Optimization of a Dissolved Air Flotation System using ...

Proceedings of the International Conference on Industrial Engineering and Operations Management

Dubai, UAE, March 10-12, 2020

© IEOM Society International

Optimization of a Dissolved Air Flotation System using

Factorial Experimental Design in a Water Treatment Plant

at Laguna De Bay

Aaron S. Cornista and Rianina D. Borres

School of Industrial and Engineering Management,

Mapúa University

658 Muralla St., Intramuros, Manila, 1002, Philippines

[email protected], [email protected]

Abstract

A Dissolved Air Flotation System was optimized using factorial experimental design at different turbidity conditions.

Using cluster analysis, historical water quality data was classified into low (0 – 181 NTU) and high (182 – 323 NTU)

turbidity cluster. A multiple regression analysis was performed for each of the cluster which revealed that for low

turbidity cluster eight factors are significant while only three factors were significant for the high turbidity cluster.

Using this knowledge, a factorial experiment was performed for both clusters. A model was generated with R2

adjusted = 86.25% for low turbidity cluster and R2 adjusted = 67.34% for the high turbidity cluster. The accuracy of

the models was tested on actual conditions and showed 92.60% accuracy for the low turbidity cluster and 88.12%

accuracy for the high turbidity cluster.

Keywords Dissolved air flotation system, turbidity reduction, water treatment plant, factorial experimental design

1. Introduction

Clean water is one of the basic necessities of human beings. Although there is more than enough supply of water to

support the increasing world population, access to sanitary water is limited. This is mostly caused by the pollutants

released in bodies of water through the wastes of human activities, specifically agricultural, domestic and industrial

wastes. According to Hu et al. (2014), the increase in urbanization results in the increase in the waste discharges in

the water sources. This is especially true for countries surrounded by large bodies of water such as the Philippines.

Most of its region is within the medium to high risk in terms of water quality (World Resource Institute, 2018). In

Manila, the most urbanized area in the Philippines, clean water source is scarce and could only support 6 million out

of its 12 million inhabitants (Metropolitan Waterworks and Sewerage System, n.d). This was a problem as well as an

opportunity to look for obscure water sources. One such water source is the Laguna de Bay. Laguna de Bay is located

in the southwest part of Metro Manila. It is the largest freshwater lake in Southeast Asia with an approximate surface

area of 90,000 km2, average depth of 2.8 m and maximum depth of 6.5 m (Tamayo-Zafaralla, Santos, Orozco, &

Elegado, 2010). It has a high volume ratio which makes it turbid with spikes in ammonia and manganese during the

major part of the year caused by the polluted inflows coming from various rivers. (Santos-Borja, 1994).

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Figure1. Philippines Overall Water Risk (World Resource Institute, 2018)

Figure 2. Map of Laguna de Bay/Lake

The main pollutants in the lake are as follows: (1) ammonia, (2) manganese, (3) total dissolved solids, and (4) turbidity.

Ammonia can be reduced by nitrification/denitrification procedures aided by ammonia microbes usually on a packed

bed or stirred tank reactors (Koren, Gould, & Bedard, 2000). It can also be reduced through the use of UV/chlorination

(Zhang, Li, Blatchley, Wang, & Pengfei, 2015). Manganese can be easily removed using oxidant such as potassium

permanganate dosing (Salem, El-Awady, & Amin, 2012). High total dissolved solids content can be removed using

reverse osmosis membranes (Fotouhi & Kresic, 2010). Turbidity reduction is done using a sedimentation system or

dissolved air flotation system. Dissolved Air Flotation is generally more effective than sedimentation especially in

algae infested waters such as the Laguna de Bay (Teixeira & Rosa, 2006). It must be noted however that although

DAF is better than conventional sedimentation processes, at turbidity higher than 100 NTU, the efficiency of DAF

turbidity reduction decreases (Kwon, Ahn, & Wang, 2004). Therefore in this study, the level of turbidity must be

defined to account for the efficiency of DAF.

A study on the scaling of up of a dissolved air flotation pilot plant showed a significant different in their performance

because full-scale plant had long retention time and turbulent mixing conditions and higher flow rate which is not

taken into consideration in studies involving pilot plant (Chung, Choi, Choi, & Kang, 2000). Therefore in this study,

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full-scale plant was considered instead of pilot plant to have a more accurate representation of data that can be used

by other plants so we can also see how the efficient is DAF at higher turbidity levels.

The parameters used varied from study to study. Some studies used only water quality and chemical dosing

relationship, some used DAF operating parameters and some used a mixture of both. Therefore, to account for all

possible significant parameters, the operating parameters as well as water quality parameters and chemical dosing

were used. Nominal parameter such as time was also included to account for human errors that may be caused by

morning and night shift.

From this the following output were aimed: (1) Formulate an optimization model of the DAF Performance per different

turbidity conditions. (2)Test the model on actual conditions to determine its effectiveness per different turbidity

conditions.

This study can be used as benchmark for other waste water and waste water treatment plant that uses Dissolved Air

Flotation system. The study focused on the existing Dissolved Air Flotation system of Putatan Water Treatment Plant.

The study was limited on the records and forms available from the plant. The study was performed prior and during

the Amihan season where turbidity values vary significantly to account for the low and high turbidity.

2. Methodology

In order to optimize a full scale dissolved air flotation system; there were several items that were prepared prior to the

experimental runs: (1) the scope of low and high turbidity was first defined; (2) then the factors that have significant

impact on DAF on different turbidity levels were identified. This was done by performing cluster analysis and multiple

regressions on the historical data.

2.1 Definition of Low and High Turbidity

Cluster analysis was used to group the historical data into different conditions of turbidity (High and Low). Using

historical data from June 2017 to November 2018, the variables included were only the inlet water quality parameters

that had direct relationship inlet turbidity: (a) turbidity, (b) TSS, (c) color, (d) organics (D’Sa, Hu, Muller-Karger, &

Carder, 2002). An agglomerative hierarchical algorithm was used using Minitab 18 Statistical Software and the results

were presented in the dendogram below. Square eucledian was used as the distance matrix and the method was

complete linkage.

Figure 3. Dendogram showing two clusters from the sample data

323317

308304319

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298288283270

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126025925825525325

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123022822622522322222

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2432 14213215212

208209203210

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1069998949087

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89838685848281

738880787977767475727157

5654697067665958686564636

16055524962535

15039323031

2448464745434238444

1403637

353433292627251723201828222

119161513141211910876

81.83

87.89

93.94

100.00

Observations

Sim

ilar

ity

DendrogramCentroid Linkage, Squared Euclidean Distance

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Cluster 1 contained samples with turbidity values ranging from 6 – 181 NTU. This was tagged as the “Low Turbidity

Cluster” while Cluster 2 contained samples with turbidity values ranging from 182 – 400 NTU were tagged as “High

Turbidity Cluster”. There were no literatures showing the distinct differences between the low and high turbidity

values. However several literatures suggested that around 200 NTU turbidities were already considered high

(Palaniandy, Adlan, Aziz, & Murshed, 2010). Therefore the 181 NTU lower limit for the high turbidity cluster was

accepted. This definition of low and high turbidity was used for the rest of the experiment.

2.2 Identification of Significant Factors

Upon identification the limits of turbidity level, multiple regression was performed for both clusters in order to

determine the significant parameters. As mentioned in the previous chapter, previous studies showed that parameters

used to optimize DAF cells vary from study to study. To be able to account the effect on various factors, inlet water

quality factors, chemical dosing factors and DAF operating parameter factors were included in the multiple

regressions. To account for the difference in human behavior during morning shift and night shift, a nominal factor

which was time/shift was added in the regression. In order to account for the interaction effect of each individual

variable, they were treated separately regardless of whether they were an inlet water quality factor, chemical dosing

factor, operating parameters or nominal factor. Below is the operational framework showing each variable that was

used in the multiple regressions.

Figure 4 Operational Framework Optimization of DAF

The historical data from June 2017 to November 2018 of all the variables above were used. Stepwise multiple

regressions were used using Minitab 18 Statistical Software and the results were presented in the ANOVA table below.

Table 1. ANOVA table for the stepwise multiple regression of low turbidity cluster

Source DF Adj SS Adj MS F-Value P-Value

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Regression 8 39.226 4.9032 460.96 0

Turbidity 1 15.5406 15.5406 1460.98 0

pH 1 1.0203 1.0203 95.92 0

Temperature 1 1.061 1.061 99.74 0

Dissolved Oxygen 1 0.3149 0.3149 29.6 0

KMnO4 1 1.5393 1.5393 144.71 0

ACH 1 1.2121 1.2121 113.95 0

Influent Flow Rate 1 0.9533 0.9533 89.62 0

Recycle Flow rate 1 0.3301 0.3301 31.04 0

Table 2. ANOVA table for the stepwise multiple regression of high turbidity cluster

Source DF Adj SS Adj MS F-Value P-Value

Regression 3 1.46997 0.48999 20.76 0

TDS 1 0.08211 0.08211 4.24 0.041

Dissolved Manganese 1 0.19584 0.19584 8.3 0.004

Influent Flow Rate 1 0.97901 0.97901 41.48 0

For the low turbidity cluster, eight (8) parameters showed significant effect on turbidity reduction and will be used for

the factorial experimental design: (a) turbidity, (b) influent flow rate, (c) pH, (d) temperature, (e) dissolved oxygen,

(f) KMnO4 dosing, (g) ACH dosing, and (i) recycle flow rate. For the high turbidity cluster, three (3) parameters

showed significant effect on turbidity reduction and will be used for the factorial experimental design: (a) TDS, (b)

dissolved Mn, and (c) influent flow rate.

2.3 Factorial Design Experimentation

For the low turbidity cluster with eight factors (28), a fractional factorial design is used with 1 replicate for each

treatment as (1) there is only limited time for the runs and (2) there are some factors that are not controlled but we

have to wait for them to happen (inlet water quality parameters). Resolution IV was chosen (1/8 fraction) with 32

runs.

For the high turbidity cluster with three factors (23), a full factorial design is used with 1 replicate for each treatment

as (1) there is only limited time for the runs (2) there are some factors that are not controlled but we have to wait for

them to happen (inlet water quality parameters) and (3) the turbidity for December 2018 is low as compared to last

year. Full factorial was chosen with 8 runs.

The experiment was performed from December 1, 2018 to January 3, 2019. The experiment was done in the actual

DAF Cell and measurements were done in the lab for the water quality and checked in the flow meter for the flow

rates. The results were then encoded in Minitab 18 for multiple regressions.

2.4 Testing the effectiveness of the model

From the mathematical model created for the dissolved air flotation system, the model was tested on actual conditions.

The prediction and actual outcome were compared using the equations below.

%𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 =𝑃𝑟𝑒𝑑𝑖𝑐𝑡𝑖𝑜𝑛

𝐴𝑐𝑡𝑢𝑎𝑙

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%𝐸𝑟𝑟𝑜𝑟 =𝐴𝑐𝑡𝑢𝑎𝑙 − 𝑃𝑟𝑒𝑑𝑖𝑐𝑡𝑖𝑜𝑛

𝐴𝑐𝑡𝑢𝑎𝑙

The experiment was performed from January 3-14, 2019. The experiment was done in the actual DAF Cell and

measurements were done in the lab for the water quality and checked in the flow meter for the flow rates.

3. Results and Discussion

3.1 Factorial Design Experimentation

The result of the experiment performed from December 1, 2018 to January 3, 2019 is shown below:

Figure 5. Pareto Chart for Standardized Effects for Low Turbidity Cluster

The top four factors (Turbidity – 46.08%; ACH-Influent Flow rate – 9.08%; ACH – dissolved oxygen – 6.71%; and,

dissolved Mn – 6.20%) constitute 68.09% contribution on the % Turbidity reduction. At low turbidity, %turbidity

reduction was more sensitive to changes in turbidity. Given the equation of turbidity reduction and that the final

turbidity was the same, increasing the initial turbidity would increase the total %turbidity reduction.

%𝑇𝑢𝑟𝑏𝑖𝑑𝑖𝑡𝑦 𝑅𝑒𝑑𝑢𝑐𝑡𝑖𝑜𝑛 =𝐼𝑛𝑖𝑡𝑖𝑎𝑙 𝑇𝑢𝑟𝑏𝑖𝑑𝑖𝑡𝑦 − 𝐹𝑖𝑛𝑎𝑙 𝑇𝑢𝑟𝑏𝑖𝑑𝑖𝑡𝑦

𝐼𝑛𝑖𝑡𝑖𝑎𝑙 𝑇𝑢𝑟𝑏𝑖𝑑𝑖𝑡𝑦

The relationship of ACH and Influent with regards to turbidity reduction was the effectiveness of coagulation through

rapid mixing. ACH was injected to the influent and coagulation was induced through the high flow rate of the influent.

A good combination of the flow and chemical dosing would lead to improvement in turbidity reduction (Kan, Huang,

& Pan, 2002).

The relationship of ACH and dissolved Oxygen with regards to turbidity reduction was the effectiveness of

coagulation and aeration. High dissolved oxygen in the system would mean that the coagulated particles can be easily

floated in the tank (Dehghani, Karimi, & Rajaei, 2016).

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The effect of dissolved Manganese with regards to turbidity reduction was on the formation of manganese dioxide.

High dissolved manganese results in color and added in the turbidity of the system (Dvorak, 2014). The regression

model generated is as follows:

𝑇𝑢𝑟𝑏𝑖𝑑𝑖𝑡𝑦 𝑅𝑒𝑑𝑢𝑐𝑡𝑖𝑜𝑛

= 3.375 − 0.01005[Turbidity(𝑁𝑇𝑈)] − 0.319[𝑝𝐻] − 0.000527[𝑇𝐷𝑆(𝑝𝑝𝑚)]− 0.1669[𝐷𝑖𝑠𝑠. 𝑀𝑛(𝑝𝑝𝑚)] + 0.058[𝐴𝐶𝐻(𝑝𝑝𝑚)] − 0.000189[𝐼𝑛𝑓𝑙𝑢𝑒𝑛𝑡 𝐹𝑙𝑜𝑤 𝑅𝑎𝑡𝑒(𝐿/𝑠)]+ 0.002219[𝑅𝑒𝑐𝑦𝑐𝑙𝑒 𝐹𝑙𝑜𝑤 𝑟𝑎𝑡𝑒(𝐿/𝑠)] − 0.3107[𝐷𝑖𝑠𝑠𝑜𝑙𝑣𝑒𝑑 𝑂𝑥𝑦𝑔𝑒𝑛(𝑝𝑝𝑚)]+ 0.001150[Turbidity(𝑁𝑇𝑈)][𝑝𝐻] − 0.00259[Turbidity(𝑁𝑇𝑈)][𝐴𝐶𝐻(𝑝𝑝𝑚)]− 0.000001[Turbidity(𝑁𝑇𝑈)][𝐼𝑛𝑓𝑙𝑢𝑒𝑛𝑡 𝐹𝑙𝑜𝑤 𝑅𝑎𝑡𝑒(𝐿/𝑠)]+ 0.002091[Turbidity(𝑁𝑇𝑈)][𝐷𝑖𝑠𝑠𝑜𝑙𝑣𝑒𝑑 𝑂𝑥𝑦𝑔𝑒𝑛(𝑝𝑝𝑚)] − 0.0062[𝑝𝐻][𝐴𝐶𝐻(𝑝𝑝𝑚)]+ 0.0374[𝑝𝐻][𝐷𝑖𝑠𝑠𝑜𝑙𝑣𝑒𝑑 𝑂𝑥𝑦𝑔𝑒𝑛(𝑝𝑝𝑚)] − 0.000006[𝑇𝐷𝑆(𝑝𝑝𝑚)][𝑅𝑒𝑐𝑦𝑐𝑙𝑒 𝐹𝑙𝑜𝑤 𝑟𝑎𝑡𝑒(𝐿/𝑠)]+ 0.000115[𝑇𝐷𝑆(𝑝𝑝𝑚)][𝐷𝑖𝑠𝑠𝑜𝑙𝑣𝑒𝑑 𝑂𝑥𝑦𝑔𝑒𝑛(𝑝𝑝𝑚)]+ 0.000118[𝐴𝐶𝐻(𝑝𝑝𝑚)][𝐼𝑛𝑓𝑙𝑢𝑒𝑛𝑡 𝐹𝑙𝑜𝑤 𝑅𝑎𝑡𝑒(𝐿/𝑠)]− 0.00808[𝐴𝐶𝐻(𝑝𝑝𝑚)][𝐷𝑖𝑠𝑠𝑜𝑙𝑣𝑒𝑑 𝑂𝑥𝑦𝑔𝑒𝑛(𝑝𝑝𝑚)]+ 0.000341[Turbidity(𝑁𝑇𝑈)][𝑝𝐻][𝐴𝐶𝐻(𝑝𝑝𝑚)]− 0.000264[Turbidity(𝑁𝑇𝑈)][𝑝𝐻][𝐷𝑖𝑠𝑠𝑜𝑙𝑣𝑒𝑑 𝑂𝑥𝑦𝑔𝑒𝑛(𝑝𝑝𝑚)]

Figure 6. Pareto Chart for Standardized Effects for Low Turbidity Cluster

At high turbidity, only handful parameters have effect on turbidity reduction as the capacity of DAF is already reached.

The saturators are clogged and the air could not push the solids upward which cause the sludge to sink on the floor.

The only parameter in the experiment that showed a significant effect is the joint interaction of TDS and influent flow

rate. The combination of TDS and influent flowrate accounts the rate of dosing ionic salts in DAF. At high turbidity,

the level of air to solids ratio is very much affected because during these conditions, the denominator is much higher

than the numerator. The presence of TDS has a significant effect on the solubility of air in the water. This results in a

much lower air to solids ratio and lower %turbidity reduction (Haarhoff & Edzwald, 2013). The regression model

generated is as follows:

𝑇𝑢𝑟𝑏𝑖𝑑𝑖𝑡𝑦 𝑅𝑒𝑑𝑢𝑐𝑡𝑖𝑜𝑛= 1.164 − 0.002037[𝑇𝐷𝑆(𝑝𝑝𝑚)] − 0.002776[𝐼𝑛𝑓𝑙𝑢𝑒𝑛𝑡 𝐹𝑙𝑜𝑤 𝑅𝑎𝑡𝑒(𝐿/𝑠)]+ 0.000009[Turbidity(𝑁𝑇𝑈)][𝐼𝑛𝑓𝑙𝑢𝑒𝑛𝑡 𝐹𝑙𝑜𝑤 𝑅𝑎𝑡𝑒(𝐿/𝑠)]

3.2 Testing the effectiveness of the model

After the models are generated, this was tested in actual setting. The result of the experiment performed from January

3 – 14, 2019 at the plant is shown below:

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Figure 7. Comparison of prediction and actual values for low turbidity cluster

The model shows high accuracy in predicting the actual values with percent difference ranging from 0.42% to 14.12%

with an average of 7.40% for 70 samples. Accuracy ranges from 85.88 to 99.58% with an average of 92.60%.

Figure 8. Comparison of prediction and actual values for high turbidity cluster

The model shows high accuracy in predicting the actual values with percent difference ranging from 7.78% to 16.77%

with an average of 11.88% for 8 samples. Accuracy ranges from 83.23 to 92.22% with an average of 88.12%

4. Conclusion

The study confirms the difference in the efficiency of dissolved air flotation system at different turbidity levels. Using

cluster analysis, water quality data from June 2017 to November 2018 was separated into two groups: Low Turbidity

cluster containing influent with turbidity values ranging from 0 – 181 NTU and High Turbidity cluster containing

influent with turbidity values ranging from 182 – 323 NTU.

Using multiple regressions on historical data, 8 significant factors were identified to have an effect on the %turbidity

reduction for low turbidity cluster: a) turbidity, (b) influent flow rate, (c) pH, (d) temperature, (e) dissolved oxygen,

(f) KMnO4 dosing, (g) ACH dosing, and (i) recycle flow rate. For the high turbidity cluster, three (3) parameters

showed significant effect on turbidity reduction: (a) TDS, (b) dissolved Mn, and (c) influent flow rate.

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All of their effects were furthered scrutinized and identified as a factorial experiment was performed on the significant

factors for both turbidity clusters. For the low turbidity cluster, five main factors, six 2-way interaction and 2 3-way

interactions show significant effect in the %turbidity reduction. The regression model generated has an r2 = 95.12%.

For the high turbidity cluster only 1 2-way interactions show significant effect in the %turbidity reduction. The

regression model generated has an r2 = 81.34%.

The models were tested on actual conditions. Low turbidity cluster showed 92.60% accuracy on the predictions and

high turbidity cluster showed 88.12% accuracy on the predictions.

Overall, this paper proved that at a certain level of inlet turbidity, the dissolved air flotation system would not be as

effective. At lower turbidity levels, the system had many options or factors that it could control or adjust to get the

optimum turbidity reduction. At higher turbidity level, the maximum reduction that can be achieved was 69%.

Adjusting other factors would only increase the operational cost.

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Biographies

Rianiña D. Borres is an Assistant Professor of School of Industrial Engineering and Engineering Management at

Mapua University in Intramuros, Manila, Philippines. She has earned her B.S degree in Industrial Engineering and

Masters of Engineering Program major in IE from Mapua University, Intramuros, Manila, Philippines. She is a

Professional Industrial Engineer (PIE) with over 15 years of experience. She has taught courses in Probability and

Statistics, Operations Research and Computer Integrated Manufacturing. She has done research projects in operations

research and human factors and ergonomics. She is a member of Philippine Institute of Industrial Engineers (PIIE).

Aaron S. Cornista graduated with a degree of Chemical Engineering (cum laude) at the University of the Philippines

Los Baños. He recently obtained his master's degree in Engineering Management and is taking his doctorate in

Environmental Engineering at the Mapua University. He is currently working as a Process and Applications Manager

in Promark Process International Corporation. His work involves designing water and waste water system to help

various industries in their water supply and sewage discharge issues. He previously worked in Maynilad as a Water

Supply Operations Officer of Putatan Water Treatment Plant Operations and works on ensuring proper water supply

to the community. Prior to that he worked in a food manufacturing industry as a process engineer who deals with

design and commissioning of complex food process flows. Prior to that, he was a project engineer who was responsible

for the construction of new manufacturing plants and rehabilitation of old factories. His research undergraduate and

master’s research focuses on wastewater and water treatment respectively.

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