Santiago (SANitation sysTem Alternative GeneratOr) is a Julia package to generate appropriate sanitation system options. It is able to
- find all possible systems given a set of sanitation technologies;
- assess the appropriateness of a technology in a given case (context);
- assess the overall appropriateness of a sanitation system in a given context;
- calculate (optionally with uncertainly quantification) the massflows for each system for
- select a meaningful subset of systems for the given case.
For non-research applications we recommend to use
Santiago via the web app sanichoice.net.
Install Julia version >= 1.4.
Santiagopackage from the Julia prompt:
] add Santiago
- To edit Julia files you may also want to install Visual Studio Code and its Julia Extension. Alternatively, see the Julia home page for support for other editors.
The example below demonstrates the typical steps needed to identify sanitation systems appropriate for a given case. See the references below for a clarification of the terminology and the recommended embedding in the strategic planning process.
Most functions have a documentation string attached that can be accessed with
?functionname on the Julia prompt.
For reproducibility it is a good idea to create a separate Julia project
virtualenv in Python) for
every analysis, see here.
using Santiago # ----------- # 1) Import technologies # we use the test data that come with the package input_tech_file = joinpath(pkgdir(Santiago), "test/example_techs.json") input_case_file = joinpath(pkgdir(Santiago), "test/example_case.json") sources, additional_sources, techs = import_technologies(input_tech_file) # ----------- # 2) Build all systems allSys = build_systems(sources, techs); # number of found systems length(allSys) # The computations can be accelerated by setting max_candidates to a low number. # However, this will result only in a *stochastic* subset of all possible systems! allSys = build_systems(sources, techs, max_candidates=100); # ----------- # 3) Calculate system properties tas, tas_components = appropriateness(input_tech_file, input_case_file) sysappscore!.(allSys) ntechs!.(allSys) nconnections!.(allSys) connectivity!.(allSys) template!.(allSys) # see all properties of the first system allSys.properties # ----------- # 4) Mass flows # Inputs for different sources in kg/year/person equivalent. # See references below. input_masses = Dict("Dry.toilet" => Dict("phosphor" => 0.548, "nitrogen" => 4.550, "totalsolids" => 32.12, "water" => 547.1), "Pour.flush" => Dict("phosphor" => 0.548, "nitrogen" => 4.55, "totalsolids" => 32.12, "water" => 1277.1), "Cistern.flush" => Dict("phosphor" => 0.548, "nitrogen" => 4.55, "totalsolids" => 32.12, "water" => 22447.1), # Urine diversion dry toilet "Uddt" => Dict("phosphor" => 0.548, "nitrogen" => 4.55, "totalsolids" => 32.12, "water" => 547.1) ) # Calculate massflows with 20 Mont Carlo iterations (probably not enough) # for all systems and save to system properties massflow_summary_parallel!(allSys, input_masses, n=20); # Alternatively, the non-parallelized version can be used: # massflow_summary!.(allSys, Ref(input_masses), n=20); # If the flows of every technology is of interest, set 'techflows=true'. # The default is 'false' as this produces as very large amount of additional data! massflow_summary_parallel!(allSys, input_masses, n=20, techflows=true); # Examples how to extract results allSys.properties["massflow_stats"]["entered"] allSys.properties["massflow_stats"]["recovery_ratio"] allSys.properties["massflow_stats"]["recovered"] allSys.properties["massflow_stats"]["lost"][:,"air loss",:] allSys.properties["massflow_stats"]["lost"][:,:,"mean"] allSys.properties["massflow_stats"]["lost"][:,:,"q_0.5"] # ----------- # 5) select a subset of systems # For example, select eight systems for further investigation selectedSys = select_systems(allSys, 8) # We can also include or exclude technologies select_systems(allSys, 8, techs_exclude=["Pour.flush", "wsp_3_trans"]) select_systems(allSys, 8, techs_include=["Pour.flush"]) # Similar for templates select_systems(allSys, 8, templates_exclude=["ST.3", "ST.15"]) select_systems(allSys, 8, templates_include=["ST.17"]) # By default the systems are selected by the `"sysappscore"` but other # properties can be used too. For example, here we prefer short systems: select_systems(allSys, 8, target="ntechs", maximize=false) # Or systems with a high phosphor recovery (run massflow calculation first): select_systems(allSys, 8, target="phosphor" => "recovery_ratio") # By default the returned systems are diverse while having a good # target score. You can ignore the diversity requirement to get the # systems with the best target scores by setting # the `selection_type` to "ranking". select_systems(allSys, 10, selection_type="ranking") # This helper function returns the systems with matching IDs: pick_systems(allSys, ["003s-QbnU-FvGB", "0JLD-YQbJ-SGAu"]) # Investigate how techs and templates are used templates_per_tech(allSys) techs_per_template(allSys) # ----------- # 6) write some properties in a DataFrame for further analysis df = properties_dataframe(selectedSys, massflow_selection = ["recovered | water | mean", "recovered | water | sd", "lost | water | air loss| q_0.5", "entered | water"]) size(df) names(df) # or you could simply export all properties (> 400!) df = properties_dataframe(allSys, massflow_selection = "all") # export as csv import CSV # the package 'CSV' needs to be installed separately CSV.write("mysystems.csv", df) # ----------- # 7) create a visualization of a system as pdf # First write a dot file dot_file(selectedSys, "system.dot") # Then, convert it to pdf (The program `graphviz` must be installed on the system) run(`dot -Tpdf system.dot -o system.pdf`) # ----------- # 8) export to JSON # Note, the JSON export is designed to interface other applications, # but not for serialization. open("system_export.json", "w") do f JSON3.write(f, selectedSys) end
Typically the information on the case specification and the available
technologies are provided via files.
Santiago can only import JSON
files. The structure must match these examples:
Many tools are available to browse and edit JSON files. For example, Firefox renders JSON files nicely, or Visual Studio allows for editing.
Santiago prints only few information. This can be
adapted by the logging level. With the package
LoggingExtras.jl (needs to
be installed extra)
different logging levels can be used for the console output and the log file:
using Logging using LoggingExtras # - on console show only infos and errors, write everything in the logfile 'mylogfile.log' mylogger = TeeLogger( MinLevelLogger(FileLogger("mylogfile.log"), Logging.Debug), # logs to file MinLevelLogger(ConsoleLogger(), Logging.Info) # logs to console ) global_logger(mylogger) ... use Santiago functions ...
Update systems for a new case profile
The generation of all systems is computationally intense. The code below demonstrates how to first generate all systems without case information and later update the system scores with case data.
using Serialization ## 1) build systems without case information and cache result sources, additional_sources, techs = import_technologies(tech_file) if isfile("mycachfile.jls") allSys, sources, additional_sources, techs = deserialize("mycachfile.jls") else allSys = build_systems(sources, techs) ... massflow_summary!.(allSys, Ref(input_masses), n=100); ... serialize("mycachfile.jls", (allSys, sources, additional_sources, techs)) # note: we need to save the techs in order to ensure the link to from systems to tech properties (tas) end sysappscore!.(allSys) # all are '-1.0' because no case profile was defined yet ## 2) read case file and update sysappscore tas, tas_components = appropriateness(tech_file, case_file); update_appropriateness!(sources, tas) update_appropriateness!(additional_sources, tas) update_appropriateness!(techs, tas) sysappscore!.(allSys) # now we have the updated SAS ## 3) select systems fewSys = select_systems(allSys, 6) ## 4) scale massflows for 100 people scale_massflows!.(fewSys, 100)
The slowest parts are
massflow_summary!. Therefore we could cache the output as shown in this
example. Steps 2 and 4 are fast and can be quickly adapted to new cases.
build_systems and especially
massflow_summary_parallel! benefit from multi-threading. As this may
involves some overhead, benchmarking is recommended. See the official
how to control the number of threads.
Spuhler, D., Scheidegger, A., Maurer, M., 2018. Generation of sanitation system options for urban planning considering novel technologies. Water Research 145, 259–278. https://doi.org/10.1016/j.watres.2018.08.021
Spuhler, D., Scheidegger, A., Maurer, M., 2020. Comparative analysis of sanitation systems for resource recovery: influence of configurations and single technology components. Water Research 116281. https://doi.org/10.1016/j.watres.2020.116281
Spuhler, D., Scheidegger, A., Maurer, M., 2021. Ex-ante quantification of nutrient, total solids, and water flows in sanitation systems. Journal of Environmental Management 280, 111785. https://doi.org/10.1016/j.jenvman.2020.111785
Santiago.jl package is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License along with this program. If not, see https://www.gnu.org/licenses/.
Copyright 2020, Dorothee Spuhler at Eawag. Contact: Dorothee Spuhler, Dorothee.Spuhler@eawag.ch