Sabino de gisi iwa - girona 2010

Giovanni De Feo 1 , Sabino De Gisi 1, Maurizio Galasso 2

A multicriteria technique for optimizing the management of personnel

of small wastewater treatment plants

Department of Civil Engineering, University of Sale rno, Italy 1

Bierrechimica S.r.l., Fisciano (SA), Settore Ricerc a e Sviluppo2

Framework

Eng. Sabino De Gisi, Department of Civil Engineerin g, University of Salerno, Italy

� Introduction

� Goals

� The wastewater treatment plants under study

� Materials & Methods

� Results & Discussion

� Conclusions

� Activity in Progress

� References

Introduction

Introduction

� Managing wastewater treatment plants involves both operation andmaintenance costs (O&M);

� The operation costs include such items as personnel, energy (typically,electrical energy), fuel, chemicals and sludge disposal;

� The maintenance costs are typically divided into ordinary maintenance(grass and vegetation cutting, machinery greasing, periodical painting,etc.) and extraordinary maintenance costs (replacement of machineryor machinery parts, fundamental interventions on parts of the plant,general reconstruction of masonry structures, etc.);


Introduction

� The adoption of small-scale treatment becomes economically attractive if thecommunities to serve are far away (decentralized systems);

� While, one of the main problems of small wastewater treatment plants (SWWTPs)is the non-existence of a qualified operator as well as the non-existence of thefunds to operate and maintain the plants (Sarikaya et al., 2003);

� In order to solve the operational problems of SWWTPs, generally it is proposedthe joint operation of several plants in the same region by the same contractor orcompany.


Introduction

� Moreover, Wilderer and Schreff (2000) suggested that closecollaboration between university researchers and industrial designers,manufacturers and marketing people is necessary in order to keepresearch and development of novel wastewater treatment methods inline with the actual field requirements;

� As a matter of fact, the present study is the result of a closecollaboration between university researchers and a water andwastewater management company (Alto Calore Servizi S.p.A.) thatmanage several SWWTPs in the same region in Southern Italy.


Goals

The aim of the proposed research was to define and apply a

simple multicriteria technique useful for the optimization of

the management (and, obviously, costs) of personnel of

SWWTPs.


The wastewater treatment plants under study

Materials & Methods


Alto Calore Servizi S.p.A. (ACS) is a public limited companyconstituted of 127 Municipalities from the Districts of Avellino andBenevento, in the Campania region in Southern Italy. ACS worksin the sectors of drinking water supplies, sewerage systems andwastewater treatment plants (Alto Calore Servizi, 2009).

SediAlto Calore Servizi S.p.A.

(Sito Web ACS, 2009)

Materials & Methods


Some informations

i Municipality

This study involved 16municipalities and 31WWTPs correspondingto an equivalentpopulation (PE) ofapproximately 36000.While, 350, 2250 and7000 inhabitants are theminimum, medium andmaximum values of PEserved, respectively.

1, 1.9 and 5 are theminimum, medium andmaximum numbers ofplants managed for eachmunicipality,respectively.

Materials & Methods

As shown, the WWTPs under study are based on a flow chart without primary settlingand with prolonged aeration.


Inlet Wastewater

Bar Screens

Grit Chamber

Denitrification

Oxidation / Nitrification Tank

Secondary Settling Tank

Disinfection

Effluent

Return Activated Sludge

Secondary Activated Sludge

Underflow to Plant Influent

Dewatered Biosolids Flow to Disposal

Drying

Flow chart of the WWTPs under study

Materials & Methods


WWTPs under study

Montella Stratola

Castelvetere sul Calore

Materials & Methods


Montella Baruso WWTPs under study

Nusco Ponteromito

Materials & Methods


Cassano Irpino WWTPs under study

Flumeri località Murge

Materials & Methods


Castel San Giorgio WWTPs under study

Melito Irpino PEEP

Materials & Methods


Melito Irpino WWTPs under study

Pietrastornina località Furno

Materials and Methods

Materials & Methods


Construction of the PE (population equivalent) Load Matrix (PELM)

Normalization of the PE Load Matrix and calucation of the PLI1

Construction of the Plants Load Matrix (PLALM)

Normalization of the Plants Load Matrix and calucation of the PLI

Construction of the Personnel Absolute Matrix (PAM)

2

Calculation of the medium personnel load index (PLI )m

Modification of the PELM and construction of the (PELM ')

Construction of the PE (population equivalent) Load Matrix (PELM ')

Elaboration of the Personnel Absolute Matrix (PAM ')

Modification of the PLALM and construction of the (PLALM ')

Construction of the Plants Load Matrix (PLALM ')

Elaboration of the Personnel Absolute Matrix (PAM '')

2Elaboration of the Personnel Absolute Matrix (PAM )

mCalcolation of the averege Personnel Load Index (PLI ) 2

Flow chart of the proposed procedure

Materials & Methods







2











1

2

3

Equivalent population

Number of plants

Materials & Methods







2











1

2

3

Characterization of the starting (not modified)

scenario

Materials & Methods


N. Municipality Plants PE Manager Assistant Laboratory Executive 1 Bonito 1 1800 0.2000 0.0440 0.0050 0.0040 2 Buonalbergo 3 3000 0.2500 0.1320 0.0160 0.0120 3 Castelvetere 1 1000 0.2000 0.0495 0.0050 0.0045

Table 1. Numbers of plants, PE, manager personnel, assistan t personnel, laboratory personnel and executive personnelfor each municipality involved in the study (Personnel Absolute Matrix, PAM).

The first step of the procedure is to construct a personnel absolute matrix (PAM) with 6 columns(Plants, PE, Manager, Assistant, Laboratory, Executive) and as many rows as the number ofmunicipalities (16 in this case).

The value 0.2000 forthe manager of themunicipality n. 1means that hespends 20% of hisworking time in theWWTP

[1]

N. Municipality Plants PE Manager Assistant Laboratory Executive 1 Bonito 1 1800 0.2000 0.0440 0.0050 0.0040 2 Buonalbergo 3 3000 0.2500 0.1320 0.0160 0.0120 3 Castelvetere 1 1000 0.2000 0.0495 0.0050 0.0045 4 Flumeri 3 1520 0.4000 0.0880 0.0090 0.0080 5 Frigento 5 1880 0.6000 0.1320 0.0140 0.0120 6 Lioni 1 5000 0.4000 0.2200 0.0230 0.0200 7 Melito Irpino 2 1100 0.3000 0.0660 0.0070 0.0060 8 Monteforte Irpino 1 2300 0.0500 0.0660 0.0070 0.0060 9 Montella 1 7000 0.8000 0.1870 0.0200 0.0170 10 Montemiletto 1 3500 0.0500 0.0990 0.0100 0.0090 11 Ospedaletto 1 1000 0.0500 0.0330 0.0040 0.0030 12 Pietrastornina 4 1725 0.2000 0.0660 0.0080 0.0060 13 Salza Irpina 1 350 0.0800 0.0099 0.0010 0.0009 14 Sturno 1 2600 0.2000 0.0990 0.0100 0.0090 15 Summonte 2 900 0.1500 0.0550 0.0060 0.0050 16 Trevico 3 1320 0.4000 0.0660 0.0070 0.0060 Average 1.9 2250 0.2706 0.0883 0.0095 0.0080 Min 1 350 0.0500 0.0099 0.0010 0.0009 Max 5 7000 0.8000 0.2200 0.0230 0.0200 Total 31 35995 4.33 1.4124 0.152 0.1284

A number between 0 and 1 has tobe specified for each of them. Ifthe number is “0”, this meansthat the personnel unit does notwork for the consideredmunicipality. On the contrary, ifthe number is “1”, this meansthat the personnel unit works allthe time (in a year) for theconsidered municipality.

Input Data

Materials & Methods


The second step consists of dividing the PE of each municipality by the corresponding valuesassigned to the four kinds of personnel units, thus obtaining a PE Load Matrix (PELM).

N. Municipality PE PE/Manager PE/Assistant PE/Laboratory PE/Executive 1 Bonito 1800 9000 40909 360000 450000 2 Buonalbergo 3000 12000 22727 187500 250000 3 Castelvetere 1000 5000 20202 200000 222222

Table 2. PE Load Matrix (PELM).

N. Municipality Plants PE Manager Assistant Laboratory Executive 1 Bonito 1 1800 0.2000 0.0440 0.0050 0.0040 2 Buonalbergo 3 3000 0.2500 0.1320 0.0160 0.0120

Table 1. Numbers of plants, PE, manager personnel, assistan t personnel, laboratory personnel and executive personnel for each municipality involved inthe study (Personnel Absolute Matrix, PAM).

PE

Kind of personnel

[2]

N. Municipality PE PE/Manager PE/Assistant PE/Laboratory PE/Executive 1 Bonito 1800 9000 40909 360000 450000 2 Buonalbergo 3000 12000 22727 187500 250000 3 Castelvetere 1000 5000 20202 200000 222222 4 Flumeri 1520 3800 17273 168889 190000 5 Frigento 1880 3133 14242 134286 156667 6 Lioni 5000 12500 22727 217391 250000 7 Melito Irpino 1100 3667 16667 157143 183333 8 Monteforte Irpino 2300 46000 34848 328571 383333 9 Montella 7000 8750 37433 350000 411765 10 Montemiletto 3500 70000 35354 350000 388889 11 Ospedaletto 1000 20000 30303 250000 333333 12 Pietrastornina 1725 8625 26136 215625 287500 13 Salza Irpina 350 4375 35354 350000 388889 14 Sturno 2600 13000 26263 260000 288889 15 Summonte 900 6000 16364 150000 180000 16 Trevico 1320 3300 20000 188571 220000 Average 2250 14322 26050 241749 286551 Min 350 3133 14242 134286 156667 Max 7000 70000 40909 360000 450000

Table 2. PE Load Matrix (PELM).

Materials & Methods

The third step is to normalize the PELM as well as calculate the Personnel Load Index 1 (PLI1).


Table 3. Normalized PELM and calculation of the Personnel Lo ad Index 1 (PLI1).

N. Municipality PE PE/Manager PE/Assistant PE/Laboratory PE/Executive PLI1 5 Frigento 1880 -0.17 -0.44 -0.48 -0.44 -1.53 15 Summonte 900 -0.12 -0.36 -0.41 -0.36 -1.26 7 Melito Irpino 1100 -0.16 -0.35 -0.37 -0.35 -1.24

minmax−−= µx

x∑

=

=4

11

iixPLI

10 Montemiletto 3500 0.83 0.35 0.48 0.35 2.01 Average 2250 0.00 0.00 0.00 0.00 0.00 Min 350 -0.17 -0.44 -0.48 -0.44 -1.53 Max 7000 0.83 0.56 0.52 0.56 2.01

[3]

The PLI1 for themunicipality number “i”(PLI1,i) is obtained byadding up the valuescorresponding to the fourpersonnel units taken intoaccount

The lowest values indicate the municipalities where the personnel do not work enough in terms ofPE managed.

On the other hand, the highest values indicate the municipalities where the personnel work toomuch (compared to the other municipalities).

The PLI1 indicates the personnel load in terms of equivalent polulation managed (PE).

Materials & Methods


The fourth step consists of dividing the number of plants of each municipality by the correspondingvalues assigned to the four kinds of personnel units, thus obtaining a Plants Load Matrix (PLALM).

N. Municipality Plants PE Manager Assistant Laboratory Executive 1 Bonito 1 1800 0.2000 0.0440 0.0050 0.0040 2 Buonalbergo 3 3000 0.2500 0.1320 0.0160 0.0120 Table 1. Numbers of plants, PE, manager personnel, assistan t personnel, laboratory personnel and executive personnel for each municipality involved in

the study (Personnel Absolute Matrix, PAM).

Number of plants

Kind of personnel

[4]

N. Municipality Plants Plants/Manager Plants/Assistant Plants/Laboratory Plants/Executive 1 Bonito 1 5.0 22.7 200.0 250.0 2 Buonalbergo 3 12.0 22.7 187.5 250.0 3 Castelvetere 1 5.0 20.2 200.0 222.2 Table 4. Plants Load Matrix (PLALM).

N. Municipality Plants Plants/Manager Plants/Assistant Plants/Laboratory Plants/Executive 1 Bonito 1 5.0 22.7 200.0 250.0 2 Buonalbergo 3 12.0 22.7 187.5 250.0 3 Castelvetere 1 5.0 20.2 200.0 222.2 4 Flumeri 3 7.5 34.1 333.3 375.0 5 Frigento 5 8.3 37.9 357.1 416.7 6 Lioni 1 2.5 4.5 43.5 50.0 7 Melito Irpino 2 6.7 30.3 285.7 333.3 8 Monteforte Irpino 1 20.0 15.2 142.9 166.7 9 Montella 1 1.3 5.3 50.0 58.8 10 Montemiletto 1 20.0 10.1 100.0 111.1 11 Ospedaletto 1 20.0 30.3 250.0 333.3 12 Pietrastornina 4 20.0 60.6 500.0 666.7 13 Salza Irpina 1 12.5 101.0 1000.0 1111.1 14 Sturno 1 5.0 10.1 100.0 111.1 15 Summonte 2 13.3 36.4 333.3 400.0 16 Trevico 3 7.5 45.5 428.6 500.0 Average 1.9 10.4 30.4 282.0 334.8 Min 1.0 1.3 4.5 43.5 50.0 Max 5.0 20.0 101.0 1000.0 1111.1

Table 4. Plants Load Matrix (PLALM).

Materials & Methods


minmax−−= µx

x

[5]

The PLI2 is obtainedby adding up thevalues correspondingto the four personnelunits taken intoaccount.

The fifth step is to normalize the PLALM as well as calculate the Personnel Load Index 2 (PLI2).

N. Municipality Plants Plants/Manager Plants/Assistant Plants/Laboratory Plants/Executive PLI2 9 Montella 1 -0.49 -0.26 -0.24 -0.26 -1.25 6 Lioni 1 -0.42 -0.27 -0.25 -0.27 -1.21 14 Sturno 1 -0.29 -0.21 -0.19 -0.21 -0.90

13 Salza Irpina 1 0.11 0.73 0.75 0.73 2.33 Average 1.9 0.00 0.00 0.00 0.00 0.00 Min 1.0 -0.49 -0.27 -0.25 -0.27 -1.25 Max 5.0 0.51 0.73 0.75 0.73 2.33

∑=

=4

12

iixPLI

Table 5. Normalized PLALM and calculation of the Personnel L oad Index 2 (PLI2).

The lowest values therefore indicate the municipalities where the personnel do not work enough interms of number of plants managed.

The highest values indicate the municipalities where the personnel work too much (compared tothe other municipalities).

The PLI2 indicates the personnel load in terms of number of plants managed.

Materials & Methods


The sixth step is to calculate for each municipality the medium Personnel Load Index (PLIm)obtained by taking the average values of the corresponding PLI1 and PLI2. The municipality can belisted in an increasing order in terms of PLIm,i.

[6]

N. Municipality P.E. Plants PLI1 PLI2 PLIm 7 Lioni 5000 1 -0.38 -1.21 -0.80 8 Melito Irpino 1100 2 -1.24 -0.20 -0.72 6 Frigento 1880 5 -1.53 0.12 -0.70

221 PLIPLI

PLI m

+=

Table 6. Calculation of the medium Personnel Load Index (PLIm).

The lowest values therefore indicate the municipalities where the personnel do not work enough.

The highest values indicate the municipalities where the personnel work too much (compared tothe other municipalities).

The PLIm indicates the average personnel load in terms of equivalent population and number ofplants managed.

Materials & Methods







2










The seventh step of the procedure consists ofmodifying the PELM (called PELM’) bysubstituting the values in the four columnsgreater than the medium plus standarddeviation (µ+σ) and lesser than the mediumminus standard deviation (µ−σ) with thecorresponding values of (µ+σ) and (µ−σ),respectively.

[7]

Materials & Methods







2










[8]

The eighth step is to obtain a firstmodification of the PAM (called PAM’) bychanging the values of its elements in linewith the PELM’.

Materials & Methods







2










[9]

The ninth step of the procedure consists ofmodifying the PLALM (called PLALM’) bysubstituting the values in the four columnsgreater than the medium plus standarddeviation (µ+σ) and lesser than the mediumminus standard deviation (µ−σ) with thecorresponding values of (µ+σ) and (µ−σ),respectively.

Materials & Methods







2










[10]

The tenth step is to obtain a secondmodification of the PAM (called PAM’’) bychanging the values of its elements in linewith the PLALM’.

Materials & Methods







2










[11]

The eleventh step is to obtain a new PAM(called PAM2) as the medium of PAM’ andPAM’’. The values of PAM2 can be used incase to modify the time that each personnelunit spends on the management of theplants of the considered municipality.

Materials & Methods







2









mCalcolation of the averege Personnel Load Index (PLI ) 2[12]

The twelfth and last step is to recalculatethe PLIm (called PLIm,2) and compare thenew values with those calculated during thestep number 6.

Results and Discussions

Results & Discussion


-2.60-2.40-2.20-2.00-1.80-1.60-1.40-1.20-1.00-0.80-0.60-0.40-0.200.000.200.400.600.801.001.201.401.601.802.002.202.402.60

Indi

ce d

i Car

ico

del P

erso

nale

Per

sonn

el lo

ad in

dex

1

Do not work enough

Work too much

Criteria 1 - Equivalent population

+

-



-2.60-2.40-2.20-2.00-1.80-1.60-1.40-1.20-1.00-0.80-0.60-0.40-0.200.000.200.400.600.801.001.201.401.601.802.002.202.402.60

Indi

ce d

i Car

ico

del P

erso

nale

Per

sonn

el lo

ad in

dex

1

The municipalities with thelowest values of PLI 1,i havemore than one plants . While,those municipalities with thehighest values of PLI 1,i haveonly one plant .

Criteria 1 - Equivalent population

Work too much +

- Do not work enough



Per

sonn

el lo

ad in

dex

1

-2.60-2.40-2.20-2.00-1.80-1.60-1.40-1.20-1.00-0.80-0.60-0.40-0.200.000.200.400.600.801.001.201.401.601.802.002.202.402.60

Indi

ce d

i Car

ico

del P

erso

nale

2P

erso

nnel

load

inde

x 2

Criteria 2 – Number of plants managed

Work too much +




Per

sonn

el lo

ad in

dex

1

-2.60-2.40-2.20-2.00-1.80-1.60-1.40-1.20-1.00-0.80-0.60-0.40-0.200.000.200.400.600.801.001.201.401.601.802.002.202.402.60

Indi

ce d

i Car

ico

del P

erso

nale

2P

erso

nnel

load

inde

x 2

The municipalities with the lowest valuesof PLI 2,i have only one plant . While, thosemunicipalities with the highest values ofPLI2,i have more than one plant , exceptedmunicipality number 11 and 13.

Criteria 2 – Number of plants managed

Work too much +




Ave

rage

Per

sonn

el L

oad

Inde

xPLIm

Work too much +




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B efore After

Work too much +




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DA

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PLI m

B efore After

Work too much +

-The difference between the maximum and minimum greatly decreased passing from PLIm(1.68+0.80=2.48) to PLIm,2 (0,89+0.71=1.50);

This suggests that the personnel load both in terms of PE and numbers on plants managed aredistributed better for the four kinds of personnel units after the application of the procedureproposed.

Do not work enough



The municipalities 7, 10 and 12 represent those where the personnel unit don’t work, incomparison with the municipalities number 9, 11 and 12.

This result in obtained both in terms of PE and numbers on plants managed.



7

10

00

i Plants Manager 1

i

i

i

i

i

i

i

i

2

18

8

4

1

9

11

14

3

1913

16

12

5 17

6

15

Before

Plants Manager 2

Plants Manager 3

Plants Manager 4

Plants Manager 5

Plants Manager 6

Plants Manager 7

Plants Manager 8

Plants Manager 9



00

2

18

8

4

1

9

11

14

3

1913

16

12

5 17

6

15

7

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After

i Plants Manager 1

i

i

i

i

i

i

i

i

Plants Manager 2

Plants Manager 3

Plants Manager 4

Plants Manager 5

Plants Manager 6

Plants Manager 7

Plants Manager 8

Plants Manager 9

Conclusions

Conclusions

The following general outcomes can be stated:

� the PE Load Matrix (PELM) is a useful instrument to evaluate the specificwork load of the personnel in terms of equivalent population managed;

� the Plants Load Matrix (PLALM) is a useful instrument to evaluate thespecific work load of the personnel in terms of numbers of plants managed;

� the Personnel Load Index 1 (PLI1) is a useful indicator to compare thespecific work load of the personnel in terms of equivalent populationmanaged;

� the Personnel Load Index 2 (PLI2) is a useful indicator to compare thespecific work load of the personnel in terms of number of plants managed;

� the medium Personnel Load Index 2 (PLIm) is a useful indicator to comparethe specific work load of the personnel both in terms of PE and number ofplants managed;


Conclusions

� the proposed procedure was able to redeploy the workforce (plantsmanagers, plants manager assistants, laboratory technicians andexecutives) both in terms of equivalent population and numbers of plantsmanaged;

� the procedure is particularly affordable for plants managers and plantsmanager assistants, while, it is less recommended for laboratorytechnicians and executives due to the difficulty in evaluating the percentageof the time that they expend for the considered municipality;

The following general outcomes can be stated:


Activity in Progress


� The proposed procedure could be improved adding other criteria estimatingthe work effort in the considered municipality, such as the number ofpumping stations, the number of extraordinary maintenance interventions,etc.; and

� finally, adding other criteria it could be useful to introduce a weightingtechnique (and a corresponding sensitivity analysis).

References

� Alto Calore Servizi (2009). Alto Calore Servizi S.p.A. Chi siamo (Who we are).http://www.altocalore.it/chisiamo.asp (accessed 4 november 2009).

� Bode H. and Grünebaum T. (2000). The cost of municipal sewage treatment – structure, origin,minimization – methods of fair cost comparison and allocation. Water, Science and Technology41(9), 289–298.

� García J., Mujeriegoa R., Obisb J. M. and Boub J. (2001). Wastewater treatment for smallcommunities in Catalonia (Mediterranean region). Water Policy, 3, 341–350.

� Masotti L. and Verlicchi P. (2005). Depurazione delle acque di piccole comunità. Tecnichenaturali e tecniche impiantistiche (Treatment of wastewater of small community. Natural andplant-engineering techniques). Hoepli, Milano, Italy.

� Rowland I. and Strongman R. (2000). Southern Water faces the small works challenge. WaterScience and Technology, 41(1), 33–39.

� Sarikaya H.Z., Sevimli M.F., Koyuncu I. and Yuksel E.(2003). Joint operation of smallwastewater treatment plants in southern Turkey. Water Science and Technology, 48(11–12), 69–76.

� Tchobanoglous G., Burton F.L, and Stensel H.D. (2003). Wastewater engineering: treatment andreuse / Metcalf & Eddy, Inc. – 4th ed., McGraw-Hill, New York.

� Wilderer P.A. and Schreff D. (2000). Decentralized and centralized wastewater management: achallenge for technology developers. Water Science and Technology, 41(1), 1–8.


Thank you for your

attention!G. De Feo, Sabino De Gisi, M. GalassoUniversity of Salerno (ITALY)

Department of Civil Engineering

Sabino de gisi iwa - girona 2010

Technology

Transcript of Sabino de gisi iwa - girona 2010