Helper Functions & Dose Chemicals

This vignette assumes a basic understanding of define_water and the S4 water class. See vignette("intro", package = "tidywater") for more information.

Chemical dosing setup

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] 6.2

As expected, a lower alum dose requires a lower caustic dose to reach the 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.

Multi-scenario setup and intro to _df functions

But what if the utility wants to test a variety of alum doses on a range of their water quality? Here, we’ll use the power of tidywater’s _df functions to extend this analysis to a full dataframe.

We’ll use tidywater’s built-in water quality data, water_df, then apply define_water_df to convert the data in the dataframe to a water object in one column of the dataframe. We use define_water_df so that other models can be added to the dataframe. This function takes a dataframe input, then outputs all parameters in a water class column. This is true for all tidywater functions with the _df suffix. _df functions are handy in a piped code block where you’ll need to use many tidywater functions, such as chemdose_ph, chemdose_toc, etc. After applying define_water_df, we’ll also use balance_ions_df to create a new variable with the ions balanced for all the “raw” water objects in the dataframe.

We’ll also set a range of alum doses to see how they affect each water quality scenario.

# Set a range of alum doses

alum_doses <- tibble(alum_dose = seq(20, 60, 10))

# use tidywater's built-in synthetic data water_df, for this example
raw_water <- water_df %>%
  slice_head(n = 2) %>%
  define_water_df(output_water = "raw") %>%
  balance_ions_df(input_water = "raw") %>%
  # join alum doses to create several dosing scenarios
  cross_join(alum_doses)

chemdose_ph_df and pluck_water

Now that we’re set up, let’s dose some alum! To do this, we’ll use chemdose_ph_df, a function with the _df suffix introduced earlier but whose tidywater base is chemdose_ph. The chemdose_ph_df function requires dosed chemicals to match the argument’s notation or have to be specified when calling the the function. Most tidywater chemicals are named with their chemical formula, all lowercase and no special characters.

There are two ways to dose chemicals.

  1. You can pass an appropriately named column into the function, or

  2. You can specify the chemical in the function.

Let’s look at both options using the alum doses from before, and adding hydrochloric acid. You should notice that the ouputs of both methods are the same.

# 1. Use existing column in data frame to dose a chemical
dose_water <- raw_water %>%
  mutate(hcl = 5) %>%
  chemdose_ph_df(input_water = "raw", alum = alum_dose, pluck_cols = TRUE) %>%
  pluck_water(input_water = "raw", parameter = "ph") %>%
  select(-c(raw, dosed_chem))

head(dose_water)
>#                                                 balanced alum_dose hcl
># 1 <S4 class 'water' [package "tidywater"] with 75 slots>        20   5
># 2 <S4 class 'water' [package "tidywater"] with 75 slots>        30   5
># 3 <S4 class 'water' [package "tidywater"] with 75 slots>        40   5
># 4 <S4 class 'water' [package "tidywater"] with 75 slots>        50   5
># 5 <S4 class 'water' [package "tidywater"] with 75 slots>        60   5
># 6 <S4 class 'water' [package "tidywater"] with 75 slots>        20   5
>#   dosed_chem_ph dosed_chem_alk raw_ph
># 1          6.60       33.04107    7.9
># 2          6.42       27.96961    7.9
># 3          6.25       22.98907    7.9
># 4          6.07       17.92141    7.9
># 5          5.87       12.96700    7.9
># 6          6.93       62.87537    8.5

# 2. Dose a chemical in the function
dose_water <- raw_water %>%
  chemdose_ph_df(input_water = "raw", alum = alum_dose, hcl = 5) %>%
  pluck_water(input_water = c("raw", "dosed_chem"), parameter = "ph") %>%
  select(-c(raw, dosed_chem))

head(dose_water)
>#                                                 balanced alum_dose hcl raw_ph
># 1 <S4 class 'water' [package "tidywater"] with 75 slots>        20   5    7.9
># 2 <S4 class 'water' [package "tidywater"] with 75 slots>        30   5    7.9
># 3 <S4 class 'water' [package "tidywater"] with 75 slots>        40   5    7.9
># 4 <S4 class 'water' [package "tidywater"] with 75 slots>        50   5    7.9
># 5 <S4 class 'water' [package "tidywater"] with 75 slots>        60   5    7.9
># 6 <S4 class 'water' [package "tidywater"] with 75 slots>        20   5    8.5
>#   dosed_chem_ph
># 1          6.60
># 2          6.42
># 3          6.25
># 4          6.07
># 5          5.87
># 6          6.93

Notice in the above code that we used the pluck_water helper function. This function creates a new column for one selected parameter from a water class object. You can choose which water column to pluck from using the input_water argument. Next, select the parameter of interest (which must match the water slot’s name). Finally, the output column’s name will default to the form water_parameter, but there is an option to name it yourself using the output_column argument. We can also directly pull out the output from a model function into its own column with pluck_cols = TRUE so that you don’t need to apply pluck_water later.

solvedose_ph_df

Remember, 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_df uses solvedose_ph to calculate the required chemical dose (as chemical, not product) based on a target pH. Similar to chemdose_ph_df, solvedose_ph_df can handle chemical selection and target pH inputs as a column or function arguments.

solve_ph <- raw_water %>%
  chemdose_ph_df("raw", alum = alum_dose) %>%
  mutate(target_ph = 8) %>%
  solvedose_ph_df(input_water = "dosed_chem", chemical = c("naoh", "mgoh2")) %>%
  select(-c(raw, dosed_chem))

head(solve_ph)
>#                                                 balanced alum_dose target_ph
># 1 <S4 class 'water' [package "tidywater"] with 75 slots>        20         8
># 2 <S4 class 'water' [package "tidywater"] with 75 slots>        30         8
># 3 <S4 class 'water' [package "tidywater"] with 75 slots>        40         8
># 4 <S4 class 'water' [package "tidywater"] with 75 slots>        50         8
># 5 <S4 class 'water' [package "tidywater"] with 75 slots>        60         8
># 6 <S4 class 'water' [package "tidywater"] with 75 slots>        20         8
>#   chemical dose
># 1     naoh  8.3
># 2     naoh 12.3
># 3     naoh 16.5
># 4     naoh 20.5
># 5     naoh 24.4
># 6     naoh  6.3

Now 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_df(input_water = "raw", output_water = "alum_dosed", alum = alum_dose) %>%
  solvedose_ph_df(input_water = "alum_dosed", target_ph = 8, chemical = "naoh") %>%
  chemdose_ph_df(input_water = "alum_dosed", output_water = "caustic_dosed", naoh = dose) %>%
  pluck_water(input_water = "caustic_dosed", "ph") %>%
  select(-c(raw:balanced, alum_dosed))

head(dosed_caustic_water)
>#   alum_dose target_ph chemical dose
># 1        20         8     naoh  8.3
># 2        30         8     naoh 12.3
># 3        40         8     naoh 16.5
># 4        50         8     naoh 20.5
># 5        60         8     naoh 24.4
># 6        20         8     naoh  6.3
>#                                            caustic_dosed caustic_dosed_ph
># 1 <S4 class 'water' [package "tidywater"] with 75 slots>             7.99
># 2 <S4 class 'water' [package "tidywater"] with 75 slots>             7.98
># 3 <S4 class 'water' [package "tidywater"] with 75 slots>             8.00
># 4 <S4 class 'water' [package "tidywater"] with 75 slots>             8.02
># 5 <S4 class 'water' [package "tidywater"] with 75 slots>             8.01
># 6 <S4 class 'water' [package "tidywater"] with 75 slots>             7.99

You can see the resulting pH from dosing caustic has raised the pH to 8 +/- 0.02 SU. Doses are rounded to the nearest 0.1 mg/L to make the calculations go a little faster.

Summary

In this tutorial, we were introduced to tidywater helper functions _df, which can be used to apply base functions to a dataframe. We also used the pluck_water helper function and the pluck_cols argument to extract parameters of interest from our dataframes.

We implemented these helper functions to complete an example dosing water with coagulant (alum) and adjusting the resulting pH to a target pH of 8 using solvedose_ph and chemdose_ph functions. To try another example with helper functions and learn about the blend_waters function, see vignette("blend_waters", package = "tidywater").