blend_waters: Determine blended water quality from multiple waters based on mass balance and acid/base equilibrium

Description

This function takes a vector of waters defined by define_water and a vector of ratios and outputs a new water object with updated ions and pH. For a single blend use blend_waters; for a dataframe use blend_waters_chain. Use pluck_water to get values from the output water as new dataframe columns.

Usage

blend_waters(waters, ratios)
blend_waters_chain(df, waters, ratios, output_water = "blended_water")

Value

blend_waters returns a water class object with blended water quality parameters.

blend_waters_chain returns a data frame with a water class column containing blended water quality

Arguments

waters: Vector of source waters created by define_water. For chain function, this can include quoted column names and/or existing single water objects unquoted.
ratios: Vector of ratios in the same order as waters. (Blend ratios must sum to 1). For chain function, this can also be a list of quoted column names.
df: a data frame containing a water class column, which has already been computed using define_water_chain
output_water: name of output column storing updated parameters with the class, water. Default is "blended_water".

Details

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

water1 <- define_water(7, 20, 50)
water2 <- define_water(7.5, 20, 100, tot_nh3 = 2)
blend_waters(c(water1, water2), c(.4, .6))


library(dplyr)

example_df <- water_df %>%
  slice_head(n = 3) %>%
  define_water_chain() %>%
  chemdose_ph_chain(naoh = 22) %>%
  mutate(
    ratios1 = .4,
    ratios2 = .6
  ) %>%
  blend_waters_chain(
    waters = c("defined_water", "dosed_chem_water"),
    ratios = c("ratios1", "ratios2"), output_water = "Blending_after_chemicals"
  )

# \donttest{
waterA <- define_water(7, 20, 100, tds = 100)
example_df <- water_df %>%
  slice_head(n = 3) %>%
  define_water_chain() %>%
  blend_waters_chain(waters = c("defined_water", waterA), ratios = c(.8, .2))

# Initialize parallel processing
library(furrr)
# plan(multisession)
example_df <- water_df %>%
  define_water_chain() %>%
  balance_ions_chain() %>%
  chemdose_ph_chain(naoh = 22, output_water = "dosed") %>%
  blend_waters_chain(waters = c("defined_water", "dosed", "balanced_water"), ratios = c(.2, .3, .5))

# Optional: explicitly close multisession processing
# plan(sequential)
# }