Development of a Software to Simulate Effluent

8/8/2019 Development of a Software to Simulate Effluent

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82 Int. J. Process Systems Engineering, Vol. 1, No. 1, 2009

Copyright 2009 Inderscience Enterprises Ltd.

Development of a software to simulate effluentcharacteristics of textile wastewater treatment

Adel Al-Kdasi

Department of Chemical and Environmental Engineering,

Faculty of Engineering, Universiti Putra Malaysia,

43400 Serdang, Selangor, Malaysia

and

Faculty of Agriculture, Sanaa University, Yemen

E-mail: [email protected]

Azni Idris

Waste Technology Centre,


Faculty of Engineering, Universiti Putra Malaysia,

43400 Serdang, Selangor, Malaysia


Luqman Chuah Abdullah


Faculty of Engineering, Universiti Putra Malaysia,43400 Serdang, Selangor, Malaysia


Mohanad El-Harbawi*

Department of Chemical Engineering,

Universiti Teknologi Petronas,

Bandar Seri Iskandar, 31750 Tronoh, Perak, Malaysia


*Corresponding author

Mogeeb AlzokryDepartment of Software Engineering,

Faculty of Computer Science and Information Technology,

Universiti Malaya,

50301, Kuala Lumpur, Malaysia



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Development of a software to simulate effluent characteristics 83

Chun-Yang YinFaculty of Chemical Engineering,

University Technology Mara,

40430 Shah Alam, Selangor, Malaysia


Abstract: The composition of textile wastewaters is extremely varied due tothe large spectrum of dyes and chemicals used in wet process resulting ingreater variability in the success of different treatment processes. A softwarewas developed to determine the required advanced oxidation process after biological treatment and to evaluate the possibility of applying advancedoxidation processes to any textile treatment plant. Regression equations relatedto the formulation of the process treatment options were successfully developedinto a software program called TexTreat with integrated graphic user interface

(GUI) component. TexTreat has the ability to determine the best option ofadvanced oxidation processes (AOPs) and predict characteristics of wastewaterdischarge in different retention times with an overall simulation accuracy ofmore than 89%. Validation of the process treatment options and TexTreatdisplay their applicability to utilise with different textile wastewater plants.

Keywords: textile wastewater; advanced oxidation process; AOP; software.

Reference to this paper should be made as follows: Al-Kdasi, A., Idris, A.,Chuah Abdullah, L., El-Harbawi, M., Alzokry, M. and Yin, C-Y. (2009)Development of a software to simulate effluent characteristics of textilewastewater treatment, Int. J. Process Systems Engineering, Vol. 1, No. 1,pp.8299.

Biographical notes: Adel Al-Kdasi is currently an Assistant Professor in theFaculty of Agriculture-Sanaa University. Prior to this, he obtained his PhDfrom the Department of Chemical and Environmental Engineering, UniversitiPutra Malaysia (UPM). He has published several papers in textile treatmentfield.

Azni Idris received his PhD in Environmental Engineering from the Universityof Newcastle upon Tyne, UK. He is currently the Head of Department ofChemical and Environmental Engineering at Faculty of Engineering, UniversitiPutra Malaysia. He is the Director of Technology Commercialisation,University Business Center, University Putra Malaysia (UPM). He is also theDirector of Waste Technology Center, UPM. As an active researcher, he hadpublished more than 100 publications in international journals and proceedings.In addition, he was awarded for research excellence and his contribution in the production of biofilter [holder of patent on organic waste treatment process

(BioFil sysytem)].

Luqman Chuah Abdullah is currently an Associate Professor in the Departmentof Chemical and Environmental Engineering, Universiti Putra Malaysia(UPM). He has also served as Head of Research Laboratory in the Institute ofTropical Forest and Forest Products (INTROP), UPM. As an active researcher,he had published 160 publications on journals and proceedings, of which manyof them are internationally cited and reputable in chemical and environmentalengineering. In addition, he won several awards in teaching and research,including Young Engineer Award from the Institution of Engineers Malaysia(IEM) in year 2006.


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84 A. Al-Kdasi et al.

Mohanad El-Harbawi received his Doctorate in Chemical and Environmental

Engineering from the University Putra Malaysia in 2006. He is part of lecturingteam at the Department of Chemical Engineering at Universiti TeknologiPETRONAS (UTP). Currently, he is working diligently in the development ofa GIS-based air pollution dispersion modelling software. He has developedseveral softwares for chemical risk assessment and air pollution assessment.Furthermore, he has several publications including ISI-indexed journal articlesand conference proceedings. In addition, he is also a reviewer for Journal ofEngineering Science and Technology (JESTEC).

Mogeeb Alzokry received his BSc in Software Engineering from TechnologyUniversity Iraq in 2002. In 2003, he was appointed as a Staff at TaizUniversity in the Faculty of Engineering. He is currently pursuing his PhD atthe University of Malaya. To date, he has published several papers in indexedjournals and conference proceedings.

Chun-Yang Yin received his PhD in Chemical Engineering from the Universityof Malaya and was appointed as a Visiting Scholar at the prestigious IvyLeague Institution, Columbia University from December 2008 to March 2009.He has over 60 publications to date including ISI-indexed journal articles andconference proceedings. He is the recipient of numerous universities, nationaland international-level awards including the IEM Tan Sri Raja Zainal Prize in2005 and 2009 as well as listed in the 25th Silver Anniversary Edition ofMarquis Whos Who in the World. He is currently a Reviewer for severalElsevier journals and is on the Editorial Board of Malaysian Journal ofChemical Engineering.

1 Introduction

Several conventional methods are used to treat textile dye wastewaters such as biological

treatment, chemical oxidation, coagulation, adsorption and filtration. However, the

efficiencies of these methods depend strongly on the types of dye in wastewater and

concentration of contaminants. Colour removal from textile effluents in aerobic

biological treatment is not an effective process since the biodegradation products are

toxic to the organisms used in the process and these result in various problem such as

sludge bulking, rising sludge and pin-point floc formation(Straley, 1984; Paprowicz and

Slodczyk, 1988; Pagga and Brown, 1986; Lin and Peng, 1996; Antonio et al., 1997;

Wilmott et al., 1998).

According to Antonio et al. (1997), biological treatments are reliable, but there are

certain substances which are unable to deal with. Thus, there are a lot of combinations of

chemical oxidation and biological treatment which can be arranged for organic removalfrom toxic wastewater. However, bio-chemical treatment of textile wastewater effluents

that contain dyes and their hydrolysis products can be a cost-effective alternative when

the effluents are pretreated chemically prior to treatment in the biological unit (Lin and

Peng, 1996).

Physico-chemical methods such as coagulation/flocculation, activated carbon

adsorption and reverse osmosis techniques have been developed in order to remove the

colour (Dae-Hee et al., 1999; Bes-Pi et al., 2003; Maria et al., 2004). However, these

methods can only transfer the contaminants from wastewater to solid waste leaving the

problem essentially unsolved (Mariana et al., 2002; Georgiou et al., 2002; Arslan et al.,


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2002). Therefore, special attention should be focused on the techniques that lead to the

complete destruction of the dye molecules.

Advanced Oxidation Processes (AOPs) is one of these developing processes that has

been used to generate hydroxyl free radicals by different techniques to destroy the

contaminants in textile wastewater. Ozone (O3), hydrogen peroxide (H2O2) and UV

irradiation are recently used to accelerate the generation of hydroxyl radical (HO). This

strong oxidant can destroy compounds that can not be readily oxidised by conventional

oxidants such as ozone, oxygen and chlorine. In addition, AOPs do not generate chemical

sludge and toxicity of wastewater is generally reduced.

Several studies have been reported about the successful application of the AOPs

(Nilsun, 1999; Arslan and Isil, 2001; Arslan et al., 2002; Georgiou et al., 2002;

Yung-Shuen and Deng-Kae, 2002; Tanja et al., 2003; Azbar et al., 2004; Ulusoy, 2004;

Shu et al., 2004; Shu and Chang, 2005). Previous studies were carried out within a certain

range of different kind of synthetic dyes and synthetic textile wastewater in the batchmode. Few studies were reported in the literature that deal with the actual wastewater due

to their inability to cope with the complex background matrices and high pollution load

normally encountered in real wastewater (Beltran et al., 1997; Wenzel et al., 1999). On

the other hand, extraordinary high oxidant doses of chemicals make the industrial scale

stand-alone and the application of chemical unfeasible from the economic point of view

(Masten and Davies, 1993; Beltran et al., 1997). As such, an integrated system of

biological treatment and AOPs would mean a cheaper option for total organic

degradation and colour removal from the textile wastewater.

Having different, easily controlled and successful processes that can deal with the

different strength of textile wastewater is the best way to ensure efficient colour and

organic pollutants removal from textile wastewater. Previous related studies were focused

on development of models to predict performance of wastewater treatment systemswithout integration of graphic user interface (GUI) to enhance user-friendly feature of the

model. Examples of such reported studies include Bernard et al. (2001), Hamed et al.

(2004) and Joksimovic et al. (2006) which focused on development of new models for

wastewater parameters estimation as applied for anaerobic, urban domestic and reclaimed

wastewater respectively. In a recent study on software development, Hidalgo et al. (2007)

developed a multi-criteria analysis user friendly software to assist responsible authorities

in determining the most efficient solutions in terms of health and safety for the

agricultural reuse of the produced effluent. From our understanding, the concept or idea

to develop a software with GUI to determine the best advanced oxidation process after

biological treatment and predict characteristics of treated textile wastewater has not been

considered in previous studies. As such, the objectives of this study were to develop

software (designated as TexTreat) to determine the required advanced oxidation process

after biological treatment and evaluate the possibility of applying AOPs to any textiletreatment plant. Development of TexTreat was divided into three distinct stages. The first

stage assigned a reaction order for each category which was determined by using the

integration method. Reaction rate coefficient (rate constant) was evaluated by substitution

using the test data at different retention time for colour, total organic carbon (TOC) and

chemical oxygen demand (COD). The second stage assigned the best fitting regression

equations between different values of the parameters and their reaction rate coefficients

were determined at different retention times using SPSS software. The final stage was to

write the program for the TexTreat software.


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2 Materials and methods

2.1 Textile wastewater collection

To achieve the formulation treatment option, samples of textile wastewater with different

concentrations were collected and studied. Each sample was studied separately using

integrated system of biological and advanced oxidation process (data not shown). The

textile wastewater was collected every five days from a local manufacturing factory

effluents of Kim Fashion Knitwear Sdn Bhd located in Senawang, Negeri Sembilan,

Malaysia. Wastewater was produced from several finishing and dyeing units, which were

used to dye fabrics, hanks and socks of different natural or synthetic fibres and mixture of

both. Three different textile wastewater factories were used to validate the TexTreat

application and the formulation of process treatment options. These three factories were

Kim Fashion Knitwear (M) Sdn Bhd located in Senawang, Negeri Sembilan, PacificPeninsula Textile Sdn Bhd located in Johor Bahru and Ramatex Textiles Industrial Sdn

Bhd located in Batu Pahat, Johor Bahru.

Figure 1 Design configuration of bio-photochemical reactor


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2.2 Experimental model setup

The laboratory setup consisted of activated sludge process (extended aeration) followed

by AOP. The reactor was designed to be operated in continuous mode. The

laboratory-scale model reactor used in this study is shown in Figure 1.

2.2.1 Activated sludge model

The activated sludge process reactor consisted of a feeding tank, aeration tank and

settling tank made of polyvinyl chloride (PVC). Raw wastewater pH in the feeding tank

was adjusted to pH 7 using 1 M hydrochloric acid and mixed continuously using a

motorised stirrer (Eurostar Digital, Germany) at a rate of 50 rpm.

A continuous flow rate of 1.26 l/h using a peristaltic pump (Heidolph PD5006,

Germany) was used. Complete mixing and aeration were assured by the continuous flowof air into the aeration tank. The dissolved oxygen concentration in aeration tank was

kept between 2 to 3 mg/l. The aeration tank was connected to the settling tank to remove

suspended solids. Biotreated effluent was subsequently discharged through a plastic pipe

to the advanced oxidation process reactor.

All experiments were conducted at room temperature range of 2528C. Samples for

analysis were collected from feeding tank, discharge point after aeration tank and

discharge point after AOP.

Microorganisms of the aeration tank were acclimatised to receive high-level of colour

and organic matter in the wastewater using actual textile wastewater without any

pretreatment for three months. The system was operated at F/M ratio of around

0.05 (g BOD /g VSS.d) and dissolved oxygen concentrations from 2 to 3 mg/l.

2.2.2 Advanced oxidation model

AOP reactor was made of 304 stainless steel with 2.5 litre capacity. The reactor was

cylindrical and equipped with a UV lamp (wavelength, = 254 nm). Both the UV-C and

quartz lamp housings were centred in the reactor tube. Five outlet discharge points were

pointed corresponding to different retention times in the system. O3 generator (OWA

350) with an O3 production rate of 350 mg/hr was used for this experiment. The O3 was

produced by natural intake of air from surrounding and was bubbled into reactor by

diffusers. The applied O3 concentrations for this experiment were 183, 152 and

101 mg/L. Ozone was introduced into the bottom of the reactor through two diffuser

points. The exhaust gas was vented from the top of the reactor passing through two

bottles of potassium iodide solution (KI) and 2% absorption solution was then vented into

a laboratory hood.Different concentrations of hydrogen peroxide (30%) were applied using a peristaltic

pump. Samples were collected from the sampling point at regular time intervals (15, 30,

60, 90 and 120 minutes)and kept for further analyses.

2.3 Analytical methods

True colour (PtCo) of the samples was measured using a spectrophotometer (HACH DR

2500, USA) calibrated according to standard platinum-cobalt method. Prior to colour

measurement, both the influent and effluent were filtered using membrane filter paper


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with a pore size of 0.45 m (Whatman, Japan). The mixed liquor suspended solids

(MLSS), total suspended solids (TSS), COD and biochemical oxygen demand (BOD)measurements were determined according to standard methods for examination of water

and wastewater(APHA et al., 1998). Ozone concentration in feed gas was determined by

KI-starch titration method (APHA et al., 1985). The TOC concentration in solution was

determined using TOC analyser (Shimadzu 5050, Japan). Wastewater pH was measured

by means of a laboratory multi-meter (WTW, InoLab Multi Level 1, Germany). The

average values of the results were obtained by three repeated experiments. Statistical

software (SPSS, Version 12) and standard Microsoft Visual Basic 6.0 for windows

computer program was used for the regression and developing of the software,

respectively.

3 Results and discussion

3.1 Formulation treatment options

Samples of textile wastewater with different concentrations were studied to obtain

formulation process treatment option for textile wastewater. Biotreated textile wastewater

was successfully categorised into four groups; corresponding to these categories,

different AOPs were formulated for use. Table 1 shows the categories of biotreated

textile wastewater and the best methods of advanced oxidation process for each category.

From the formulation treatment options, a software (TexTreat) was developed to assist

operation in selecting the most appropriate AOP for the textile wastewater and also to

estimate the discharge effluent concentration.

Table 1 Categories of biotreated textile wastewater and best selected methods of advancedoxidation

Category Condition Best method of AOPS

Category 1 Colour 400 and TC 80 mg/l 0.25 ml/l H2O2/UV

Colour 400 and TC > 80 mg/l 0.5 ml/l H2O2/UV

Category 2 400 < colour 800 0.75 ml/l H2O2/UV/50 mg/l O3

Category 3 800 < colour 1200 1.5 ml/l H2O2/UV/134 mg/l O3

Category 4 Colour > 1200 2 ml/l H2O2/UV/183 mg/l O3

3.2 Determination of the reaction order and reaction rate coefficient

Integration method was used to determine the order of the reaction for the selected

process. Three reaction orders (zero, first and second order) were plotted. As shown in

Figures 24, the experimental data fitted the second-order (straight line for the

parameters removal) the best among the three orders. Only the second order curve was

shown for reason of brevity. As shown in Figure 2, the second order give the straight line

for the colour removal where R2 (coefficient of determination), were higher than R2 for

plots C and log[C/Co] (zero and first order).


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Figure 2 Colour graphical analysis for the determination of reaction order for Category 1

Category 1R2 = 0.9961

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0 15 30 45 60 75 90 105 120 135

Retention Time min

1/Colour

Figure 3 TOC graphical analysis for the determination of reaction order for Category 1

Category 1 R2 = 0.6141

0

0.020.04

0.06

0.08

0.1

0.12

0.14

0 15 30 45 60 75 90 105 120 135

Retention Time (min)

1/TOC

Figure 4 COD graphical analysis for the determination of reaction order for Category 1

Category 1 R2 = 0.9194

0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0.04

0.045

0.05

0 15 30 45 60 75 90 105 120 135


1/COD


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R2 for the second, third and fourth categories are shown in Table 2. Reaction rate

coefficients were determined for colour at the different retention times for the differentcategories. Table 2 shows reaction rate coefficients for colour at the different retention

time for the different categories. These reaction rate coefficients were utilised in

TexTreat to predict the residual of parameters undergoing AOPs.

Table 2 Reaction rate coefficient for colour removal at different retention times

Reaction rate coefficientCategories

15 min 30 min 60 minR2

Category 1 0.0015205 0.0014929 0.0012348 0.9961

Category 2 0.0010198 0.0009644 0.0009576 0.9769

Category 3 0.0004342 0.0005370 0.0006033 0.9912

Category 4 0.0000977 0.0001482 0.0002188 0.9614

The reaction orders for TOC and COD were determined and the reaction rate coefficients

were evaluated as well. As shown in Figures 34, TOC and COD removal data fit the

second-order quite well in whichR2 was higher thanR2 for zero and first-order.R2 for the

second, third and fourth categories are shown in Table 3.

Table 3 Reaction rate coefficients for TOC and COD removals

Reaction rate coefficientCategories

15 min 30 min 60 minR2

TOC

Category 1 0.002187 0.0018628 0.0009978 0.614

Category 2 0.0014545 0.0010726 0.0009206 0.961

Category 3 0.0013813 0.0010085 0.0008118 0.981

Category 4 0.0003819 0.0004395 0.0004803 0.998

COD

Category 1 0.0008333 0.0004605 0.0003293 0.919

Category 2 0.0006871 0.0004825 0.0004198 0.984

Category 3 0.0006030 0.0006792 0.000621 0.893

Category 4 0.0001502 0.000233 0.0003635 0.955

The reaction rate coefficients for TOC and COD were evaluated at different retention

time for all the categories. Table 3 shows the reaction rate coefficients for TOC and COD

at different retention time for all the different categories. The entire reaction ratecoefficients were used to get the relationship between the reaction rate coefficients and

parameters and then used in the TexTreat to determine the values of the parameters after

the different retention time of AOPs.

Once the reaction rate and coefficient are known, the second integrated form could be

used. The concentration of the sample could be determined at selected retention time

treatment of AOPs if the initial concentration of parameter, retention time and reaction

rate coefficient are known. The integrated form for-second order can be written as

follows:


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O

1 1= kt

C C (1)

O

1 1= + kt

C C(2)

O OC C / (1 k * t *C )= + (3)

where

C = concentration of constituent

CO = concentration of constituent at zero time

k = reaction rat coefficient, (mg/L3)

n1/min

t = time

3.3 Determination of the best fitting regression equation

Relationship between different values of the parameters and their rate constants were

determined at different retention times. For each parameter, three formulas were

determined (first for the 15 min and second for 30 min and the last for 60 min). SPSS was

used to determine the best fitting curve and the regression equations. The second-order

was the best equation that adequately describes the relationship between the

concentration of parameters and 1/k. The coefficients of determinations (R2) were more

than 0.9 and the standard errors were around zero with the second regression equations.

This indicated all the input data fitted the second-order very well and that since all the R2

values for all regression coefficients were all very close to unity, it implied that all these

equations are applicable for data input.

Figures 57 show the regression equations fitted between different parameters at

15 min and 1/k using Microsoft Excel. All regression fitting were similar to SPSS

regression. The regression equations at the different retention times were written in the

TexTreat code. Table 4 shows the regression equations andR2at different retention time.

Figure 5 Relationship between 1/k and colour at 15 min

y = 5E-05x + 1.8787x - 148.85

R2 = 0.9982

0

2000

4000

6000

8000

10000

12000

0 1000 2000 3000 4000 5000 6000Colour

Data

Equation plot


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Figure 6 Relationship between 1/k and TOC at 15 min

y = 0.6153x2 - 21.382x + 685.15

R2

= 0.9943

0

500

1000

1500

2000

2500

3000

10 20 30 40 50 60 70 80

1/k

TOC

Data

Equation plot

Figure 7 Relationship between 1/k and COD at 15 min

y = 0.2736x2

- 44.725x + 3103.2

R2

= 0.9987

0

1000

2000

3000

4000

5000

6000

7000

50 70 90 110 130 150 170 190 210 230 250

COD

1/K

Data

Equation plot

Table 4 Regression equations

Parameter Time Regression equations R2

15 min Y=(5E-05x2 + 1.8787x 148.85)1 0.998

30 min Y=(2E-05x2 + 1.4466x + 103.5)1 0.999

Colour

60 min Y=(4E-05x2 + 1.0801x + 382.33)1 0.998

15 min Y=(0.6153x2 21.382x + 685.15)1 0.994

30 min Y=(0.1339x2 + 17.1x + 197.21)1 0.985

TOC

60 min Y=(0.1967x2 + 0.44x + 912.68)1 1

15 min Y=(0.2736x2 44.725x + 3103.2)1 0.999

30 min Y=(0.3133x2 80.454x + 6706.6)1 0.989

COD

60 min Y=(0.3196x2 98.492x + 8864.7)1 0.999


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3.4 Formulation of computer algorithm

All equations that determined the removal of colour, TOC and COD at the different

retention time for the parameters at different retention times were written in TexTreat

code. The program was written in standard Microsoft Visual Basic 6.0 and distributed in

the source code. The computational of the mathematical models for the result estimation

was calculated by using VB program code.

The main interface has three buttons (enter, information and exit) as shown in

Figure 8. The enter button allows the user to go into the decision support interface for

textile wastewater treatment. The information button provides the user detailed

description about TexTreat. It is designed using front page and HTML (hyper text

markup language). The exit button is used to close the TexTreat.

Figure 8 The main and submenus interfaces (see online version for colours)


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The decision support interface is used to insert the characteristics of textile biotreated

wastewater. The decision support interface of TexTreat contains three main stages. Thedifferent stages and instructions to use the TexTreat are shown in Figure 9. In the first

stage, input data stage, the characteristics of the biotreated textile wastewater were

inserted to the processes of running the TexTreat. In the input data stage, different

buttons and text boxes for different parameters of characteristics of textile wastewater

and the run (process) of the program were used. In the second stage, the required

treatment method is achieved in text box. This stage also has buttons to estimate the

results by using the determination method. In the final stage, the estimated results using

the determination method appear at different text boxes. The results estimated appear for

different parameters (colour, TOC and COD) in different retention time (15, 30 and

60 min). Three different separated columns were used to show the result for the different

retention time for the different parameters.

Figure 9 Flowchart of instructions of using the TexTreat (see online version for colours)

Figure 10 shows the TexTreat algorithm. An algorithm is basically a succession of

instructions or a process used for calculation and data processing. At the beginning of the

algorithm sequence, data pertaining to characteristics of wastewater are put into the

TexTreat. After commencement of a run, TexTreat will basically determine the level of

hydrogen peroxide/UV needed based on two main parameters, namely, colour and total


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carbon. Subsequent to initiation of treatment, the characteristics of the effluent will be

predicted. The TC and colour values for the algorithm are obtained from Table 1.

Figure 10 Flow diagram of the TexTreat algorithm


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3.5 Validation of TexTreat and process treatment options

3.5.1 Sample collection and characteristics

Three different textile wastewater factories were applied as database test for TexTreat.

The characteristics of raw and biotreated wastewater for the different factories are shown

in Table 5. All factories show different characteristics in wastewater which are ideal for

the purpose of validation.

Table 5 The characteristics of raw and biotreated wastewater for the validation purpose

Kim fashion knitwear Pacific peninsulatextile Ramatex textilesindustrialParameter

Raw Biotreated Raw Biotreated Raw Biotreated

Colour 486 326 1744 1200 1119 680TOC 138 23.3 195 41.4 236 46.12

COD 563 134 676 154 689 136

Note: All unites mg/l except colour PtCo.

3.5.2 Software validation

Based on the characteristics obtained from the biotreated process in textile wastewaters of

different industries, TexTreat had recommended different methods of advanced oxidation

process to be used. Based on the characteristics of biotreated wastewater of Kim Fashion

Knitwear, Pacific Peninsula Textile and Ramatex Textile Industrial, 0.25 ml/l of

H2O2/UV, 1.5 ml/l of H2O2/134 mg/l of O3/UV and 0.75 ml/l of H2O2/50 mg/l of O3/UV

were recommended, respectively. Figure 11 shows the predicted and lab results of colour

obtained using the recommended methods. Lab result shows that obtained colour were 0,

41 and 0 PtCo after 60 min while simulated results using TextTreat were 12, 26 and

18 PtCo for Kim Fashion Knitwear, Pacific Peninsula Textile and Ramatex Textile

Industrial, respectively. The difference in terms of removal between TexTreat predicted

results and lab results was lower than 4%.

Figure 11 TexTreat and lab result of colour value

-200

0

200

400

600

800

1000

1200

1400

0 15 30 45 60 75


ColourPtCo

Kim software result

Kim lab result

Pacific software result

Pacific lab result

Ramatex software resultRamatex lab result


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As shown in Figure 12, lab results of the TOC of biotreated textile wastewater after

advanced oxidation of Kim Fashion Knitwear, Pacific Peninsula Textile and Ramatex

Textile Industrial were 8, 16 and 18 mg/l while the predicted results were 9, 14 15 mg/l,

respectively. Figure 13 shows the lab and simulated results using TextTreat of COD.

After 60 min, COD of the lab results were 31, 34 and 36 mg/l whereas the simulated

results were 20, 19 and 20 mg/l for Kim Fashion Knitwear, Pacific Peninsula Textile and

Ramatex Textile Industrial respectively. However, the differences in terms of removal of

TOC and COD between TexTreat and lab results were lower than 6% and 11%,

respectively.

Figure 12 TexTreat and lab result of TOC value

0

5

10

15

20

25

30

3540

45

50

0 15 30 45 60 75


TOC(

mg/l)

Kim software resultKim lab result

Pacific software resultPacific lab resultRamatex software resultRamatex lab result

Figure 13 TexTreat and lab result of COD value

0

20

40

60

80

100

120

140

160

180

0 10 20 30 40 50 60 70


COD(

mg/l)

Kim software result

Kim lab result

Pacific software result

Pacific lab result

Ramatex software result

Ramatex lab result

4 Conclusions

Regression equations related to the formulation of the process treatment options were

successfully developed into a software code (TexTreat). The results have shown that

treatment process options were successfully simulated using TexTreat. The difference

between the lab results and TexTreat predicted results were less than 11%. It was also

shown that the applied GUI and algorithm were simple and therefore will be useful to aid


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in training of wastewater technicians. While it is understandable that the characteristics of

textile wastewater are statistically more varied from parameters presented here, it is fairto imply that prediction of COD, TOC and colour effluent parameters using TexTreat will

significantly aid in decision support regarding the type and level of oxidative treatment

most suitable to treat textile wastewater. TexTreat could also be used to predict the

capability of the combined advanced oxidation process to treatment plants of any factory

textile plants in Malaysia.

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Development of a Software to Simulate Effluent

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