tidywater (version 0.9.0)

decarbonate_ph: Apply decarbonation to a water

Description

Calculates the new water quality (pH, alkalinity, etc) after a specified amount of CO2 is removed (removed as bicarbonate). The function takes an object of class "water" and a fraction of CO2 removed, then returns a water class object with updated water slots. For a single water, use decarbonate_ph; to apply the model to a dataframe, use decarbonate_ph_chain. For most arguments, the _chain helper "use_col" default looks for a column of the same name in the dataframe. The argument can be specified directly in the function instead or an unquoted column name can be provided.

Usage

decarbonate_ph(water, co2_removed)
decarbonate_ph_chain(
  df,
  input_water = "defined_water",
  output_water = "decarbonated_water",
  co2_removed = "use_col"
)

Value

A water with updated pH/alk/etc.

decarbonate_ph_chain returns a data frame with a column containing a water with updated pH, alk, etc.

Arguments

water

Source water of class "water" created by define_water

co2_removed

Fraction of CO2 removed

df

a data frame containing a water class column, which has already been computed using define_water_chain. The df may include a column with names for each of the chemicals being dosed.

input_water

name of the column of water class data to be used as the input for this function. Default is "defined_water".

output_water

name of the output column storing updated parameters with the class, water. Default is "dosed_chem_water".

Details

decarbonate_ph uses water@h2co3 to determine the existing CO2 in water, then applies chemdose_ph to match the CO2 removal.

For large datasets, using fn_once or fn_chain may take many minutes to run. These types of functions use the furrr package for the option to use parallel processing and speed things up. To initialize parallel processing, use plan(multisession) or plan(multicore) (depending on your operating system) prior to your piped code with the fn_once or fn_chain functions. Note, parallel processing is best used when your code block takes more than a minute to run, shorter run times will not benefit from parallel processing.

See Also

chemdose_ph

Examples

water <- define_water(ph = 4, temp = 25, alk = 5) %>%
  decarbonate_ph(co2_removed = .95)


example_df <- water_df %>%
  define_water_chain() %>%
  decarbonate_ph_chain(input_water = "defined_water", output_water = "decarb", co2_removed = .95)