Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.
Resilience.IO WASH
Training Workshop
Rembrandt Koppelaar, Xiaonan Wang,
Department of Chemical Engineering, Imperial Colle...
Outline
 Installation
 resilience.io Package Overview
 Using the model – step by step
 resilience.io Testing Capabilit...
Installation
3
Everything in one folder
4
 resilienceIO_final
 Copy folder resilienceIO_final from the pen-drive to
your hard-drive at ...
Resilience.IO
package overview
5
 A data-driven simulation model of a synthetic
population
 To experiment with different scenarios by generating
demand p...
Everything in one folder
7
1. Creation of Synthetic
Population Change
2. Simulate demands
3. Examine what
infrastructure c...
In Sub-folders storage of data-files
8
 File storage of Synthetic Population Change:
C:resilienceIO_finalresilience.io.ab...
Using Resilience.IO WASH
step by step
9
How to use the model: step-by-step
10
 main folder: start_resilience.io_socio_dem_model
Step 1: Double clicks the
resilie...
How to use the model: step-by-step
11
 results folder: population and companies master tables
ResilienceIO/ resilience.io...
How to use the model: step-by-step
12
 main folder: start_resilience.io_demand_model
Step 1: Double clicks the
resilience...
How to use the model: step-by-step
13
Running: calculations are going on
Stopped: results are ready now
Agents/people are ...
How to use the model: step-by-step
14
 results folder: demand and costs
All results are stored
in the folder
ResilienceIO...
How to use the model: step-by-step
15
 main folder: double click resilience.io_supply_model
 Equivalently, you can click...
Resilience.IO WASH
Testing Capabilities
16
Demographics module
17
 Loads Population and Company Master Table
C:resilienceIO_finalresilience.io.abmdataagent_dataGAMA...
Demographics module
18
 Calculates changes in population for each
population type per year for X number of years (e.g.
fe...
Demographics module – how to change?
19
 Open YAML file with text editor (notepad)
C:resilienceIO_finalresilience.io.abmd...
Demographics module – how to change?
20
 Change file in text editor (notepad)
 Example larger immigration rate
 Order o...
Demographics module – Additional Settings
21
 Changes from low income to medium income population
(value for lowtomediums...
Demand Systems module – what can be changed?
22
 Setting water demands in litres / day / person
 Currently: Medium-incom...
Demand Systems module – what can be changed?
23
 Costs for water and toilets for calculation assuming
100% demands at end...
Supply infrastructure module – what can be changed?
24
 Load the desired starting scenario file by copying
from folder:
C...
Supply infrastructure module – what can be changed?
25
 Number and name of districts and coordinates
Coordinates of “cell...
 Technology data
Supply infrastructure module – what can be changed?
26
Capacity of technologies per half year (182.5
day...
 Technology-Resource data
Supply infrastructure module – what can be changed?
27
Which resources are available in the
mod...
 Technology-Cost data
Supply infrastructure module – what can be changed?
28
Investment cost per technology in order
Sour...
 Technology-Cost data
Supply infrastructure module – what can be changed?
29
Operational cost for technology
Source water...
 Settings for what to optimise (find lowest cost)
Supply infrastructure module – what can be changed?
30
Set objectives t...
Supply infrastructure module – what can be changed?
31
 Settings for resource to meet demands
If true reads simulated dem...
 Settings for pipes and flows
Supply infrastructure module – what can be changed?
32
Pipe type names (potable water and w...
 Settings for meeting resource import needs (e.g. outside
GAMA or outside WASH sector).
Supply infrastructure module – wh...
 Initial infrastructure already in place
Supply infrastructure module – what can be changed?
34
Every row is an MMDA, and...
 Initial pipe infrastructure already in place from/to
Supply infrastructure module – what can be changed?
35
AM  potable...
 Pipe connections which are allowed to be built by model
Supply infrastructure module – what can be changed?
36
AM2  pot...
 Cost of building trunk pipes and operating them
Supply infrastructure module – what can be changed?
37
Capital cost of p...
 Additional settings for resource to meet demands
Supply infrastructure module – what can be changed?
38
Number of major ...
 Additional Settings
Supply infrastructure module – what can be changed?
39
Amount of potable water turned into waste-wat...
Resilience.IO WASH
use examples
40
Already prepared Use cases and Scenarios
41
Use Case 3
Toilets & Waste-water
Use Case 1:
Water & Waste-water
Baseline
Use ...
Example 1 – editing data
42
Example, change the costs of a technology
43
 We have new/improved data for the costs of a
technology such as conventiona...
 Go to the investment cost table VIJA
 Look up which row is the source water treatment plant
 Adjust the value and save...
Example, change the costs of a technology
45
 We have new/improved data for the costs of a
technology such as conventiona...
Example 2 – comparing
scenarios
46
Example, effect change in pipe leakage
47
 We want to run for 2025 the impacts of a 10% pipe
leakage reduction for improv...
Example, effect change in pipe leakage
48
 We want to run for 2025 the impacts of a 10% pipe
leakage reduction for improv...
Example, effect change in pipe leakage
49
 First step  Run Demographics module for 15 years
(from 2010 to 2025) with inp...
Example, effect change in pipe leakage
50
 Third step  Run baseline demand situation for 2015
demographics with input se...
Example, effect change in pipe leakage
51
 Fifth step  Save all generated results for
demographics, demands, and supply ...
Example, effect change in pipe leakage
52
 We now have the results for baseline_scenario for the
year 2015 with 27% pipe ...
Example, effect change in pipe leakage
53
 Sixth step  Rename the earlier generated population
data for 2025 in the agen...
Example, effect change in pipe leakage
54
 Eight step  Run Supply to meet generated demands
for 2025 by using scenario f...
Example, effect change in pipe leakage
55
 Tenth step  Adjust YAML file Central_pipe_4_2025.yml
 Change leakage rate: ...
Example, effect change in pipe leakage
56
 Now we should have in folder
 c:ResilienceIO_FinalScenario_Results20_June_lea...
A Sample of Results
57
 Population in 2025 near 7 million
 Water Demand in 2025 close to 636,000 m3/day (will
differ som...
A Sample of Results – 2025 w 27% leakage
58
 Investment cost 2015-2025  3.26 billion USD
 Operational cost in 2025  10...
Interpreting Results
59
 The supply side outcomes are influenced by the
constraints and limitations
 For example: It inv...
Example 3 – Adding
entirely new technologies
(and demands)
60
Advanced Example: Adding Biogas into model
61
Start with the desired YAML file
62
 Take and copy to the input folder:
C:resilienceIO_finalresilience.io.rtnoutputyaml_i...
Example: Adding Biogas into model
63
read_ABM : false
ODS:
- [4632193 , 3705754, 200]
- [89126797 , 71301437, 200]
- [1196...
Example: Adding Biogas into model
64
read_ABM : false
ODS:
- [4632193 , 3705754, 2000]
- [89126797 , 71301437, 2000]
- [11...
Example: Adding Biogas into model
65
j: Technologies List
1 [source_water_treatment_plant,
2 borehole_source_water_system,...
MU: Technologies * Resources
[raw_source_water, electricity, labour_hours, potable_water, sludge,
carbon_dioxide, influent...
VIJA: capital expenditure, operational cost, environmental cost
- [45197947,0,0]
- [3325541,0,0]
- [50000,0,0]
- [43065,0,...
VIJA: capital expenditure, operational cost, environmental cost
- [45197947,0,0]
- [3325541,0,0]
- [50000,0,0]
- [43065,0,...
69
Results: new investment on infrastructure
Investments('decentralised_anaerobic_biogas_treatment_plant'.AMA.2030) =4
Inv...
70
Results: new investment on infrastructure
Investments('biogas_plant'.AMA.2030) = 1
What happened if costs reduced for a...
71
Results: new investment on infrastructure
Investments('biogas_plant'.AMA.2030) = 2
24000 m3 capacity per year each plant
72
Results: new investment on infrastructure
ProductionRate('biogas_plant'.ADMA.1.2030) = 930
ProductionRate('biogas_plant...
 Supply module  Sometimes the connection to the
visualisation software does not work, and you get an
error in the code, ...
Troubleshooting
74
 Demand module  restarting the interface instead of
running the model a few times
 You can always em...
Q & A
75
Upcoming SlideShare
Loading in …5
×

resilience.io WASH sector prototype debut training workshop

907 views

Published on

Installation
resilience.io Package Overview
Using the model –step by step
resilience.io Testing Capabilities (and Limitations)
resilience.io Use Examples
Q&A / Interactive Session

Published in: Government & Nonprofit
  • Be the first to comment

resilience.io WASH sector prototype debut training workshop

  1. 1. Resilience.IO WASH Training Workshop Rembrandt Koppelaar, Xiaonan Wang, Department of Chemical Engineering, Imperial College London, UK IIER – Institute for Integrated Economic Research Accra - June 2016 Resilience.IO platform
  2. 2. Outline  Installation  resilience.io Package Overview  Using the model – step by step  resilience.io Testing Capabilities (and Limitations)  resilience.io Use Examples  Q&A / Interactive Session 2
  3. 3. Installation 3
  4. 4. Everything in one folder 4  resilienceIO_final  Copy folder resilienceIO_final from the pen-drive to your hard-drive at C:  (640 mb folder)
  5. 5. Resilience.IO package overview 5
  6. 6.  A data-driven simulation model of a synthetic population  To experiment with different scenarios by generating demand profiles  And to find supply from a description of technologies and networks using optimisation with key performance metrics The approach: Resilience.IO Model 6
  7. 7. Everything in one folder 7 1. Creation of Synthetic Population Change 2. Simulate demands 3. Examine what infrastructure can best supply demands  Double-click to run:  start_resilience.io_socio_de mographics_calculation  start_resilience.io_demand_c alculation  start_resilience.io_supply_cal culation  In Main folder c:/resilienceIO_final
  8. 8. In Sub-folders storage of data-files 8  File storage of Synthetic Population Change: C:resilienceIO_finalresilience.io.abmdataagent_data  File storage of simulated demands: C:resilienceIO_finalresilience.io.abmfileoutput  File storage of infrastructure supply simulation C:resilienceIO_finalresilience.io.rtnvisual_outputs C:resilienceIO_finalresilience.io.rtntext_outputs
  9. 9. Using Resilience.IO WASH step by step 9
  10. 10. How to use the model: step-by-step 10  main folder: start_resilience.io_socio_dem_model Step 1: Double clicks the resilience.io_socio_dem_mod el file Step 2: User can inputs the years to be simulated after the instruction line (the starting base year is 2010 with existing complete information) and press Enter key. Step 3: The generated data is stored into two categories of spreadsheets to record the population and business sectors information respectively.
  11. 11. How to use the model: step-by-step 11  results folder: population and companies master tables ResilienceIO/ resilience.io.abm / data agent_data By changing the selected year's file name to “GAMA_Agent_ma stertable” and “GAMA_Company _mastertable”, users can plan the supply matching with any year’s data.
  12. 12. How to use the model: step-by-step 12  main folder: start_resilience.io_demand_model Step 1: Double clicks the resilience.io_demand_model file Step 2: Check the parameters to the left if you want to change any settings, otherwise the default parameters are used. Step 3: Click on Initialize model to load the map and agents, and click Run to start simulation. Initialize model / Run
  13. 13. How to use the model: step-by-step 13 Running: calculations are going on Stopped: results are ready now Agents/people are starting their daily activities: pink- female blue- male
  14. 14. How to use the model: step-by-step 14  results folder: demand and costs All results are stored in the folder ResilienceIO/resilienc e.io.abm/FileOutput with a comprehensive list of the WASH sector key characteristics, especially the water demand file and waste to be treated
  15. 15. How to use the model: step-by-step 15  main folder: double click resilience.io_supply_model  Equivalently, you can click on resilience.io_supply_textoutputs to store results in spreadsheets/ text format
  16. 16. Resilience.IO WASH Testing Capabilities 16
  17. 17. Demographics module 17  Loads Population and Company Master Table C:resilienceIO_finalresilience.io.abmdataagent_dataGAMA_Agent_ Mastertable.csv C:resilienceIO_finalresilience.io.abmdataagent_dataGAMA_Compa ny_Mastertable.csv
  18. 18. Demographics module 18  Calculates changes in population for each population type per year for X number of years (e.g. female, unemployed, access to drinking water)  Adds births (specify no births per 1000 people)  Subtracts deaths (specify no deaths per 1000 people)  Adds immigration (specify no immigrants per 1000 people  Adds emigration (specify no emigrants per 1000 people
  19. 19. Demographics module – how to change? 19  Open YAML file with text editor (notepad) C:resilienceIO_finalresilience.io.abmdatasocio_economic_data_input.yml
  20. 20. Demographics module – how to change? 20  Change file in text editor (notepad)  Example larger immigration rate  Order of MMDA values for all district specific data  Change value in immigration rate row for Accra (second value)  Save file  Now the module can be operated with new settings!
  21. 21. Demographics module – Additional Settings 21  Changes from low income to medium income population (value for lowtomediumstart, 0.003  0.3% per year)  Changes from medium to high income population (value for mediumtohighstart, 0.003  0.3% per year)  Maximum employment of 15+ year population (Value for maximumEmployment15plus, 0.80  80%)  Ageing of population from 0-14 to 15+ (Value for ageintRate14to15, 0.06  6% per year )
  22. 22. Demand Systems module – what can be changed? 22  Setting water demands in litres / day / person  Currently: Medium-income  1 * 70 to 90 litres  70-90  Low-income  0.73 * 70 to 90  51 to 66 litres  High-income  1.56 * 70 to 90  109 to 140 litres  Setting toilet use, faeces and urine per toilet use
  23. 23. Demand Systems module – what can be changed? 23  Costs for water and toilets for calculation assuming 100% demands at end point would be met (no non- revenue, ideal situation) Tariffs as set by PURC Estimated market values calculated from GHS to USD
  24. 24. Supply infrastructure module – what can be changed? 24  Load the desired starting scenario file by copying from folder: C:resilienceIO_finalresilience.io.rtnoutputyaml_input_filesuse _case_x_yaml_files  And pasting to folder: C:resilienceIO_finalresilience.io.rtnoutputyaml_input_files  Store any other existing files in another folder (or delete them if not useful)  Open Scenario YML file to change settings
  25. 25. Supply infrastructure module – what can be changed? 25  Number and name of districts and coordinates Coordinates of “cells” (MMDAs) based on real coordinate systems, in the order of “names_of_cells” Values entered twice, once for calculation and once for visualisation MMDAs, the order is important for further data input!
  26. 26.  Technology data Supply infrastructure module – what can be changed? 26 Capacity of technologies per half year (182.5 days) Names of technologies, the order is important for further data input! Load factor of technologies (75% - 85%)  Boreholes  15,000 m3 per year capacity * 75% load  11,250 m3 per year operation
  27. 27.  Technology-Resource data Supply infrastructure module – what can be changed? 27 Which resources are available in the model (again the order is important for further settings!). Also which resources can flow (usually both are set to the same) Input and output of resources for technologies. Every row is a technology and every column a resource Negative value is input, and positive value is output Input of raw_source_water
  28. 28.  Technology-Cost data Supply infrastructure module – what can be changed? 28 Investment cost per technology in order Source water treatment plant  45,197,947 USD Borehole source water system  3,325,541 USD (boreholes + local town water system) Protected well or protected spring  50,000 USD
  29. 29.  Technology-Cost data Supply infrastructure module – what can be changed? 29 Operational cost for technology Source water treatment plant  0.23 USD per m3 Borehole source water system  0.237 USD per m3 Protected well or protected spring  1 USD per m3 And greenhouse gas emissions for technology use Source water treatment plant  0.017 kg per m3 Borehole source water system  0.0065 USD per m3 Protected well or protected spring  0 USD per m3
  30. 30.  Settings for what to optimise (find lowest cost) Supply infrastructure module – what can be changed? 30 Set objectives to minimize capital & operational expenditure & CO2 emissions (do not change!) Set importance in minimization for objectives. Values are multipliers. Currently: CAPEX  [1] so as to represent total capital cost OPEX  [15] so as to represent 15 years of OPEX CO2  [0.5] arbitrarily chosen Set which resource demands to meet, values correspond to order in resource column, additional demands can be added! Set % of demands to meet [1,1]  100%, 100%
  31. 31. Supply infrastructure module – what can be changed? 31  Settings for resource to meet demands If true reads simulated demands from file, if false reads demands from ODS demands for set resources per year, only used if read_ABM is set false, Every row is demand for an MMDA in order of names of cells as set earlier: [ Adenta 3010999, 2408799] [ Accra_Metropolitan 175684715, 6054772] Numbers represent resources for which demands are set in file (in this case water and influent waste-water), additional demand values can be added here!
  32. 32.  Settings for pipes and flows Supply infrastructure module – what can be changed? 32 Pipe type names (potable water and waste- water). Order is important! Resources which flow through pipes pw_pipe  potable_water ww_pipe  influent_wastewater Leakage % in pipes (currently 27%) Capacity per pipe per year for resource [4,7]
  33. 33.  Settings for meeting resource import needs (e.g. outside GAMA or outside WASH sector). Supply infrastructure module – what can be changed? 33 MMDAs which can import resources Import maximum (50,000,000) per MMDA The resources which can be imported raw_source_water  from waterbodies Electricity  from electricity sector Labour_hours  from population Liquid_effluent  special settings to make waste-water calculation work Cost of imports Electricity  0.02 USD per MJ Labour-hours  2.4 USD per hour
  34. 34.  Initial infrastructure already in place Supply infrastructure module – what can be changed? 34 Every row is an MMDA, and every column is number of technologies  Boreholes in AMA  329 * 15,000 m3 per year capacity is equal to 5 million m3 per year, or 13,500 m3 per day
  35. 35.  Initial pipe infrastructure already in place from/to Supply infrastructure module – what can be changed? 35 AM  potable water pipes AM1  waste-water pipes  If all values are 0, then no pipes are in existence prior to model run, such as for waste-water pipes Pipe exists from/to From Accra Metropolitan To La-Dade Kotopon
  36. 36.  Pipe connections which are allowed to be built by model Supply infrastructure module – what can be changed? 36 AM2  potable water pipes AM3  waste-water pipes  If all values are 0, then no pipes can be built, if all values are 1 then all connections can be built Pipe allowed from/to From Ga-South To Ga-West
  37. 37.  Cost of building trunk pipes and operating them Supply infrastructure module – what can be changed? 37 Capital cost of pipe per km Potable water pipe  2,350,000 USD Waste-water pipe  235,000 USD Operational cost of pipe per m3 per flowable resource value for potable water set to  0.001 USD per m3
  38. 38.  Additional settings for resource to meet demands Supply infrastructure module – what can be changed? 38 Number of major periods (years) and minor periods in a year (two)  don’t change setting Year which is printed in the output results (doesn’t influence model) Split for minor periods in year (8760 hours per year), in this case 1756 hours and 7008 hours  These settings are for the model to calculate sub-periods within a year when useful
  39. 39.  Additional Settings Supply infrastructure module – what can be changed? 39 Amount of potable water turned into waste-water Available budget for investment + operation per year Set all facilities forced to full operation (100%) No investments are allowed (can lead to not being able to meet demands  no solution) The number of solutions tried out (Lower is better, higher is faster), 0.01 is highest value allowed
  40. 40. Resilience.IO WASH use examples 40
  41. 41. Already prepared Use cases and Scenarios 41 Use Case 3 Toilets & Waste-water Use Case 1: Water & Waste-water Baseline Use Case 2 Water supply Baseline City-Wide Decentralised districts Low pipe leakage variants Local Pipe Source Central Pipe Source High immigration variants Baseline Public toilet and local district treatment Sustainable Development Goal targets Private toilets and central GAMA treatment  Various Input files in folder: C:resilienceIO_finalresilience.io.rtnYAML_INPUT_FILES
  42. 42. Example 1 – editing data 42
  43. 43. Example, change the costs of a technology 43  We have new/improved data for the costs of a technology such as conventional water treatment  First step  Edit the YAML file(s) that you want to run the model with:  Open: C:ResilienceIO_Finalresilience.io.rtnoutputYAML_INPUT_FILESuse_ca se_2_yaml_filesCentral_pipe_4_2025.yml
  44. 44.  Go to the investment cost table VIJA  Look up which row is the source water treatment plant  Adjust the value and save the file Example, change the costs of a technology 44
  45. 45. Example, change the costs of a technology 45  We have new/improved data for the costs of a technology such as conventional water treatment  Second step  Copy the YAML file to the base folder that you want to run with  From: C:ResilienceIO_Finalresilience.io.rtnoutputYAML_INPUT_FILESuse_ca se_2_yaml_filesCentral_pipe_4_2015.yml  To: C:ResilienceIO_Finalresilience.io.rtnoutputYAML_INPUT_FILESCentral _pipe_4_2015.yml
  46. 46. Example 2 – comparing scenarios 46
  47. 47. Example, effect change in pipe leakage 47  We want to run for 2025 the impacts of a 10% pipe leakage reduction for improved potable water.  Use case 2 scenario files are for potable water only  Decide what to compare? Situation / year 2015 2025 Scenario A Baseline 27% Continuation 27% leakage Scenario B Reduction to 17% leakage
  48. 48. Example, effect change in pipe leakage 48  We want to run for 2025 the impacts of a 10% pipe leakage reduction for improved potable water.  Use case 2 scenario files are for potable water only  Decide what to compare? Situation / year 2015 2025 Scenario A Baseline 27% Continuation 27% leakage Scenario B Reduction to 17% leakage
  49. 49. Example, effect change in pipe leakage 49  First step  Run Demographics module for 15 years (from 2010 to 2025) with input settings.  Second step  Rename the earlier generated population data for 2025 in the folder before demands calculation  Take file  C:ResilienceIO_FinalResilienceIO_Finalresilience.io.abmdata agent_dataagentMasterTable-2015  Rename into  C:ResilienceIO_FinalResilienceIO_Finalresilience.io.abmdata agent_dataGAMA_Agent_mastertable  And do the same for companyMasterTable-2015 and rename into GAMA_Company_mastertable
  50. 50. Example, effect change in pipe leakage 50  Third step  Run baseline demand situation for 2015 demographics with input settings.  Fourth step  Run Supply to meet generated demands for baseline using baseline scenario file use Case 2  C:ResilienceIO_Finalresilience.io.rtnoutputYAML_INPUT_FIL ESuse_case_2_yaml_filesBaseline_1_2015.yml  The baseline scenario files contain a “dummy” technology called “unimproved_w_inv” and “unimproved_ww_inv” for adding unimproved sources “to meet demands”  without investment (no cost)
  51. 51. Example, effect change in pipe leakage 51  Fifth step  Save all generated results for demographics, demands, and supply in a new folder (for example c:ResilienceIO_FinalScenario_Results20_June_leakage)  Files can be found in the following folders: C:resilienceIO_finalresilience.io.abmdataagent_data C:resilienceIO_finalresilience.io.abmfileoutput C:resilienceIO_finalresilience.io.rtnvisual_outputs C:resilienceIO_finalresilience.io.rtntext_outputs
  52. 52. Example, effect change in pipe leakage 52  We now have the results for baseline_scenario for the year 2015 with 27% pipe leakage! Situation / year 2015 2025 Scenario A Baseline 27% Continuation 27% leakage Scenario B Reduction to 17% leakage
  53. 53. Example, effect change in pipe leakage 53  Sixth step  Rename the earlier generated population data for 2025 in the agent_data folder to run demands  Take file  C:ResilienceIO_FinalResilienceIO_Finalresilience.io.abmdata agent_dataagentMasterTable-2025  Rename into  C:ResilienceIO_FinalResilienceIO_Finalresilience.io.abmdata agent_dataGAMA_Agent_mastertable  And do the same for companyMasterTable-2025 and rename into GAMA_Company_mastertable  Seventh step  Run demand simulation based on 2025 demographics with input settings.
  54. 54. Example, effect change in pipe leakage 54  Eight step  Run Supply to meet generated demands for 2025 by using scenario file: C:ResilienceIO_Finalresilience.io.rtnoutputYAML_INPUT _FILESuse_case_2_yaml_filesCentral_pipe_4_2025.yml  Ninth step  Save all generated results for demographics, demands, and supply in the new folder Situation / year 2015 2025 Scenario A Baseline 27% Continuation 27% leakage Scenario B Reduction to 17% leakage
  55. 55. Example, effect change in pipe leakage 55  Tenth step  Adjust YAML file Central_pipe_4_2025.yml  Change leakage rate:   Eleventh step  Run Supply to meet generated demands for 2025 by using adjusted YAML scenario file.  Last step  Save all generated results for demographics, demands, and supply in the new folder for 17% leakage rate. Situation / year 2015 2025 Scenario A Baseline 27% Continuation 27% leakage Scenario B Reduction to 17% leakage
  56. 56. Example, effect change in pipe leakage 56  Now we should have in folder  c:ResilienceIO_FinalScenario_Results20_June_leakage  - Results for baseline 27% run for 2015  - Results for 2025 100% improved water  27% leakage  - Results for 2025 100% improved water  17% leakage  We can now compare results for changes in population, changes in demands (2015-2025), difference in costs between 27% and 17% leakage, etc. using the csv files, text output file for supply, and generated graphs
  57. 57. A Sample of Results 57  Population in 2025 near 7 million  Water Demand in 2025 close to 636,000 m3/day (will differ somewhat for each model run and number of agents)  C:ResilienceIO_Finalresilience.io.abmFileOutputday-0- waterDemandTotal
  58. 58. A Sample of Results – 2025 w 27% leakage 58  Investment cost 2015-2025  3.26 billion USD  Operational cost in 2025  105 million USD
  59. 59. Interpreting Results 59  The supply side outcomes are influenced by the constraints and limitations  For example: It invests in conventional water treatment at Lake Weija mainly because  There are no limits to expansion at Lake Weija  Building treatment plants are similar in cost at Lake Weija are at Volta River / Kpone  Only the distance for pipe connections are taken into account (greater distance to Volta River versus Lake Weija)  Elevation and difference in source water intake are not taken into account
  60. 60. Example 3 – Adding entirely new technologies (and demands) 60
  61. 61. Advanced Example: Adding Biogas into model 61
  62. 62. Start with the desired YAML file 62  Take and copy to the input folder: C:resilienceIO_finalresilience.io.rtnoutputyaml_input_files use_case_1_yaml_filesSustainable_Development_Goals_4 _2030.yml  Since we are running additional demands (for biogas) - which are not generated by the demand module - we want to open the YAML file and flag  read_abm: false  Now we can make further adjustments!
  63. 63. Example: Adding Biogas into model 63 read_ABM : false ODS: - [4632193 , 3705754, 200] - [89126797 , 71301437, 200] - [11961616 , 9569293, 0] - [7504044 , 6003235, 0] - [8506051 , 6804841, 0] - [28814317 , 23051454, 0] - [12085454 , 9668363, 0] - [6670931 , 5336745, 0] - [8770558 , 7016447, 0] - [6908802 , 5527041, 0] - [9799336 , 7839469, 0] - [12679806 , 10143845, 200] - [3126596 , 2501277, 0] - [5024429 , 4019543, 0] - [1550251 , 1240201, 0] - [1,1,1] Pilot: Which districts would like to use bio-gas? [ADMA, AMA, ASHMA, GCMA, GSMA, GWMA,GEMA, KKMA, LADMA, LANKMA, LEKMA, TEMA, ASMA, ASEMA, NAMA, VOLTA] Demand of biogas: 2000 m3 per year for the selected district each
  64. 64. Example: Adding Biogas into model 64 read_ABM : false ODS: - [4632193 , 3705754, 2000] - [89126797 , 71301437, 2000] - [11961616 , 9569293, 0] - [7504044 , 6003235, 0] - [8506051 , 6804841, 0] - [28814317 , 23051454, 0] - [12085454 , 9668363, 0] - [6670931 , 5336745, 0] - [8770558 , 7016447, 0] - [6908802 , 5527041, 0] - [9799336 , 7839469, 0] - [12679806 , 10143845, 2000] - [3126596 , 2501277, 0] - [5024429 , 4019543, 0] - [1550251 , 1240201, 0] - [1,1,1] Pilot: Increased biogas production can satisfy regional energy demand. [ADMA, AMA, ASHMA, GCMA, GSMA, GWMA,GEMA, KKMA, LADMA, LANKMA, LEKMA, TEMA, ASMA, ASEMA, NAMA, VOLTA]
  65. 65. Example: Adding Biogas into model 65 j: Technologies List 1 [source_water_treatment_plant, 2 borehole_source_water_system, 3 protected_wellspring_rainwater, 4 sachet_drinking_water, 5 bottled_water, 6 unimproved_tanked_vendor, 7 unimproved_other, 8 waste_water_treatment_plant, 9 waste_stabilisation_pond, aerated_lagoon, 10 decentralized_activated_sludge_system, 11 faecal_sludge_polymer_separation_drying_plant, 12 decentralised_anaerobic_biogas_treatment_plant, 13 decentralised_aerobic_treatment_plant, 14 desalination_plant, 15 biogas_plant] Capacity: 2400 m3 per year each plant Capacity factor: 0.75
  66. 66. MU: Technologies * Resources [raw_source_water, electricity, labour_hours, potable_water, sludge, carbon_dioxide, influent_wastewater, drink_water_satchet, liquid_effluent, sludge_effluent, influent_faecal_sludge, biogas] - [-1,-0.75,-0.002,1,0.0924,0.017,0,0,0,0,0,0] -[-1.3,0,-0.35,1,0,0.00065,0,0,0,0,0,0] -[-1.1,0,-0.20,1,0,0,0,0,0,0,0,0] -[-1,-15.1,-4,1,0,1.39,0,2000,0,0,0,0] -[-1.46,-240,-7.65,1,0,2.1,0,0,0,0,0,0] -[-1,0,0,1,0,0,0,0,0,0,0,0] -[-1,0,0,1,0,0,0,0,0,0,0,0] -[0,-1.07,-0.02,0,0,0.04,1,0,-1,0.00024,0,0] -[0,-0.05,-0.0025,0,1.49,0.38,1,0,-1,0.0015,0,0] -[0,-5.99,-0.0063,0,1.39,1.01,1,0,-1,0.0014,0,0] -[0,-0.36,-0.004,0,0,1.13,1,0,-1,0.16,0,0] -[0,-1,-0.2,0,0.05,0,1,0,-0.86,0,0,0] -[0,0,-0.5,0,0,0,1,0,-0.98,0,0,0.5] -[0,-6.21,-0.5,0,0,7.1,1,0,-0.97,0.03,0,0] -[-1,-28.5,-0.001,0.41,0.11,1.78,0,0,0,0,0,0] -[0,0.02,-0.2,0,0,0.1,0,0,0,0,0,1] Example: Adding Biogas into model 66
  67. 67. VIJA: capital expenditure, operational cost, environmental cost - [45197947,0,0] - [3325541,0,0] - [50000,0,0] - [43065,0,0] - [2478334,0,0] - [150,0,0] - [100,0,0] - [53398778,0,0] - [14145810,0,0] - [768544,0,0] - [1516850,0,0] - [4816845,0,0] - [3092,0,0] - [244500,0,0] - [130000000,0,0] - [7200,0,0] Example: Adding Biogas into model 67 What else do you need to change? - - -
  68. 68. VIJA: capital expenditure, operational cost, environmental cost - [45197947,0,0] - [3325541,0,0] - [50000,0,0] - [43065,0,0] - [2478334,0,0] - [150,0,0] - [100,0,0] - [53398778,0,0] - [14145810,0,0] - [768544,0,0] - [1516850,0,0] - [4816845,0,0] - [3092,0,0] - [244500,0,0] - [130000000,0,0] - [7200,0,0] Example: Adding Biogas into model 68 What else do you need to change? - VPJ - [0,0.08,0] - N_alloc_matrix: no existing plants, all 0 - dp: 1 Qmax: 10000
  69. 69. 69 Results: new investment on infrastructure Investments('decentralised_anaerobic_biogas_treatment_plant'.AMA.2030) =4 Investments('decentralised_anaerobic_biogas_treatment_plant'.LEKMA.2030) = 3020 Investments('decentralised_anaerobic_biogas_treatment_plant'.TEMA.2030) = 2 Investments('decentralised_anaerobic_biogas_treatment_plant'.ASMA.2030) = 1
  70. 70. 70 Results: new investment on infrastructure Investments('biogas_plant'.AMA.2030) = 1 What happened if costs reduced for affordable large-scale biogas technology?
  71. 71. 71 Results: new investment on infrastructure Investments('biogas_plant'.AMA.2030) = 2 24000 m3 capacity per year each plant
  72. 72. 72 Results: new investment on infrastructure ProductionRate('biogas_plant'.ADMA.1.2030) = 930 ProductionRate('biogas_plant'.ADMA.2.2030) = 3699 ProductionRate('biogas_plant'.TEMA.1.2030) = 393 ProductionRate('biogas_plant'.TEMA.2.2030) = 1570
  73. 73.  Supply module  Sometimes the connection to the visualisation software does not work, and you get an error in the code, or graphs don’t appear:  Click Ctrl-Alt-Delete  go to task manager  click on process called Rserve.exe and  end task  Now rerun the model Troubleshooting 73
  74. 74. Troubleshooting 74  Demand module  restarting the interface instead of running the model a few times  You can always email:  Xiaonan.wang@imperial.ac.uk  Koppelaar@iier.ch
  75. 75. Q & A 75

×