1. Introduction The environment degradation is consequence of industrial effluents sewage in a inadequate way, that has been bringing about serious problems of contamination on several world coutries [1]. The industrial process produces a large toxic molecules range that could foul the air and water, because result in negatives impacts for ecosystem and human being [2]. The phenols are toxic compound to microorganisms and can be hard to be removed for the system over biological treatment or by natural procedure in aquatic environment [3]. Phenol is one of the most ordinary organic compound and its found like effluent in many industries, such as oil refining, petrochemical, pharmaceutical products, dyes, Textile Industry, manufacture of organic chemicals and others [4]. According [5], believes that the necessary process at chemical area; chemical engineering specifically; will be achieved by multidisciplinary and a comprehensive approach, that it will be through constants scientific innovations and powerful computational tools.

According [6] increase the importance of shaping and simulation on chemical engineering development. These methods are critical technologies to reach the industrial purpose in process area. This work consists on Monte Carlo simulation appliance at phenolic effluent treatment. It was used a comparative approach of simulation relative to orthogonal array of Taguchi for the Environment variables, such as, Total Organic Carbon (TOC), phenols and Chemical Oxygen Demand (COD). 2. literature review 2.1. Operational Research Human mistakes over an operation and consequent operational risk enlargement are the main concern for all sections. Besides, human reliability analysis with probabilistic Risk analysis is a key element in reducing the operational risk. A prevision technique average of human error and risk of standardized plan and the reliability human methods has been used to quantify distinct human errors categories [7].

20

Ana Paula Barbosa Rodrigues de Freitas et al.: Taguchi Orthogonal Array Combined with Monte Carlo Simulation in the

Optimization of Wastewater Treatment

2.2. Monte Carlo Simulation

in an optimal solution. Concluded by a binary model that

polymers with increasing conduction band need better The Monte Carlo simulation is an associated method of solutions and are dependents of greatest value. use of random numbers and statistical probability [8].

According [9], Monte Carlo Simulationis applied in chemical process, being studied in kinetic behavior of

3. Materials and Methods

hydrogenation of acetylene. The significant activation energy 3.1. raw material for this system shows the presence of carbon monoxide

absorbed increase material selection, and activation energies

Experimental data will be used at Laboratory of

obtained 16,7 and 56 KJ mol-1.

Environment Engineering School of Lorena (EEL / USP). At

The Monte Carlo methodis conducted for model this step the phenolic effluents were treat over a design

calibration used on incineration of waste in a landfill. Experimental (Orthogonal array of Taguchi L16) using

Sensibility analysis also was realized to identify most advanced oxidation process. Obtained results were applied on

sensible factors. At huge cities and metropolis with large Monte Carlo simulation.

creation of solids urban wastes, possibility of large scale construction incineration installations raise, while, for 3.2. Methods

median and small cities, incineration of waste get down [10].

The Monte Carlo simulation associated to the genetic

2.3. GeneticAlgorithms

algorithm Metaheurнstica will do a scanning deterministic simulation of experimental design. It's a multiojective

Genetics Algorithms (AG) are an efficient technic of problem being realized an optimization using the CP function.

sweep the space of solutions and find close great values,

Simulation experiments will be held using a Minitab®,

being one of the several techniques of computational Crystal Ball® and Statistica® computational package.

intelligence worthy of study [11].

Joint application of genetics algorithms and Monte Carlo 4. Results and Discussions simulation was realized for the study of determining the

value of real options of several technical market uncertainties. 4.1. Monte Carlo Simulation

The application obtained the goal of approach a great decision rule and determine the value of the real option in a way of having several investment in a project.[12]. According [13] Genetic Algorithms were applied in a electronics properties studies and optics of polymeric structures, in order of obtain a suitable copolymer for a given application. Evaluate the effect of polymer properties when subject to a system of electrical discontinuity when immersed

In this work were performed a deterministic simulation of multiple TOC, COD and Phenols responses respectively represented by Y1, Y2 e Y3according to table 1. This Table were obtained the target values of deterministic simulation realized, so we can compare the values obtained in the experimental planning. Target values showed huge relevance if compared to Taguchi. Mathematic model coefficients follows a distribution.

Table 1. Monte Carlo Simulation the deterministic values

Factors Response Y1 Y2 Y3

X1 0 21,57 44,13 98,83

X2 X3

X4

00

0

Software target

33,27

67,64

100

X5

X6

0

0

Model Coefficients

B0

Y1

21,6

Y2

44,1

Y3

93,8

X1 X2 3,4 1,4 7,3 0,6 -6,0 -0,7

X1X2 X3 X2X3 X5 X1X5 X6 X1X6 X4 X1X4

-0,19 -1,3 0,7

-2,6 -1,3 1,4 -0,8 0,11 -0,3

-0,1 -0,2 1,4 1,2

-0,3 -5,6 8,9 7,1

3

-1,6 1,5 -2,6

0,5 0,2 3,8 0,4 2,4 -3,7

Figure 1. Best solution with Monte Carlo simulation (% TOC)

American Journal of Theoretical and Applied Statistics 2014; 3(6-1): 19-22

21

Figure 2. Best solution with Monte Carlo simulation (% COD)

On Monte Carlo simulation were realized 5000 interactions. In Figure 1, the percentage optimization of Total Organic Carbon (TOC) removal was realized, in which the optimization time was 1 minute and 04 seconds and, then, obtained a stabilization of the best solution. Software improved the outcome of 21,47 to 33,27, in other words, 54,26% was an improvement in the variable response. In Figure 2, the optimization of removal percentage of Chemical Oxigen Demand (COD) was realized and, in a time of 1 minute and 18 seconds obtained a better stabilization solution. Software improved the outcome of 44,13 to 67,64, in other words, 53,28% was an improvement in the variable response. In Figure 3, the optmization of removal percentage of total phenols was realizes and, in a time of 1 minute and 47 seconds obtained a better stabilization solution. Software improved the outcome of 93,83 to 100, in other words, 6,28%

was an improvement in the variable response. Presented data showed a significant improvement ofvariable response percentage removal which proves the validity of the optimization method. On Table 2, the individual values of each variable response were optimized by Compromise Programming (CP) function. The problem is multiobjective, and, therefore the optimization is performed in this way, in which the target value obtained is used and the weights assigned to each variable. The obtained value in CP function was 5,9, in other words, features a value of great importance, because the rejection rate was around 6,95%. This indicates a great quality of obtained data, because in experimental chemical problems the maximum allowed value must be less than 10%. In CP function was obtained weights for variable responses, in which the values are like 0,333; in other words, being also significant for the optimization process.

Table 2. Simulation of CP function of variable response (Multiobjective)

X1 X2 X3

X4 X5

X6

Factors 1

-1

Response

Y1

30,46

Y2

65,76

Y3

90,35

FCP

5,9

-1

-0,6 1

1

Software target

Model

B0 Coefficients

X1

33,27

Y1

21,6 3,4

67,64

Y2

44,1 7,3

100

Y3

93,8 -6,0

DPM

Ё6,95%

FCP Weights

0,3

X2 X1X2

X3

1,4 -0,19 -2,6

0,6 -1,3

1,4

-0,7 0,7

0,11

0,3 0,3 1FCP

X/X3 X5 -1,3 -0,1 -0,8 -0,3 -0,3 3

X1X5 X6 -0,2 1,4 -5,6 8,9 -1,6 1,5

X1X6 X4 1,2 0,5 7,1 3,8 -2,6 2,4

X1X4 0,2 0,4 -3,7

Figure 3. Best solution with Monte Carlo simulation (% Total Phenols)

4.2. Steps for Further Work The next step to be realized will be the confirmation of Laboratory experiments, and then,a stochastic planning data. In this work will be comparatively evaluated using Monte

Carlo method with the optimization approach of experiments planning in the study of phenolic effluents treatment by advanced oxidation processes. Thus, behavior of variable input and regression coefficients as stochastic variables will be studied, proposes to reduce the experimental number, as

22

Ana Paula Barbosa Rodrigues de Freitas et al.: Taguchi Orthogonal Array Combined with Monte Carlo Simulation in the

Optimization of Wastewater Treatment

well as the use of Metaheurнsica genetic algorithm to solve Monte Carlo Simulation. The proposal is to work with stochastic coefficients, so, witFh probability distribution associated.

[5] Charpentier, J.C. In the frame of globalization and sustainability, process intensification the path to the future of chemical and process engineering (molecules into money). Chemical Engineering Journal, vol. 134, p. 84-92, 2007.

[6] Dixon, D.A.; Feller, D. Computational Chemistry and process

5. Conclusion

design. Chemical Engineering Science, vol. 54, p. 1929, 1999.

In this work was realizedmultiobjective optimization of Mathematical Modeling obtained from experimental planning. Deterministic simulation was realized and improvements

[7] Barati, R.; Setayeshi, S. On the operator action to reduceoperational risk analysis in research reactorsRaminBarati *, Saeed Setayeshi. Process Safety and environmental protection, 2 0 1 4.

were obtained in 3 studied variable responses. Enhance [8] Angelotti, W.F.D.; Fonseca, A.L.; Torres, G.B.; Custodio, R. A

responses were adjusted on CP function and obtained

simplified approach to quantum Monte Carlo method: the

consistent values. The software has enabled an upgrading of the variables

sluзгo integral to the problem of electronic distribution. Quнmica Nova, v. 31, p. 433-444, 2008.

planning value of 54,26% of TOC, 53,28% of DQO and 6,58% [9] Mcleod, A.S; Blackwell, R. Monte Carlo simulation of the

of Total Phenols, that feature the importance of the method

selective hydrogenation of acetylene. Chemical Engineering

for the experimental optimizations, and thereafter reduction

Science, vol. 59, p. 4715 - 4721, 2004.

of experiments to be realized in the job first step.

[10] Bao-guo, T .; Ji-tao, S .; Yan.Z .; Hong-tao, W .; Ji-ming, H.

References

Approach of technical decision-making by element analysis and Monte-Carlo simulation of municipal solid waste stream flow. Journal of Environmental Sciences, v. 19, p. 633-640, 2007.

[1] Han, F .; Kambala, V.S.R .; Srinivasan, M .; Rajarathan, D .; Naidu, R.Tailored titanium dioxide photocatalysts for the degradation of organic dyes in wastewater treatment: A review. Applied Catalysis A: General, Vol. 359, p. 25-40, 2009. [2] Search, G.; Berardinelli, S.; Resini, C.; Arrighi, L. Technologies for the removal of phenol from fluid streams: A short review of recent Developments. Journal of Hazardous

[11] Linden, R. Genetic Algorithms: An Important Tool in Computational Intelligence. Publisher Brasport books Multimedia LTD. Rio de Janeiro: Brasport399 p. availableem:http://books.google.com.br/books?id=it0kv6UsE MEC&printsec=frontcover&hl=ptBR&source=gbs_ge_summ ary_r&cad=0#v=onepage&q&f=false. Accessed on 01/11/2014

Materials, vol. 160, p. 265-288, 2008. [3] Doocey, D.J.; Sharratt, P.N.; Cundy, C.S.; Plaisted, RJ ZeoliteMediated Adavanced model chlorinated phenolic oxidation of aqueous waste Part 2: solid phase Catalysis. Institution of Chemical Engineers, v.82, p. 359-364, 2004.

[12] LazoLazo, J. G. Determination of the value of real options by simulation with montecarlo approach for fuzzy numbers and algorithms genйticos.190 f. Thesis (PhD Electrical Engineering) - Catholic University PUC-Rio, Rio de Janeiro, 2004.

[4] Gogate, P. R. Treatment of wastewater streamscontaining phenolic compounds using hybrid techniques Bades on cavitation: A review of the G culo status and the way forward.UltrasonicsSonochemistry, v.5, p. 1-15, 2008.

[13] Kaur, A.; Bakhshi, A. K. Change in optimum genetic algorithm solution with changing banddiscontinuities and band widths of electrically Conducting copolymers. Chemical Physics, vol. 369, p. 122-125, 2010.

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