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Urban Water Journal

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Analysis of the long-term performance of an on-site greywater treatment plant

using novel statistical approaches

Elena Aizenchtadt a; Dov Ingman a; Eran Friedler b

a Department of Quality Assurance and Reliability, Technion - Israel Institute of Technology, Haifa, Israel b

Faculty of Civil and Environmental Engineering/Environmental, Water and Agricultural Engineering Division, Technion - Israel Institute of Technology, Haifa, Israel

Online Publication Date: 01 October 2009

To cite this Article Aizenchtadt, Elena, Ingman, Dov and Friedler, Eran(2009)'Analysis of the long-term performance of an on-site greywater treatment plant using novel statistical approaches',Urban Water Journal,6:5,341 — 354

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RESEARCH ARTICLE

Analysis of the long-term performance of an on-site greywater treatment plant using novel

statistical approaches

Elena Aizenchtadta, Dov Ingmanaand Eran Friedlerb*

a

Department of Quality Assurance and Reliability, Technion – Israel Institute of Technology, Haifa 32000, Israel;bFaculty of Civil and Environmental Engineering/Environmental, Water and Agricultural Engineering Division, Technion – Israel Institute of

Technology, Haifa 32000, Israel

(Received 20 November 2007; final version received 5 December 2008)

This paper analyses the performance of three greywater treatment systems: RBC, MBR and stand-alone sand filter. Pollutants’ concentrations in raw greywater exhibited a long-term increase. The RBC and MBR exhibited high removal efficiency, while the sand filter performance was much poorer. In order to apply control measures, dynamic regression SPC (DRSPC), SPC (statistical process control) with variable control limits, was developed. DRSPC often better described underlying long-term trends. For RBC effluent, DRSPC had the highest advantage for TKN. For MBR effluent, it had the highest advantage for turbidity and CODt. For the stand-alone sand filter effluent, the DRSPC advantage was much lower. Constant density curves showed low negative correlation between inflow and outflow VSS, and no correlation between inflow and outflow BODt for the RBC. For the MBR, no slope was identified for VSS and low negative slope for BODt. The BODtof the sand filter effluent revealed positive correlation with inflow.

Keywords:greywater treatment; MBR; RBC; sand filtration; process control; SPC

Introduction

The urban sector in many countries is known to be a major consumer of potable water. Its water demand tends to grow continuously as a result of ever-increasing urbanisation and rising specific water demand. This leads to water shortage even in places that traditionally were conceived as water-ample (Europe, Japan, etc.). Specific domestic water demand in industrialised countries approximates 100–180 l c71d71 (litre/capita/day), 60–70% of which is transformed into greywater, while most of the rest is consumed for toilet flushing and released as blackwater (Butler et al. 1995, Almeida et al. 1999, Dixon et al. 1999b, Birks et al. 2003, Wheatley and Surendran 2003, Friedler et al. 2005). Accordingly, greywater reuse for toilet flushing can reduce domestic water consumption by 40–60 l c71d71, and thus lead to 10–20% reduction of the overall urban water con-sumption (Friedler 2008). Additional reduction can be achieved by reusing greywater in garden irrigation, which can be significant in suburban areas in arid/ semi-arid regions (Australia, Arizona, California, Israel, Jordan, etc.).

Nevertheless, greywater may contain various pol-lutants such as organic matter (COD – chemical oxygen demand of up to a 1,000 mgl71), indicator microorganisms (faecal coliforms, 104–108 cfu(100 ml)71), skin pathogens (Staphylococcus aureus sp., Pseudomonas aeruginosa sp., 9.9 103 and 3.3103 cfu(100 ml)71respectively) and detergents (Almeida et al. 1999, Dixon et al. 1999a, Diaper et al. 2001, Patterson 2001, Friedler et al. 2006, Gilboa and Friedler 2008). Thus, unless appropriately treated, greywater may pose health risks and exhibit negative environmental and aesthetic effects.

Indeed, increasing interest has been taken in recent years in on-site greywater reuse, especially in Australia, the EU, the Middle East, Japan and the USA. Various types of treatment and reuse systems are described in the literature, including natural systems (constructed wetlands), minimum treatment ones (filtration and disinfection), biological treatment (BAF – biological aerated filter, and RBC – rotating biological contactor) and membrane embedded treatment systems (MBR, direct membrane filtration) (Nolde 1999, Ogoshiet al. 2001, Al-Jayyousi 2003, Gardner 2003, Lazarovaet al. 2003, Wheatley and Surendran 2003, March et al.

*Corresponding author. Email: eranf@tx.technion.ac.il Vol. 6, No. 5, November 2009, 341–354

ISSN 1573-062X print/ISSN 1744-9006 online

Ó2009 Taylor & Francis DOI: 10.1080/15730620902795210 http://www.informaworld.com

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2004, Friedler et al. 2006, 2008, Gross et al. 2007). Since this practice is relatively new, scarce information exists on the long-term performance of these systems. This is the goal of the current paper.

The paper analyses the long-term performance of three pilot-scale greywater treatment systems, each representing a different type of treatment: low-energy biological treatment (RBC), membrane embedded treatment (MBR – membrane bioreactor) and mini-mum treatment (stand-alone sand filtration). The analysis was performed by employing novel statistical methods, which reveal the long-term performance of these systems, and unveil underlying long-term trends. Special attention was given to the input–output deviation statistics, which are very important where on-site reuse (short reuse cycle) is considered, due to the relatively close proximity between the general public and the reused water.

Materials and methods

Statistical process control (SPC)

Shewhart (1981) explained that a process is in control when, using past experience, the probability that the observed values of a product characteristic will fall within given limits can be predicted. This prediction is possible when the probability distribution of the characteristic is known. As variability is always present, the characteristic can be regarded as a random variable described by its probability distribution. The parameters of this distribution are referred to as control parameters. Shewhart’s control charts and their various later modifications aim to detect the causes of variation, hence to reduce the overall variability of the control parameters. The Shewhart approach actually ‘fits the process to the model’, which is the reverse of statistical modelling (Hoerl and Palm 1992).

According to the SPC approach, the system can either be in-control (IC), when the control parameters equal their target values, or out-of-control (OOC), which is associated with special or assignable causes of variation. IC can be described by some probability distribution with fixed parameters. OOC can be detected by means of SPC, and the process is adjusted accordingly (Montgomery 1996, Lipnik 2000).

The chemical industry typically uses two sigma (2 s) process control to set the SPC control limits. First the average of the data points (X) is calculated. Next the UCL (upper control limit) and LCL (lower control limit) can be calculated:

UCL¼Xþ2sX ð1Þ

LCL¼X2sX ð2Þ

wheresX¼psffiffin, ands(standard deviation) is calculated as:

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

Pn

i¼1ðXiXÞ 2

N1

s

ð3Þ

where Xi is each individual observation; N¼total

number of observations.

It should be noted that in the case of greywater (or wastewater) treatment OOC points above the UCL are much more important than OOC points below the LCL, as the first type of OOC points in many cases means that discharge limits are violated, while the latter type often means that a very high quality of effluent was reached (sometime much higher than the required quality).

Following this approach, greywater (or wastewater in general) treatment deviations can be divided into two categories:

(1) Input deviations (external) resulting from variability of the input properties, i.e. varia-bility of the quality of the raw greywater. (2) System deviations (internal) stemming from the

vast number of reactions involved in organic matter utilization, and from failures of electro-mechanical equipment (pumps, mixers, aerators, etc.).

Internal deviations are in many cases compensated through adaptation of the biological-physical-chemical system with its rather significant time constant and high variability. Thus, the system is relaxed. External deviations may be smoothed by the treatment system. The residual variability of the output in common cases is controlled by SPC.

Description of pilot plant

Light greywater (i.e. greywater discharged from baths/ showers and washbasins) was collected separately from seven flats in an eight-storey-high building. It was conveyed by a gravity collector to a pilot plant situated in the basement of the building. The plant consisted of pretreatment followed by three treatment trains in parallel (Figure 1), namely: an RBC based system, which represents attached growth biological treatment of low energy consumption and minimum maintenance, having a small footprint, making it very suitable for small plants; an MBR, which represents the state-of-the-art compact intensive treatment, which has the potential to produce effluents of very high quality, and has a small footprint; and a stand-alone filtration unit, which is the most widely used system for greywater treatment and represents the ‘minimal treatment’ approach (UK

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Environment Agency 2000, Diaper et al. 2001,

Al-Jayyousi 2003, Birks et al. 2003, Wheatley and

Surendran 2003, Marchet al. 2004).

Pretreatment

Pretreatment consisted of a fine screen (1 mm square mesh) for removal of coarse solids, hair, etc. This was followed by an equalisation basin (capacity 0.3 m3; mean residence time51–10 h) for regulation between raw greywater inflow (instantaneous, highly variable) and withdrawal to the treatment trains (continuous and steady), and for equalisation of temperature and quality. Then the greywater was distributed to three parallel processes.

1. RBC

This system comprised two 15 l RBC basins in series, each equipped with 13 circular discs (diameter 0.22 m) on a horizontal axis having a total surface area of 1 m2, 40% of their area submerged in the liquid. Rotational speed of the discs was set at 0.22 s71(13 rpm), creating linear velocity of 0.15 m s71 at the outer edge of the discs, which is the same velocity as that at the outer edge of full-scale discs (2–3 m in diameter) rotating at 1–1.5 rpm (normal rotational speed in full-scale systems). Flow was perpendicular to the rotation axis set to 7.5 lh71, resulting in a hydraulic retention time (HRT) of 2 h in each basin. The second RBC basin discharged into a sedimenta-tion basin (7.5 l, and HRT of 1 h). Sludge was removed from the bottom of this basin manually. The sedimentation basin was followed by a pre-filtration storage tank (volume 15 l, mean HRT 1.1 h) that regulated between the discharges of the RBC (steady) and the sand filter (intermittent). The sand filter was identical to the stand-alone sand filter (see below), apart from the fact that it was operated intermittently 11 times a day (filtration rate 8.3 m h71, 0.25 h each time), and backwashed manually once a week (once every 77 filtration cycles – 230 bed volumes).

2. MBR

The MBR (Triqua B.V., Netherlands) consisted of an aeration basin of 0.1 m3in which the HRT was 5–8 h. Mixed liquor was wasted daily, setting sludge age at 15– 20 d. A centrifugal pump withdrew 1.671073m3s71

of mixed liquor from the aeration basin into

the membrane module creating cross-flow velocity of 4.0 ms71. The head at the entrance to the membrane was 3 atm, while the head loss along the membranes was kept at 1 atm. Permeate was discharged through a small

holding tank, and retentate was recirculated back to the aeration basin.

The membrane unit (side membranes) comprised two modules (in series) of four parallel polysulphone tubular cross-flow ultrafiltration (UF) membranes (BTU-P4V/02AE; Berghoff GmbH, Germany), having a diameter of 0.0115 m, total surface area 0.34 m2 (eight elements), and MWCO (molecular weight cut-off) of 100,000 Dalton. Permeate flux ranged from 0.0588 to 0.0382 m3m72h71 (equivalent to 20– 13 lh71) through clean membranes and membranes just before cleaning respectively. The system was cleaned once a month with hypochlorite solution.

3. Stand-alone filtration unit

This consisted of a gravity filter (0.1 m diameter), filled with a 0.7 m layer of quartz sand (size 0,d100.63 mm,

d60 0.78 mm, uniformity coefficient 1.24 and porosity

0.36) supported by a 0.1 m gravel layer (diameter 2.2 mm). Filtration rate was set at 8.3 mh71. The filter was operated manually for 0.25 h once a day, after which it was backwashed.

Sampling and analyses

The systems were sampled usually twice a week over a period of 8 months at the following points (Figure 1): raw greywater, settled RBC effluent, RBCþsand filter effluent, MBR effluent, and stand-alone sand filter effluent. All samples were grab samples, while special attention was given to taking the samples on different days and times of day in order to avoid bias. It should be noted that not all sampling points were sampled at the same frequency (raw greywater and RBC effluent*50 times; RBCþsand filter and MBR *30 times; stand-alone sand filter*20 times) and not all parameters were analysed in each sample (BODt, BODd and EC were

analysed at a much lower frequency than other parameters, 10–20 and 6–16 times respectively; Table 1). All quality parameters except nitrate were analysed in accordance with the Standard methods (APHA,

AWWA and WEF 1998): pH (4500-Hþ), turbidity

(2310), TSS (total suspended solid) (2540-D), VSS (volatile suspended solid) (2540-E), BOD5(biological

oxygen demand) (5210-B), COD (5220-B), TKN (total Kjeldahl nitrogen) Norg. B), ammonia (4500-NH3), and TP (total phosphorus) (4500-P). Nitrate was

analysed by the sodium salicylate method (Scheiner 1974).

Results and discussion

Although only light greywater was collected, its quality was far from being suitable for reuse (Table 1),

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stressing the need for appropriate treatment. The average COD of the raw greywater was 211 mgl71 and average BOD was 69 mgl71, about half of both being dissolved (CODd and BODd respectively).

The COD/BOD ratio was *3, indicating that the

raw greywater was relatively easily biodegradable.

The raw greywater contained 9.7 mg l71 TKN,

33% of which was in the form of ammonia-nitrogen. Nitrate concentrations were extremely low, which fits very well with the short travel time from the source of greywater to the treatment unit. Faecal coliforms concentration was of the order of 105 cfu(100 ml)71, two orders of magnitude lower than in municipal wastewater (Gilboa and Friedler 2008), but still too high to allow safe reuse (without treatment, either for toilet flushing or for garden irrigation). All quality parameters in the raw grey-water exhibited very high variability as demonstrated by the high standard deviations and the large differences between the maximum and minimum values observed (which in many cases was larger than an order of magnitude).

The removal efficiency of the RBCþsand filtra-tion treatment was high in respect of turbidity, BODt

and TSS (99%, 98% and 92% removal respectively).

Faecal coliforms removal in this treatment train, although high (99.8%, 2.7 logs removal), was not high enough (3.7102cfu(100 ml)71in the effluent) and thus a disinfection step should be added before the effluent can be safely reused. COD removal was significantly lower than BOD removal (80% vs. 98% and 61% vs. 97% for total and dissolved BOD and COD respectively), indicating that the greywater contains slowly/non-biodegradable organic matter, especially in a dissolved form.

The MBR was highly efficient in removing turbidity (99.7%), BOD (99%), TSS (87%) and faecal coliforms (99.998%, 4.7 logs removal), producing effluent of very high quality (turbidity 0.2 NTU, BOD 1.1 mgl71,

TSS 12 mgl71 and faecal coliforms 27 cfu

(100 ml)71), while its efficiency in removing COD was somewhat lower (81%). As UF membranes act as a physical barrier to bacteria, faecal coliforms found in the MBR effluent resulted from transfer from other treatment units by aerosols and/or contamination of some of the samples (in 65% of the samples no faecal coliforms were detected).

Both the MBR and the RBC were highly efficient in removing anionic detergents, from an average MBAS concentration of 8.1 mgl71in the raw greywater to

Figure 1. General scheme of the experimental greywater pilot plant.

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Table 1. Quality of raw greywater and treated effluent.

Parameter

Raw greywater

RBC effluent

RBCþsand filter effluent

MBR effluent

Stand-alone sand filter effluent

CODt[mgl71]

Average 211 47 42 40 130

Min 53 27 22 22 70

Max 948 106 75 80 234

STD 141 17 15 16 37

n 49 48 26 20 21

RE (%) – 78 80 (11)a 81 38

CODd[mgl71]

Average 108 48 42 37 87

Min 26 22 22 22 38

Max 302 147 91 75 132

STD 47 22 19 14 28

n 49 47 24 22 18

RE (%) 56 61 (13)a 66 20

BODt[mgl 71

]

Average 69 3.7 1.8 1.1 62

Min 23 0.0 0.0 0.0 30

Max 134 24 8.5 5.4 92

Stdev 33 5.5 2.2 1.7 21

n 20 18 16 9 11

RE (%) – 95 98 (28)a 99 10

BODd[mgl71]

Average 36 1.5 1.4 0.5 40

Min 5 0.0 0.0 0.0 2

Max 81 6.4 7.6 3.3 79

STD 20 1.5 1.9 1.1 23

n 20 18 16 9 10

RE (%) – 96 97 (7)a 99 0b

TSS [mgl71]

Average 92 13 7.5 12 32

Min 18 1.8 1.8 2.0 12

Max 649 47 17.2 26 62

STD 115 11 4.7 8.0 13

n 47 47 29 26 19

RE (%) – 86 92 (42)a 87 65

VSS [mgl71]

Average 64 8.6 6.1 8.4 26

Min 6 1.0 0.3 1.0 12.4

Max 413 32 17 25 48

Stdev 76 7.0 4.5 6.9 7.9

n 47 48 29 25 19

RE (%) – 87 90 (29)a 87 59

Turbidity [NTU]

Average 65 1.6 0.6 0.2 35

Min 12 0.3 0.2 0.1 9.0

Max 350 12 1.6 0.5 125

STD 68 1.9 0.4 0.1 25

n 48 50 30 26 21

RE (%) – 98 99 (62)a 99.7 46

NO3-N [mgl71]

Average 0.05 1.4 1.3 0.7 0.1

Min 0.00 0.02 0.02 0.03 0.00

Max 0.4 6.8 6.9 3.2 1.9

STD 0.1 1.7 1.5 0.8 0.5

n 31 46 29 20 18

NH4-N [mgl 71

]

Average 3.8 0.2 0.1 0.8 4.1

Min 0.4 0.02 0.01 0.01 0.5

Max 9.3 2.0 0.8 4.3 10

STD 5.2 0.9 0.5 1.4 2.9

n 35 36 24 25 21

RE (%) – 94 97 (50)a 79 78c

(continued)

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0.05 and 0.12 mg l71in the MBR and RBC effluent, respectively. These correspond to 99.3% removal (MBR) and 98.5% removal (RBC).

The quality of the RBC and MBR effluent was very high, meeting the ‘excellent-quality’ requirements for urban reuse set by the Israel Ministry of Health (Halprin and Aloni 2003) in terms of BOD (maximum of 10 mgl71) and turbidity (maximum of 2 NTU). However, both systems failed to meet the microbial quality requirements (0 cfu(100 ml)71 faecal coli-forms in 75% of the samples and a maximum of 23 cfu(100 ml)71). The latter suggests that a disinfec-tion stage should be added prior to possible reuse of these effluents.

The stand-alone filter produced effluent which was not suitable for on-site reuse (either for toilet flushing or garden irrigation). This unit succeeded in removing only 65% of the TSS, 46% of the turbidity, 10% of the BOD and 38% of the COD. The results demonstrate that the widely used minimum treatment approach

Table 1. (Continued).

Parameter

Raw greywater

RBC effluent

RBCþsand filter effluent

MBR effluent

Stand-alone sand filter effluent

TKN [mgl71]

Average 9.7 1.2 1.0 1.4 7.5

Min 3.1 0.0 0.2 0.03 2.2

Max 28 3.8 2.5 11 14

STD 5.2 0.9 0.5 2.5 2.9

n 46 48 28 24 20

RE (%) – 88 90 (17)a 86 23

TP (as PO4) [mgl71]

Average 4.7 2.3 2.0 1.3 3.2

Min 1.3 0.3 0.4 0.0 1.4

Max 13 9.5 9.1 3.5 6.8

STD 2.6 1.9 1.5 1.0 1.3

n 49 50 30 25 21

RE (%) – 51 57 (9)a 72 32

pH

Average 7.2 7.7 7.7 7.9 7.3

Min 6.7 6.5 7.4 7.4 7.0

Max 7.9 8.3 8.2 8.5 7.6

STD 0.3 0.4 0.2 0.3 0.2

n 48 49 29 26 21

ECd[dSm71]

Average 1.0 1.0 1.0 1.0 1.0

Min 0.9 0.9 0.9 0.9 0.9

Max 1.1 1.1 1.1 1.2 1.1

STD 0.1 0.1 0.1 0.1 0.1

n 16 16 6 16 6

Faecal coliforms [cfu(100 ml)71]

Geo meane 1.9Eþ5 1.4Eþ3 3.7Eþ2 2.7Eþ1f 8.1Eþ4

Max 2.0Eþ6 1.0Eþ5 4.0Eþ5 2.0Eþ2 6.0Eþ5

Min 9.8Eþ3 1.0Eþ2 8.0Eþ0 51 9.0Eþ3

STD 4.1Eþ5 2.0Eþ4 1.2Eþ5 5.6Eþ1 1.4Eþ5

n 32 25 26 27 21

RE (%) – 99.2 99.8 (73.6)1 99.998 57

RE, removal efficiency.

aValues in brackets are relative removal efficiency (relative to the effluent of preceding treatment step).

bNegative BOD

dremoval is impossible; the calculated value was711%. cNegative ammonia removal may occur due to mineralization of TKN.

dAs none of the systems was capable of removing salinity, removal efficiency of EC was not calculated.

eGeometric mean.

fArithmetic average (in 65% of the observations faecal coliforms were not detected).

Table 2. DRSPC efficiency for several quality character-istics in raw greywater and treated effluent.

Parameter

Raw greywater

RBCþ

sand filter effluent

MBR effluent

Stand-alone sand filter

effluent

TSS 0.072 0.000 0.000 0.031

VSS 0.056 0.014 0.000 0.056

Turbidity 0.116 0.092 0.677 0.005

CODt 0.118 0.000 0.607 0.062

BODt 0.213 0.088 0.552 0.000

CODt/BODt 0.118 0.098 0.216 0.000

TP 0.074 0.000 0.148 0.156

TKN 0.000 0.206 0.376 0.077

NH3 0.135 0.119 0.595 0.000

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does not produce effluent of acceptable quality. Other studies also reported poor performance of stand-alone sand filtration units treating greywater (Al-Jayyousi 2003, Marchet al. 2004).

The RBC and the MBR units removed 90 and 86% of the nitrogen, which corresponds to removal of 8.5 and 8.3 mg l71 respectively, while only 1.3 and 0.7 mgl71 of nitrate was detected in the effluent. This indicates that some of the degraded TKN was assimilated by the biomass and the rest was removed by denitrification that occurred in the inner layers of the biofilm (RBC), or within the bioflocs (MBR). The stand-alone sand filter succeeded in removing only 23% of the TKN.

As discussed above, raw greywater quality dis-played high variability. In order to reveal a possible underlying long-term pattern in this variability and to examine whether this variability affects the perfor-mance of the treatment systems, several statistical methods were employed.

Application of process control methods

The quality of raw greywater not only exhibited high vari-ability, but also reveals that the concentrations of some quality parameters slowly changed with time (Figure 2). TSS and VSS concentrations rose with time and the resultant turbidity showed the same general pattern. Both CODt and BODt concentrations also gradually

increased. On the other hand, the COD/BOD ratio decreased, indicating that as the organic load increased it became more biodegradable. TKN concentrations exhibited the highest variability, but with no clear long-term pattern.

Compared with pollutants concentrations in the inflow, which generally exhibited an upward tendency with time, their concentrations in the RBC effluent slowly decreased (Figure 3). This opposite tendency may indicate that as the load on the RBC became higher, the unit became more efficient in removing

TSS, VSS, CODt and TP. The same is true for

turbidity and BODt, which do not show any

long-term trend although both slowly increased in the raw greywater. TSS and VSS concentrations in the MBR effluent were stable, not showing any long-term trend (Figure 4a,b); the same was true for turbidity (Figure 4c). This was expected as these were removed by the UF membrane and its rejection efficiency should not change with time. COD and BOD concentrations in the MBR effluent decreased with time (Figure 4d,e). This indicates that biodegradation in the MBR became more efficient as the organic load increased. The increase in the COD/BOD ratio (Figure 4f) strengthens the above observation. TP and TKN concentrations (Figure 4g,h) in the MBR effluent did not exhibit any

clear long-term behaviour. The stand-alone sand filter unit did not show any clear long-term pattern.

In order to apply control measures, both on input quality (raw greywater) and on the performance of the treatment units, the SPC control method could be applied. However, it can be seen from Figures 2 – 4 that the control limits of classic static SPC in certain cases are very wide and may not reflect the dynamic nature of the process, and therefore may be non-optimal as control measures. Thus, a modified approach is suggested, dynamic regression SPC (DRSPC), which is an SPC having variable control limits.

DRSPC limits are based on a dynamic average n degree polynomial function that is represented by:

pðxÞ ¼a0þa1xþa2x2þ þan1xn1þanxn

ð4Þ

wherep(x)¼dynamic average of a quality parameter of the wastewater in this study, a0, . . . ,an are

coefficients of the polynomial, n¼degree (n 4 0), andx¼the explanatory variable (in this study: serial number of observation).

The optimal order polynomial is chosen by the minimum variance, which is given by:

s2DRSPC¼

PN1

i¼0 ðtipiÞ2

Nd ð5Þ

where s2

DRSPC ¼variance of DRSPC, ti¼individual

observation i, pi¼polynomial predicted value for

observationi,N¼total number of observations, and d ¼degrees of freedom.

It should be noted that the minimum variance was determined by repeatedly calculating Equation (4), with different polynomial degrees (n) leading to different pi values and degrees of freedom, d. Then,

the variance of each DRSPC polynomial (s2

DRSPC) was calculated and the one with the lowest s2DRSPC was selected.

The modified UCL and LCL can then be described by the following equations:

UCL¼pðxÞ þZ1a

2

½ sDRSPC ð6Þ

LCL¼pðxÞ Z1a

2

½ sDRSPC ð7Þ

wherea¼confidence level, 1a 2

¼confidence inter-val, Z1a

2

½ ¼upper percentage point of the standard normal distribution (in our caseZ1a

2

½ ¼2).

Figures 2–4 show that the DRSPC limits are in many cases closer than those of the classic SPC, as expected for series of data exhibiting a general trend, and the two DRSPC standard deviations suffice for

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tracking the process according to the dynamic mode. In these cases DRSPC better describes the long-term process and thus can serve as a better control and prediction tool.

In order to compare DRSPC and SPC it is proposed to use a comparative beta error (the probability of a statistical test generating a false-positive error: affirming a non-null pattern by chance)

Figure 2. Time series of raw greywater quality. (a) TSS, (b) VSS; (c) turbidity; (d) CODt; (e) BODt; (f) CODt/BODt; (g) TP; (h) TKN. Horizontal dotted lines, classic SPC; inclined dotted lines, dynamic regression SPC.

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of DRSPC to classic SPC. In our case the beta error is more misleading than the alpha error (the prob-ability of a statistical test generating a false-negative

error: failing to assert a defined pattern of deviation from a null pattern in circumstances where the defined pattern exists). This is due to the fact that

Figure 3. Time series of RBC effluent quality. (a) TSS, (b) VSS; (c) turbidity; (d) CODt; (e) BODt; (f) CODt/BODt; (g) TP; (h) TKN. Horizontal dotted lines, classic SPC; inclined dotted lines, dynamic regression SPC.

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having data in the OOC zone and assuming them to be in the IC zone is more disadvantageous than the reverse (data in the IC zone and assuming them to be in the OOC zone). This comparative parameter

will be called DRSPC efficiency, which can be calculated by:

Ef¼1sDRSPC sSPC

ð8Þ

Figure 4. Time series of MBR effluent quality. (a) TSS, (b) VSS; (c) turbidity; (d) CODt; (e) BODt; (f) CODt/BODt; (g) TP; (h) TKN. Horizontal dotted lines, classic SPC; inclined dotted lines, dynamic regression SPC.

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where Ef¼DRSPC efficiency, sDRSPC¼standard

deviation of DRSPC, sSPC ¼standard deviation of

classic SPC.

DRSPC efficiencies for TSS, VSS and TP in the raw greywater entering the treatment plant were 0.072, 0.056 and 0.074 respectively (Table 2); the DRSPC

Figure 5. Constant density curves for VSS and BODt. (a) Input/after RBCþsand filter; (b) input/after MBR; (c) input/after stand-alone sand filter. Both axes are nominal deviations from average values, i.e. each observation is represented as a deviation from the average value for bothxandyaxes (x7x;y7y), and the zero point in each axis represents the average value.

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efficiencies for turbidity, CODtand BODtwere higher

(0.116, 0.118 and 0.213 respectively), while the DRSPC efficiency for TKN was 0.000. Zero efficiency means that the distance between the lower and upper limits of DRSPC is identical to the distance between the lower and the upper limits of classic SPC, while the higher the efficiency the lower the beta error. For example, for influent BODt the DRSPC efficiency is 0.213, which

means that by employing DRSPC and not classic SPC the beta error was reduced by 21.3%. Thus, the higher the DRSPC efficiencies, the better the long-term trends in the inflow and outflow qualities are represented by DRSPC.

Examination of DRSPC efficiencies for the RBCþ sand filter unit effluent (Table 2) shows that for CODt,

TP and TSS, DRSPC does not have any advantage over classic SPC, while for TKN it has the highest advantage (reducing the beta error by over 20%). For the MBR effluent DRSPC has the highest advantage for turbidity and CODt (0.677 and 0.607) and no advantage over

classic SPC for TSS and VSS. For the stand-alone sand filter effluent, the advantage of DRSPC was generally found to be much lower, with a highest value of 0.156 (TP), and all the rest being lower than 0.1. This indicates there were probably no long-term trends in the behaviour of this treatment unit.

Constant density curves

Output (treated effluent)/input (raw greywater) pairs of quality data were plotted. Both axes were presented in the form of nominal deviations from average values, and each observation is accordingly represented as deviation from the average value. Then a constant density curve (ellipse) was plotted, using a method described by Grabov and Ingman (1996), D’Ambrosio (1998), Riberio (2004) and others. The curve is the boundary of the true control region representing a set of points with a given common property (the mutual orthogonality of sample means and standard devia-tions was taken into account in its derivation). To accomplish this, the probability density function for multinomial distribution can be set to be equal to the control parameter. The control parameter was chosen so that 99.73% (+3s) of the total quantity of data (points) would fall within the ellipse. This means that the distance between any data point outside the ellipse and the average is more than 3s. The resultant

equation is the ellipse, with the following

characteristics:

. Its major and minor axes represent deviations from average of the parameters in the x and y axes respectively. Thus, the larger the axis the higher is the deviation of the relevant parameter.

. The slope of the major axis represents the correlation between the variables explored. Thus, the steeper the slope the higher is the correlation between the two variables.

. The direction of this slope shows whether the correlation is positive or negative.

. The process is considered to be IC (in control) for any point falling within the boundary and OOC (out-of-control) for points falling outside the set boundary.

As an example of the capability of this technique, constant density curves for VSS and BODtbefore and

after the three types of treatments are presented in Figure 5. The RBCþsand filter exhibits no

correla-tion between inflow and outflow VSS and BODt

(Figure 5a). This again indicates that the performance of the RBC was not affected by perturbations of the organic load on the system. Further it can be seen that inflow quality variation is much higher than outflow quality variation, as the major axis of the ellipse is much wider (*140 mgl71for both VSS and BODt)

than its minor axis (*20 and *10 mgl71for VSS and BODtrespectively). For the MBR (Figure 5b), no

slope could be identified for VSS and a low negative slope for BODt. Here again the outflow quality

variation was much lower than the inflow one for both VSS and BODt(*30 vs.*140 mg l

71

for VSS and *1.5 vs. 100 mg l71 for BODt). The BODt of

the stand-alone sand filter exhibited positive correla-tion with inflow BODt (Figure 5c), which indicates

again that unit efficiency drops as the organic load rises. The same phenomenon was revealed for phos-phorus (data not shown).

Conclusions

The results show that the quality of the light greywater was far from being suitable for reuse, stressing the need for appropriate treatment. Further, raw greywater quality exhibited very high variability. The removal efficiency of the RBCþsand filter unit was high in respect of turbidity, BOD and TSS, while its removal efficiency of faecal coliforms, although high (99.8%), was not high enough. The MBR was highly efficient in removing turbidity, BOD, TSS and faecal coliforms, producing effluent of excellent quality. COD removal in both systems was lower than BOD removal, indicating that greywater contains some slowly/non-biodegradable organic matter. Both the MBR and the RBC efficiently removed anionic detergents (over 98%). The stand-alone sand filter exhibited much poorer performance.

Raw greywater quality not only exhibited high variability, but the concentrations of many pollutants

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slowly increased with time (TSS, VSS, turbidity, CODt and BODt). In contrast, pollutant

concentra-tions in the RBC effluent slowly decreased. This may indicate that the unit became more efficient as pollutant load increased. The same general behaviour was observed in the MBR effluent, while the stand-alone sand filter unit did not show any clear long-term pattern.

In order to apply control measures on input quality (raw greywater) and on the performance of the treatment units a modified SPC approach was devel-oped – dynamic regression SPC (DRSPC) – which is an SPC with variable control limits. In many cases DRSPC better described the underlying long-term process and thus could serve as a better control and prediction tool. Further, a comparative beta error of DRSPC vs. classic SPC was developed – DRSPC efficiency – where zero efficiency means that both DRSPC and classic SPC limits are identical, while the higher the efficiency the lower the beta error is and the better the underlying long-term trend is represented by DRSPC. For example, DRSPC efficiency for influent BODt was found to be 0.213, which means that by

employing DRSPC (and not classic SPC) the beta error was reduced by 21.3%. Examination of DRSPC efficiencies for the RBC þsand filter effluent showed that for CODt, TP and TSS, DRSPC did not have any

advantage over classic SPC, while for TKN it had the highest advantage (reducing the beta error by over 20%). For the MBR effluent, DRSPC had the highest advantage for turbidity and CODt (0.677 and 0.607).

For the stand-alone sand filter effluent, the advantage of DRSPC was found to be much lower, indicating that probably there were no long-term trends in the performance of this treatment unit.

The use of the density curves technique showed that the RBC þsand filter exhibited no correlation between inflow and outflow VSS and BODt. For the

MBR, no slope (correlation) was identified for VSS and a low negative slope for BODt. The BODtof the

stand-alone sand filter effluent exhibited positive correlation with inflow BOD, indicating again that unit efficiency dropped as the organic load rose.

Acknowledgement

This research was partially supported by The Israeli Water and Wastewater Authority and by The Grand Water Research Institute, Technion.

List of abbreviations

BAF biological aerated filter

BOD biological oxygen demand

COD chemical oxygen demand

DRSPC dynamic regression statistical process control

EC electrical conductivity

HRT hydraulic retention time

IC in-control

LCL lower control limit

MBAS methyl blue active substance (measure of anionic surfactants)

MBR membrane bioreactor

MWCO molecular weight cut-off

OOC out-of-control

RBC rotating biological contactor

SPC statistical process control

TKN total Kjeldahl nitrogen

TP total phosphorus

TSS total suspended solid

UCL upper control limit

UF ultrafiltration

VSS volatile suspended solid

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Figure

Figure 1.General scheme of the experimental greywater pilot plant.

Figure 1.General

scheme of the experimental greywater pilot plant. p.5
Table 1.Quality of raw greywater and treated effluent.

Table 1.Quality

of raw greywater and treated effluent. p.6
Table 2.DRSPC efficiency for several quality character-istics in raw greywater and treated effluent.

Table 2.DRSPC

efficiency for several quality character-istics in raw greywater and treated effluent. p.7
Table 1. (Continued).

Table 1.

(Continued). p.7
Figure 2.Time series of raw greywater quality. (a) TSS, (b) VSS; (c) turbidity; (d) CODt; (e) BODt; (f) CODt/BODt; (g) TP; (h)TKN

Figure 2.Time

series of raw greywater quality. (a) TSS, (b) VSS; (c) turbidity; (d) CODt; (e) BODt; (f) CODt/BODt; (g) TP; (h)TKN p.9
Figure 3.Time series of RBC effluent quality. (a) TSS, (b) VSS; (c) turbidity; (d) CODt; (e) BODt; (f) CODt/BODt; (g) TP; (h)TKN

Figure 3.Time

series of RBC effluent quality. (a) TSS, (b) VSS; (c) turbidity; (d) CODt; (e) BODt; (f) CODt/BODt; (g) TP; (h)TKN p.10
Figure 4.Time series of MBR effluent quality. (a) TSS, (b) VSS; (c) turbidity; (d) CODt; (e) BODt; (f) CODt/BODt; (g) TP; (h)TKN

Figure 4.Time

series of MBR effluent quality. (a) TSS, (b) VSS; (c) turbidity; (d) CODt; (e) BODt; (f) CODt/BODt; (g) TP; (h)TKN p.11
Figure 5.Constant density curves for VSS and BODstand-alone sand filter. Both axes are nominal deviations from average values, i.e

Figure 5.Constant

density curves for VSS and BODstand-alone sand filter. Both axes are nominal deviations from average values, i.e p.12

References

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