, the performance prediction model of
the Weibull distribution, presented in Fig. 4, identified
2 mL aniline admixed inhibitor in steel reinforced concrete as exhibiting optimal
inhibition effectiveness, compared to the control specimen with no admixed inhibitor.
At a predicted inhibiting quality of 402.60 mV (CSE) (Table 2)
and reliability of 56.99% probability, this modelling, however, represents a
wide margin of overprediction in comparison with the actual mean OCP of 371.63
mV (CSE) observed at a probability of 52.47%. Though obtained at a good KS
goodness of fit which confirms the null hypothesis, this margin of quality overprediction
is, however, wider than that obtainable with any of the admixed inhibitors in
NaCl medium. It could also be observed that the reliability obtained as probability
percentage in this medium (H_{2}SO_{4}) is higher than any obtainable
in NaCl medium. Furthermore, the H_{2}SO_{4} medium was confirmed
in this study as a harsh corroding environment to steel reinforced concrete
even as observed in literatures (Hewayde et al.,
2007; Parande et al., 2006). At this, the
optimal inhibiting quality favouring 2 mL aniline admixture in steel reinforced
concrete was only able to assuage the predicted corrosion condition to the range
of high (>90%) risk that corrosion will occur from the severe corrosion range
modelled for the control specimen, according to ASTM C87691 R99, in Table
4. However, the 3 and 5 mL admixtures closely followed the 2 mL inhibitor
concentration in order of effectiveness with quality/reliability of 455.21
mV (CSE)/59.81% and 466.22 mV (CSE)/58.35%, respectively, compared to measured
OCP mean/probability of 384.69 mV (CSE)/52.15% and 399.50 mV (CSE)/50.57%,
as shown in Table 2. The other inhibitor admixture concentrations
appeared (in no particular order) to come after the control in effectiveness,
revealing that no specific relationship exists between increasing concentrations
and inhibitor effectiveness.
All the synergetic inhibitor admixtures considered in this study are modelled
as exhibiting poor inhibiting performance both in NaCl and H_{2}SO_{4}
media. Of these, only the synergetic combination of 3.0 g NaNO_{2} +
1 mL aniline admixtures in NaCl medium could be considered as somewhat close
to the control specimen as to be suggestive of the need for further studies
involving optimal increase of the concentrations of the inhibitor combinations
needed for effective inhibition in that medium.
CONCLUSION The performance of sodium nitrite and aniline as inhibitors on the corrosion of steel reinforced concrete has been modelled in this study using the Weibull PDF and the KolmogorovSmirnov statistics for the goodness of fit test of the modelled data. From the study the following conclusions could be drawn:
• 
Of the twosets of fifteen specimens of inhibitor admixture
in steel reinforced concrete tested, all the fifteen specimens in NaCl medium
followed the fittings of the Weibull distribution model along with eight
out of the fifteen specimens in H_{2}SO_{4} medium. The
remaining seven specimens in H_{2}SO_{4} medium were characterised
with large scatter of measured data in the harsh sulphate corrosive environment
and thus did not follow the Weibull model fittings according to the KS
criteria 
• 
The statistical model identified 7.5 g (0.679 M) NaNO_{2} inhibitor
admixture in steel reinforced concrete as exhibiting optimal performance
of inhibition effectiveness in NaCl medium with Weibull quality modelling
of 270.10 mV (CSE) at 47.80% probability, followed, in order, by 4.5 g
NaNO_{2} and 4 mL aniline admixtures 
• 
In the H_{2}SO_{4} medium, 2 mL (0.137 M) aniline inhibitor
admixture in steel reinforced concrete was modelled as exhibiting optimum
performance of inhibition effectiveness, with Weibull quality of 402.60
mV (CSE) at 56.99% probability 
• 
All the synergetic admixtures employed in the study performed poorly in
inhibiting effectiveness compared to the control specimens in the two media
considered 
• 
The Weibull modelling of corrosion potential for each concrete samples
have enabled interpretation of the OCP data in accordance with ASTM C87691
R99 

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