resilience.io wash sector prototype debut training workshop

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

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

Installation

3

Everything in one folder

4

resilienceIO_final

Copy folder resilienceIO_final from the pen-drive to

your hard-drive at C:\ (640 mb folder)

Resilience.IO package overview

5

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

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

In Sub-folders storage of data-files

8

File storage of Synthetic Population Change:

C:\resilienceIO_final\resilience.io.abm\data\agent_data\

File storage of simulated demands:

C:\resilienceIO_final\resilience.io.abm\fileoutput\

File storage of infrastructure supply simulation

C:\resilienceIO_final\resilience.io.rtn\visual_outputs\

C:\resilienceIO_final\resilience.io.rtn\text_outputs\

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

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

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

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

Running: calculations are going on

Stopped: results are ready now

Agents/people are starting their daily activities:

pink- female

blue- male


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

main folder: double click resilience.io_supply_model

Equivalently, you can click on resilience.io_supply_textoutputs to store

results in spreadsheets/ text format

Resilience.IO WASH Testing Capabilities

16

Demographics module

17

Loads Population and Company Master Table

C:\resilienceIO_final\resilience.io.abm\data\agent_data\GAMA_Agent_

Mastertable.csv

C:\resilienceIO_final\resilience.io.abm\data\agent_data\GAMA_Compa

ny_Mastertable.csv

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

Demographics module – how to change?

19

Open YAML file with text editor (notepad)

C:\resilienceIO_final\resilience.io.abm\data\socio_economic_data_input.yml

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!

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 )

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

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

Supply infrastructure module – what can be changed?

24

Load the desired starting scenario file by copying

from folder:

C:\resilienceIO_final\resilience.io.rtn\output\yaml_input_files\use

_case_x_yaml_files\

And pasting to folder:

C:\resilienceIO_final\resilience.io.rtn\output\yaml_input_files\

Store any other existing files in another folder (or

delete them if not useful)

Open Scenario YML file to change settings


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!

Technology data


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

Technology-Resource data


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 outputInput of raw_source_water

Technology-Cost data


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

Technology-Cost data


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

Settings for what to optimise (find lowest cost)


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

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!

Settings for pipes and flows


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]

Settings for meeting resource import needs (e.g. outside

GAMA or outside WASH sector).


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

Initial infrastructure already in place


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

Initial pipe infrastructure already in place from/to


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

Pipe connections which are allowed to be built by model


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

Cost of building trunk pipes and operating them


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

Additional settings for resource to meet demands


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

Additional Settings


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

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 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_final\resilience.io.rtn\YAML_INPUT_FILES\

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 conventional water treatment

First step Edit the YAML file(s) that you want to run

the model with:

Open:

C:\ResilienceIO_Final\resilience.io.rtn\output\YAML_INPUT_FILES\use_ca

se_2_yaml_files\Central_pipe_4_2025.yml

Go to the investment cost table VIJA

Look up which row is the source water treatment plant

Adjust the value and save the file


44


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_Final\resilience.io.rtn\output\YAML_INPUT_FILES\use_ca

se_2_yaml_files\Central_pipe_4_2015.yml

To:

C:\ResilienceIO_Final\resilience.io.rtn\output\YAML_INPUT_FILES\Central

_pipe_4_2015.yml

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 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

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?


Scenario A

Baseline 27%

Continuation

27% leakage


17% 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_Final\ResilienceIO_Final\resilience.io.abm\data\

agent_data\agentMasterTable-2015

Rename into


agent_data\GAMA_Agent_mastertable

And do the same for companyMasterTable-2015 and rename

into GAMA_Company_mastertable


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_Final\resilience.io.rtn\output\YAML_INPUT_FIL

ES\use_case_2_yaml_files\Baseline_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

Fifth step Save all generated results for

demographics, demands, and supply in a new folder (for

example c:\ResilienceIO_Final\Scenario_Results\20_June_leakage)

Files can be found in the following folders:

C:\resilienceIO_final\resilience.io.abm\data\agent_data\

C:\resilienceIO_final\resilience.io.abm\fileoutput\

C:\resilienceIO_final\resilience.io.rtn\visual_outputs\

C:\resilienceIO_final\resilience.io.rtn\text_outputs\


52

We now have the results for baseline_scenario for the

year 2015 with 27% pipe leakage!


Scenario A

Baseline 27%

Continuation

27% leakage


17% leakage


53

Sixth step Rename the earlier generated population

data for 2025 in the agent_data folder to run demands

Take file


agent_data\agentMasterTable-2025

Rename into


agent_data\GAMA_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

Eight step Run Supply to meet generated demands

for 2025 by using scenario file:

C:\ResilienceIO_Final\resilience.io.rtn\output\YAML_INPUT

_FILES\use_case_2_yaml_files\Central_pipe_4_2025.yml

Ninth step Save all generated results for

demographics, demands, and supply in the new folder


Scenario A

Baseline 27%

Continuation

27% leakage

Scenario B Reduction to 17%

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.


Scenario A

Baseline 27%

Continuation

27% leakage


17% leakage


56

Now we should have in folder

c:\ResilienceIO_Final\Scenario_Results\20_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

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_Final\resilience.io.abm\FileOutput\day-0-

waterDemandTotal

A Sample of Results – 2025 w 27% leakage

58

Investment cost 2015-2025 3.26 billion USD

Operational cost in 2025 105 million USD

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

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_final\resilience.io.rtn\output\yaml_input_files\

use_case_1_yaml_files\Sustainable_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!

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

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

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

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]


66

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]


67

What else do you

need to change?

-

-

-

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]


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

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


Investments('biogas_plant'.AMA.2030) = 1

What happened if costs reduced for affordable large-scale biogas technology?

71


Investments('biogas_plant'.AMA.2030) = 2

24000 m3 capacity per year each plant

72


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

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

Troubleshooting

74

Demand module restarting the interface instead of

running the model a few times

You can always email:

[email protected]

[email protected]

mailto:[email protected]

mailto:[email protected]

Q & A

75

resilience.io wash sector prototype debut training workshop

Government & Nonprofit