chemdose_toc_once function

Apply chemdose_toc function and output a data frame

This function allows chemdose_toc to be added to a piped data frame. Its output is a data frame with updated TOC, DOC, and UV254.

chemdose_toc_once(
  df,
  input_water = "defined_water",
  alum = 0,
  ferricchloride = 0,
  ferricsulfate = 0,
  coeff = "Alum"
)

Arguments

• df: a data frame containing a water class column, which has already been computed using define_water_chain. The df may include a column named for the coagulant being dosed, and a column named for the set of coefficients to use.
• input_water: name of the column of Water class data to be used as the input for this function. Default is "defined_water".
• alum: Hydrated aluminum sulfate Al2(SO4)3*14H2O + 6HCO3 -> 2Al(OH)3(am) +3SO4 + 14H2O + 6CO2
• ferricchloride: Ferric Chloride FeCl3 + 3HCO3 -> Fe(OH)3(am) + 3Cl + 3CO2
• ferricsulfate: Amount of ferric sulfate added in mg/L: Fe2(SO4)3*8.8H2O + 6HCO3 -> 2Fe(OH)3(am) + 3SO4 + 8.8H2O + 6CO2
• coeff: String specifying the Edwards coefficients to be used from "Alum", "Ferric", "General Alum", "General Ferric", or "Low DOC" or named vector of coefficients, which must include: k1, k2, x1, x2, x3, b

Returns

A data frame with an updated DOC, TOC, and UV254 concentration.

Details

The data input comes from a water class column, as initialized in define_water or balance_ions.

If the input data frame has a column(s) name matching a valid coagulant(s), the function will dose that coagulant(s). Note: The function can only dose a coagulant as either a column or from the function arguments, not both.

The column names must match the coagulant names as displayed in chemdose_toc. To see which coagulants can be passed into the function, see chemdose_toc.

tidywater functions cannot be added after this function because they require a water class input.

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.

Examples

library(purrr)
library(furrr)
library(tidyr)
library(dplyr)

example_df <- water_df %>%
  define_water_chain() %>%
  balance_ions_chain() %>%
  chemdose_ph_chain(alum = 30) %>%
  chemdose_toc_once(input_water = "dosed_chem_water")

example_df <- water_df %>%
  define_water_chain() %>%
  balance_ions_chain() %>%
  mutate(
    ferricchloride = seq(1, 12, 1),
    coeff = "Ferric"
  ) %>%
  chemdose_toc_once(input_water = "balanced_water")

example_df <- water_df %>%
  define_water_chain() %>%
  balance_ions_chain() %>%
  chemdose_toc_once(input_water = "balanced_water", alum = 40, coeff = "General Alum")

# Initialize parallel processing
plan(multisession, workers = 2) # Remove the workers argument to use all available compute
example_df <- water_df %>%
  define_water_chain() %>%
  balance_ions_chain() %>%
  mutate(ferricchloride = seq(1, 12, 1)) %>%
  chemdose_toc_once(input_water = "balanced_water", coeff = "Ferric")

# Optional: explicitly close multisession processing
plan(sequential)

See Also

chemdose_toc