solvedose_alk_once function

Apply solvedose_alk to a dataframe and create a new column with numeric dose

This function allows solvedose_alk to be added to a piped data frame. Its output is a chemical dose in mg/L.

solvedose_alk_once(
  df,
  input_water = "defined_water",
  output_column = "dose_required",
  target_alk = NULL,
  chemical = NULL
)

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 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. Default is "defined_water".
• output_column: name of the output column storing doses in mg/L. Default is "dose_required".
• target_alk: set a goal for alkalinity using the function argument or a data frame column
• chemical: select the chemical to be used to reach the desired alkalinity using function argument or data frame column

Returns

A data frame containing the original data frame and columns for target alkalinity, chemical dosed, and required chemical dose.

Details

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

If the input data frame has column(s) named "target_alk" or "chemical", the function will use the column(s) as function argument(s). If these columns aren't present, specify "target_alk" or "chemical" as function arguments. The chemical names must match the chemical names as displayed in solvedose_alk. To see which chemicals can be dosed, see solvedose_alk.

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() %>%
  mutate(
    target_alk = 300,
    chemical = rep(c("naoh", "na2co3"), 6)
  ) %>%
  solvedose_alk_once()

# When the selected chemical can't raise the alkalinity, the dose_required will be NA
# Eg,soda ash can't bring the alkalinity to 100 when the water's alkalinity is already at 200.

example_df <- water_df %>%
  define_water_chain() %>%
  solvedose_alk_once(input_water = "defined_water", target_alk = 100, chemical = "na2co3")

example_df <- water_df %>%
  define_water_chain() %>%
  mutate(target_alk = seq(100, 210, 10)) %>%
  solvedose_alk_once(chemical = "na2co3")

# Initialize parallel processing
plan(multisession, workers = 2) # Remove the workers argument to use all available compute
example_df <- water_df %>%
  define_water_chain() %>%
  mutate(target_alk = seq(100, 210, 10)) %>%
  solvedose_alk_once(chemical = "na2co3")

# Optional: explicitly close multisession processing
plan(sequential)

See Also

solvedose_alk