Type:	Package
Title:	Process Biogas Data and Predict Biogas Production
Version:	1.64.0
Date:	2025-04-05
Maintainer:	Sasha D. Hafner <sasha.hafner@bce.au.dk>
VignetteBuilder:	knitr
Depends:	R (≥ 3.5.0)
Suggests:	minpack.lm, knitr, ggplot2, tinytest
Description:	Functions for calculating biochemical methane potential (BMP) from laboratory measurements and other types of data processing and prediction useful for biogas research. Raw laboratory measurements for diverse methods (volumetric, manometric, gravimetric, gas density) can be processed to calculate BMP. Theoretical maximum BMP or methane or biogas yield can be predicted from various measures of substrate composition. Molar mass and calculated oxygen demand (COD') can be determined from a chemical formula. Measured gas volume can be corrected for water vapor and to standard (or user-defined) temperature and pressure. Gas quantity can be converted between volume, mass, and moles. A function for planning BMP experiments can consider multiple constraints in suggesting substrate or inoculum quantities, and check for problems. Inoculum and substrate mass can be determined for planning BMP experiments. Finally, a set of first-order models can be fit to measured methane production rate or cumulative yield in order to extract estimates of ultimate yield and kinetic constants. See Hafner et al. (2018) <doi:10.1016/j.softx.2018.06.005> for details. OBA is a web application that provides access to some of the package functionality: https://biotransformers.shinyapps.io/oba1/. The Standard BMP Methods website documents the calculations in detail: https://www.dbfz.de/en/BMP.
License:	GPL-2
URL:	https://github.com/sashahafner/biogas/, https://www.dbfz.de/en/BMP
NeedsCompilation:	no
Packaged:	2025-04-05 10:08:48 UTC; sasha
Author:	Sasha D. Hafner [aut, cre], Charlotte Rennuit [aut], Camilla Justesen [aut], Nanna Lojborg [aut], Jacob Mortensen [aut], Jonas Ohlsson [aut], Sergi Astals [ctb], Konrad Koch [ctb], Soeren Weinrich [ctb], Jin Mi Triolo [ctb], Ali Heidarzadeh Vazifehkhoran [ctb]
Repository:	CRAN
Date/Publication:	2025-04-05 14:20:06 UTC

Biogas Volume and Mass Loss from BMP bottles

Description

Gas density biochemical methane potential (GD-BMP) measurements for 9 batch anaerobic bottles.

Usage

data("UQGDBiogas")

Format

A data frame with 135 observations on the following variables.

id

identification code, a unique value for each bottle in the dataset. A factor.

time.d

elapsed time of measurements (from bottle setup) in days, a numeric vector.

vol

measured biogas volume at the end of a sampling interval, a numeric vector.

mass.init

total bottle mass prior to biogas venting in g, a numeric vector.

mass.final

total bottle mass after biogas venting in g, a numeric vector.

Details

These data are meant to be example data for calcBgGD, for gas density-based determination of biogas production (GD-BMP). Reactors were glass serum bottles with butyl rubber septa. Masses were measured with an analytical balance. Data in UQGDSetup are from the same bottles.

Source

Measurements were made by Sergi Astals and are described in the following paper.

Justesen, C.G., Astals, S., Mortensen, J.R., Thorsen, R., Koch, K., Weinrich, S., Triolo, J.M., Hafner, S.D. 2019. Development and validation of a low-cost gas density method for measuring biochemical potential (BMP) Water (MDPI) 11(12): 2431.

Examples

data(UQGDBiogas)

BMP Bottle Setup Information

Description

Details on bottle contents and more from GD-BMP experiment.

Usage

data("UQGDSetup")

Format

A data frame with 9 observations on the following variables.

id

identification code, a unique value for each bottle in the dataset. A factor.

descrip

description of substrate, a factor with levels Inoculum, Cellulose, Substrate C.

m.inoc

mass of inoculum added to the bottle at the start in g. Numeric.

m.sub

mass of substrate added to the bottle at the start in g. Numeric.

m.tot

total mass added to the bottle at the start in g. Numeric.

m.sub.vs

mass of volatile solids (VS) of substrate added to the bottle at the start in g. Numeric.

vol.hs

bottle headspace in mL. Numeric.

isr

VS-based inoculum-to-substrate ratio.

Details

These data are meant to be example data for calcBgGD, for gas density-based determination of biogas production (GD-BMP). Reactors were glass serum bottles with butyl rubber septa. Data in UQGDBiogas are from the same bottles.

Source

Measurements were made by Sergi Astals and are described in the following paper.

Justesen, C.G., Astals, S., Mortensen, J.R., Thorsen, R., Koch, K., Weinrich, S., Triolo, J.M., Hafner, S.D. 2019. Development and validation of a low-cost gas density method for measuring biochemical potential (BMP) Water (MDPI) 11(12): 2431.

Examples

data(UQGDSetup)

Mass Loss and Biogas Composition from BMP bottles

Description

Gravimetric biochemical methane potential measurements for 9 batch anaerobic bottles.

Usage

data("UQGravBiogas")

Format

A data frame with 135 observations on the following variables.

id

identification code, a unique value for each bottle in the dataset. A factor.

day

elapsed time of measurements (from bottle setup) in days, a numeric vector.

mass.init

total bottle mass prior to biogas venting in g, a numeric vector.

mass.final

total bottle mass after biogas venting in g, a numeric vector.

xCH4

biogas methane content as a mole fraction (normalized so methane and carbon dioxide sum to 1.0), a numeric vector.

Details

These data are meant to be example data for calcBgGrav, for gravimetric determination of biogas production. Reactors were glass serum bottles with butyl rubber septa. Masses were measured with an analytical balance. Data in UQGravSetup are from the same bottles.

Source

Measurements were made by Sergi Astals and are described in the following paper.

Justesen, C.G., Astals, S., Mortensen, J.R., Thorsen, R., Koch, K., Weinrich, S., Triolo, J.M., Hafner, S.D. 2019. Development and validation of a low-cost gas density method for measuring biochemical potential (BMP) Water (MDPI) 11(12): 2431.

Examples

data(UQGravBiogas)

BMP Bottle Setup Information

Description

Details on bottle contents and more from a gravimetric BMP test.

Usage

data("UQGravSetup")

Format

A data frame with 9 observations on the following variables.

id

identification code, a unique value for each bottle in the dataset. A factor.

descrip

description of substrate, a factor with levels Inoculum, Cellulose, Substrate C.

m.inoc

mass of inoculum added to the bottle at the start in g. Numeric.

m.sub

mass of substrate added to the bottle at the start in g. Numeric.

m.tot

total mass added to the bottle at the start in g. Numeric.

vol.hs

bottle headspace in mL. Numeric.

m.sub.vs

mass of volatile solids (VS) of substrate added to the bottle at the start in g. Numeric.

m.inoc.vs

mass of volatile solids (VS) of inoculum added to the bottle at the start in g. Numeric.

isr

VS-based inoculum-to-substrate ratio.

Details

These data are meant to be example data for calcBgGrav, for gravimetric determination of biogas production. Reactors were glass serum bottles with butyl rubber septa. Data in UQGravBiogas are from the same bottles.

Source

Measurements were made by Sergi Astals and are described in the following paper.

Justesen, C.G., Astals, S., Mortensen, J.R., Thorsen, R., Koch, K., Weinrich, S., Triolo, J.M., Hafner, S.D. 2019. Development and validation of a low-cost gas density method for measuring biochemical potential (BMP) Water (MDPI) 11(12): 2431.

Examples

data(UQGravSetup)

Calculate Biogas Production from Gas Density Data (GD-BMP)

Description

calcBgGD (for calculate biogas from GD (gas density) measurements) calculates cumulative biogas, methane production and production rates from mass loss and volume measurements for any number of bottles.

Usage

calcBgGD(
  # Main arguments
  dat, 
  temp.vol,
  temp.grav,
  pres.vol,
  pres.grav,
  # Column names
  id.name, 
  time.name, 
  vol.name,
  m.pre.name = NULL,
  m.post.name,
  comp.name = 'xCH4',       
  # Settings
  vented.mass = FALSE,
  averaging = 'final',  
  temp.init = NULL,       
  pres.init = NULL,      
  headspace = NULL,     
  vol.hs.name = NULL,  
  headcomp = 'N2',    
  # Calculation method and other settings
  vmethod = 'vol',   
  comp.lim = c(0, 1), 
  comp.sub = NA,     
  imethod = 'linear',
  extrap = FALSE,
  addt0 = TRUE,
  showt0 = TRUE,
  dry = FALSE,
  # Warnings and messages
  std.message = TRUE,
  check = TRUE,
  # Units and standard conditions
  temp.std = getOption('temp.std', as.numeric(NA)),
  pres.std = getOption('pres.std', as.numeric(NA)),
  unit.temp = getOption('unit.temp', 'C'),
  unit.pres = getOption('unit.pres', 'atm')
  ) 

Arguments

Details

Using volume and mass loss data from dat, this function will calculate standardized biogas and methane production for each observation using the gas density (GD) method. See reference below for details on the method.

Standard values and units for temperature and pressure can be globally set using the function options. See stdVol.

Value

a data frame with all the columns originally present in dat, plus others including these:

Units are based on units in input data.

Author(s)

Sasha D. Hafner, Camilla Justesen, Jacob Mortensen

References

Justesen, C.G., Astals, S., Mortensen, J.R., Thorsen, R., Koch, K., Weinrich, S., Triolo, J.M., Hafner, S.D. 2019. Development and validation of a low-cost gas density method for measuring biochemical potential (BMP) Water (MDPI) 11(12): 2431.

See Also

calcBgMan, calcBgVol, summBg, interp, stdVol, options

Examples

 

data("UQGDBiogas")
data("UQGDSetup")

head(UQGDBiogas)
head(UQGDSetup)

cbg <- calcBgGD(UQGDBiogas,
                temp.vol = 20, pres.vol = 1013.25,
                temp.grav = 30, pres.grav = 1500,
                id.name = 'id', vol.name = 'vol',
                m.pre.name = 'mass.init', m.post.name = 'mass.final',
                time.name = 'time.d', unit.pres = 'mbar')


BMP <- summBg(cbg, UQGDSetup, id.name = "id",
              time.name = 'time.d', descrip.name = 'descrip',
              inoc.name = "Inoculum", inoc.m.name = "m.inoc", norm.name = "m.sub.vs",
              when = 'end')

BMP

Calculate Biogas Production from Gravimetric Data

Description

calcBgGrav calculates cumulative biogas, methane production and production rates from mass loss (gravimetric measurements) and volume measurements for any number of bottles.

Usage

calcBgGrav(
  # Main arguments
  dat,
  comp = NULL,              
  temp = NULL,
  pres = NULL,
  data.struct = 'longcombo',
  id.name = 'id',
  time.name = 'time',
  mass.name,    
  xCH4.name = 'xCH4', 
  xCO2.name = NULL,   
  xN2.name = NULL,    
  headspace = NULL,  
  vol.hs.name = 'vol.hs',
  temp.init = NULL, 
  pres.init = NULL,
  pres.resid = NULL, 
  headcomp = 'N2',  
  cmethod = 'removed',
  imethod = 'linear',
  extrap = FALSE,
  addt0 = TRUE,
  showt0 = TRUE,
  std.message = !quiet,
  check = TRUE,
  temp.std = getOption('temp.std', as.numeric(NA)),
  pres.std = getOption('pres.std', as.numeric(NA)),
  unit.temp = getOption('unit.temp', 'C'),
  unit.pres = getOption('unit.pres', 'atm'),
  quiet = FALSE
  ) 

Arguments

Details

Using mass loss data from dat and composition data from dat or comp, this function will calculate standardized biogas and methane production for each observation using a grvimetric method. See reference below for details on the method.

Standard values and units for temperature and pressure can be globally set using the function options. See stdVol.

Value

a data frame with all the columns originally present in dat, plus others including these:

Units are based on units in input data.

Author(s)

Sasha D. Hafner

References

Hafner, S.D., Rennuit, C., Triolo, J.M., Richards, B.K. 2015. Validation of a simple gravimetric method for measuring biogas production in laboratory experiments. Biomass and Bioenergy 83, 297-301.

See Also

calcBgMan, calcBgVol, calcBgGD, summBg, interp, stdVol, options

Examples

 

# Combined data frame with mass and composition for \code{data.struct = 'longcombo'} (default).

data('UQGravBiogas')
data('UQGravSetup')

cbg <- calcBgGrav(UQGravBiogas, temp = 35, pres = 1.5,
                   id.name = 'id', time.name = 'day', 
		   mass.name = 'mass.final',
                   xCH4.name = 'xCH4')

head(cbg)

# With separate mass and composition data frames
data('strawMass')
data('strawComp')
data('strawSetup')

cbg0 <- calcBgGrav(strawMass,
                   comp = strawComp, temp = 35, pres = 1.5,
                   data.struct = 'long', id.name = 'bottle',
                   time.name = 'time', mass.name = 'mass',
                   xCH4.name = 'xCH4')
warnings()

cbg <- calcBgGrav(strawMass,
                  comp = strawComp, temp = 35, pres = 1.5,
                  data.struct = 'long', id.name = 'bottle',
                  time.name = 'time', mass.name = 'mass',
                  xCH4.name = 'xCH4', extrap = TRUE)

head(cbg)

Calculate Cumulative Biogas Production from Pressure Data

Description

calcBgMan (for cumulative biogas manometric) calculates cumulative biogas, methane production and production rates from individual pressure and composition measurements for any number of reactors.

Usage

calcBgMan(
  # Main arguments
  dat, comp = NULL, temp, interval = TRUE,
  data.struct = 'longcombo',
  # Column names 
  id.name = 'id', time.name = 'time',
  pres.name = 'pres', comp.name = NULL,
  # Additional arguments
  pres.resid = NULL, temp.init = NULL, pres.init = NULL,
  rh.resid = NULL,
  rh.resid.init = 1, headspace = NULL, 
  vol.hs.name = 'vol.hs',
  absolute = TRUE, pres.amb = NULL,
  # Calculation method and other settings
  cmethod = 'removed', imethod = 'linear', extrap = FALSE,
  addt0 = TRUE, showt0 = TRUE, 
  # Warnings and messages
  std.message = !quiet, 
  check = TRUE,
  # Units and standard conditions
  temp.std = getOption('temp.std', as.numeric(NA)), 
  pres.std = getOption('pres.std', as.numeric(NA)),
  unit.temp = getOption('unit.temp', 'C'),
  unit.pres = getOption('unit.pres', 'atm'),
  quiet = FALSE
  ) 

Arguments

Details

Using pressure data from dat and gas composition from comp, this function will calculate standardised biogas and methane production (if comp is provided) for each observation, interpolating comp.name (from comp argument) to each time.name in dat if needed, and summing these for cumulative values. Inputs units are specified with unit.pres, and output volumes are the same as headspace volume, e.g., mL or L.

Biogas composition (comp.name column in comp) is specified as the mole fraction of methane in dry biogas, normalised so mole fractions of methane and carbon dioxide sum to unity (Richards et al. 1991). Alternatively, if cmethod is set to "total", biogas composition is the mole fraction of methane in dry biogas (include all the other gases except water).

Standard values and units for temperature and pressure can be globally set using the function options. See stdVol. To surpress volume correction to a “standard” temperature and pressure, leave temp as NULL (the default).

If check = TRUE, the input values of mole fraction of methane in biogas are checked, and a warning is returned if the are outside 0, 1.

See associated vignette (calcBgMan_function.Rnw) for more information.

Value

a data frame with all the columns originally present in dat, plus these others:

Author(s)

Sasha D. Hafner and Nanna Lojborg

References

Hafner, S.D., Rennuit, C., Triolo, J.M., Richards, B.K. 2015. Validation of a simple gravimetric method for measuring biogas production in laboratory experiments. Biomass and Bioenergy 83, 297-301.

Hansen, T.L., Schmidt, J.E., Angelidaki, I., Marca, E., Jansen, J. la C., Mosbak, H. and Christensen, T.H. 2004. Method for determination of methane potentials of solid organic waste. Waste Management 24, 393-400

Richards, B.K., Cummings, R.J., White, T.E., Jewell, W.J. 1991. Methods for kinetic analysis of methane fermentation in high solids biomass digesters. Biomass and Bioenergy 1: 65-73.

See Also

cumBg, calcBgVol, calcBgGD, summBg, interp, stdVol, options

Examples

# Example with longcombo structured input data frame
data("sludgeTwoBiogas")
data("sludgeTwoSetup")


head(sludgeTwoBiogas)
head(sludgeTwoSetup)

# Calculate cumulative production and rates 
# Pressure is gauge (not absolute) so absolute argument needed
# Data structure is default of longcombo
cbg <- calcBgMan(sludgeTwoBiogas, temp = 30,
                 id.name = "id", time.name = "time.d", 
                 pres.name = "pres", comp.name = "xCH4n",
                 temp.init = 30, pres.resid = 0, pres.init = 0,
                 headspace = sludgeTwoSetup, vol.hs.name = "vol.hs",
                 pres.amb = 1013, absolute = FALSE,
                 unit.pres = "mbar")
head(cbg)

# Plot results
## Not run: 
  # Not run just because it is a bit slow
  library(ggplot2)

  ggplot(cbg, aes(time.d, cvCH4, colour = factor(id))) + 
    geom_point() +
    geom_line(aes(group = id)) +
    labs(x = "Time (d)", y = "Cumulative methane production  (mL)", colour = "Bottle id")  + 
    theme_bw() 

## End(Not run)

# This sludgeTwoBiogas dataset has original xCH4 as well as normalized values
# So "method 2" can also be used by changing comp.name and cmethod arguments
cbg2 <- calcBgMan(sludgeTwoBiogas, temp = 30,
                  id.name = "id", time.name = "time.d", 
                  pres.name = "pres", comp.name = "xCH4",
                  temp.init = 30, pres.resid = 0, pres.init = 0,
                  headspace = sludgeTwoSetup, vol.hs.name = "vol.hs",
                  pres.amb = 1013, cmethod = 'total', absolute = FALSE,
                  unit.pres = "mbar")
head(cbg2)

# Compare
quantile(cbg2$vCH4 - cbg$vCH4)
# Median difference of 0.2 mL

# Example with long structured input data frame
data("strawPressure")
data("strawComp")
data("strawSetup")

# Need to specify data structure with \code{data.struct} argument
# Using default values for time.name, pres.name
cbg <- calcBgMan(strawPressure, comp = strawComp, temp = 31,
                 data.struct = "long",
                 id.name = "bottle", comp.name = "xCH4",
                 temp.init = 21.55, pres.resid = "pres.resid", pres.init = 0,
                 headspace = strawSetup, vol.hs.name = "headspace",
                 pres.amb = 101.3, absolute = FALSE,
                 unit.pres = "kPa")

# Because of missing composition measurements at last time for some bottles
# CH4 volume will be missing
# Can estimate xCH4 here by extrapolation using argument of same name

cbg2 <- calcBgMan(strawPressure, comp = strawComp, temp = 31,
                 data.struct = "long",
                 id.name = "bottle", comp.name = "xCH4",
                 temp.init = 21.55, pres.resid = "pres.resid", pres.init = 0,
                 headspace = strawSetup, vol.hs.name = "headspace",
                 pres.amb = 101.3, absolute = FALSE, 
                 extrap = TRUE, unit.pres = "kPa")


# For example with wide structured input data frame calcBgVol() help file

Calculate Cumulative Biogas Production from Volumetric Data

Description

calcBgVol (for calculation of biogas production from volumetric) measurements) calculates cumulative biogas, methane production and production rates from individual volume and composition measurements for any number of bottles.

Usage

calcBgVol(
  # Main arguments
  dat, comp = NULL, temp = NULL,
  pres = NULL, interval = TRUE, 
  data.struct = 'longcombo',
  # Column names
  id.name = 'id', time.name = 'time', vol.name = 'vol',
  comp.name = NULL,
  # Additional arguments
  headspace = NULL, vol.hs.name = 'vol.hs',
  # Calculation method and other settings
  cmethod = 'removed', imethod = 'linear', extrap = FALSE, 
  addt0 = TRUE, showt0 = TRUE,
  dry = FALSE, 
  empty.name = NULL,
  # Warnings and messages
  std.message = !quiet,
  check = TRUE,
  # Units and standard conditions
  temp.std = getOption('temp.std', as.numeric(NA)),
  pres.std = getOption('pres.std', as.numeric(NA)),
  unit.temp = getOption('unit.temp', 'C'),
  unit.pres = getOption('unit.pres', 'atm'),
  quiet = FALSE
  ) 

Arguments

Details

Using volume data from dat and gas composition from comp, this function will calculate standardised biogas and methane production (if comp is provided) for each observation, interpolating comp.name (from comp argument) to each time.name in dat if needed, and summing these for cumulative values. All volumes (input and output) have the same units, e.g., mL, L, SCF.

Biogas composition (comp.name column in comp) is specified as the mole fraction of methane in dry biogas, normalised so mole fractions of methane and carbon dioxide sum to unity (Richards et al. 1991). Alternatively, if cmethod is set to "total", biogas composition is the mole fraction of methane in dry biogas (include all the other gases except water).

Standard values and units for temperature and pressure can be globally set using the function options. See stdVol. To surpress volume correction to a “standard” temperature and pressure, leave temp as NULL (the default).

If check = TRUE, the input values of mole fraction of methane in biogas are checked, and a warning is returned if the are outside 0, 1.

See associated vignette (calcBgVol_function.Rnw) for more information.

Value

a data frame with all the columns originally present in dat, plus these others:

Author(s)

Sasha D. Hafner and Nanna Lojborg

References

Hafner, S.D., Rennuit, C., Triolo, J.M., Richards, B.K. 2015. Validation of a simple gravimetric method for measuring biogas production in laboratory experiments. Biomass and Bioenergy 83, 297-301.

Richards, B.K., Cummings, R.J., White, T.E., Jewell, W.J. 1991. Methods for kinetic analysis of methane fermentation in high solids biomass digesters. Biomass and Bioenergy 1: 65-73.

See Also

cumBg, calcBgMan, calcBgGD, summBg, interp, stdVol, options

Examples

 
# Example with long structured input data frame

data("s3lcombo")
s3lcombo

# Calculate cumulative production and rates from s3lcombo
# With default data structure comp argument is not needed
# Necessary to extrapolate because first observations are missing xCH4
cbg <- calcBgVol(s3lcombo, 
                 temp = 25, pres = 1,
                 id.name = 'id', time.name = 'time.d', 
                 vol.name = 'vol.ml', comp.name = 'xCH4', 
                 extrap = TRUE)
head(cbg)

# Plot results
## Not run: 
  # Not run just because it is a bit slow
  ggplot(cbg, aes(time.d, cvCH4, colour = id)) + 
         geom_point() + geom_line(aes(group = id)) +
         labs(x = "Time (d)", y = "Cumulative methane production  (mL)", colour = "Bottle ID")  + 
         theme_bw() 

  plot(ggplot)

## End(Not run)


# Wide data structure, from AMPTS II in this case
data("feedVol")
head(feedVol)

# By default biogas is assumed to be saturated with water vapor
# Composition is set to a single value. 
# Data are cumulative
args(calcBgVol)
cbg <- calcBgVol(feedVol, comp = 1, temp = 0, pres = 1,
                 interval = FALSE, data.struct = 'wide',
                 id.name = "id", time.name = 'time.d', vol.name = '1', 
                 dry = TRUE)

head(cbg)


# Calculate cumulative production and rates from vol and comp
# Biogas volume and composition can be in separate data frames
data("vol")
data("comp")

head(vol)
head(comp)

# extrap = TRUE is needed to get CH4 results here because first xCH4 values are missing
cbg <- calcBgVol(vol, comp = comp, temp = 20, pres = 1, 
                 data.struct = "long",
                 id.name = "id", time.name = "days", comp.name = "xCH4", 
                 vol.name = "vol", extrap = TRUE)

head(cbg)

Calculate Oxygen Demand

Description

calcCOD is used to calculate the oxygen demand (“calculated oxygen demand”, or COD' as described by Rittmann and McCarty (2001)) of a compound.

Usage

calcCOD(form)

Arguments

Details

Based on Eqs. (2.2) and (2.3) in Rittmann and McCarty (2001) (p 128), but using molar mass calculated with molMass. calcCOD is not case-sensitive as long as all elements have single letter abbreviations. The function is vectorized.

Value

A numeric vector with length equal to length of form with COD' in g of oxygen per g of compound.

Author(s)

Sasha D. Hafner and Charlotte Rennuit

References

Rittmann, B., McCarty, P. 2001 Environmental Biotechnology. McGraw-Hill.

See Also

molMass, predBg

Examples

  calcCOD("C6H12O6")

  calcCOD("CH3COOH")

  calcCOD("CH3CH2OH")

  calcCOD("CH4")

  calcCOD("ch4")

  calcCOD(c("C6H12O6", "CH3COOH", "CH3CH2OH"))

Methane Content of Biogas

Description

Methane content (biogas composition) measurements from nine batch reactors.

Usage

data("comp")

Format

A data frame with 132 observations on the following 4 variables.

id

identification code, a unique value for each reactor in the dataset. A factor with levels 2_1, 2_2, 2_3, 2_4, 2_5, 2_6, 2_7, 2_8, 2_9, 2_10, 2_11, and 2_12

date.time

date and time of mass measurement, a POSIXct object.

days

elapsed time of mass measurements (from reactor setup) in days, a numeric vector.

xCH4

biogas methane content as a mole fraction, excluding water and all other gases other than carbon dioxide, a numeric vector

Details

These data are meant to be example data for multiple functions, e.g., interp, cumBg, or summBg. Reactors were 500 mL glass serum bottles with butyl rubber septa and screw caps. Methane and carbon dioxide contents were determined by gas chromatography using a thermal conductivity detector and normalised so methane and carbon dioxide sum to 1.0. Data in vol, mass, comp, and setup are from the same reactors.

Source

Measurements by Charlotte Rennuit and Ali Heidarzadeh Vazifehkhoran.

Examples

data(comp)

Methane Content of Biogas

Description

Methane content (biogas composition) measurements from 15 batch reactors.

Usage

data("comp2")

Format

A data frame with 360 observations on the following 3 variables.

bottle

identification code, a unique value for each reactor in the dataset. A factor with levels 1_1, 1_2, 1_3, 2_1, 2_2, 2_2, ... through 5_3. The first number indicates the sample, the second the replicate.

days

elapsed time of mass measurements (from reactor setup) in days, a numeric vector.

CH4.conc

biogas methane content as a mole fraction, excluding water and all other gases other than carbon dioxide, a numeric vector

Details

These data are meant to be example data for multiple functions, e.g., interp, cumBg, or summBg. Reactors were 500 mL or 1000 mL glass serum bottles with butyl rubber septa and screw caps. Methane and carbon dioxide contents were determined by gas chromatography using a thermal conductivity detector and normalised so methane and carbon dioxide sum to 1.0. Data in vol2, comp2, and setup2 are from the same reactors.

Source

Measurements made by Ali Heidarzadeh Vazifehkhoran

Examples

data(comp2)

Calculate Cumulative Biogas Production

Description

cumBg (for cumulative biogas) calculates cumulative biogas, methane production and production rates from individual volume (or mass) and composition measurements for any number of reactors.

Usage

cumBg(
  # Main arguments
  dat, dat.type = 'vol', comp = NULL, temp = NULL, 
  pres = NULL, interval = TRUE,
  data.struct = 'long',
  # Column names for volumetric method
  id.name = 'id', time.name = 'time', dat.name = dat.type,
  comp.name = 'xCH4', 
  # Additional arguments for manometric and gravimetric methods
  pres.resid = NULL, temp.init = NULL, pres.init = NULL,
  rh.resid = NULL,
  rh.resid.init = 1, headspace = NULL, 
  vol.hs.name = 'vol.hs', headcomp = 'N2',
  absolute = TRUE, pres.amb = NULL,
  # Additional arguments for GCA method
  mol.f.name = NULL, vol.syr = NULL,
  # Calculation method and other settings
  cmethod = 'removed', imethod = 'linear', extrap = FALSE,
  addt0 = TRUE, showt0 = TRUE, 
  dry = FALSE,
  empty.name = NULL,
  # Warnings and messages
  std.message = !quiet, 
  check = TRUE,
  # Units and standard conditions
  temp.std = getOption('temp.std', as.numeric(NA)), 
  pres.std = getOption('pres.std', as.numeric(NA)),
  unit.temp = getOption('unit.temp', 'C'),
  unit.pres = getOption('unit.pres', 'atm'),
  quiet = FALSE
  ) 

Arguments

Details

Using volume, mass, pressure, or molar quantity data from dat and gas composition from comp, this function will calculate standardised biogas and methane production (if comp is provided) for each observation, interpolating comp.name (from comp argument) to each time.name in dat if needed, and summing these for cumulative values. Use of volumetric measurements is the default case. However, this function is essentially deprecated for both volumetric and manometric methods, and the new calcBgMan and calcBgVol functions are recommended instead.

Alternatively, if reactor mass is given in dat, a gravimetric approach (Hafner et al. 2015) can be applied by setting dat.type to "mass". In this case, to determine total cumulative biogas production for batch reactors, an initial mass should be included for each reactor. If pressure is measured in dat, a manometric method can be applied by setting dat.type to "pres". Lastly, molar quantity of methane can be used in the absolute GC method by setting dat.type to "gca".

For the volumetric and absolute GC methods, all volumes (input and output) have the same units, e.g., mL, L, SCF. For the gravimetric method, output volumes will be in mL if input masses are in g (or in L if input masses are in kg, etc.). For the manometric method, inputs units are specified with unit.pres, and output volumes are the same as headspace volume, e.g., mL or L.

Biogas composition (comp.name column in comp) is specified as the mole fraction of methane in dry biogas, normalised so mole fractions of methane and carbon dioxide sum to unity (Richards et al. 1991). Alternatively, if cmethod is set to "total", biogas composition is the mole fraction of methane in dry biogas (include all the other gases except water).

Standard values and units for temperature and pressure can be globally set using the function options. See stdVol. To surpress volume correction to a “standard” temperature and pressure, leave temp as NULL (the default).

If check = TRUE, the input values of mole fraction of methane in biogas are checked, and a warning is returned if the are outside 0, 1.

Value

a data frame with all the columns originally present in dat, plus these others:

Author(s)

Sasha D. Hafner and Charlotte Rennuit

References

Hafner, S.D., Rennuit, C., Triolo, J.M., Richards, B.K. 2015. Validation of a simple gravimetric method for measuring biogas production in laboratory experiments. Biomass and Bioenergy 83, 297-301.

Hansen, T.L., Schmidt, J.E., Angelidaki, I., Marca, E., Jansen, J. la C., Mosbak, H. and Christensen, T.H. 2004. Method for determination of methane potentials of solid organic waste. Waste Management 24, 393-400

Richards, B.K., Cummings, R.J., White, T.E., Jewell, W.J. 1991. Methods for kinetic analysis of methane fermentation in high solids biomass digesters. Biomass and Bioenergy 1: 65-73.

See Also

calcBgMan, calcBgVol, calcBgGD, summBg, interp, stdVol, options

Examples

data("vol")
data("comp")

head(vol)
head(comp)

# Calculate cumulative production and rates from vol and comp
cum.prod <- cumBg(vol, comp = comp, temp = 20, pres = 1, id.name = "id", time.name = "days", 
		  comp.name = "xCH4", dat.name = "vol")
head(cum.prod)

# Note warnings and related NAs in results
# Set extrap = TRUE to extrapolate xCH4 to earliest times

# Calculate cumulative production and rates from vol and comp
cum.prod <- cumBg(vol, comp = comp, temp = 20, pres = 1, id.name = "id", time.name = "days", 
		  comp.name = "xCH4", dat.name = "vol", extrap = TRUE)
head(cum.prod)

# In this case, we can use default values for some column names, so this call is identical
cum.prod <- cumBg(vol, comp = comp, temp = 20, pres = 1, time.name = "days", extrap = TRUE)

# Plot results
## Not run: 
# Not run just because it is a bit slow
library(ggplot2)
qplot(x = days, y = cvCH4, data = cum.prod, xlab = "Time (d)", 
      ylab = "Cumulative methane production (mL)",color = id, geom = "line")

## End(Not run)

# Omit added time zero rows
cum.prod <- cumBg(vol, comp = comp, temp = 20, pres = 1, time.name = "days", extrap = TRUE, 
		  showt0 = FALSE)
head(cum.prod)

## Not run: 
# Not run just because it is a bit slow
qplot(x = days, y = cvCH4, data = cum.prod, xlab = "Time (d)", 
      ylab = "Cumulative methane production (mL)", color = id, geom = "line")

## End(Not run)

# Previous is different from never adding them in the first place (rates not calculated for first 
# observations here)
cum.prod <- cumBg(vol, comp = comp, temp = 20, pres = 1, time.name = "days", extrap = TRUE, 
		  addt0 = FALSE)
head(cum.prod)

## Not run: 
# Not run just because it is a bit slow
qplot(x = days, y = cvCH4, data = cum.prod, xlab = "Time (d)", 
      ylab = "Cumulative methane production (mL)", color = id, geom = "line")

## End(Not run)

# Can use POSIX objects for time (but cumBg cannot add t0 rows here)
class(vol$date.time)
class(comp$date.time)
cum.prod <- cumBg(vol, comp = comp, temp = 20, pres = 1, time.name = "date.time", extrap = TRUE)
head(cum.prod)

## Not run: 
# Not run just because it is a bit slow
qplot(x = date.time, y = cvCH4, data = cum.prod, xlab = "Time (d)", 
      ylab = "Cumulative methane production (mL)", color = id, geom = "line")

## End(Not run)

# Can leave out composition data, and then CH4 is not included in results
cum.prod <- cumBg(vol, temp = 20, pres = 1, time.name = "days")
head(cum.prod)

# Leave out pres or temp, and results are not standardised
cum.prod <- cumBg(vol, time.name = "days")
head(cum.prod)

# Example with input data frames with different column names
data("vol2")
data("comp2")

head(vol2)
head(comp2)

cum.prod <- cumBg(vol2, comp = comp2, temp = 20, pres = 1, 
                  id.name = "bottle", time.name = "days", 
		  dat.name = "meas.vol", comp.name = "CH4.conc")
head(cum.prod)
tail(cum.prod)

# Note warnings and related NAs in results
warnings()

# Set extrap = TRUE to avoid
cum.prod <- cumBg(vol2, comp = comp2, temp = 20, pres = 1, 
                  id.name = "bottle", time.name = "days", 
		  dat.name = "meas.vol", comp.name = "CH4.conc", 
                  extrap = TRUE)
head(cum.prod)

## Not run: 
# Not run just because it is a bit slow
qplot(x = days, y = cvCH4, data = cum.prod, xlab = "Time (d)", 
      ylab = "Cumulative methane production (mL)", 
      color = bottle, geom = "line")

## End(Not run)

# Mass example
data("mass")

mass

# Need to specify data type with dat.type argument (using default 
# values for id.name, dat.name, and comp.name)
cum.prod <- cumBg(mass, dat.type = "mass", comp = comp, temp = 35, 
                  pres = 1, time.name = "days")
cum.prod

# Drop time 0 rows
cum.prod <- cumBg(mass, dat.type = "mass", comp = comp, temp = 35, 
                  pres = 1, time.name = "days", showt0 = FALSE)
cum.prod

# Add initial headspace correction (alternatively, headspace could 
# be a data frame with a different volume for each reactor)
cum.prod <- cumBg(mass, dat.type = "mass", comp = comp, temp = 35, 
                  pres = 1, time.name = "days", headspace = 300, 
                  headcomp = "N2", temp.init = 20, showt0 = FALSE)
cum.prod

# Pressure example
data("strawPressure")
data("strawComp")
data("strawSetup")

cum.prod <- cumBg(strawPressure, dat.type = 'pres', 
                  comp = strawComp, temp = 35, 
                  id.name = 'bottle', time.name ='time', 
                  dat.name = 'pres', comp.name = 'xCH4',
                  pres.resid = 'pres.resid', temp.init = 20, 
                  pres.init = 101.325,
                  headspace = strawSetup, 
                  vol.hs.name = 'headspace', 
                  extrap = TRUE, 
                  unit.pres = 'kPa', pres.std = 101.325)

head(cum.prod)

# Absolute GC method (long format is the only option)

# Generate some data--two bottles with identical results
# Units for n1 and n2 are micromoles of CH4
biogas <- data.frame(id = rep(c('A', 'B'), each = 5), 
                     time.d = rep(1:5, 2), 
                     n1 = rep(c(1.1, 2.4, 3.8, 5.9, 2.3), 2), 
                     n2 = rep(c(NA, NA, NA, 1.2, NA), 2))

# Bottles were vented after the measurements on day 4
biogas

# Syringe volume is 0.25 mL
# Headspace volume is 50 mL

cp <- cumBg(biogas, id.name = 'id', time.name = 'time.d', 
            dat.name = 'n1', mol.f.name = 'n2', vol.syr= 0.25,
            headspace = 50, dat.type = 'gca')
cp

# Suppose the bottles had two difference headspace volumes
setup <- data.frame(id = c('A', 'B'), vol.hs = c(50, 60))

cp2 <- cumBg(biogas, id.name = 'id', time.name = 'time.d', 
            dat.name = 'n1', mol.f.name = 'n2', vol.syr= 0.25,
            headspace = setup, vol.hs.name = 'vol.hs', dat.type = 'gca')
cp2


# Different data structures
# Load example data
data("s3voll")
data("s3volw")
data("s3compl")
data("s3compw")
data("s3lcombo")

# wide

# Check data structure first
s3volw

s3compw

cum.prod <- cumBg(s3volw, comp = s3compw, temp = 25, pres = 1,
                  time.name = 'time.d',
                  data.struct = 'wide',
                  dat.name = 'D', comp.name = 'D',
                  extrap = TRUE)

cum.prod

# longcombo
s3lcombo

cum.prod <- cumBg(s3lcombo, temp = 25, pres = 1,
                  id.name = 'id', time.name = 'time.d',
                  data.struct = 'longcombo',
                  dat.name = 'vol.ml', comp.name = 'xCH4',
                  extrap = TRUE)

# Compare wide and longcombo to long (default)
s3voll
s3compl

cum.prod <- cumBg(s3lcombo, comp = s3compl, temp = 25, pres = 1,
                  id.name = 'id', time.name = 'time.d',
                  dat.name = 'vol.ml', comp.name = 'xCH4',
                  extrap = TRUE)

cum.prod

Setup Details for Batch Reactors

Description

Information on bottle id, substrates and, inoculum initial masses for 12 anaerobic bottles with animal feed ingredients for substrate.

Usage

data("feedSetup")

Format

A data frame with 12 observations on the following 3 variables:

descrip

description of bottle contents.

id

identification code, a unique value for each bottle in the dataset. Numeric with all values from 1 to 12.

m.inoc

mass of inoculum added to the bottle at the start in g, a numeric vector.

m.sub.vs

mass of volatile solids (VS) of substrate added to the bottle at the start in g, a numeric vector.

Details

These data are meant to be example data for summBg. Inoculum was digestate from a stable laboratory reactor (400 L) operated at mesophilic temperatures (38 degrees C) and a retention time of ca. 98 days Substrates were cellulose, wheat straw (WS), and animal feed ingredients (FI). Additinally, 1 mL each of a vitamin mixture and trace element solution was added to each BMP bottle.

BMP tests were operated with a total volume of ca. 400 mL at 38 degrees C.

Data in feedVol and feedSetup are from the same bottles.

Source

Data were originally collected by Soeren Weinrich and others at DBFZ in Leipzig Germany.

Examples

data(feedSetup)

Biogas Volume from Batch Reactors

Description

Cumulative standardized measurements of biogas volume from 12 batch bottles, organized in a “wide” format.

Usage

data("feedVol")

Format

A data frame with 44 observations on the following 13 variables (note that all biogas volume columns, 2-13, are similar):

time.d

elapsed time of volume measurements in days, a numeric vector.

1

cumulative standardized volume of biogas produced at time given in time from bottle “1” in mL, a numeric vector.

2

volume of biogas from bottle “2”.

3

volume of biogas from bottle “3”.

4

volume of biogas from bottle “4”.

5

volume of biogas from bottle “5”.

6

volume of biogas from bottle “6”.

7

volume of biogas from bottle “7”.

8

volume of biogas from bottle “8”.

9

volume of biogas from bottle “9”.

10

volume of biogas from bottle “10”.

11

volume of biogas from bottle “11”.

12

volume of biogas from bottle “12”.

Details

These data are meant to be example data for calcBgVol and summBg. Inoculum was digestate from a stable laboratory reactor (400 L) operated at mesophilic temperatures (38 degrees C) and a retention time of ca. 98 days Substrates were cellulose, wheat straw (WS), and animal feed ingredients (FI). Additinally, 1 mL each of a vitamin mixture and trace element solution was added to each BMP bottle. BMP tests were operated with a total volume of ca. 400 mL at 38 degrees C.

Biogas productions was measured using the online volumtric AMPTS II method. Carbon dioxide was produced using an alkaline trap, so biogas volume is equal to methane volume.

Data in feedSetup and feedVol are from the same bottles.

Source

Data were originally collected by Soeren Weinrich and others at DBFZ in Leipzig Germany.

Examples

data(feedVol)

Fit a First-Order Model to Biogas or Other Data

Description

fitFOM (FOM is for first order model) is a flexible function for fitting first-order models to batch biogas production data, typically from a biochemical methane potential (BMP) test.

Usage

fitFOM(dat, n.pool = 1, time.name = 'time.d', resp.name = 'cvCH4',
       fit.to = 'yield', method = 'Nelder-Mead', abs.err = FALSE, trans = TRUE,
       init = if (n.pool == 1) c(B = 'yield', k = 0.5) 
                else c(B = 'yield', f = 0.5, k1 = 0.01, k2 = 0.5), 
       fixed = NULL, fit.last = FALSE, lower = NULL, upper = NULL, lag.phase = FALSE)

Arguments

Details

Use for fitting a first-order model (estimation of best-fit parameter values). Intended for extracting kinetic constants and maximum methane potential from biochemical methane potential (BMP) test measurements.

Value

A list with parameter estimates and additional information. Most useful elements are coefs and coef.tab (best-fit parameter values), summ (a summary that includes model efficiency, error, and convergence information), and pred (model predicted or fitted values).

Author(s)

Sasha D. Hafner

References

Hafner, S.D., Koch, K., Carrere, H., Astals, S., Weinrich, S., Rennuit, C. 2018. Software for biogas research: Tools for measurement and prediction of methane production. SoftwareX 7 205-210. doi:10.1016/j.softx.2018.06.005

Examples

 
# First use example data to generate a specific methane potential (SMP) curve
library(biogas)
data('feedVol')
data('feedSetup')

# Cumulative biogas and CH4 production
cbg <- calcBgVol(feedVol, comp = 1, temp = 0, pres = 1,
                 interval = FALSE, data.struct = 'wide',
                 id.name = 'id', time.name = 'time.d', vol.name = '1', 
                 dry = TRUE)

# Get SMP
SMP <- summBg(vol = cbg, setup = feedSetup, time.name = 'time.d', 
               inoc.name = 'BK', inoc.m.name = 'm.inoc', when = 'meas',
               norm.name = 'm.sub.vs', show.obs = TRUE)

# Select bottle 9
s9 <- subset(SMP, id == 9)

# Fit model
mod1 <- fitFOM(s9, n.pool = 1, time.name = 'time.d', resp.name = 'cvCH4')

# View summary
mod1$summ

# Add model predictions
s9$cvCH4.pred <- mod1$pred

# And plot
plot(cvCH4 ~ time.d, data = s9, type = 'o')
lines(cvCH4.pred ~ time.d, data = s9, col = 'red')

# Fit to rates instead
mod1b <- fitFOM(s9, n.pool = 1, time.name = 'time.d', resp.name = 'cvCH4', fit.to = 'rate')
mod1b$summ

# Try 2 pools
mod2 <- fitFOM(s9, n.pool = 2, time.name = 'time.d', resp.name = 'cvCH4')
mod2$summ

# First pool effectively ignored in the fit
# Try different method (this one required minpack.lm package)
## Not run: 
mod2 <- fitFOM(s9, n.pool = 2, time.name = 'time.d', resp.name = 'cvCH4', method = 'LM')
mod2$summ

## End(Not run)

# Unfortunately, here is a big effect of method on the result!

s9$cvCH4.pred2 <- mod2$pred

plot(cvCH4 ~ time.d, data = s9, type = 'o')
lines(cvCH4.pred ~ time.d, data = s9, col = 'red')
lines(cvCH4.pred2 ~ time.d, data = s9, col = 'blue')

# Drop (exclude) lag phase
mod3 <- fitFOM(s9, n.pool = 2, time.name = 'time.d', resp.name = 'cvCH4', lag.phase = TRUE)
mod3$summ
s9$cvCH4.pred3 <- mod3$pred

lines(cvCH4.pred3 ~ time.d, data = s9, col = 'darkgreen')

Interpolate Biogas Composition or Cumulative Production

Description

interp interpolates (or extrapolates) biogas composition (methane concentration) or cumulative production data to a wanted time using one of several possible methods.

Usage

interp(times, y, time.out, method = "linear", extrap = FALSE)

Arguments

Details

interp is really a wrapper for the interpolation functions approx and spline. For cumulative production, which (usually) must monotonically increase, method = "hyman" is the best choice. Extrapolation behavior depends on method. For method = "linear", extrap = TRUE simply returns the value of the closest y. For manometric measurement with mix of venting/no venting with composition measurements only when venting use method = "f1". See approx and spline for more information.

Value

Interpolated estimates of y at given times.

Author(s)

Sasha D. Hafner and Charlotte Rennuit

See Also

cumBg

Examples

# Fake composition data 
dat <- data.frame(time = c(1, 7, 14, 28), 
                  xCH4 = c(0.3, 0.5, 0.61, 0.65))
interp(dat$time, dat$xCH4, time.out = 10)
interp(dat$time, dat$xCH4, time.out = 10, method = "natural")
interp(dat$time, dat$xCH4, time.out = c(10, 30))
interp(dat$time, dat$xCH4, time.out = c(10, 30), method = "natural")
interp(dat$time, dat$xCH4, time.out = c(10, 30), extrap = TRUE)

# Actual data
data(comp)
# Work with one reactor
bgc <- subset(comp, id=="2_1")

# With numeric time, interpolate to 1, 7, and 30 days
interp(bgc$days, bgc$xCH4, time.out = c(1, 7, 30))

# If extrapolation is OK
interp(bgc$days, bgc$xCH4, time.out = c(1, 7, 30), extrap = TRUE)

# Or POSIXct
interp(bgc$date.time, bgc$xCH4, 
       time.out = as.POSIXct("2014-07-12 13:00:00"))

# For cumulative gas production
data(vol)
# Work with one reactor
bgv <- subset(vol, id=="2_1")
# Calculate cumulative volume
bgv <- cumBg(bgv, time.name = "days")
# The interpolate of cumulative production to 1, 7, and 30 days
interp(bgv$days, bgv$cvBg, time.out = c(1, 7, 30), method = "hyman")

Mass Change of Batch Reactors

Description

Mass measurements for 9 batch anaerobic reactors.

Usage

data("mass")

Format

A data frame with 18 observations on the following 4 variables.

id

identification code, a unique value for each reactor in the dataset. A factor with levels 2_1, 2_2, 2_3, 2_4, 2_5, 2_6, 2_10, 2_11, and 2_12

date.time

date and time of mass measurement, a POSIXct object.

days

elapsed time of mass measurements (from reactor setup) in days, a numeric vector.

mass

total reactor mass in g, a numeric vector.

when

when was measurement taken? A character vector with two values.

Details

These data are meant to be example data for mass2vol, for gravimetric determination of biogas production. Reactors were 500 mL glass serum bottles with butyl rubber septa and screw caps. Masses were measured with an electronic balance to 100 mg. Data in vol, mass, massw, comp, and setup are from the same reactors.

Source

Measurements by Charlotte Rennuit and Sasha Hafner.

Examples

data(mass)

Calculate Biogas Production Gravimetrically

Description

mass2vol calculates biogas volume based on measured reactor mass loss.

Usage

mass2vol(mass, xCH4, xCO2 = 1 - xCH4 - xN2, xN2 = 0, temp, pres, 
  temp.std = getOption('temp.std', as.numeric(NA)), 
  pres.std = getOption('pres.std', as.numeric(NA)), 
  unit.temp = getOption('unit.temp', 'C'), 
  unit.pres = getOption('unit.pres', 'atm'), 
  value = "CH4", headspace = NULL, headcomp = 'N2', 
  temp.init = NULL, std.message = TRUE)

Arguments

Details

This function uses the method described in Hafner et al. (2015) to calculate biogas production from reactor mass loss. It is essential that the only change in reactor mass is due to biogas removal! Users are advised to read the original reference before applying the method. This function is vectorized. Instead of using this function directly when working with multiple measurements on multiple reactors, use the cumBg function (which can call up mass2vol).

Standard values and units of temperature and pressure can be globally set using the function options.

Value

volume of methane in mL as a numeric vector (if value = "CH4") or a matrix with columns for biogas, methane, and carbon dioxide volumes in mL.

All volumes are standardised using pres.std and temp.std arguments (see stdVol).

Author(s)

Sasha D. Hafner and Charlotte Rennuit

References

Hafner, S.D., Rennuit, C., Triolo, J.M., Richards, B.K. 2015. Validation of a simple gravimetric method for measuring biogas production in laboratory experiments. Biomass and Bioenergy 83, 297-301.

See Also

vol2mass, stdVol, cumBg, options

Examples

  # Volume of methane if measured mass loss was 3.1 g
  mass2vol(3.1, xCH4 = 0.65, temp = 35, pres = 1)

  # More details
  mass2vol(3.1, xCH4 = 0.65, temp = 35, pres = 1, value = "all")
  
  # Vectorized
  data("massw")
  massw$massloss <- massw$start - massw$end
  massw$vCH4 <- mass2vol(massw$massloss, xCH4 = 0.65, temp = 35, 
                         pres = 1)
  massw
  

Mass Change of Batch Reactors

Description

Mass measurements for 9 batch anaerobic reactors in a “wide” format (separate columns for initial and final mass).

Usage

data("massw")

Format

A data frame with 9 observations on the following 3 variables.

id

identification code, a unique value for each reactor in the dataset. A factor with levels 2_1, 2_2, 2_3, 2_4, 2_5, 2_6, 2_10, 2_11, and 2_12

start

total reactor mass at the start of its incubation in g, a numeric vector.

end

total reactor mass at the end of its incubation in g, a numeric vector.

Details

These data are meant to be example data for application of mass2vol, for gravimetric determination of biogas production. Reactors were 500 mL glass serum bottles with butyl rubber septa and screw caps. Masses were measured with an electronic balance to 100 mg. These are the same data as in mass, but simply in a “wide” format. Data in vol, mass, massw, comp, and setup are from the same reactors.

Source

Measurements by Charlotte Rennuit and Sasha Hafner.

Examples

data(massw)

Calculate Molar Mass of Compound

Description

molMass calculates the molar mass of any organic compound (and some salts) based on its chemical formula.

Usage

molMass(form)

Arguments

Details

Standard atomic weights are from CIAAW, and were rounded to three or more digits (with the exception of Li) depending on the range of the reported interval for “normal materials”. In general form should follow capitalization rules for elements (i.e., first letter capitalized, second lower-case). However, if form contains single-letter elements only, it is possible to use lower-case letters for all elements (but for clarity this is not recommended). The function is vectorized.

Value

Numeric vector with length equal to length of form with molar mass (g/mol)

Author(s)

Charlotte Rennuit and Sasha D. Hafner

References

CIAAW <http://www.ciaaw.org/atomic-weights.htm>

See Also

calcCOD

Examples

  molMass("C6H12O6")

  molMass("CH3COOH")

  molMass("CH3CH2OH")

  molMass("CH4")

  molMass("ch4")

  molMass(c("C6H12O6", "CH3COOH", "CH3CH2OH"))

  # Case-sentitive for two letter elements
  molMass("NaHCO3")

  # Complex formulas OK
  molMass("H3C(CH2)5COOH")

  molMass("(C6H12O6)0.24999 (H3COOH)0.75001")

  # Database is quite complete 
  molMass('CdSiO3')

  # Use care for, e.g., hydrates
  molMass('FeSO4(H2O)7')

Claculate Inoculum and Substrate Mass for BMP Experiments

Description

planBMP assists in the design of BMP experiments. It can be used to determine inoculum and substrate masses based on inoculum-to-substrate ratio and volatile solids concentrations, or to calculate inoculum-to-substrate ratio based on masses.

Usage

planBMP(vs.inoc, vs.sub, isr = NA, m.inoc = NA, m.sub = NA, 
        m.tot = m.inoc + m.sub, m.vs.sub = vs.sub*m.sub/1000,
        digits = 3, warn = TRUE, nice = TRUE)

Arguments

Details

BMP experiments should be designed giving consideration to the inoculum-to-substrate ratio (ISR), the substrate VS mass, and the mixture VS concentration. This function calculates inoculum and substrate masses based on VS concentrations and ISR, along with either total mixture mass or substrate VS mass. Alternatively, it can be used to calculate ISR if the masses have been selected. Warnings are based on the guidelines of Holliger et al. (2016).

Value

A named numeric vector, or (if any of the first 7 input arguments have a length > 1, i.e., a vectorized call), a data frame. Names and interpretation are identical to the first 7 input arguments, and also include:

For non-vectorized calls, the results are returned invisibly and a easy-to-read summary is printed (see nice argument).

Note

Calculations used in this function are trivial, and they could also be done with a spreadsheet or even pencil and paper. The advantage here is ease and some flexibility. In addition to ISR and the other parameters included in this function, expected biogas production rate and bottle headspace volume are important, depending on the method. For more details, see Holliger et al. (2016).

Author(s)

Sasha D. Hafner, based on suggestion by Konrad Koch

References

Holliger, C., Alves, M., Andrade, D., Angelidaki, I., Astals, S., Baier, U., Bougrier, C., Buffiere, P., Carbella, M., de Wilde, V., Ebertseder, F., Fernandez, B., Ficara, E., Fotidis, I., Frigon, J.-C., Fruteau de Laclos, H., S. M. Ghasimi, D., Hack, G., Hartel, M., Heerenklage, J., Sarvari Horvath, I., Jenicek, P., Koch, K., Krautwald, J., Lizasoain, J., Liu, J., Mosberger, L., Nistor, M., Oechsner, H., Oliveira, J.V., Paterson, M., Pauss, A., Pommier, S., Porqueddu, I., Raposo, F., Ribeiro, T., Rusch Pfund, F., Stromberg, S., Torrijos, M., van Eekert, M., van Lier, J., Wedwitschka, H., Wierinck, I., 2016. Towards a standardization of biomethane potential tests. Water Science and Technology 74, 2515-2522.

See Also

calcBgVol, calcBgMan, calcBgGD, cumBg, summBg, predBg

Examples

  # Bottles are 500 mL, substrate is wastewater sludge.
  # Assume we want no more than 250 mL reacting volume (~250 g)
  # First try setting ISR and total mass.
  # VS concentrations: 20 g/kg in inoculum, 70 g/kg for substrate, ISR = 2.
  planBMP(vs.inoc = 20, vs.sub = 70, isr = 2, m.tot = 250)
  # Get 31 g substrate, 220 g inoculum.
  
  # After setup, we can check final values.
  planBMP(vs.inoc = 18, vs.sub = 72, m.sub = 32, m.inoc = 218)
  # We didn't quite meet our target in this case--next time use more inoculum to be sure
  
  # We can alternatively specify substrate VS mass
  planBMP(vs.inoc = 20, vs.sub = 70, isr = 2, m.vs.sub = 2)
  
  # Some options
  planBMP(vs.inoc = 20, vs.sub = 70, isr = 2, m.vs.sub = 2, nice = FALSE)
  # Perhaps we want to use three different ISRs
  planBMP(vs.inoc = 20, vs.sub = 70, isr = 2:4, m.vs.sub = 2, nice = FALSE)

Predict Biogas Production

Description

predBg predicts biogas and methane production based on composition and other, optional, details.

Usage

predBg(form = NULL, mass = 1, mol = NULL, fs = 0, fd = 1, 
       mcomp = NULL, COD = NULL, conc.sub = NULL, pH = NULL, 
       temp = NULL, mu = 0.1, shortform = NULL, value = "CH4")

Arguments

Details

predBg is a flexible function that returns different details depending on the data provided. Calculations can be based on form, COD, or mcomp (at least one of these is required). See ‘Examples’ for more information.

In its simplest usage, predBg calculates theoretical biochemical methane potential (theoretical BMP). With more parameters, it can also predict carbon dioxide partitioning, total biogas production and composition, as well as microbial biomass production and nitrogen requirement. Stoichiometry is based on Eq. 13.5 in Rittmann and McCarty (2001). Partitioning of carbon dioxide is based on an equilibrium speciation model using temperature-dependent parameters (Henry's law constant and dissociation constants) based on Hafner et al. (2012). predBg is vectorized for all arguments except mcomp, and will recycle argument elements as needed.

Value

Standardised volume (at 0 degrees C and 1 atmosphere) of methane produced in mL (for value = "CH4"), or a data frame with some of these columns (depending on provided arguments):

Note

Predictions will only be as good as the parameter values provided, and maybe not even that good. fs should be interpreted as $f_s$ in Ritttmann and McCarty (2001), i.e., the effective value after decay of microbial biomass, and not $f_s^0$. The original reference (Section 2.3 and Eq. (3.33) in Rittmann and McCarty (2001)) and the predBg vignette provide more details. Partitioning of carbon dioxide is based on equilibrium between all biogas produced and the solution, and represents a continuous reactor running at steady-state.

Author(s)

Sasha D. Hafner and Charlotte Rennuit

References

Hafner, S.D. 2007 Ammonia Speciation in Anaerobic Digesters. PhD dissertation, Cornell University.

Hafner, S.D., Montes, F., Rotz, C.A. 2012 The role of carbon dioxide in emission of ammonia from manure. Atmospheric Environment 66, 63-71.

Moller, H.B., Sommer, S.G., Ahring, B.K. 2004 Methane productivity of manure, straw and solid fractions of manure. Biomass and Bioenergy 26, 485-495.

Rittmann, B., McCarty, P. 2001 Environmental Biotechnology. McGraw-Hill.

Triolo, J.M., Sommer, S.G., Moller, H.B., Weisbjerg, M.R., Jiang, X.Y. 2011 A new algorithm to characterize biodegradability of biomass during anaerobic digestion: Influence of lignin concentration on methane production potential. Bioresource Technology 102, 9395-9402.

See Also

calcCOD, molMass

Examples

  # BMP of cellulose in mL CH4/g
  predBg("C6H10O5")

  # How much is produced in a real reactor? Assume 10% goes to 
  # biomass production
  predBg("C6H10O5", fs = 0.1)

  # And substrate is 80% degradable
  predBg("C6H10O5", fs = 0.1, fd = 0.8)

  # More detailed results
  predBg("C6H10O5", value = "all")

  # Given a substrate with a COD of 1.4 g
  predBg(COD = 1.4)

  # But CH4 production is reduced if substrate is 80% degradable 
  # and 10% goes to cell synthesis
  predBg(COD = 1.4, fd = 0.8, fs = 0.1)

  # Can use macromolecule composition, e.g., for pig manure
  predBg(mcomp = c(vfa = 0.2, protein = 0.25, carbohydrate = 0.32, 
                   lipid = 0.15, lignin = 0.08), 
         value = "all")

  # Function expects sum(mcomp) == 1 but this is not required
  # But unless mass is set, mass is taken as sum(mcomp)
  predBg(mcomp = c(vfa = 20, protein = 25, carbohydrate = 32, 
                   lipid = 15, lignin = 8), 
         value = "all")

  # Specify mass if this is not the case
  predBg(mcomp = c(vfa = 20, protein = 25, carbohydrate = 32, 
                   lipid = 15, lignin = 8), 
         mass = 1, value = "all")

  # Can mix formulas and macromolecular groups in mcomp
  predBg(mcomp = c(C6H12O6 = 0.5, protein = 0.5))

  # For CO2 partitioning, must provide conc.sub, pH, and temp 
  # cattle manure example from Hafner (2007)
  predBg("C13H20O11N", mass = 1, fs = 0.1, fd = 0.56, 
         conc.sub = 85, pH = 7.7, temp = 35, value = "all")

  # Or, mix of waste paper and waste vegetable oil from vignette
  predBg(mcomp = c(C6H10O5 = 5/6, C54H100O7 = 1/6), mass = 1, 
	 fd = 0.8, fs = 0.1, conc.sub = 50, pH = 7.5, temp = 35, 
	 value = "all")

  # Note that form can also be used for mixtures, but here it is 
  # molar ratios that are specified
  predBg('(C6H10O5)5(C54H100O7)1)', mass = 1, 
	 fd = 0.8, fs = 0.1, conc.sub = 50, pH = 7.5, temp = 35, 
	 value = "all")
  
  # Function is vectorized for everything except mcomp, e.g., 
  # fs
  predBg("C6H10O5", fs = c(0, 0.05, 0.1), value = "all")

  # form
  predBg(c("C6H10O5", "C10H19O3N", "CH3CH2OH"), value = "all")
  
  # pH
  predBg("C13H20O11N", conc.sub = 85, fs = 0.1, fd = 0.56, 
         pH = 7+0:10/10, temp = 35, value = "all")

Methane Content of Biogas from Batch Reactors

Description

Methane content (biogas composition) measurements from three batch bottles, organized in a “long” format.

Usage

data("s3compl")

Format

A data frame with 14 observations on the following 3 variables:

id

identification code, a unique value for each bottle in the dataset.

time.d

elapsed time of mass measurements (from bottle setup) in days, a numeric vector.

xCH4

biogas methane content as a mole fraction, excluding water and all other gases other than carbon dioxide, a numeric vector

Details

These data are meant to be example data for cumBg. Reactors were 300 mL glass serum bottles with butyl rubber septa and screw caps. Substrate was municipal wastewater sludge. Methane and carbon dioxide contents were determined by gas chromatography using a thermal conductivity detector and normalised so methane and carbon dioxide sum to 1.0. Data in s3voll, s3compl, s3volw, s3compw, and s3lcombo are from the same bottles.

Source

Sasha D. Hafner. Measurements by Johanna Maria Pedersen.

Examples

data(s3compl)

Methane Content of Biogas from Batch Reactors

Description

Methane content (biogas composition) measurements from three batch bottles, organized in a “wide” format.

Usage

data("vol")

Format

A data frame with 5 observations on the following 4 variables:

time.d

elapsed time of mass measurements (from bottle setup) in days, a numeric vector.

D

biogas methane content at time from bottle “D” as mole fraction, a numeric vector.

E

biogas methane content at time from bottle “E” as mole fraction, a numeric vector.

F

biogas methane content at time from bottle “F” as mole fraction, a numeric vector.

Details

These data are meant to be example data for cumBg. Reactors were 300 mL glass serum bottles with butyl rubber septa and screw caps. Substrate was municipal wastewater sludge. Methane and carbon dioxide contents were determined by gas chromatography using a thermal conductivity detector and normalised so methane and carbon dioxide sum to 1.0. Data in s3voll, s3compl, s3volw, s3compw, and s3lcombo are from the same bottles.

Source

Sasha D. Hafner. Measurements by Johanna Maria Pedersen.

Examples

data(s3compw)

Biogas Volume and Methane Content from Batch Bottles

Description

Biogas volume and methane content measurements from three batch bottles, organized in a “long” format.

Usage

data("s3lcombo")

Format

A data frame with 21 observations on the following 4 variables:

id

identification code, a unique value for each bottle in the dataset.

time.d

elapsed time of mass measurements (from bottle setup) in days, a numeric vector.

vol.ml

volume of biogas removed at time in mL, a numeric vector.

xCH4

biogas methane content as a mole fraction, excluding water and all other gases other than carbon dioxide, a numeric vector

Details

These data are meant to be example data for cumBg. Reactors were 300 mL glass serum bottles with butyl rubber septa and screw caps. Substrate was municipal wastewater sludge. Volume was measured using syringes. Methane and carbon dioxide contents were determined by gas chromatography using a thermal conductivity detector and normalised so methane and carbon dioxide sum to 1.0. Data in s3voll, s3compl, s3volw, s3compw, and s3lcombo are from the same bottles.

Source

Sasha D. Hafner. Measurements by Johanna Maria Pedersen.

Examples

data(s3lcombo)

Biogas Volume from Batch Bottles

Description

Biogas volume measurements from three batch bottles, organized in a “long” format.

Usage

data("s3voll")

Format

A data frame with 21 observations on the following 4 variables:

id

identification code, a unique value for each bottle in the dataset.

time.d

elapsed time of mass measurements (from bottle setup) in days, a numeric vector.

vol.ml

volume of biogas removed at time in mL, a numeric vector.

cvol.ml

cumulative sum of vol.

Details

These data are meant to be example data for cumBg. Reactors were 300 mL glass serum bottles with butyl rubber septa and screw caps. Substrate was municipal wastewater sludge. Volume was measured using syringes. Data in s3voll, s3compl, s3volw, s3compw, and s3lcombo are from the same bottles.

Source

Sasha D. Hafner. Measurements by Johanna Maria Pedersen.

Examples

data(s3voll)

Biogas Volume from Batch Reactors

Description

Biogas volume measurements from three batch bottles, organized in a “wide” format.

Usage

data("vol")

Format

A data frame with 7 observations on the following 5 variables:

time.d

elapsed time of mass measurements (from bottle setup) in days, a numeric vector.

D

volume of biogas removed at time from bottle “D” in mL, a numeric vector.

E

volume of biogas removed at time from bottle “E” in mL, a numeric vector.

F

volume of biogas removed at time from bottle “F” in mL, a numeric vector.

Details

These data are meant to be example data for cumBg. Reactors were 300 mL glass serum bottles with butyl rubber septa and screw caps. Volume was measured using syringes. Data in s3voll, s3compl, s3volw, s3compw, and s3lcombo are from the same bottles.

Source

Sasha D. Hafner. Measurements by Johanna Maria Pedersen.

Examples

data(vol)

Setup Details for Batch Reactors

Description

Description of reactor substrates and information on reactor, substrates and, inoculum initial masses for twelve anaerobic reactors.

Usage

data("setup")

Format

A data frame with 12 observations on the following 11 variables:

id

identification code, a unique value for each reactor in the dataset. A factor with levels 2_1, 2_2, 2_3, 2_4, 2_5, 2_6, 2_7, 2_8, 2_9, 2_10, 2_11, and 2_12

descrip

description of substrate, a factor with levels A B inoc.

msub

mass of substrate added to the reactor at the start in g, a numeric vector.

minoc

mass of inoculum added to the reactor at the start in g, a numeric vector.

mvs.sub

mass of volatile solids (VS) of substrate added to the reactor at the start in g, a numeric vector.

mvs.inoc

mass of volatile solids (VS) of inoculum added to the reactor at the start in g, a numeric vector.

mcod.sub

mass of chemical oxygen demand (COD) of substrate added to the reactor at the start in g oxygen, a numeric vector.

mcod.inoc

mass of chemical oxygen demand (COD) of inoculum added to the reactor at the start in g oxygen, a numeric vector.

m.tot

total mass added to the reactor at the start in g, a numeric vector.

mvs.tot

total mass of VS in the reactor at the start in g, a numeric vector.

mcod.tot

total mass of COD in the reactor at the start in g oxygen, a numeric vector.

Details

These data are meant to be example data for summBg. Reactors were 500 mL glass serum bottles with butyl rubber septa and screw caps. Data in vol, mass, massw, comp, and setup are from the same reactors.

Source

Measurements made by Charlotte Rennuit and Ali Heidarzadeh Vazifehkhoran

Examples

data(setup)

Setup Details for Some Batch Reactors

Description

Information on reactor substrates and substrate and inoculum masses for 15 anaerobic reactors.

Usage

data("setup2")

Format

A data frame with 15 observations on the following 4 variables:

bottle

identification code, a unique value for each reactor in the dataset. A factor with levels 1_1, 1_2, 1_3, 2_1, 2_2, 2_2, ... through 5_3. The first number indicates the sample, the second the replicate.

description

description of substrate, a factor.

sub.vs

mass of substrate volatile solids (VS) added to the reactor at the start in g, a numeric vector

inoc.mass

mass of inoculum added to the reactor at the start in g, a numeric vector.

Details

These data are meant to be example data for summBg. Reactors were 500 mL or 1000 mL glass serum bottles with butyl rubber septa and screw caps. Data in vol2, xCH42, and setup2 are from the same reactors.

Source

Measurements made by Ali Heidarzadeh Vazifehkhoran

Examples

data(setup2)

Headspace Pressure, Mass measurements, and Methane and Carbondioxid Content from Batch Bottles

Description

Interval-based measurements of headspace pressure, mass, and methane and carbondioxide content from anaerobic batch bottles, organized in a “longcombo” format.

Usage

data("sludgeTwoBiogas")

Format

A data frame with 224 observations on the following 8 variables:

id

identification code, a unique value for each bottle in the dataset.

time.d

elapsed time of mass and pressure measurements in days, a numeric vector.

pres

bottle headspace pressure at time in mbar (gauge), a numeric vector

mass.init

total reactor mass at the start of its incubation in g, a numeric vector.

mass.final

total reactor mass at the end of its incubation in g, a numeric vector.

xCH4

biogas methane content as a mole fraction, excluding water, a numeric vector

xCO2

biogas carbon dioxide content as a mole fraction, excluding water, a numeric vector

xCH4n

biogas methane content as a mole fraction in dry biogas, normalized so the sum of mole fractions of CH$_4$ and CO$_2$ is unity, a numeric vector.

Details

These data are meant to be example data for cumBg and calcBgMan for respectively, gravimetric and manometric determination of biogas production. BMP test were carried out in 160 mL serum bottles sealed with a butyl rubber septum retained with an aluminum crimp seal at mesophilic conditions (37 degrees C). Substrate was primary wastewater sludge.

Cumulative methane production was measured from the same set of bottles by both manometric and gravimetric methods. BMP was evaluated at 18 days At each sampling event, headspace pressure was measured using a manometer and composition (CH4 and CO2) with a gas chromatograph). All reported pressure measurements are gauge unless stated otherwise. After each sampling event, the headspace of the serum bottles (including blanks) was vented to near atmospheric pressure.

Data in sludgeTwoBiogas and sludgeTwoSetup are from the same batch bottles.

Source

Data were originally collected by Sergi Astals at the University of Queensland and are described in the following paper. Hafner, S.D., Astals, S. 2019.Systematic error in manometric measurement of biochemical methane potential: Sources and solutions. Waste Management 91, 147-155.

Examples

data(sludgeTwoBiogas)

Setup Details for Batch Reactors

Description

Description of reactor substrates and information on bottle, substrates, inoculum initial masses, and headspace volume for 18 anaerobic bottles with primary wastewater sludge for substrate.

Usage

data("sludgeTwoSetup")

Format

A data frame with 18 observations on the following 5 variables:

id

identification code, a unique value for each bottle in the dataset. Numeric with all values within a range of 1-21.

descrip

treatment of the substrate (primary wastewater sludge), a character.

vol.hs

bottle headspace volume, in mL. A numeric vector.

m.inoc

mass of inoculum added to the bottle at the start in g, a numeric vector.

m.sub.vs

mass of volatile solids (VS) of substrate added to the reactor at the start in g, a numeric vector.

Details

These data are meant to be example data for summBg. Substrate was primary wastewater sludge.

BMP test were carried out in 160 mL serum bottles sealed with a butyl rubber septum retained with an aluminum crimp seal at mesophilic conditions (37 degrees C). All bottles contained inoculum and the amount of primary sludge required to achieve an inoculum-to-substrate ratio (ISR) of 2 (VS basis). Bottles containing only inoculum (blanks) were used to correct for the background CH4 potential of the inoculum (endogenous CH4 production).

Data in sludgeTwoBiogas and sludgeTwoSetup are from the same batch bottles.

Source

Data were originally collected by Sergi Astals at the University of Queensland and are described in the paper listed below. Hafner, S.D., Astals, S. 2019.Systematic error in manometric measurement of biochemical methane potential: Sources and solutions. Waste Management 91, 147-155.

Examples

data(sludgeTwoSetup)

Correct Gas Volume to 'Standard' Conditions

Description

stdVol corrects gas volumes to dry conditions at a specified temperature and pressure.

Usage

stdVol(vol, temp, pres, rh = 1, 
       temp.std = getOption('temp.std', as.numeric(NA)), 
       pres.std = getOption('pres.std', as.numeric(NA)), 
       unit.temp = getOption('unit.temp', 'C'), 
       unit.pres = getOption('unit.pres', 'atm'), 
       std.message = TRUE, warn = TRUE)

Arguments

Details

Standardisation is done in a two-step process following Hafner et al. (2015). First, the contribution of water vapor is removed and volume is corrected to the standard pressure:

vd = vm*(pm - rh*pw)/ps

where vd = dry volume at standard pressure ps, vm = measured volume at pressure pm, pw = saturation vapor pressure of water (all pressures in Pa), and rh = relative humidity (rh above). Water vapor pressure pw is calculated from the Magnus form equation given in Alduchov and Eskridge (1996) (Eqs. (21) and (22)). In the second step, the volume is adjusted for temperature.

vs = vd*Ts/T

where vs = standardised volume and Ts = standardisation temperature (K, converted from temp.std argument). This approach is based on Charles's and Boyle's laws. Comparison with calculations using the Peng-Robinson equation of state suggests that error in stdVol is around 0.1% for typical biogas with volume measured at 25 degrees C, and higher at higher temperatures (up to 0.3% at 55 degrees C).

Standard temperature and pressure and their units can be defined by the user using the temp.std, pres.std, temp.unit, and pres.unit arguments. Alternatively, standard values and units of temperature and pressure can be globally set using the function options. Default values are 0 degrees C and 1.0 atm. stdVol is vectorized, and if one argument has a shorter length than the others, it will be recycled.

Value

Standardised gas volume in the same units as vol. A numeric vector.

Author(s)

Sasha D. Hafner and Charlotte Rennuit

References

Hafner, S.D., Rennuit, C., Triolo, J.M., Richards, B.K. 2015. Validation of a simple gravimetric method for measuring biogas production in laboratory experiments. Biomass and Bioenergy 83, 297-301.

Richards, B.K., Cummings, R.J., White, T.E., Jewell, W.J. 1991. Methods for kinetic analysis of methane fermentation in high solids biomass digesters. Biomass and Bioenergy 1, 65-73.

See Also

cumBg, summBg, stdVol, options

Examples

  # 100 mL, measured at 35 C
  stdVol(100, temp = 35, pres = 1)

  # Or, with different units
  stdVol(100, temp = 35, pres = 103, unit.pres = "kPa", pres.std = 101.325)
  
  # Vectorized
  data(vol)
  head(vol)
  vol$vol.std <- stdVol(vol$vol, temp = 20, pres = 1.02)
  head(vol)
  
  #using options() (recommended!)
  oldoptions <- options(temp.std = 273.15, pres.std = 101325, 
                        unit.temp = 'K', unit.pres = 'Pa')
  vol$vol.std <- stdVol(vol$vol, temp = 293.15, pres = 101325)
  head(vol)
  options(oldoptions)

Methane Content of Biogas

Description

Methane content (biogas composition) measurements from 12 anaerobic batch reactors with straw for substrate.

Usage

data("comp")

Format

A data frame with 63 observations on the following 4 variables.

bottle

identification code, a unique value for each reactor in the dataset. Integer with all values from 1 to 12.

date.time

date and time of mass measurement, a POSIXct object.

time

elapsed time of mass measurements (from reactor setup) in days, a numeric vector.

xCH4

biogas methane content as a mole fraction, excluding water and all other gases other than carbon dioxide, a numeric vector

Details

These data are meant to be example data for cumBg. Reactors were ca. 600 mL glass serum bottles with butyl rubber septa and screw caps. Pressure was measured using an electronic manometer. Data in strawMass, strawSetup, and strawPressure are from the same reactors.

Source

Measurements by Charlotte Rennuit.

Examples

data(strawComp)

Mass Change of Batch Reactors

Description

Mass measurements for 12 batch anaerobic reactors with ground straw as a substrate.

Usage

data("mass")

Format

A data frame with 89 observations on the following 4 variables.

bottle

identification code, a unique value for each reactor in the dataset. Integer with all values from 1 to 12.

date.time

date and time of mass measurement, a POSIXct object.

time

elapsed time of mass measurements (from reactor setup) in days, a numeric vector.

mass

total reactor mass in g, a numeric vector.

Details

These data are meant to be example data for mass2vol or cumBg, for gravimetric determination of biogas production. Reactors were ca. 600 mL glass serum bottles with butyl rubber septa and screw caps. Masses were measured with an electronic balance to 10 mg. Data in strawPressure, strawSetup, and strawComp are from the same reactors.

Source

Measurements by Charlotte Rennuit.

Examples

data(strawMass)

Headspace Pressure in Batch Reactors

Description

Interval-based measurements of headspace pressure in 12 anaerobic batch reactors with straw as the substrate.

Usage

data("strawPressure")

Format

A data frame with 72 observations on the following 5 variables:

bottle

identification code, a unique value for each reactor in the dataset. Integer with all values from 1 to 12.

date.time

date and time of mass measurement, a POSIXct object.

time

elapsed time of mass measurements (from reactor setup) in days, a numeric vector.

pres

absolute bottle headspace pressure at the stated time, in kPa. Biogas accumulated in bottles from the previous time.

pres.resid

absolute bottle headspace pressure after venting, in kPa.

Details

These data are meant to be example data for cumBg. Reactors were ca. 600 mL glass serum bottles with butyl rubber septa and screw caps. Pressure was measured using an electronic manometer. Data in strawMass, strawSetup, and strawComp are from the same reactors.

Source

Measurements by Charlotte Rennuit.

Examples

data(vol)

Setup Details for Batch Reactors

Description

Description of reactor substrates and information on reactor, substrates and, inoculum initial masses for 12 anaerobic reactors with straw for substrate.

Usage

data("setup")

Format

A data frame with 12 observations on the following 6 variables:

bottle

identification code, a unique value for each reactor in the dataset. Integer with all values from 1 to 12.

treatment

treatment of the substrate (ground straw), a factor.

start

starting date and time, when the reactors were set up.

sub.mass

mass of substrate added to the reactor at the start in g, a numeric vector.

inoc.mass

mass of inoculum added to the reactor at the start in g, a numeric vector.

headspace

bottle headspace volume, in mL. A numeric vector.

Details

These data are meant to be example data for summBg. Reactors were ca. 600 mL glass serum bottles with butyl rubber septa and screw caps. Masses were measured with an electronic balance to 10 mg. Data in strawPressure, strawMass, and strawComp are from the same reactors.

Source

Measurements by Charlotte Rennuit.

Examples

data(setup)

Summarise and Normalise Cumulative Methane Production

Description

From cumulative gas production, use summBg to standardise, interpolate, subtract innoculum contribution, normalise by substrate mass, and summarise the output calculating mean and standard devations for each type of sample (groups of replicates). The function is flexible: some, all, or none of these operations can be carried out in a call. Typically summBg is used to calculate biochemical methane potential (BMP) from cumulative methane production.

Usage

summBg(vol, setup, id.name = "id", time.name = "time", 
       descrip.name = "descrip", inoc.name = NULL, inoc.m.name = NULL,
       norm.name = NULL, norm.se.name = NULL,  vol.name = "cvCH4", 
       imethod = "linear", extrap = FALSE, when = 30, 
       when.min = 0, rate.crit = 'net', 
       show.obs = FALSE, show.rates = FALSE, show.more = FALSE,
       sort = TRUE, set.name = 'set', quiet = FALSE)

Arguments

Details

summBg was primarily designed to calculate the biochemical methane potential (BMP) from cumulative methane production of a set of batch bottles through these steps: interpolation of cumulative production to a specified time (if needed), subtratction of apparent innoculum contribution, normalisation of the results by substrate mass (typically volatile solids (VS) mass, but could be the mass of anything within the bottle) and calculation of mean and standard deviation for each sample type (set of replicates, identified by descrip.name).

If needed summBg can return values for all observations and be used for simpler operations e.g., determining cumulative biogas production at some specified time or normalising gas volume by different substrate characteristics.

To summarise data, the setup data frame should have a column with a description of bottle substrate (or a code for this). The name of the column is set by the descrip.name argument. If the inoculum effect is to be subtracted out, a column named descrip.name (for identifying replicates) and a column with the mass of inoculum present (any units), with a name set by inoc.m.name are both required in setup. To normalise by substrate mass (or any mass to be used for normalisation), an additional column with the mass of substrate is needed in setup –its name is set by norm.name.

This function is probably easier to understand by example. See ‘Examples’.

Value

a data frame, with the colums:

If show.more = TRUE additional columns are returned: summ2 <- summ2[ , c(descrip.name, time.name, 'mean', 'se', 'sd', 'n', 'rsd.inoc', 'fv.inoc', 'se1', 'se2', 'se3')]

If show.obs = TRUE even more columns are returned:

Note

Reported standard deviation and standard error includes an estimate of variability from subtracting the inoculum contribution when this is done.

Author(s)

Sasha D. Hafner and Charlotte Rennuit

See Also

cumBg

Examples

data("vol")
data("comp") 
data("setup") 

# First need to calculate cumulative methane production data
cum.prod <- cumBg(vol, comp = comp, temp = 20, pres = 1, 
                  time.name = "days", extrap = TRUE)

head(cum.prod)

# Cumulative methane production (default) at 30 d 
# Uses default names for some columns
summBg(vol = cum.prod, setup = setup, time.name = "days", when = 30)

# Or total cumulative biogas
summBg(vol = cum.prod, setup = setup, time.name = "days", 
       vol.name = "cvBg", when = 30)

# Cumulative CH4 only, subtract inoculum contribution
summBg(vol = cum.prod, setup = setup, time.name = "days", 
       inoc.name = "inoc", inoc.m.name = "minoc", when = 30)

# And normalise by mvs.sub column (mass of substrate VS here) (so the result is BMP)
summBg(vol = cum.prod, setup = setup, time.name = "days", 
       inoc.name = "inoc", inoc.m.name = "minoc", when = 30, 
       norm.name = "mvs.sub")

# Same example, but return results for three times
summBg(vol = cum.prod, setup = setup, time.name = "days", 
       inoc.name = "inoc", inoc.m.name = "minoc", when = c(10, 30, 60), 
       norm.name = "mvs.sub")

# Back to earlier example, but return all individual observations 
# (and total production and individual contributions of substrate 
# and inoculum) 
summBg(vol = cum.prod, setup = setup, time.name = "days", 
       inoc.name = "inoc", inoc.m.name = "minoc", when = 30, 
       norm.name = "mvs.sub", show.obs = TRUE)

# Something different: interpolated biogas production rates
summBg(vol = cum.prod, setup = setup, time.name = "days", 
       vol.name = "rvBg", when = 30, show.obs = TRUE)

# The when argument could also be 'meas', 'end', or '1p3d' (or related) for any of these examples 
summBg(vol = cum.prod, setup = setup, time.name = "days", 
       inoc.name = "inoc", inoc.m.name = "minoc", when = "end", 
       norm.name = "mvs.sub")

summBg(vol = cum.prod, setup = setup, time.name = "days", 
       inoc.name = "inoc", inoc.m.name = "minoc", when = "meas", 
       norm.name = "mvs.sub")

summBg(vol = cum.prod, setup = setup, time.name = "days", 
       inoc.name = "inoc", inoc.m.name = "minoc", when = "1p3d", 
       norm.name = "mvs.sub")

# Or combine multiple values of when in a list

summBg(vol = cum.prod, setup = setup, time.name = "days", 
       inoc.name = "inoc", inoc.m.name = "minoc", 
       when = list(30, "1p3d", "end"), 
       norm.name = "mvs.sub")

# If you want to apply the 1% criterion but also want a single fixed time for all bottles,
# you have to use two calls
summBg(vol = cum.prod, setup = setup, time.name = "days", 
       inoc.name = "inoc", inoc.m.name = "minoc", when = "1p3d", 
       norm.name = "mvs.sub")

# From the first call, the longest time is 42 days, so use when = 42
summBg(vol = cum.prod, setup = setup, time.name = "days", 
       inoc.name = "inoc", inoc.m.name = "minoc", when = 42, 
       norm.name = "mvs.sub")

# If an error is thrown because a bottle doesn't meet the 1% criterion, use 
# show.rates = TRUE to see rates
## Not run: 
# Will return error
cpshort <- cum.prod[cum.prod$days < 10, ]
summBg(vol = cpshort, setup = setup, time.name = "days", 
       inoc.name = "inoc", inoc.m.name = "minoc", when = "1p", 
       norm.name = "mvs.sub")

## End(Not run)

# So then use this to see which bottles are causing problems
cpshort <- cum.prod[cum.prod$days < 10, ]
summBg(vol = cpshort, setup = setup, time.name = "days", 
       inoc.name = "inoc", inoc.m.name = "minoc", when = "1p", 
       norm.name = "mvs.sub", show.rates = TRUE)


# Example with dataset with different column names
data("vol2")
data("comp2")
data("setup2")

# First need to calculate cumulative methane production data
cum.prod <- cumBg(vol2, comp = comp2, temp = 20, pres = 1, 
                  id.name = "bottle", time.name = "days", 
		  dat.name = "meas.vol", comp.name = "CH4.conc", 
                  extrap = TRUE)

head(cum.prod)

# Cumulative CH4 production at 30 d, subtract inoculum contribution 
# and normalise by sub.vs column (mass of substrate VS here) (look at setup2).
summBg(vol = cum.prod, setup = setup2, id.name = "bottle", 
       time.name = "days", descrip.name = "description", 
       inoc.name = "Inoculum", inoc.m.name = "inoc.mass", 
       norm.name = "sub.vs", when = 30)

Biogas Volume from Batch Reactors

Description

Biogas volume measurements from twelve batch reactors.

Usage

data("vol")

Format

A data frame with 288 observations on the following 4 variables:

id

identification code, a unique value for each reactor in the dataset. A factor with levels 2_1, 2_2, 2_3, 2_4, 2_5, 2_6, 2_7, 2_8, 2_9, 2_10, 2_11, and 2_12

date.time

date and time of mass measurement, a POSIXct object.

days

elapsed time of mass measurements (from reactor setup) in days, a numeric vector.

vol

volume of biogas removed at date.time in mL, a numeric vector.

Details

These data are meant to be example data for multiple functions, e.g., stdVol, cumBg, or summBg. Reactors were 500 mL glass serum bottles with butyl rubber septa and screw caps. Volume was measured using syringes. Data in vol, mass, xCH4, and setup are from the same reactors.

Source

Measurements by Charlotte Rennuit and Ali Heidarzadeh Vazifehkhoran.

Examples

data(vol)

Biogas Volume from Batch Reactors

Description

Biogas volume measurements from 15 batch reactors.

Usage

data("vol2")

Format

A data frame with 216 observations on the following 4 variables:

bottle

identification code, a unique value for each reactor in the dataset. A factor with levels 1_1, 1_2, 1_3, 2_1, 2_2, 2_2, ... through 5_3. The first number indicates the sample, the second the replicate.

days

elapsed time of mass measurements (from reactor setup) in days, a numeric vector.

meas.vol

volume of biogas removed at days in mL, a numeric vector

Details

These data are meant to be example data for multiple functions, e.g., stdVol, cumBg, or summBg. Reactors were 500 mL or 1000 mL glass serum bottles with butyl rubber septa and screw caps. Volume was measured using syringes. Data in vol2, xCH42, and setup2 are from the same reactors.

Source

Measurements by Ali Heidarzadeh Vazifehkhoran.

Examples

data(vol2)

Calculate Mass of Biogas

Description

vol2mass calculates the mass of biogas removed from a reactor, based on its composition, temperature, and pressure. This function is the inverse of mass2vol.

Usage

vol2mass(volBg, xCH4, temp.hs, temp.vol, pres.hs, pres.vol, 
  unit.temp = getOption('unit.temp', 'C'), 
  unit.pres = getOption('unit.pres', 'atm'), 
  rh.hs = 1, rh.vol = 1)

Arguments

Details

This function is vectorized. Argument elements will be recycled as needed. Note that this function is conceptually but not numerically the inverse of mass2vol, because the volBg argument here is not standardised, and is assumed to be saturated with water vapor just prior to removal. The mass that is calculated may not be equal to the mass of the biogas at the time of volume measurement–as long as temp.vol is less than temp.hs, some of the water lost from the reactor condenses and is not present in the biogas at the time of volume measurement.

Standard values and units of temperature and pressure can be globally set using the function options.

Value

biogas mass in g as a numeric vector.

Author(s)

Sasha D. Hafner and Charlotte Rennuit

References

Hafner, S.D., Rennuit, C., Triolo, J.M., Richards, B.K. In review. A gravimetric method for measuring biogas production. Biomass and Bioenergy.

See Also

mass2vol, options

Examples

# Mass loss from reactor for 100 mL biogas measured at 20 degrees C 
# and 1.0 atm, with headspace at 1.5 atm and 35 degrees C at the 
# time of biogas exit
vol2mass(100, xCH4 = 0.65, temp.hs = 35, temp.vol = 20, 
         pres.hs = 1.5, pres.vol = 1)

# If the measured volume has already been standardised to dry 
# conditions at 0 C and 1 atm
vol2mass(100, xCH4 = 0.65, temp.hs = 35, temp.vol = 0, 
         pres.hs = 1.5, pres.vol = 1, rh.vol = 0)

# Here vol2mass *is* numerically the inverse of mass2vol
vol2mass(mass2vol(1.234, xCH4 = 0.65, temp = 35, pres = 1.5, 
                  value = "Bg"), 
         xCH4 = 0.65, temp.hs = 35, temp.vol = 0, pres.hs = 1.5, 
         pres.vol = 1, rh.vol = 0)

Calculate Moles of a Gas

Description

vol2mol calculates the moles of a gas for a measured volume, temperature, and pressure.

Usage

vol2mol(vol, gas = "CH4", temp, pres, rh = 0, 
	unit.temp = getOption('unit.temp', 'C'), 
	unit.pres = getOption('unit.pres', 'atm'), 
	tp.message = TRUE)

Arguments

Details

This function uses a simple and approximate approach for the conversion, based on Charles's and Boyle's laws, with NIST values for 0 degrees C and 1.0 atm taken as the reference state (Lemmon et al. 2011). Measured volume is normalized to 1.0 atm and 0 degrees C using stdVol, and the result is divided by the molar volume at the same conditions, as reported by NIST. Resulting error should be within 0.5% for temperature and pressure close to ambient, and is usually below 0.2%.

Value

gas amount in moles as a numeric vector.

Author(s)

Sasha D. Hafner

References

Lemmon EW, McLinden MO, Friend DG. Thermophysical Properties of Fluid Systems. In Linstrom PJ, Mallard WG, editors. Chemistry WebBook, NIST Standard Reference Database Number 69. Gaithersburg, MD: National Institute of Standards and Technology, 2011.

See Also

vol2mass, mass2vol, options

Examples

# Assume we have measured 253 mL CH4 at 1.0 atm and 22 C, dry
vol2mol(253, "CH4", temp = 22, pres = 1)

# Compare to results to NIST values for the least ideal gas
# Results are within 0.5%
# Expect 0.04108 mol
vol2mol(1000, "CO2", temp = 25, pres = 1)

# Expect 0.08258 mol
vol2mol(1000, "CO2", temp = 25, pres = 2)

# Expect 0.07482 mol
vol2mol(1000, "CO2", temp = 55, pres = 2)