Oct 19, 2015 - using Multi Criteria Decision Analysis (MCDA): A Case Study of Mysore Campus (cluster of office buildings) of Infosys Limited, India. Prepared ...
Evaluation of Sewage Sludge Treatment Techniques using Multi Criteria Decision Analysis (MCDA): A Case Study of Mysore Campus (cluster of office buildings) of Infosys Limited, India Prepared by: Moni Mohan Mondal, M.Sc. Institute of Sanitary Engineering and Waste Management (ISAH) Leibniz Universität Hannover, Germany. Supervised by: PD Dr.-Ing. habil. Dirk Weichgrebe Head, Division of Waste Management Institute of Sanitary Engineering and Waste Management (ISAH) Leibniz Universität Hannover, Germany. Awarded for (Second Position): German Water Partnership (GWP) - Award India 2015 for Young Water Professionals
3rd Indian-GWP Day 2015, Kochi, Kerala, India
19 October, 2015
Contents of the Presentation Study background and problem identification Material and methods Investigation of sewage sludge management alternatives Solar sludge drying greenhouse Anaerobic sludge digestion Sludge composting with green waste
Evaluation of sludge management alternatives Application of MCDA tools o ELECTRE; and o Compromise Programming
Evaluation of results
Alternative recommendation
Study Background and Problem Identification Infosys Limited: A leading IT industry in India Services: IT, business consulting and outsourcing 11 campuses (cluster of commercial office buildings) Water use by its employee for all potable & non-potable needs (total 160,000 Employees)
Study Focus: Mysore campus, Infosys 110 commercial office buildings 3 STPs (Activated sludge) - (30,000 PE) Sludge generation: 4 ton per day
Problems with sludge handling Open bed drying (odor, emission, aesthetic) Reuse or disposal (after unregulated treatment)
Materials and Methods Specification of Value System Climatic variables (Temp., Rainfall, Solar rad., Humidity, Air density, Wind velocity) State variables (sludge amount, water content, VS, C/N ratio, density, pH, COD, BOD5) Control variables (ventilation rate, air mixing rate)
Process Indicators and Analytics for Design Solar sludge drying greenhouse Anaerobic sludge digestion Sludge composting with green waste
MCDA Evaluation Tools ELECTRE Compromise Programming
Assessment of the Status Quo Diagnosis
Sludge Management Alternatives Evaluation and Ranking of Sludge Management Alternatives
Preferable Sludge Management Technique for Mysore Campus, Infosys
Design
Evaluation
Recomme ndation
Sludge characteristics data for Mysore Campus SL 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Parameters Water Content Dry solid contents (DSC) Organic content (% of DSC) Inorganic (Ash) content (% of DSC) BOD5 COD pH-Value Density Carbon Nitrogen Phosphorous (as P2O5) Potassium (as K2O) Magnesium (as MgO) Calcium (as CaO)
Test results 80 20 79 21 15873 76190 7.03 1005 24 38.4 36.5 4.2 9.7 73.7
Unit (%) (%) (%) (%) mg/l mg/l Kg/m3 % g/kg DS g/kg DS g/kg DS g/kg DS g/kg DS
Investigation and Design of Sludge Management Alternatives for the Context of Mysore Campus
Alternative 1: Solar Sludge Drying Greenhouse Alternative 2: Anaerobic Sludge Digestion Alternative 3: Sludge Composting With Green Waste Alternative 4: Existing Open Bed Sludge Drying
Alternative 1: Solar Sludge Drying Greenhouse Exhaust Ventilator Air Mixing Fans Controlled Air Inlet
Sludge Turner
Source: Huber Technology, 2012
Key Design Requirements - Evaporation rate - Area needed - Energy consumption With respect to sludge state and local climatic conditions
Evaporation Rate: Solar Sludge Drying Greenhouse Months
Qv 3 (m /m2h)
Qm 3 (m /m2h)
To (°C)
Ro (W/m2)
40 40 40 40 40
DSC (kg solids/ kg sludge) 0.2 0.2 0.2 0.2 0.2
January February March April May
80 80 80 80 80
June July August September October November December
189.21 195.39 234.14 230.09 211.61
Air density, ρ (kg/m3) 1.19 1.14 1.05 1.04 1.05
Evaporation rate , E(kg/m2d) 8.27 8.60 8.89 9.09 8.54
22.48 24.50 25.90 26.91 25.70
80
40
0.2
24.36
201.39
1.09
8.29
80 80 80 80 80 80
40 40 40 40 40 40
0.2 0.2 0.2 0.2 0.2 0.2
22.66 23.37 23.04 23.27 22.96 22.02
161.20 172.75 183.89 171.17 195.21 195.83
1.18 1.18 1.18 1.18 1.18 1.19
7.86 8.22 8.30 8.18 8.48 8.23
Qv = The ventilation rate (m3 [air]/m2h), Qm = Air mixing rate (m3 [air]/(m2h), To = Outdoor temperature (°C), Ro = Outdoor solar radiation (W/m2), σ = DSC, Dry solid content, (kg[solids]/ kg[sludge]), ρ = Air density (kg/m3)
Example calculation for May: Solar Sludge Drying Greenhouse Changes of moisture contents and occurring drying rates sludge drying rate
0,90
9,0
0,80
8,0
0,70
7,0
0,60
6,0 Sludge Dry Solid content 80% Drying time -8.2 days
0,50 0,40
5,0 4,0
0,30
3,0
0,20
2,0
0,10
Warming-up period
0,00 0
1
2
Constant drying period
3
4
1,0
Falling drying period
5
Drying time (Days)
6
7
0,0 8
9
10
Drying rate (kg water/m2d)
Moisture content (kg water/kg sludge)
sludge moisture content
Drying performances: Solar Sludge Drying Greenhouse Months
January February March April May June July August September October November December Total
Sludge Average Input sludge drying (ton) rate (kg/m2.d) 124 5.08 112 6.52 124 6.85 120 6.85 124 5.72 120 5.19 124 4.20 124 5.19 120 4.59 124 4.59 120 5.20 124 5.08 1460
Water DS removed Stabilization (ton) (ton) 93.08 2.98 84.08 2.46 93.08 2.48 90.08 2.40 93.08 3.22 90.08 3.36 93.08 3.72 93.08 3.47 90.08 3.48 93.08 3.60 90.08 3.12 93.08 2.98 1096 37
Sludge output (ton) 28 25 28 28 28 27 27 27 26 27 27 28 327
Mass reduction (%) 77 77 77 77 78 78 78 78 78 78 78 77 78
Alternative 2: Anaerobic Sludge Digestion Estimation of Anaerobic Digester Design Parameters Sl
Parameters
1 Organic loading rate (OLR)
Design Unit
Unit
Amount
(Assumed)
Kg VS/m3.d
2
2 Digester volume (DV)
= VS Load/OLR = 632(kg VS/d)/2 (kg VS/m3.d)
m3
316
3 Solids retention time (SRT)
= Digester volume/sludge flow = 316 m3/ 19.61 (m3/d)
day
16
%
57
4 Volatile solids destroyed (VSd) = 13.7*ln (SRT) + 18.9
5 Biogas production (Biogas yield= 1.1 m3/kg VSd)
= OLR*Biogas yield* DV* VSd = 2 (kg VS/m3.d)*1.1 m3/kg VS*316 m3* 0.57
m3/day
417
6 Methane production
= 417 m3/d * 65% of biogas
m3/day
271
7 Heat energy recovery
= biogas yield * heating value = 417 (m3/d)/22.4 (MJ/m3)
GJ/year
3409.4
8 Heating requirement
= heating raw sludge+heat loss = 218.4 MJ/d + 152 MJ/d
GJ/year
135.2
9 Biosolids production
= sludge input (m3/d)* 25kg/m3
Ton/year
179
(heating value biogas=22.4 MJ/m3)
Sludge production: 4000 kg/d, Sludge flow: 19.61m3/d, Sludge DSC: 20% (800kg/d), Volatile Solids (% DSC): 79 % (632kg/d)
Mass Balance: Anaerobic Sludge Digestion Process After Digestion CH4, CO2, H2O
Before Digestion 800 kg/d Raw sludge (Dry Weight) Digestion converts volatiles to CH4, CO2, and H2O
380 kg/d 79% Volatile
21% Fixed
632 kg/d
168 kg/d
Remaining Digested sludge 252 kg/d
57% Volatile
168 kg/d
43% Fixed
Alternative 3: Sludge Composting with Green Waste Estimation of Sludge Composting Parameters 1
2
Sludge characteristics Amount Unit Sludge production 4 ton/day Solids concentration 20 % Nitrogen content 2.5 % Carbon content 24 % Bulking agent (green waste) characteristics Solids concentration 65 % Nitrogen content 1 % Carbon content 55 %
Parameters Weight, Input for IR Weight, Input for IR Water content C/N ratio DM oDM mDM
Unit Ton/d ton/a % ton/a ton/a ton/a
Typical C/N ratio for composting = 26-31 Assume ‘1’ part sludge with ‘Y’ part bulking agent
If Mixture C/N ratio is 30 = Y = 2.04 Required Bulking Materials = 4 t/d * 2.04 = 8 t/d
Sewage sludge 4 1,460 80 9.6 292 230.68 61.32
Bio-waste 8 2,920 35 55 1,898 1,328.6 569.4
total 12 4,380 50 30 2,190 1,559.28 654.08
Results – all Alternatives: Cost-Benefit Analysis The capital costs are annualized using the capital recovery factor CFACR (i,n) = (annuity factor) CFACR, considering project life (n) and interest rate (i) Alternatives Alternative 1
Alternative 2
Alternative 3
Components Annualized capital costs Operating or running costs Revenues from dried biosolids Net costs Annualized capital costs Operating or running costs Revenues from biosolids and biogas Net costs Annualized capital costs Operating or running costs Revenues from composts Net costs
(+) (+) (-) (+) (+) (-) (+) (+) (-)
Euro/a 20,516 45,831 8,175 58,173 127,713 286,160 38,569 375,304 25,543 657,000 61,320 621,223
INR/a 1,600,248 3,574,818 637,650 4,537,494 9,961,614 22,320,480 3,008,382 29,273,712 1,992,354 51,246,000 4,782,960 48,455,394
Summary : Costing, Space Requirements and Bio-solids Production Alternatives Alternative-1 Alternative-2 Alternative-3 Alternative-4 (existing)
Cost (€1000/a) 58 375 621 42
Land area requirement (100 m2) 5.7 1.5 18 8.5
Reuse of end products (100 ton/a) 3.3 1.8 22 0.5
Evaluation of Sludge Management Alternatives using MCDA Tools: ELECTRE; and Compromise Programming
Methods applied: Multi‐Criteria Decision Analysis (MCDA) I. ELECTRE (Elemination Et Choix Traduisant la Realité) ELECTRE is an outranking method, Uses the dominance principle to compare project alternatives: Concordance principle
Discordance principle
Where, k-decision variables; αk -weights of the decision variables k; nik -performance value of criterion k for alternative i; njk -performance value of criterion k for alternative j.
Methods applied: Multi‐Criteria Decision Analysis (MCDA) II. Compromise Programming Compromise Programming - distance based method Determines a distance of the alternatives to an ideal point. The smaller the distance, the better the alternative is rated.
Where, di -distance of alternative i to the ideal point; α -weights of the decision variables k; nik -performance value of criterion k for alternative i, and p -compensation factor.
List of Alternatives and Decision Variables Alternatives investigated
Decision variables considered
A-1 Solar sludge drying greenhouse
k-1 Cost
A-2 Anaerobic sludge digestion
k-2 Land area requirement
A-3 Sludge composting with green waste
k-3 User friendliness
A-4 Sludge drying in open bed (existing)
k-4 Environmental Impacts
k-5 Reuse of end products
Results - all alternatives: Numerical values of the decision variables Alternatives
Cost, k1, (€1000/a)
Land area requirement , k2 (100 m2)
User friendly , k3, (0-10)
Environmental Impacts, k4, (0-10)
Reuse of end products, k5, (100 ton/a)
A- 1 A- 2 A -3 A -4
58 375 621 42
5.7 1.5 18 8.5
7 4 5 8
2 5 8 10
3.3 1.8 22 0.5
k-1, k-2 & k-5 : Design calculation results; k-3 & k-4 : Assumption of the consequences
Normalization of the Variables and Weight Assignment k- 1 (€1000)
k- 2 (100 m2)
k- 3 (0-10)
k- 4 (0-10)
k-5 (100 ton/a)
0
0
10
0
25
Critical value
700
20
0
10
0
Alternative-1
Performance values (0-1) of decision variables 0.9 0.7 0.7 0.8
0.2
Alternative-2
0.5
0.9
0.4
0.5
0.1
Alternative-3
0.1
0.1
0.5
0.2
0.9
Alternative-4
0.9
0.5
0.8
0.0
0.02
Weights
0.4
0.2
0.15
0.15
0.1
Ideal value
Explanation: for example, Alternative-1 against k-1 (cost) = (critical - numerical value)/(critical –ideal value) = (700-58)/(700-0) = 0.9
Evaluation Result: with ELECTRE (from performance values) Concordance Matrix Alternative-1 Alternative-2 Alternative-3 Alternative-4
Discordance Matrix Alternative-1 Alternative-2 Alternative-3 Alternative-4
Alternative-1
Alternative-2
Alternative-3
Alternative-4
x 0.2 0.1 0.35
0.8 x 0.25 0.55
0.9 0.75 x 0.75
0.65 0.45 0.25 x
Alternative-1 x 0.4 0.8 0.8
Alternative-2 0.2 x 0.8 0.5
Alternative-3 0.7 0.8 x 0.88
Alternative-4 0.1 0.4 0.8 x
i > j if Cij ≥ Cthreshold and dij ≤ dthreshold
Cthreshold = 0.5;
dthreshold = 0.6
Results: Alternative-1 > Alternative-2; Alternative-1 > Alternative-4 Alternative-4 > Alternative-2;
Ranking of alternatives,
Alternative-1 > Alternative-4 > Alternative-2 > Alternative-3
Evaluation Result: with Compromise Programming (calculated from performance values table) Alternatives Alternative-1 Alternative-2 Alternative-3 Alternative-4 Weight
K-1 0.9 0.5 0.1 0.9 0.4
K-2 0.7 0.9 0.1 0.5 0.2
K-3 0.7 0.4 0.5 0.8 0.15
K-4 0.8 0.5 0.2 0.0 0.15
K-5 0.2 0.1 0.9 0.02 0.1
dci (p=3) dci (p=6) 0.3963 0.5459 0.5585 0.6400 0.8108 0.8436 0.6469 0.7893
Compensation factor, p: usually between 2 and 10 (here chosen as 3 and 6) The larger the p, the more a very bad performance value influences the result.
The best alternative is the one with the smallest distance dc to the ideal point
Ranking, Alternative 1 > Alternative 2 > Alternative 4 > Alternative 3 Most Preferable alternative Alternative 1 (Solar Sludge Drying Greenhouse) is the best solution (ELECTRE and Compromise Programming, with chosen boundary values).
Alternative Recommendation: Sludge Drying Greenhouse Selection Incentives: Tropical climate in India (less fluctuation of temperature and solar radiation) Less expensive compare to other techniques (around 95% of total energy from Solar radiation) Dried biosolids constitute potential organic fertilizer Environment friendly (only 24 kg CO2/ton of evaporated water, from thermal drying-170 kg CO2/t) Water removed through evaporation: 1096 t/a STP: Mysore Campus, Infosys
Sewage sludge input Raw sludge : 4 t/d Raw sludge : 1460 t/a Sludge DSC : 20% Sludge DSC : 292 t/a
Solar drying halls: 1 Drying area : 554
m2
Length
: 46 m
Width
: 12 m
Max evaporation : 3070.65 kg/m2a Average drying rate: 1978.3 kg/m2a
Average drying period/batch: 9 d
Sludge Input: 1460 t/a Water evaporated: 1096 t/a Dry solid Stabilization: 37 t/a
Sludge output: 327 t/a
Sewage sludge output Sludge output: 327 t/a Output DSC: 80% Output DSC: 262 t/a
Thank You All
Objectives of the Study Overall objective To Identify an appropriate sewage sludge management concept for the STPs of Infosys. Specific targets To investigate and design sewage sludge management alternatives; To evaluate sludge management techniques using analytical tool (MCDA); and To recommend the appropriate technique.
General Overview of the STP Location (Mysore Campus, Infosys) Study Area: Infosys Ltd., Mysore Campus Karnataka, India
Boundary Conditions
New sewage treatment works are built- sludge production is increasing Environmental quality standards become more stringent. Still, sludge are disposed off on agricultural lands for irrigation or manure purposes without treatment. Current sludge management in Infosys: drying with unsafe and unregulated open bed.
Infosys identifies disruption of sludge management as a key operational risk.
Working Principles: Solar Sludge Drying Greenhouse Driving force: difference of partial vapor pressure between sludge and ambient air Naturally: water vapor is lighter than dry air
The warmer the air more water vapor can be transported Source: IST, 2010
Monthly outdoor temperature and solar radiation in Mysore Max
Avg
Min
30 25 20 15 10
solar radiation (W/m2)
Temperature (0C)
35
240 220 200 180 160 140 120 100
Estimation of evaporation rate: Multiplicative Model [Developed by Seginer and Bux, 2006]
Multiplicative model for the prediction of evaporation rate[kg/m2h] is based on vapor balance equation:
Where, the humidity ratio difference (Δw) is modeled as a product of power factors, one for each of five predictors (Ro, To, Qv, Qm, σ), as follows:
Qv = The ventilation rate (m3 [air]/m2h), Qm = Air mixing rate (m3 [air]/(m2h), To = Outdoor temperature (°C), Ro = Outdoor solar radiation (W/m2),, σ = DSC, Dry solid content, (kg[solids]/ kg[sludge]), ρ = Air density (kg/m3)
Effect of Ventilation Rate and Air Mixing Rate on DSC Different values of Qv (10, 50, 80, 100 and 150 m3/m2h) and Qm (0, 40, 90 and 150 m3/m2h) are substituted into the equation. The values for other parameters of the model were set up as constants. The higher evaporation rate E is obtained for higher value for Qv & Qm 0,50
0,50 0,45 0,40
Evaporation Rate (kg water/m2.h)
Qv=10 (m3/m2.h) Qv=50 (m3/m2.h) Qv=80 (m3/m2.h) Qv=100 (m3/m2.h) Qv=150 (m3/m2.h)
0,55
0,35
0,30 0,25 0,20 0,15 0,10
0,45
Qm=0 (m3/m2.h) 0,40
Qm=40 (m3/m2.h) Qm=90 (m3/m2.h) Qm=150 (m3/m2.h)
0,35
0,30 0,25
0,05 0,00
Sludge DSC (Kg solids/Kg sludge)
0,00 0,05 0,10 0,15 0,20 0,25 0,30 0,35 0,40 0,45 0,50 0,55 0,60 0,65 0,70 0,75 0,80 0,85 0,90 0,95 1,00
0,85
0,80
0,75
0,70
0,65
0,60
0,55
0,50
0,45
0,40
0,35
0,30
0,25
0,20
0,15
0,10
0,05
0,20 0,00
Evaporation Rate (kg water/m2.h)
0,60
Sludge Soild Content (Kg solids/Kg sludge)
Air drying principle: drying rate of porous materials is not linear or constant. Stages of drying rate: warming-up period, constant rate, and falling rate of drying.
Drying rate, (kg/m2d)
Air Drying Rates and Period: Batch Drying System
Constant drying rate Falling rate
Time (d)
Estimation of monthly drying period: Batch drying Months
January February March April May June July August September October November December Average
Evaporation Warming up Constant drying Falling drying rate period period (days) period (days) (kg water/ m2d) (days) 8.27 8.60 8.89 9.09 8.54 8.29 7.86 8.22 8.30 8.18 8.48 8.23 8.41
0.4 0.3 0.2 0.2 0.4 0.6 0.8 0.6 0.7 0.7 0.6 0.5 0.5
4.65 4.47 4.33 4.23 4.51 4.64 4.90 4.68 4.64 4.71 4.54 4.68 4.58
2.54 2.36 1.90 1.58 4.00 4.82 5.61 4.35 5.26 5.50 3.85 2.94 3.73
Drying time per batch (days) 8 7 6.5 6.5 8.5 9.5 11 10 10.5 10.5 9 8 9
Monthly Drying Period (one complete drying cycle) Warm-up period Falling drying period
Constant drying period Full drying period
12,0
11 9,5
Drying period (day)
10,0 8,0 6,0 4,0 2,0 0,0
8,5
8 7
6,5
6,5
10
10,5
10,5 9 8
Sludge Drying Within Greenhouse: Results 140 Input 120
Amount (ton)
100 80 60 40 20 0
Stabilized
Evaporated
Output
Calculation of the area required for Greenhouse
Required Area, A = Drying area, m2 E = Evaporation rate in (kg[water]/ (m2[ground]a)) Mwet= the amount of (wet) sludge production, (kg/a) Ϭin= % of the DSC of the wet sludge at the beginning of the drying period Ϭout= % of the DSC of the wet sludge at the end of drying period
= 554 m2
Degradation of DM and oDM during composting (Initial oDMintense,o = 1,559.28 ton/a) Rotting period
Time
weeks
(t)
Exp(-kt)
oDMt
DMt
mDMt
(ton)
(ton)
(ton)
oDM DM degradation degradation
ILt
(%)
(%)
(%)
Intensive rotting 0
0.00
1.00
1559.28
2190
654.08
16.43
16.67
71
1
0.02
0.86
1335.13 1989.21
654.08
28.44
24.31
67
2
0.04
0.73
1143.21 1797.29
654.08
38.73
31.61
64
3
0.06
0.63
978.87 1632.95
654.08
47.54
37.86
60
4
0.08
0.54
838.16 1492.24
654.08
55.08
43.22
56
Secondary rotting 5
0.10
0.48
755.78 1409.86
654.08
59.49
46.35
54
6
0.12
0.42
647.14 1301.22
654.08
65.32
50.49
50
7
0.13
0.36
554.11 1208.19
654.08
70.30
54.03
46
8
0.15
0.30
474.46 1128.54
654.08
74.57
57.06
42
9
0.17
0.26
406.25 1060.33
654.08
78.23
59.65
38
Degradation of dry matter and organic dry matter during composting Time-dependent degradation of the organic substance is often described with First Order degradation kinetics. oDMEnd = oDMBegin * exp(-k*t) kIntens = 8.07 (d-1) Ksecondary = 5.38 (d-1) – (measured)
Degradation during rotting period 90
oDM Degradation
80
After Intensive rotting oDM degraded : 55.08% DM degraded : 43.22%
70 Degradation in %
DM Degradation
60 50 40
After Secondary rotting oDM degraded : 81.36% DM degraded : 61.87%
30 20
Intensive rotting
10
Secondary rotting
0 0
1
2
3
4 5 6 7 Rotting Period in Weeks
8
9
10
