Case Study SA-JW6
COST-BENEFIT MODEL FOR BIOGAS PRODUCTION
Anaerobic digestion and biogas generation technology is well established in South Africa. However, the
development of models to allow for scenario simulation to provide approximate costing and biogas yields
projections associated with CHP are not widely used or available. This case study explores the use of a simplified
model to assist planning and financing requirements, as a replicable approach for further case studies.
Page | 1
Cost-Benefit Models and Simulation to Plan, Motivate and Cost Biogas Production
Description of Process:
Combined Heat and Power (CHP) refers to the thermo-dynamics of cleaning and combustion of gas that will
result in 40% of the energy source as heat and 35% as electrical power. In order to assess and project the outputs
and cost of this technology, a simplified model was developed by Johannesburg Water to create the
experimental conditions for the CHP biogas project in a way that will translate to the expected outcomes for
later comparison to the directly measured outcomes upon project commissioning.
CHP is done via prime movers such as gas turbines or reciprocating engines, following a course of cell lysis and
biogas scrubbing. The project has been commissioned, but practice indicated that modeling of the expected (and
later confirmed) outputs would add benefit to the motivation and planning processes that preceded the actual
design and construction phases.
The model and simulation have been developed in such way that it would be evaluated by its consistency to
empirical data and to reproducible results, using ‘Microsoft Office Excel’ as basis. The following factors have
been used to verify the simplified biogas model:




Ability to explain observations and predict future observations
Cost of use, especially in combination with other models
Estimation of the degree of confidence in the model
Simplicity or even aesthetic appeal.
Potential Interventions

Ensure a cost-benefit model is in place whereby CHP can be modelled and simulated in the planned
environment

Ensure that biogas yield, input flow, sludge volume energy cost, capex and opex figures are contained in the
CHP model in order to simulate various scenarios.

Ensure that the % heat and % electricity from CHP is determined upfront.
Range of potential Savings
CHP is capable of producing 9.2 MWe electrical energy and 10.0 MW heat from 5 wastewater treatment plants
treating 1 022 700 m3/day (2011). The simulation model serves to project and predict the associated cost and
biogas yield against variable input flow scenarios.
Case Study SA-JW6
Page | 2
COST-BENEFIT MODEL FOR BIOGAS PRODUCTION
Case Study SA-JW6
Ref
Response information, description and remarks
Location:
Northern Wastewater Treatment Plant situated in South Africa,
Gauteng Province. High density urban space. Central Gauteng
with plant located in the northern catchment
Sector:
Sewage sludge
Works Owner or Operator:
The plant is owned by the City of Johannesburg (CoJ) Metropolitan
Municipality and operated by Johannesburg Water of behalf of CoJ,
as part of the City of the Future – Municipal Entities.
Size:
Plant size is 450 Ml/day; actual flow is 430 000 m3/dy. Produce 85
tons dry sludge/day. PE estimated at 990 000 households, with an
average of 6 persons per household, water consumption: 75 ℓ/c/d;
sewer production: 60 ℓ/c/d (=80% of water consumption).
Energy Provider:
Power is in form of electricity, provided by the national electricity
agency, ESKOM. Plant requires 7.5 MW energy, with potential to
generate 4.4 MW electrical energy and 4.8 MW heat.
Process:
Anaerobic digestion of sewage sludge with optimization of methane
gas for energy production purposes. CHP plant is under
construction and commissioning planned for November 2013.
Component:
Sludge digestion, whereby ‘sludge’ consist of primary-, excess
secondary-, and waste activated sludge. The model uses plant flow
and sludge volume as input parameter.
Motivation for the case study:
The increased electricity tariffs by ESKOM have been a key driver to
investigate biogas generation. Electrical power costs will treble over
the next 7 - 10 years, with 25% increases already incurred over past 3
years. ESKOM has incentives in place for off-peak power use, and
for green energy development. In order to develop biogas
generation, a model was developed to overcome problems
associated with yield estimation, capex and operational cost
requirements and ROI. 16% over next 5 yrs proposed
Process/Plant changes:
The model incorporates various input values, including input flow,
sludge volume energy cost, and electricity cost (at 2012 tariffs), and
simulates different scenarios in order to produce output values.
1
2
3
4
5
6
7
8
9
Page | 3
Civil/Physical Changes:
The model does not involve any civil or physical changes to the CHP
plant.
Operational Changes:
The model does not involve any operational changes to the plant,
but can be used in conjunction with operational software to
compliment the output. The design and use of the model does
required engineering or scientific expertise.
10
11
Risks and Dependencies:
12
13
Implementation:
The model was designed and tested by in-house expertise within the
municipality.
Energy Efficiency gains
The model project that a flow of 100 Ml/day will save R9.32/kWh
(14.32 – 5.0), which will result in payback period of <3 years (years
2012 – 2014)
Cost / Benefit analysis:
Payback period of <3 years in asset terms, reduction of 35 000 ton
CO2 in terms of emissions / environmental benefit
Project review:
The model and simulation can be refined after commissioning of the
CHP plant by making accuracy comparisons between the model
output and actual real time outputs.
Confidence grade:
Low confidence until such time that model output data can be
verified with actual output data.
14
15
16
17
Low risk involved if applied properly with correct input values. When
model is tested and output compared to actual outputs from the CHP
system (once commissioned), adjustments can be made before
replicating the use of the model elsewhere.
It is estimated that by 2020, the cost of electricity for the treatment of wastewater in Johannesburg would have
risen from the present R 97 m per annum (2010) to around R 300 m per annum (excluding the proposed 16%
increase for the next 5 years), making the existing wastewater treatment operation possibly unaffordable.
Failure of the wastewater treatment operations would have a devastating effect on the economy, environment,
health services and social activities of the City.
Johannesburg Water operates and maintains six wastewater treatment works and treated about 1 022 700 m3 /
day (2011 figures), generating approximately 260 dry tons of sludge per day. Combined Heat and Power
generation has been installed firstly at the Northern WWTW during 2011 as a test facility, with commissioning of
the installation due in 2013. A simplified model and simulation exercise benefitted the City and its stakeholders
Page | 4
to plan, project and cost the expected outputs from this project, and may serve as a replicable model for other
municipal users who endeavour to install CHP in similar projects.
The Process
A simplified model was developed by Johannesburg Water to create the experimental conditions for the CHP
biogas project in a way that will translate to the expected outcomes for later comparison to the directly
measured outcomes upon project commissioning. Direct measurement of outcomes under controlled
conditions will always be more reliable than modelled estimates of outcomes. However, modelling of the
scenario and its expected outcomes hold significant benefit to the municipality for various reasons:

Allow for simulation which represent the implementation of the model

Allow a steady state simulation which provide information about the system at a specific instant in time

Allow for dynamic simulation that will provide information over time

Mode simulation brings a model to life and shows how a particular object or phenomenon will behave,
thereby being useful for testing, analysis, or training in those cases where real-world systems or
concepts are being tested or piloted.
The model and simulation have been developed in such way that it would be evaluated by its consistency to
empirical data and to reproducible results, using ‘Microsoft Office Excel’ as basis. The following factors have
been used to verify the simplified biogas model:





Ability to explain observations
Ability to predict future observations
Cost of use, especially in combination with other models
Estimation of the degree of confidence in the model
Simplicity or even aesthetic appeal.
Results:
The model allows for the following input and output parameters (input parameters marked in Yellow):
Input:

flow treated in Ml/day

kWe selection for associated flow

sludge production (e.g. between 0.5-0.65 for 50-65% sludge production) – maximum will be 0.80 when
input sewage to plant is augmented with a carbon rich supplement, cell lysis, power factor correction etc.

Electricity (power) costs selection
Assumptions:

heat:power= = 1.25:1

94 m3 biogas per Ml treated where 1 m 3=35.3 ft3

38% efficiency

92% system availability
Page | 5

4500 BTU/h per kWh where 1 kJ=0.95 BTU
Output:

Civil, mechanical, electrical and total Capex as kWe (across scenario 280 to 3040 kWe

Fixed cost per annum

Variable cost per kWh per annum

Total O&M cost per annum

Total sludge (required and installed) as kWe

Sludge flow from stated baseline with projections and annual % increases over time (e.g. 2013 to 2020)

Power cost after saving

Total cost which represent Return on Investment in years.
The model assumes that the installation is a “green fields” project. The O and M function is outsourced to a
private company for a 7 year period and that the O and M costs consist of a fixed monthly cost and variable
monthly cost for the kWh produced. The variable cost also allows for the refurbishment of the prime movers after
a period of 6 years operation.
Figure: Snapshot profile of input and output scenario where the flow is 100 Ml/day
(only alter number in yellow box)
Page | 6
kWe
280
380
Civil
0.9
1.0
1.3
1.7
1.9
2.0
2.6
3.2
3.7
4.3
4.9
Mechanical
6.3
7.8
10.5
13.4
15.2
18.0
23.3
28.6
33.9
39.2
44.5
Electrical
3.3
4.1
5.5
7.1
8.0
11.5
14.9
18.3
21.8
25.2
28.6
Total Capex
10.5
12.9
17.3
22.2
25.1
31.5
40.8
50.1
59.4
68.7
78.0
Fixed cost per annum Rmil
0.5
0.6
0.7
0.8
0.8
1.0
1.2
1.4
1.6
1.8
2.0
Variable cost per kWh pa
1.0
1.1
1.2
1.4
1.6
1.7
2.0
2.3
2.6
2.9
3.2
Total O and M pa
1.5
1.7
1.9
2.2
2.4
2.7
3.2
3.7
4.2
4.7
5.2
3420
3800
4180
4560
4940
5320
5700
6080
6460
6840
7220
kWe
560
760
880
1140
1520
1900
2280
2660
3040
Civil
5.5
6.1
6.7
7.2
9.7
10.4
11.1
11.8
12.5
13.2
14.0
Mechanical
49.8
55.2
60.5
65.8
75.3
80.9
86.5
92.1
97.8
103.5
109.0
Electrical
32.0
35.3
38.7
42.2
39.5
42.5
45.5
48.5
51.4
54.3
57.3
Total Capex
87.3
96.6
105.9
115.2
124.5
133.8
143.1
152.4
161.7
171.0
180.3
Fixed cost per annum Rmil
2.2
2.4
2.6
2.8
3.0
3.2
3.4
3.6
3.8
4.0
4.2
Variable cost per kWh pa
3.5
3.8
4.1
4.4
4.7
5.0
5.3
5.6
5.9
6.2
6.5
Total O and M pa
5.7
6.2
6.7
7.2
7.7
8.2
8.7
9.2
9.7
10.2
10.7
Key aspects taken from case study:
The model proved itself valuable to simulate various scenarios by opting for different input parameters: biogas
yield, input flow, sludge volume, energy cost, capex and opex figures.
Acknowledgements:

Johannesburg Water, Shaun Deacon.
Page | 7