OPTIMIZATION OF FLAME RETARDANT ADDITIVES FOR GREEN FLAME RETARDANT COATING FORMULATION

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1 OPTIMIZATION OF FLAME RETARDANT ADDITIVES FOR GREEN FLAME RETARDANT COATING FORMULATION En.Chien Le Cao, PhD. Dat Trinh Minh, En. Ha Le Thi Song, En. Qui Vu Ngoc Centre for Organic Materials & Construction Chemical - VIBM Manuscript received on 00/00/015, accepted manuscript on 00/00/015 Abstract The formulation of waterborne flame retardant coating depends on many factors such as the ratio of binder, water dilution ratio, flame retardant additives ratio, technological conditions and other additives ratio etc. This study focused on the optimization of ingredient additives, which include Ammonium Polyphosphates (APP), Pentaerythritol (PER), Melamine (ME) and water dilution ratio (Water/Binder -W/B). The formulation for the flame retardant coating was optimized by combining the results from laboratory studies with the experimental design. A two-level full factorial design for three factors showed that the objective function was less affected by water dilution ratio. Afterwards, we used Central Composite Design based on response surface methodology (RSM) in optimizing the APP ratio and PER ratio. The results showed that the optimal ratios of APP and PER compared to the total amount of flame-retardant additives used were respectively and The flame retardant coating fabricated using the optimal formulation was covered on the wood samples. Its properties reached the level V-0 according to ANSI/UL94:010 and the coating after being dried did not affect wood grain colour. Keywords: Flame retardant coating, Optimization, Experimental design, Wood coatings. 1. Introduction Along with the strong economic growth and the rapid increase of tall construction works such as high-rise buildings, office towers apartment towers, blocks of flats, shopping malls etc., and the number of fires, either small or large also occur more frequently. According to the report of the Police Department for Fire Prevention and Fighting, Rescue and Salvage, nearly 600 fires occurred last year in Viet Nam, killing 14 people and injuring 349 people. The figure represented a 10 percent rise in comparison with that of 01. Property damage was estimated at more than VND1500 billions. In 013, fires involving buildings, markets, commercial centers and apartment tended to rise. When a fire or an explosion occurs, wooden, plastic and fabric furniture are more combustible. It produces a high amount of heat that contributes to maintaining and developing the fire. In addition, it will create a large amount of smoke that cause gas asphyxiation and reduce visibility, making it difficult for victims to escape. Vietnam has recorded an increasing number of fires and explosions particularly in high-rise buildings, stores, supermarkets and factories. Along with firefighting work with many difficulties for both man and equipments, it is urgent to improve our fire prevention, for the reason that it is the most proactive and effective method of reducing emergencies and damages caused by harmful fires. However, the most efficient fire 10 Vol

2 prevention measure is to eliminate all the fire resulting from human activities; unfortunately, it is still impossible in practice, because this is also the most complicated and difficult issue. One more reason is that some fires occurring beyond human control, such as earthquakes, volcanoes, lightning etc. Therefore, paralleling with the researches addressing the origin causes of fire and explosion, using fire retardant materials in daily life has been carried out to reduce the fire consequences to the minimum. Two kinds of fire retardant materials commonly used in the world presently are paint and fire retardant coating. Through theoretical research and experimental exploration, we found that there were many factors affecting fire retardant properties of the coating on wood. In which the ratio of using fire retardant additives APP, PER and ME had the greatest influence. For assuring the least number of survey experiments but still identify the optimal condition for the objective function stated as minimizing the mass loss rate after burning, we used experimental design method to define the linear or non-linear regression and afterwards to define the optimal formulation in coating fabrication which was consistent with the objectives mentioned above. We use softwares: MS Excel and Maple 17.0 during survey process, data analysis and optimization calculation.. Materials and Methods - Test equipment: Busen Burner... Research Methods..1. Theoretical Research Study the mechanism of wood burning, principles and mechanisms of flame retardant of common additives such as organohalogen compounds, organophosphorus compounds, mineral compounds and nitrogen compounds. Based on the results of theoretical research, we carried out experimental exploration on such additives mixing with the base organophosphorus additive system and nitrogen additive system to determine preliminary formulation, limiting the number of experiments that were far from optimal area, in which the proportion of the flame retardant additives system used was the most important. For example, when making the flame retardant intumescent coatings, the APP ratio was usually between 0.4 to 0.7, PER ratio was from 0:15 to 0:5 and ME ratio was from 0.14 to 0.4 [1,, 3, 4, 5].... Experiment Research a. Experiment method [6] - The specimens were held at one end in the vertical position (see Fig.1). A burner flame was applied to the free end of the specimen for two 10-second intervals separated by the time it took for flaming combustion to cease after the first application. Two sets of 5 specimens were tested..1. Materials and Test equipments - Materials: Vinyl Acrylic and Vinyl Acetate Veova Emulsion - EMULTEX 800 was supplied by Synthomer (UK). Fire-retardant additives were composed of three fire retardant additives: an acid source - Ammonium polyphosphate (APP) as a form APP II was supplied by Sino- Harvest (China); a carbon source - Pentaerythritol (PER) was supplied by Shandong Xinruida chemical (China) and blowing agent - Melamine additives was supplied by Metal & Plastic Co., Nanjing Aoshuo., Ltd... (China). Figure 1. Test method ANSI/UL94:010 for vertical burn V-0, V-1, V- Vol

3 Table 1. Material Classification ANSI/UL94:010 Classification Criteria Conditions V-0 V-1 V- Total flaming combustion for each specimen 10s; Total flaming combustion for all 5 specimens of any set 50s; Flaming and glowing combustion for each specimen after second burner flame application 30s; Afterflame or afterglow of any specimen up to the holding clamp: not allowed Cotton indicator ignited by flaming particles or drops: not allowed Total flaming combustion for each specimen 30s; Total flaming combustion for all 5 specimens of any set 50s; Flaming and glowing combustion for each specimen after second burner flame application 60s; Afterflame or afterglow of any specimen up to the holding clamp: not allowed Cotton indicator ignited by flaming particles or drops: not allowed Total flaming combustion for each specimen 30s; Total flaming combustion for all 5 specimens of any set 50s; Flaming and glowing combustion for each specimen after second burner flame application 60s; Afterflame or afterglow of any specimen up to the holding clamp: not allowed Cotton indicator ignited by flaming particles or drops: allowed - Determination of mass loss after burning: The test specimens were made of bamboo rods 15mm 13mm size 5mm. Dry the specimen to constant mass. Then the test sample was covered with coating layers. Proceed to dry after 48 hours and dry the sample to constant mass - mo (g). Fire tests were conducted in accordance with standard ANSI/UL94:010 for vertical burn. After the completion of the combustion process, re-weigh the sample weight - m1 (g). The mass loss after burning of sample was determined by the formula: m = m 0 - m 1 m 0, % b. Experimental design method As mentioned above coating formulation process depends on many factors, in which the ratio of using fire retardant additives APP, PER and ME has a great influence on fire retardant properties of the coating. To use - level full factorial design to identify interactions between separate experimental factors and the effect that such interactions have on the experimental response. The primary purpose of the experiment is to select or screen out the few important main effects from the many less important ones. To know optimizing responses when factors are proportions of a mixture objective then need a response surface method (RSM) - Central Composite Design (CCD). The experiment was designed to allow us to estimate interaction and even quadratic effects, and therefore gave us an idea of the shape of the response surface we were investigating. Therefore, in this study we chose the mixture experiment design to optimize the ratio calculation of three factors APP, PER and ME respectively. 3. Experiment Results 3.1. Full Factorial Designs In an experiment, we deliberately changed one or more process factors in order to observe how the effect changed in one or more response 1 Vol

4 variables. The design of experiments began with determining the objectives of an experiment and selecting the process factors for the study such as the following [7, 8]: - Ratio APP per total additives mixture (M) (AP- P/M) was levels of the factors range 0.55 to 0.65; - Ratio PER per total additives mixture (M) (PER/M) was levels of the factors range 0.18 to Water dilution ratio (Water/Binder - W/B) was levels of the factors range 1.80 to.80. There are 3 factors, each at levels, a full factorial design has k runs: N = k = 3 = 8 experiments. The full factorial design table is shown in Table 3. The results determining the mass loss after burning of the sample are shown in Table 4. Table. Levels of the factors Name factors Coded factors Level factors Rate (δ) APP/M PER/M W/B Table 3. Full factorial design table Run Coded value Actual value Weight of compounds (g) APP/M PER/M W/B Binder Water APP PER ME Table 4. Mass loss after burning of the samples Run Coded value Mass loss after burning, % Effect variance (Si m 1 m m 3 m ) Vol

5 Analysis the Reliability of Experiment Model by Cochran Standard The relationship between the objective response - Y is a function that depends on the control factors,,, and the random effects uncontrollable which is considered noise - ξ. b 13 was the interaction coefficient of three. Each coefficient b characterized the influence of these factors to the flame-retardant properties of the coating system. Calculated by the following formula: Y = f(,, ) + ξ It was assumed that experiment model was reliable, then the mean and variance of ξ respectively was Eξ = 0, Dξ = σ, meaning ξ ~ N(0, σ ). We were testing whether the null hypothesis was H0: Dξ = σ, against hypothesis H1: Dξ σ. From the experiment values obtained in the table, conduct testing the reliability of the results found by Cochran standard. The order of calculation was as follows: - Repeated measures of variance S ll was calculated by formula: S ll = Ʃ S i = Cochran's statistic for data series (G tn ) calculated by formula: G test = max S i S ll = = Lookup in the critical value from table of standard Cochran [7]: G table = G α (f 1, f ). Level of significance was set at α = 0.05; Degrees of freedom: f 1 = m 1 = (m was repeated experiment number m = 3); f = N = 8, we got G table = From G table > G test : Accept H0, so the experimental values obtained were reliable Calculate the linear regression calculator of full factorial designs Regression formula: Y = b 0 + b 1 + b + b 3 + b 1 + b 13 + b 3 + b 13 Where Y was response variable - Mass loss after burning, %: b 0 was regression coefficient; b 1, b, b 3 were linear coefficients; b 1, b 13, b 3 were the interaction coefficient pair; From experimental data, by applying the formula we got the linear regression calculator of full factorial designs that took the following form: Y = Testing the significance of regression coefficient in regression model First. we had the standard deviation: To check the significance of regression coefficient in regression model. We were testing whether the null hypothesis was H 0 : θ j = 0, against hypothesis H 1 : θ j 0. We had a statistic: (If θ j = 0), Therefore follows the Student's t-distribution. Lookup in the critical value from table of standard Student's t-distribution [7] with degrees of freedom was f = N - k - 1, where N was experiment number N = 8, k was variable number k = 3. So f = 4. Level of significance was set at α = 0.05, we got a critical value that was: t table =.13 and t table xs bδ =, = So based on Student value t table > t bj - Accept H 0. The coefficient b of any variables in the linear regression calculator that had = 1407, then that coefficient did not affect or had 14 Vol

6 insignificant effect on the objective function (here, it was the mass loss after burning of the samples), which made it possible to remove those coefficients. So the equation of the objective function was: Y = Testing the compatibility of the model To obtain suitable equation for the experiment the two variance must be equal. Therefore, testing whether the null hypothesis was H 0 : S mh = S, against hypothesis H : ll 1 S mh S. We had ll a statistic: Variance of the model was calculated by formula follow the Fisher distribution. with: l: significant coefficient number of model (l=4) N: experiment number (N=8) i: average value of each experiment Y i : calculated value of the model Variance of the model is: and: reflects rule of the objective function - mass loss after burning of the samples with the experimental variables,. 3.. Experiment to find the stop domain From the experimental planning level 1 result, we saw: When the Ratios APP/M and PER/M lower than the objective function mass loss after burning decrease. From then, conduct experiment to find the stop domain with these results below: From the result of experiment to find stop domain we saw at APP/M = 0.59 and PER/M = 0.18 we obtained the minimum mass loss after burning. Thus, we used those values for central composite design to find the optimal values according to response surface methodology (RSM) Optimization of the independent variables- Response surface methodology For the optimization of process parameters, statistical experimental design advanced approach was used to provide information on the interactive effect of variables; finally, verification of experiments was used to validate the results under specific experimental conditions. The influence of APP ratio and PER ratio was defined using RSM such as the following: - Ratio APP/M was level of the factors range 0.57 to 0.61; Lookup in the critical value from table of standard Fisher [9] F α(f1, f, level of significance was α ) = 0.05; f 1 = n - 1 = 4; f = m - 1 =, we got F table = F 0.05(4,) = 19.5 so F table = 19.3 > F tt = Accept H 0 - therefore the empirical model found - Ratio PER/M was level of the factors range 0.16 to 0.0; - Ratio between flame retardant additives powder and solution was 18/8; Order APP/M PER/M Table 5. Experiment to find the stop domain results Weight of compounds (g) Water Binder APP PER ME Mass loss after burning (%) Vol

7 Table 6. Levels of the factors Name factors Coded value Levels of the factors Rate (δ) APP/B X PER/B X Table 7. Experimental plan for central composite design Run Coded value Actual value Weight of compounds, (g) APP/M PER/M Water Binder APP PER ME Water dilution ratio was 4.85/ The experiment was designed according to response surface methodology, consisting of three components: The first four points formed a k = = 4 design. Because they were on the corners of the cube in k-dimensional space (k = factors effect), they were called cube points or corner points. The next four points from two pairs of points along the two coordinate axes and were therefore called axial points or star points. Distance from center point to axial points was α. The value of α for rotatability depended on the number of points in the factorial portion of the design. The choice α = (F) 1/4, yields a rotatable central composite design where F was the number of points used in the factorial portion of the design. Therefore α = (4) 1/4 = [7, 8.9]. The last five runs were at the center of design Figure 1. A central composite design in two dimensions region were called centre points. These 13 distinct points of the design were represented in the two-dimensional space as in Fig.. 16 Vol

8 Experiment numbers were N = k + k + m = + * + 5 = 13 experiments. The objective response - Y was mass loss after burning. Experimental plan for optimization of flame retardant additives using response surface methodology such as following: The results determining the mass loss after burning of the sample were shown in Table Calculate the second-order polynomial equation The following second-order polynomial equation was adopted to study the effects of variables to the response: Y = b 0 + b 1 + b + b 1 + b 11 + b The coefficients b in second-order polynomial equation was calculated by the following formulas [9, 10]: Where: The response equation from the above set of experiments could be written as: Y = Testing the statistical significance of the value coefficient The standard errors S bj of the value coefficient b j were calculated by the following formulas: Where was the sum of the squared values from vector X j in design matrix. S ll was repeated measure of variance: m was repeated experiment number m = 5. Y 0 was average value of point centre of experiment design. Table 8. Mass loss after burning for central composite design Run Actual value Weight of compounds, (g) APP/B PER/B Water Binder APP PER ME m (%) Vol

9 Table 9. t statistics of each value coefficient t bj Coefficient b 0 b 1 b b 1 b 11 b 1 S bj t bj Y 0α was calculated value of point centre of experiment design. To check the statistical significance of value coefficient in the second-order polynomial equation. We were testing whether the null hypothesis was H 0 : θ j = 0, against hypothesis H 1 : θ j 0. We had a statistic: l: significant coefficient number of model (l = 5); N: experiment number (N = 13); Y i : average value of each experiment; Y i : calculated value of the model. Variance of the model: and: (if θ j = 0), therefore t bj followed by the Student's t-distribution. Lookup in the critical value from table of standard Student's t-distribution, with f = m - 1 = 4 and level of significance was set at α = We got t table =.13 [9]. Then calculate t statistics of each value coefficient b j. So based on Student value t table > t bj - Accept H 0. Then the coefficient b 1 did not affect or had insignificant effect on the objective function. This made it possible to remove that coefficient in the second-order polynomial equations, so the response equation could be written as: Y = Testing the compatibility of the model according the standard Fisher To obtain suitable equation for the experiment, the two variance must be equal. Therefore, testing whether the null hypothesis was H 0 : S mh = S ll, against hypothesis H 1 : S mh S ll. We follow the Fisher distri- had a statistic: bution. Variance of the model was calculated by formula:, with Lookup in the critical value from table of standard Fisher [9], level of significance was α = 0.05; f 1 = n - l = 8; f = m - 1 = 4, we got F table = F 0.05(8,4) = F table = 6.04 > F tt = Accept H 0 - therefore the empirical model found reflected rule of the objective function- mass loss after burning of the samples with the experimental variables,. The three dimensional response surface curves were plotted by statistically significant model using Maple 17.0 software to understand the interaction of the medium components and the optimum concentration of each component required for optimum mass loss after burning was described in Fig.3. And their respective D contour plots (Fig.4) provided a visual interpretation of the interaction between two factors. The interactive effect of two variables (APP ratio and PER ratio) at mass loss after burning was described in Fig.3. Response surface (Fig. 3) mass loss after burning and contour graphs showing the interaction of each factor and from these graphs we could determine the optimal 18 Vol

10 Figure 3. Response surface mass loss obtained by a RMS-CCD with two variables Figure 4. Contour graphs showing the interaction between factors Table 10. Result of optimize formulation to manufacture a green flame retardant coating Coded value Ratio Optimize formulation, (%) APP/B PER/B Water Binder APP PER ME value of each factor makes to reach minimum response levels. The ratio of APP/M, PER/M was 0.574, 0.156, respectively. This resulted in minimum predicted mass loss after burning was m min = 3.484%. That ratio has used to build a green flame retardant coating formulation, was listed in Table Conclusion The process of defining the optimal formulation of flame-retardant additive system was the most important stage in the fabrication process of fire-retardant coating. It determined the flame-retardant properties of the coating. Using the response surface method (RSM) - central composite design (RSM-CCD), we identified the optimal ratio for coating fabricationas follows: APP additive ratio was 10.33%, PER additive ratio rate was.81% and ME additive ratio was 4.86% per the total mass of additives used. Using this formulation to fabricate the solution and covering on plastic or wood materials to make fire retardant coating, using ANSI/UL94:010 standard for testing its properties. The sample proved to obtain the minimum mass loss after burning, and achieved the requirements of V-0 level. In addition, after getting dried, this coating became colorless and did not affect the wood surface. In conclusion, it could be confirmed that the research result was totally suitable for fabrication of coating on furniture and architecture finishes, indoor and outdoor./. Vol

11 REFERENCES [1]. Guojian Wang, Jiayun Yang. Influences of molecular weight of epoxy binder onfire protection of waterborne intumescent fire resistive coating, Surface & Coatings Technology 06, pp , 01. []. Guojian Wang, Yilong Wang, Jiayun Yang. Influences of polymerization degree of ammonium polyphosphate on fire protection of waterborne intumescent fire resistive coating, Surface & Coatings Technology 06, pp 75-80, 01. [3]. Chih-Shen Chuang et all. Fire retardancy and CO/CO emission of intumescent coatings on thin plywood panel with waterborne vinyl acetate-acrylic resin, Wood Sci Technol 47, pp , 013. [4]. Gilman, J.W and Kashiwagi, Intumescent flame retardant, a Biweekly capsule newsletter highlighting NIST activities, research and service - Fire Science, [5]. Camino G., Costa L., Martinasso G., Intumescent Fire retardant Systems, Polymer Degradation and Stability, 3, , [6]. UL 94: 010 Standard for Safety, Test for Flammability of Plastic Materials for Parts in Devices and Appliances, Underwritters Laboratories Inc, 010. [7]. Gopal K. Kanji, 100 STATISTICAL TESTS, SAGE Publications Ltd, 006. [8]. NIST/SEMATECH, Handbook of Statistical Methods, NIST, 00. [9]. C. F. Jeff Wu, Michael Hamada, Experiments Planning, Analysis, and Parameter Design Optimization, JOHN WILEY & SONS, INC, Vol