This vignette assumes a basic understanding of
define_water and the S4 water class. See
vignette("intro", package = "tidywater") for more
information.
To showcase tidywater’s acid-base equilibrium functions, let’s use a common water treatment problem. In this analysis, a hypothetical drinking water utility wants to know how much their pH will be impacted by varying doses of alum. They also want to ensure that their finished water has a pH of 8.
We can create a quick model by manually inputting the utility’s typical water quality. Then we’ll dose the water with their typical alum dose of 30 mg/L, and then a proposed 20mg/L dose. Finally, we’ll see how much caustic is required to raise the pH back to 8.
# Use define_water to prepare for tidywater analysis
no_alum_water <- define_water(ph = 8.3, temp = 18, alk = 150)
# Dose 30 mg/L of alum
alum_30 <- no_alum_water %>%
chemdose_ph(alum = 30) %>%
solvedose_ph(target_ph = 8, chemical = "naoh")
alum_30 # Caustic dose required to raise pH to 8 when 30 mg/L of alum is added
># [1] 10.3
# Dose 20 mg/L of alum
alum_20 <- no_alum_water %>%
chemdose_ph(alum = 20) %>%
solvedose_ph(target_ph = 8, chemical = "naoh")
alum_20 # Caustic dose required to raise pH to 8 when 20 mg/L of alum is added
># [1] 6.2As expected, a lower alum dose requires a lower caustic dose to reach the target pH.
But what if the utility wants to test a variety of alum doses on a
range of their water quality? We’ll use the power of tidywater’s
_chain functions to extend this analysis to a full
dataframe. For more information on tidywater’s _chain
functions, please see the
vignette("help_functions_blend_waters", package = "tidywater").
We’ll use tidywater’s built-in water quality data,
water_df, then apply define_water_chain and
balance_ions_chain to convert the data to a
water object. We’ll also set a range of alum doses to see
how they affect each water quality scenario.
chemdose_ph_chainNow that we’re set up, let’s dose some alum! To do this, we’ll use
chemdose_ph_chain, a function whose tidywater base is
chemdose_ph. The chemdose_ph_chain function
requires dosed chemicals to match the argument’s notation. In this case,
our chemical is already properly named. Other chemicals, such as
caustic, ferric sulfate, soda ash and more would need to be named
naoh, fe2so43, and na2co3,
respectively. Most tidywater chemicals are named with their chemical
formula, all lowercase and no special characters.
There are two ways to dose chemicals.
You can pass an appropriately named column into the function, or
You can specify the chemical in the function.
Let’s look at both options.
# 1. Use an existing column in your data frame to dose a chemical.
# Here, we use the alum column as the dosed chemical.
dose_column_water <- raw_water %>%
chemdose_ph_chain(input_water = "balanced_water") %>% # The function recognizes the 'alum' column as the chemical dose
pluck_water(input_water = "dosed_chem_water", parameter = "ph") %>%
select(-c(defined_water, balanced_water))
head(dose_column_water)
># dosed_chem_water alum
># 1 <S4 class 'water' [package "tidywater"] with 63 slots> 10
># 2 <S4 class 'water' [package "tidywater"] with 63 slots> 20
># 3 <S4 class 'water' [package "tidywater"] with 63 slots> 30
># 4 <S4 class 'water' [package "tidywater"] with 63 slots> 40
># 5 <S4 class 'water' [package "tidywater"] with 63 slots> 50
># 6 <S4 class 'water' [package "tidywater"] with 63 slots> 60
># dosed_chem_water_ph
># 1 7.17
># 2 6.86
># 3 6.64
># 4 6.45
># 5 6.28
># 6 6.10
# 2. Dose a chemical in the function. Rename the alum column so it doesn't get used in the function
dose_argument_water <- raw_water %>%
rename(coagulant = alum) %>%
chemdose_ph_chain(input_water = "balanced_water", alum = 30) %>%
pluck_water(input_water = "dosed_chem_water", parameter = "ph") %>%
select(-c(defined_water, balanced_water))
head(dose_argument_water)
># coagulant dosed_chem_water alum
># 1 10 <S4 class 'water' [package "tidywater"] with 63 slots> 30
># 2 20 <S4 class 'water' [package "tidywater"] with 63 slots> 30
># 3 30 <S4 class 'water' [package "tidywater"] with 63 slots> 30
># 4 40 <S4 class 'water' [package "tidywater"] with 63 slots> 30
># 5 50 <S4 class 'water' [package "tidywater"] with 63 slots> 30
># 6 60 <S4 class 'water' [package "tidywater"] with 63 slots> 30
># dosed_chem_water_ph
># 1 6.64
># 2 6.64
># 3 6.64
># 4 6.64
># 5 6.64
># 6 6.64For Option 1, notice that the ph column has a different
result for each row. This shows how powerful the tidywater functions are
for simulating multiple doses for multiple scenarios. Option 2 creates a
column, alum to show what was dosed. This may be useful for
plotting or remembering how much alum was dosed.
solvedose_ph_onceRemember, our original task is to see how alum addition affects the
pH, but the finished water pH needs to be 8. First, we’ll use caustic to
raise the pH to 8. solvedose_ph_once uses
solvedose_ph to calculate the required chemical dose (as
chemical, not product) based on a target pH.
Note: How can you remember the difference between
solvedose_ph vs chemdose_ph? Any function
beginning with “solve” is named for what it is solving for based on one
input: SolveWhatItReturns_Input. So, solvedose_ph is
solving for a dose based on a target pH. Other treatment functions are
set up as WhatHappensToTheWater_WhatYouSolveFor. So with
chemdose_ph, chemicals are being dosed, and we’re solving
for the resulting pH (and other components of acid/base chemistry).
chemdose_toc models the resulting TOC after chemicals are
added, and dissolve_pb calculates lead solubility in the
distribution system.
Let’s get back to our analysis. Similar to
chemdose_ph_chain, solvedose_ph_once can
handle chemical selection and target pH inputs as a column or function
arguments.solvedose_ph_once outputs a pH, not a
water object. Thus, solvedose_ph_chain doesn’t
exist because the water isn’t changing, so chaining this
function to a downstream tidywater function can be done using normal
tidywater operations.
# 1. Use existing columns in your dataframe to set a target pH and the chemicals to dose
raise_ph <- tibble(
chemical = c("naoh", "mgoh2"),
target_ph = c(8, 8)
)
solve_column <- raw_water %>%
chemdose_ph_chain(input_water = "balanced_water") %>%
cross_join(raise_ph) %>%
solvedose_ph_once(input_water = "dosed_chem_water") %>%
select(-c(defined_water:dosed_chem_water))
head(solve_column)
># alum chemical target_ph dose_required
># 1 10 naoh 8 4.3
># 2 10 mgoh2 8 3.2
># 3 20 naoh 8 8.3
># 4 20 mgoh2 8 6.1
># 5 30 naoh 8 12.4
># 6 30 mgoh2 8 9.0
# 2. Set the target pH and chemical needed to raise the pH inside the function
solve_argument <- raw_water %>%
chemdose_ph_chain(input_water = "balanced_water") %>%
solvedose_ph_once(input_water = "dosed_chem_water", chemical = "naoh", target_ph = 8) %>%
select(-c(defined_water:dosed_chem_water))
head(solve_argument)
># alum target_ph chemical dose_required
># 1 10 8 naoh 4.3
># 2 20 8 naoh 8.3
># 3 30 8 naoh 12.4
># 4 40 8 naoh 16.5
># 5 50 8 naoh 20.4
># 6 60 8 naoh 24.6Now that we have the dose required to raise the pH to 8, let’s dose caustic into the water!
dosed_caustic_water <- raw_water %>%
chemdose_ph_chain(input_water = "balanced_water", output_water = "alum_dosed") %>%
solvedose_ph_once(input_water = "alum_dosed", chemical = "naoh", target_ph = 8) %>%
rename(
naoh = dose_required,
coagulant = alum
) %>% # rename alum column so it doesn't get dosed twice
chemdose_ph_chain(input_water = "alum_dosed", output_water = "caustic_dosed") %>%
pluck_water(input_water = "caustic_dosed", "ph") %>%
select(-c(defined_water:chemical))
head(dosed_caustic_water)
># caustic_dosed naoh caustic_dosed_ph
># 1 <S4 class 'water' [package "tidywater"] with 63 slots> 4.3 7.99
># 2 <S4 class 'water' [package "tidywater"] with 63 slots> 8.3 7.98
># 3 <S4 class 'water' [package "tidywater"] with 63 slots> 12.4 8.02
># 4 <S4 class 'water' [package "tidywater"] with 63 slots> 16.5 8.01
># 5 <S4 class 'water' [package "tidywater"] with 63 slots> 20.4 7.99
># 6 <S4 class 'water' [package "tidywater"] with 63 slots> 24.6 8.01You can see the resulting pH from dosing caustic has raised the pH to 8 +/- 0.02 SU.
chemdose_ph is powerful because it can handle multiple
chemical inputs. To demonstrate this, we will assume the utility
regularly operates under enhanced coagulation. Assuming their alum dose
is always 30 mg/L and their acid (HCl) dose is always 10 mg/L.
enhanced_coag_water <- raw_water %>%
mutate(alum = 30) %>%
chemdose_ph_chain(input_water = "balanced_water", output_water = "alum_dosed", hcl = 10) %>%
pluck_water("alum_dosed", "ph") %>%
solvedose_ph_once(input_water = "alum_dosed", target_ph = 8, chemical = "naoh", output_column = "naoh") %>%
select(-c(alum, hcl)) %>% # remove chemical columns so they don't get dosed again in the next line.
chemdose_ph_chain(input_water = "alum_dosed", output_water = "ph_adjusted") %>%
select(-c(defined_water:alum_dosed, target_ph, chemical))
head(enhanced_coag_water)
># alum_dosed_ph ph_adjusted naoh
># 1 6.15 <S4 class 'water' [package "tidywater"] with 63 slots> 23.5
># 2 6.15 <S4 class 'water' [package "tidywater"] with 63 slots> 23.5
># 3 6.15 <S4 class 'water' [package "tidywater"] with 63 slots> 23.5
># 4 6.15 <S4 class 'water' [package "tidywater"] with 63 slots> 23.5
># 5 6.15 <S4 class 'water' [package "tidywater"] with 63 slots> 23.5
># 6 6.15 <S4 class 'water' [package "tidywater"] with 63 slots> 23.5