tidywater (version 0.9.0)

biofilter_toc: Determine TOC removal from biofiltration using Terry & Summers BDOC model

Description

This function applies the Terry model to a water created by define_water to determine biofiltered DOC (mg/L). All particulate TOC is assumed to be removed so TOC = DOC. For a single water use biofilter_toc; for a dataframe use biofilter_toc_chain. Use pluck_water to get values from the output water as new dataframe columns. For most arguments in 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

biofilter_toc(water, ebct, ozonated = TRUE)
biofilter_toc_chain(
  df,
  input_water = "defined_water",
  output_water = "biofiltered_water",
  ebct = "use_col",
  ozonated = "use_col"
)

Value

biofilter_toc returns water class object with modeled DOC removal from biofiltration.

biofilter_toc_chain returns a data frame containing a water class column with updated DOC, TOC, and UV254 water slots.

Arguments

water

Source water object of class "water" created by define_water.

ebct

The empty bed contact time (min) used for the biofilter.

ozonated

Logical; TRUE if the water is ozonated (default), FALSE otherwise.

df

a data frame containing a water class column, which has already been computed using define_water_chain. The df may include a column indicating the EBCT or whether the water is ozonated.

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 "biofiltered_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

library(tidywater)
water <- define_water(ph = 7, temp = 25, alk = 100, toc = 5.0, doc = 4.0, uv254 = .1) %>%
  biofilter_toc(ebct = 10, ozonated = FALSE)


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

example_df <- water_df %>%
  define_water_chain() %>%
  biofilter_toc_chain(input_water = "defined_water", ebct = 10, ozonated = FALSE)

example_df <- water_df %>%
  define_water_chain() %>%
  mutate(
    BiofEBCT = c(10, 10, 10, 15, 15, 15, 20, 20, 20, 25, 25, 25),
    ozonated = c(rep(TRUE, 6), rep(FALSE, 6))
  ) %>%
  biofilter_toc_chain(input_water = "defined_water", ebct = BiofEBCT)

# \donttest{
# Initialize parallel processing
library(furrr)
# plan(multisession)
example_df <- water_df %>%
  define_water_chain() %>%
  biofilter_toc_chain(input_water = "defined_water", ebct = c(10, 20))

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