solvedose_alk: Calculate a desired chemical dose for a target alkalinity

Description

This function calculates the required amount of a chemical to dose based on a target alkalinity and existing water quality. Returns numeric value for dose in mg/L. Uses uniroot on the chemdose_ph function. For a single water, use solvedose_alk; to apply the model to a dataframe, use solvedose_alk_once. For most arguments, the _once 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

solvedose_alk(water, target_alk, chemical)
solvedose_alk_once(
  df,
  input_water = "defined_water",
  output_column = "dose_required",
  target_alk = "use_col",
  chemical = "use_col"
)

Value

solvedose_alk returns a numeric value for the required chemical dose.

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

Arguments

water: Source water of class "water" created by define_water
target_alk: The final alkalinity in mg/L as CaCO3 to be achieved after the specified chemical is added.
chemical: The chemical to be added. Current supported chemicals include: acids: "hcl", "h2so4", "h3po4", "co2", bases: "naoh", "na2co3", "nahco3", "caoh2", "mgoh2"
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".

Details

solvedose_alk uses stats::uniroot() on chemdose_ph to match the required dose for the requested alkalinity target.

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

Run this code

dose_required <- define_water(ph = 7.9, temp = 22, alk = 100, 80, 50) %>%
  solvedose_alk(target_alk = 150, "naoh")

library(dplyr)

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

# \donttest{
# Initialize parallel processing
library(furrr)
# plan(multisession)
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)
# }