An Evaluation of the Inhibitory Effect of Dandelion Root Extract in a HCl Solution by Statistical Analysis ^†

Abstract

In this paper, the influence of steel type, inhibitor concentration, and time on the inhibitory effect of dandelion root extract in a 4% HCl solution was investigated. Two types of steel of known composition were used, and the inhibitory effect was monitored in a time interval of 2, 4, 6, 24, and 48 h. Dandelion root extract was obtained by the Soxhlet method with 96% ethanol, and then dilutions (0.5 g/L, 1.0 g/L, and 1.5 g/L) were made from the obtained extract in a 4% HCl solution. The optimization of the experimentally obtained results was performed using MINITAB 21 software. The optimal concentration of dandelion roots in the 4% HCl solution in terms of the inhibitory effect is 1.5 g/L, while time has no great influence on the inhibitory effect because the beneficial effect was already achieved after 2 h. In addition, better inhibition efficiency was observed in steel type 1.

1. Introduction

Steel and other iron alloys are the most commonly used materials for the construction of buildings, pipelines, process equipment, and automobiles due to their excellent thermal conductivity and mechanical workability [1,2]. However, these materials do not have sufficient thermodynamic stability, and when in contact with oxidizing agents (e.g., water or air), they tend to return to their original form (sulfides, oxides, hydroxides), i.e., they corrode [3,4,5]. The International Union of Pure and Applied Chemistry (IUPAC) has defined corrosion as an irreversible interfacial reaction of a material with its environment which results in consumption of the material or in dissolution into the material of a component of the environment [6]. In this process, redox reactions (anodic and cathodic) occur, in which electrons are accepted or donated [7]. In the corrosion process, a series of reactions are possible that will lead to local changes in pH values, changes in the composition and properties of the metal surface (oxide formation), the diffusion of metal cations into the coating matrix, and changes in their electrochemical potential [8]. Although hydrochloric acid is widely used in industry for the pickling, cleaning, descaling, and etching of metals, improper use can lead to corrosion of the metal surface [9].

The petroleum and petrochemical industries, as well as the automotive sector, suffer significant economic losses due to corrosion [10]. It is estimated that around EUR 2.3 trillion is spent annually on solving problems caused by corrosion, which amounts to approximately 3–4% of the gross world product (GWP) per year [11]. Therefore, efforts are being made to protect these materials. Metals can be protected from corrosion using various methods, such as coatings, corrosion inhibitors, and galvanization [12,13,14]. Due to their ability to modify the corrosion potential and electrical resistance, protective coatings (organic and inorganic) on the surface of metals have been the most common means of protecting metals [7]. Another effective method to prevent the deterioration of metal surfaces in acidic environments is the use of inhibitors [12]. They work by slowing down the corrosion process by forming a thin layer of molecules on the metal surface that reduces metal dissolution and prevents direct contact of the corrosive medium with the metal [10]. Several conditions must be met to select a suitable inhibitor, including (a) the price and quantity of the inhibitor, (b) long-term toxicological effects, and (c) the availability and stability of the inhibitor in the environment [9].

The action of inhibitors is based on the formation of a protective film on the metal surface by the process of adsorption (physisorption, chemisorption, or mixed). Physisorption occurs due to ionic interactions between the charged corrosion inhibitor and the metal surface, which reduces the number of active sites where corrosion can occur [15]. On the other hand, if the inhibitors contain heteroatoms such as nitrogen (N), oxygen (O), sulfur (S), and phosphorus (P) that have unshared electron pairs, they can donate electrons to the metal. This leads to the process of chemisorption of the inhibitor, whereby a protective layer is formed on the metal surface that prevents contact of the corrosive medium with the metal [16]. Mixed adsorption occurs when adsorption takes place on both the anodic and cathodic sites of the corroding metal [9,10,17]. There are inorganic and organic inhibitors. The basic mechanism of inorganic inhibitors (such as nitrites, arsenates, nitrates, phosphates, and chromates) consists of the formation of a passive film on the metal anode, while the mechanism of organic inhibitors (such as pyridine, amine, etc.) consists either of the formation of a film on the metal surface or the adsorption of various functional groups from the inhibitor onto the metal [10]. Due to their toxicity and strict environmental regulations, there is an increasing effort to gradually remove both protective coatings and organic inhibitors; therefore, in recent years, there has been a development of green chemistry and environmentally friendly technologies [1].

In the past decade, plant extracts have been widely used as alternative corrosion inhibitors due to their low cost, availability, and environmental friendliness [2]. Most plants and plant extracts are non-toxic and biodegradable, which makes them suitable as substitutes for harmful organic inhibitors. These corrosion inhibitors can be obtained from various plant components, including pulp, leaves, bark, roots, seeds, and rinds [18]. Plant extracts contain various organic compounds, such as tannins, alkaloids, flavonoids, amino acids, anthraquinones, and saponins, which possess good inhibitory properties [19,20]. These organic compounds are rich in conjugated double bonds, electronegative groups, and heteroatoms (N, O, and S), all of which serve as important adsorption sites in effective corrosion inhibitors [12].

There are several studies on the use of dandelion root extract (Taraxacum officinale) as a corrosion inhibitor. In the work of Mitrović et al., it was determined that dandelion root extract acts as an anodic inhibitor and that inhibition occurs primarily by the adsorption of various compounds from the extract onto the steel surface. The highest inhibition efficiency (80–85.35%) was achieved at an inhibitor concentration of 1.5 g/L, while at 1.0 g/L, the inhibition drops to 50% and at 0.5 g/L to 30%, which means that this inhibitor is ineffective at lower concentrations [21]. The optimization of process parameters for determining the inhibitory effect of dandelion root extract was carried out in the work of Vasiljević et al. [22]. It was determined that dandelion root extract is an effective corrosion inhibitor and that to achieve inhibitor efficiency over 75%, an inhibitor concentration greater than 1.4 g/L is required. On the other hand, in the work of Žbulj et al., it was found that dandelion root extract as an inhibitor for carbon steel in a CO₂-saturated brine solution achieves the highest inhibition efficiency in static conditions of 98.37% at a concentration of 12 mL/L, while in flow conditions, the maximum inhibition efficiency of 82.8% is achieved at a concentration of 14 mL/L. It was also found that the inhibitor is of a mixed type and that dandelion root extract is almost completely biodegradable (0.96) [23]. The aim of this study is to examine the extract obtained from dandelion root (Taraxacum officinale) for preventing steel corrosion in an aggressive HCl environment. The influence of time (2–48 h) and inhibitor concentration (0.5–1.5 mg/L) on the inhibition of corrosion of two types of steel was examined. In this way, a deeper understanding of the inhibitory effects of dandelion root extract will be enabled.

2. Materials and Methods

To determine the inhibitory effect of dandelion root extracts in a 4% HCl (Lach:ner, Neratovice, Czech Republic) solution, two types of steel samples (dimensions 31 × 31 mm) of known composition were used. The sample surface was chemically prepared, and then the corrosion rate was tested in uninhibited and inhibited solutions. All experiments were performed indoors and at room temperature.

The steel samples were prepared by first degreasing them with a detergent and then rinsing them with running and distilled water. The washed samples were immersed in a solution for chemical degreasing at a temperature of 80–90 °C for 20 min. The samples were washed again with running and distilled water and immersed in an etching solution (20% H₂SO₄) for a time t = 1 min at a temperature of 60–70 °C. After etching, the samples were rinsed with running and distilled water. The next in the series of operations is drying, but in order to make the drying faster, the samples were previously washed in alcohol (96% ethanol). The sample was dried for 5 min, after which the mass of the sample was measured. The measured mass represented the initial mass of the sample before corrosion. After the measurement, the surface of the sample was activated in 20% H₂SO₄ at t = 60–70 °C and time t = 2 s. After that, the sample was washed with running and distilled water and immersed in non-inhibited or inhibited solutions in a time interval of 2, 4, 6, 24, and 48 h.

Extracts used for inhibition were prepared by extracting dandelion roots with 96% ethanol using the Soxhlet method. Appropriate dilutions (0.5 g/L, 1.0 g/L, and 1.5 g/L) were made from the obtained extract in a 4% HCl solution.

Based on the mass loss of the steel samples during the time spent in the prepared solutions, the corrosion rate Km⁻, the depth corrosion indicator π, and the inhibitor efficiency (degree of inhibitor protection) z were calculated.

Experimental data were fitted to a second-order polynomial model to obtain the regression coefficients. The generalized second-order polynomial model is as follows:

Y = a₀ + Σa_iX_i + Σa_iiX_i² + Σa_ijX_iX_j

(1)

where Y represents the experimental response, a₀ is a constant, a_i, a_ii, and a_ij are coefficients of linear, quadratic, and interactive regression models, and X_i and X_j are independent variables in coded values.

The coefficient of determination (R²) and p-value obtained by an analysis of variance (ANOVA) were used to assess the adequacy of the developed model. Regression analysis and contour plots were generated to explain the effects of independent variables on response.

3. Results and Discussion

Table 1. Effect of steel type, inhibitor concentration, and time on output variable.

Input			Output			Input			Output
Steel Type	Inhib. Conc. [g/L]	Time [h]	Km− [g/m2 h]	π [mm/year]	z [%]	Steel Type	Inhib. Conc. [g/L]	Time [h]	Km− [g/m2 h]	π [mm/year]	z [%]
1	0	2	0.6959	0.8467	0	2	0	2	0.2308	0.2808	0
1	0	4	0.683	0.831	0	2	0	4	0.1923	0.234	0
1	0	6	0.7131	0.8676	0	2	0	6	0.2137	0.26	0
1	0	24	0.7109	0.8649	0	2	0	24	0.2308	0.2808	0
1	0	48	0.7088	0.8624	0	2	0	48	0.2308	0.2808	0
1	0.5	2	0.232	0.2823	66.66	2	0.5	2	0.1795	0.2184	22.23
1	0.5	4	0.2191	0.2666	67.92	2	0.5	4	0.1667	0.2028	13.31
1	0.5	6	0.2405	0.2926	66.27	2	0.5	6	0.1709	0.2079	20.03
1	0.5	24	0.2298	0.2796	67.67	2	0.5	24	0.1688	0.2054	26.86
1	0.5	48	0.2287	0.2782	67.73	2	0.5	48	0.1677	0.204	27.34
1	1	2	0.1804	0.2195	74.08	2	1	2	0.1538	0.1871	33.36
1	1	4	0.1933	0.2352	71.7	2	1	4	0.1282	0.16	33.33
1	1	6	0.189	0.2247	73.5	2	1	6	0.1453	0.1768	32.01
1	1	24	0.1847	0.2247	74.02	2	1	24	0.16	0.1947	30.68
1	1	48	0.1836	0.2234	74.1	2	1	48	0.16	0.1947	30.68
1	1.5	2	0.0773	0.094	88.89	2	1.5	2	0.0513	0.0624	77.77
1	1.5	4	0.1031	0.1254	84.9	2	1.5	4	0.0641	0.078	66.67
1	1.5	6	0.0945	0.115	86.75	2	1.5	6	0.094	0.1144	56.01
1	1.5	24	0.0881	0.1072	87.61	2	1.5	24	0.0791	0.0962	65.73
1	1.5	48	0.0891	0.1084	87.43	2	1.5	48	0.0812	0.0988	64.82

shows the influence of process parameters on the corrosion rate (Km⁻), depth corrosion indicator (π), and inhibitor efficiency (z).

By a preliminary analysis of Table 1, it can be observed that different types of steel react differently. Thus, at the same inhibitor concentration, type 1 steel has a higher inhibition efficiency than type 2 steel. Observing the inhibitor concentration as a process parameter, it is clear that without the inhibitor (0 g/L), the corrosion rate is maximum, while with the growth of the inhibitor concentration, there is a decrease in the Km⁻ and π values, with a simultaneous increase in z. By monitoring the corrosion process over time (2, 4, 6, 24, and 48 h), it is observed that the Km⁻ and π values are relatively stable, which may indicate that the inhibitor is effective throughout the whole measurement period.

In order to determine the influence of process parameters on the output, an ANOVA and evaluation of the obtained models are used.

The experimental data of each measurement variable were fitted into a quadratic model. The F-value and a p-value were also calculated for each member of the regression model. Choosing a confidence level of 95%, a p-value greater than 0.05 was not considered statistically significant.

Table 2. Results of ANOVA.

Source	DF a	Km− [g/m2 h]				π [mm/year]				z [%]
		Adj SS b	Adj MS c	F-Val.	p-Val. d	Adj SS	Adj MS	F-Val.	p-Val.	Adj SS	Adj MS	F-Val.	p-Val.
Regresion	8	1.23434	0.154292	29.04	0.001	1.82660	0.228325	29.01	0.001	37,113.4	4639.17	31.36	0.001
Steel type (A)	1	0.35925	0.359245	67.61	0.001	0.53066	0.530657	67.42	0.001	889.4	889.44	6.01	0.020
Inhib. conc. (B)	1	0.47820	0.478195	90.00	0.001	0.70886	0.708859	90.06	0.001	5314.5	5314.47	35.92	0.001
Time (C)	1	0.00005	0.000047	0.01	0.926	0.00007	0.000074	0.01	0.923	0.1	0.06	0.00	0.984
BB	1	0.07655	0.076554	14.41	0.001	0.11344	0.113444	14.41	0.001	1070.2	1070.19	7.23	0.011
CC	1	0.00014	0.000137	0.03	0.874	0.00019	0.000194	0.02	0.876	0.5	0.49	0.00	0.954
AB	1	0.25244	0.252441	47.51	0.001	0.37451	0.374511	47.58	0.000	433.9	433.95	2.93	0.097
AC	1	0.00016	0.000162	0.03	0.862	0.00021	0.000211	0.03	0.871	0.2	0.16	0.00	0.974
BC	1	0.00003	0.000033	0.01	0.937	0.00005	0.000049	0.01	0.938	7.9	7.88	0.05	0.819
Error	31	0.16472	0.005314			0.24400	0.007871			4586.3	147.95
Total	39	1.39906				2.07060				41,699.7
Coeff. of determination		R2 = 0.8823 Adjusted R2 = 0.8519 Predicted R2 = 0.7997				R2 = 0.8822 Adjusted R2 = 0.8517 Predicted R2 = 0.7995				R2 = 0.8900 Adjusted R2 = 0.8616 Predicted R2 = 0.8132

^a Degree of freedom, ^b adjusted sum of squares, ^c adjusted mean square, ^d p < 0.05 indicates statistical significance.

shows the ANOVA results of the influence of input parameters on corrosion rate (Km⁻), inhibitor efficiency (z), and depth corrosion indicator (π). The R² values for the corrosion rate, depth corrosion indicator, and inhibitor efficiency were 0.8823, 0.8822, and 0.8900, respectively. This showed that the response variability was well explained in the generated model, as the models were able to explain 88.23% of the variation in the corrosion rate, 88.22% of the variation in the depth corrosion indicator, and 89.00% of the variation in the inhibitor efficiency.

The adjusted R² is the corrected value for R² after eliminating terms in the model that do not have a significant effect on the responses. The values of the corrosion rate, depth corrosion indicator, and inhibitor efficiency are 0.8519, 0.8517, and 0.8616, respectively. These values are very close to the R² values, which means that the proposed models can explain very easily the different variations even by eliminating the members whose p-values are greater than 0.05.

The predicted R² is used to determine how well a regression model makes predictions. The values for the predicted R² for the corrosion rate, depth corrosion indicator, and inhibitor efficiency were 0.7997, 0.7995, and 0.8132, respectively. The difference between the adjusted R² and predicted R² for all output variables were extremely small, which means that the obtained model provides valid predictions for the new observations.

Figure 1 shows the Pareto diagrams for the corrosion rate, depth corrosion indicator, and inhibitor efficiency.

By analyzing the p-value from Table 2 and the Pareto diagram (Figure 1a), it was determined that the following parameters have an effect on the corrosion rate (factors that exceed the red significance threshold line − 2.04, indicating statistical relevance): inhibitor concentration (B), steel type (A), the mutual interaction of the steel type and inhibitor concentration (AB), and the square of inhibitor concentration (BB). Other parameters have a p-value greater than 0.05 and can be excluded.

Secondly, observing Figure 1a,b, it can be seen that Pareto diagrams are almost identical for the corrosion rate and depth corrosion indicator, which means that input factors affect both outputs equally.

Finally, it was determined from Table 2 and the Pareto diagram from Figure 1c that the following parameters have the greatest influence on the inhibitor efficiency: inhibitor concentration (B), the square of inhibitor concentration (BB), and steel type (A). The other members have a p-value greater than 0.05 and can be excluded.

By discarding the elements that are not significant, the abbreviated regression equations for all outputs had the following form:

Corrosion rate (Km⁻) = 0.9896 − 0.3658 A − 0.9211 B + 0.1750 BB + 0.2842 AB

(2)

Depth corrosion indicator (π) = 1.203 − 0.445 A − 1.121 B + 0.2130 BB + 0.3462 AB

(3)

Inhibitor efficiency (z) = 29.2 − 18.2 A + 97.1 B − 20.69 BB

(4)

In order to evaluate the influence of the process parameters on the output value, contour diagrams of the steel type (A) and inhibitor concentration (B), as well as contour diagrams of the influence of the inhibitor concentration (B) and time (C), were constructed (Figure 2). The contour diagram in which the influence of the steel type and time on the output is given is not processed, because based on the number of degrees of freedom, it is dependent on the previous diagrams.

From Figure 2a, it can be seen that in a pure 4% HCl solution (without the addition of inhibitors), steel type 2 (S2) has significantly greater resistance to corrosion, and its mass loss (corrosion rate) is within the limits of 0.2–0.3 g/m² h, while for steel type 1 (S1), the mass loss is greater than 0.6 g/m² h. Additionally, it can be seen that the inhibitor concentration has a significant effect on the reduction in mass loss and that the mass loss does not change significantly with time. Moreover, it can be seen that for S1, the optimal inhibitor concentration is 1.3–1.5 g/L and the mass loss at these concentrations is less than 0.1 g/m² h. For S2 under the same conditions, mass loss below 0.1 g/m² h is achieved at an inhibitor concentration of 0.8–1.2 g/L. At inhibitor concentrations above 0.7 g/L in 4% HCl, the mass loss is less than 0.2 g/m² h.

From Figure 2c, it can be seen that for S1, the protective factor (inhibitor efficiency) is greater than 80% at an inhibitor concentration greater than 1.2 g/L. For S2, the protective factor is significantly lower (40–60%) and is the highest at inhibitor concentrations higher than 0.8 g/L. The influence of the exposure time of the steel to the corrosion environment has no effect on the inhibitor efficiency, i.e., the inhibitor efficiency does not change with time for certain inhibitor concentrations, but the change in inhibitor concentration in 4% HCl has a significant effect on the inhibitor efficiency of both steels (S1 and S2). The optimal concentration of dandelion root extract as an inhibitor for both types of steel is 1–1.5 g/L. At those inhibitor concentrations, the inhibitor efficiency for S1 is greater than 80%, while for S2, it is up to 60%. Steel S2 is more resistant to corrosion (more prone to self-passivation) in 4% HCl than S1, so the protective effect of the used inhibitor is lower. However, the obtained results showed that dandelion root extract can be successfully used to protect both types of steel.

4. Conclusions

In this paper, the influence of steel type, inhibitor concentration, and time on the inhibitory effect of dandelion root extract in a 4% HCl solution was investigated using the statistical program MINITAB 21. The R² values for the corrosion rate, inhibitor efficiency, and depth corrosion indicator were closed to unity, indicating that the generated model well explained the response variability. The adjusted R² and predicted R² were very close to the R² values, indicating that the proposed models can easily explain the various variations even after excluding members with p-values greater than 0.05 and can make valid predictions for future observations. By optimizing process parameters, it was determined that dandelion root extract acts as an effective corrosion inhibitor, significantly reducing the corrosion rate for both types of steel. However, the inhibition effect is more pronounced in steel S1. In 4% HCl without inhibitor, steel S1 is more corrosion-resistant than steel S2, but with the addition of inhibitors in concentrations higher than 1.2 g/L, the mass loss of both steels is significantly reduced. The loss of mass is significantly lower in steel S1 after the addition of inhibitors, and the degree of inhibitor efficiency in steel S1 is over 80%, while in steel S2, it is up to 60%. The inhibitory effect is most pronounced in the first 2 h for both types of steel, but the corrosion rate does not change significantly with the extension of time to 48 h. The obtained results show that the influence of inhibitor concentration is the most significant parameter for reducing the corrosion process of steel in 4% HCl. Furthermore, the type of steel has a significant influence, while the effect of time on the rate of corrosion is least important. The obtained results showed that dandelion root extract can be successfully used as a corrosion inhibitor of used and similar steels.