Materials Performance

OCT 2017

Materials Performance is the world's most widely circulated magazine dedicated to corrosion prevention and control. MP provides information about the latest corrosion control technologies and practical applications for every industry and environment.

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49 NACE INTERNATIONAL: VOL. 56, NO. 10 MATERIALS PERFORMANCE OCTOBER 2017 rectly adds sodium hydroxide (NaOH) into the SCW system, is widely used in the ther- mal and nuclear power stations. 5-8 It has been shown that DO, pH, and temperature should be considered for Cu corrosion studies. However, little attention has been paid on the interactive ef fect of these parameters. Therefore, further studies are still necessary. This report employs response surface methodolog y (R SM) with Box-Behnken design (BBD) to establish a mathematical correlation involving temperature, pH, and DO and finally optimize these parameters in order to decrease the Cu corrosion rate. Scanning electron microscopy (SEM) was implemented to research the morphology of deposits formed on the Cu surface. Visual Minteq † (VM) software was used to calculate the species in solutions at various pH values. The interactive effects were dis- cussed and an applied optimum condition was obtained. Experimental Procedures The weight-loss measurements used 40 by 13 by 2 mm pure (99.99%) Cu specimens. The specimens were polished by a series of emery papers (from grades 500 to 1200) and then mirror-polished using 0.5- µ m dia- mond polishing paste. Each polished speci- men was successively rinsed in acetone, twice-distilled water, and then dried in a d e si c cator at 25 °C. S o lution pH wa s adjusted by 0.1-mol/L NaOH solution to the range of 7.5 to 9.5. The solutions were pre- pared using analytical grade chemicals and twice-distilled water. The weight-loss mea- surements were performed in a 450-mL b eaker. Cu ions were m easured by an atomic absor ption sp ectrophotom et er (Beijing Purkinje General) after the speci- mens were immersed for 72 h. SEM was performed using a QUANTA 200 † . Before SEM examinations, the specimens were stored in a desiccator. The corrosion rate was calculated from the weight loss of the specimens after exposure. The calculation formula of the corrosion rate was as follows in Equation (1): r = ⋅ ⋅ ⋅ V 8.76 (W – W ) S t 0 1 (1) where V is the corrosion rate of Cu (mm/y); W 0 and W 1 are the weight of the specimen in grams before and after the experiment, respectively ; ρ i s th e density of m etal (g/cm 3 ); S is the surface area of the speci- men (m 2 ); and t is the corrosion time (h). The BBD and RSM were employed to investigate the effects of the three inde- pendent variables on the response func- tion. The independent variables were tem- perature (A), pH (B), and D O (C). Th e corrosion rate was analyzed to obtain the optimum anticorrosion condition. Each experiment was repeated three times and values averaged. The low, center, and high levels of each variable are designed as –1, 0, and +1, respectively. The ranges of inde- pendent variables and experimental con- ditions derived from BBD are summarized in Table 1. Results and Discussion The BBD was employed to investigate the behavior of Cu corrosion in simulated SCW. RSM was used to optimize the three key independent operating parameters, namely temperature, pH, and DO. Two sec- o n d - o rd e r p o ly n o m i a l m o d e l s , u si n g D e si g n E xp er t † 8 . 0 5 ( D E ) e xp erim ent design software, in terms of coded factors, are shown in Equation (2): † Trade name. TABLE 1. EXPERIMENTAL RANGES AND LEVELS OF THE INDEPENDENT VARIABLES Coded levels Factors Symbol –1 0 1 Temperature °C A 40 60 80 pH B 7.5 8.5 9.5 DO mg/L C 0 1 2 TABLE 2. ANALYSIS OF VARIANCE RESULTS FOR SIGNIFICANT MODEL TERMS Source Sum of Squares df Mean Square F Value p-value Prob > F Model 2.6714 E-03 9 2.9682E-04 20.4956 0.0003 A 6.8635 E-04 1 6.8635E-04 47.3917 0.0002 B 9.0451E-05 1 9.0453E-05 6.2455 0.0410 C 1.8605 E-05 1 1.8605 E-05 1.2846 0.2944 AB 4.2250E-07 1 4.2250E-07 0.0291 0.8692 AC 4.4100E-06 1 4.4100E-06 0.3045 0.5982 BC 1.0000E-06 1 1.0000E-06 0.0690 0.8003 A2 9.3321E-04 1 9.3321 E-04 64.4371 < 0.0001 B2 2.7201E-04 1 2.7201 E-04 18.7816 0.0034 C2 4.8319E-04 1 4.8319 E-04 33.3631 0.0007 Residual 1.0137E-04 7 1.4482E-05 — — Lack of fit 6.5937E-05 3 2.1979E-05 2.4815 0.2004 Pure error 3.5440E-05 4 8.8600 E-06 — — Cor total 2.7728E-03 16 — — —

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