tidywater (version 0.9.0)

pac_toc: Calculate DOC Concentration in PAC system

Description

Calculates DOC concentration multiple linear regression model found in 2-METHYLISOBORNEOL AND NATURAL ORGANIC MATTER ADSORPTION BY POWDERED ACTIVATED CARBON by HYUKJIN CHO (2007). Assumes all particulate TOC is removed when PAC is removed; therefore TOC = DOC in output. For a single water use pac_toc; for a dataframe use pac_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.

water must contain DOC or TOC value.

Usage

pac_toc(water, dose, time, type = "bituminous")
pac_toc_chain(
  df,
  input_water = "defined_water",
  output_water = "pac_water",
  dose = "use_col",
  time = "use_col",
  type = "use_col"
)

Value

pac_toc returns a water class object with updated DOC, TOC, and UV254 slots.

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

Arguments

water

Source water object of class "water" created by define_water

dose

Applied PAC dose (mg/L). Model results are valid for doses concentrations between 5 and 30 mg/L.

time

Contact time (minutes). Model results are valid for reaction times between 10 and 1440 minutes

type

Type of PAC applied, either "bituminous", "lignite", "wood".

df

a data frame containing a water class column, which has already been computed using define_water_chain. The df may include columns named for the dose, time, and type

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 "pac_water".

Details

The function will calculate DOC concentration by PAC adsorption in drinking water treatment. UV254 concentrations are predicted based on a linear relationship with DOC.

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

water <- define_water(toc = 2.5, uv254 = .05, doc = 1.5) %>%
  pac_toc(dose = 15, time = 50, type = "wood")


library(dplyr)

example_df <- water_df %>%
  define_water_chain("raw") %>%
  mutate(dose = seq(11, 22, 1), PACTime = 30) %>%
  pac_toc_chain(input_water = "raw", time = PACTime, type = "wood")

# \donttest{
# Initialize parallel processing
library(furrr)
# plan(multisession)
example_df <- water_df %>%
  define_water_chain("raw") %>%
  pac_toc_chain(input_water = "raw", dose = 4, time = 8)

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