In sashahafner/ATM99: Simulate Anaerobic Biodegradation
Overview
Point of these tests is to check behavior of abm() when slurry mass is variable.
Development of the relevant code has been challenging, so it is important to check behavior of multiple scenarios after an update.
Prep
devtools::load_all()
Case 1, consecutive removals
Slurry mass data.
slurry_mass_dat <- data.frame(time = c(0, 1, 10, 15, 30), slurry_mass = c(1000, 7000, 2000, 1500, 3000))
plot(slurry_mass ~ time, data = slurry_mass_dat, type = 'o')
Default, with "early" behavior.
out1 <- abm(30, delta_t = 0.1, man_pars = man_pars1.0, grp_pars = grp_pars1.0,
add_pars = list(storage_depth = 4, area = 1000, floor_area = 0,
slurry_mass = slurry_mass_dat, evap = 0, rain = 0))
Late
out2 <- abm(30, delta_t = 0.1, man_pars = man_pars1.0, grp_pars = grp_pars1.0,
add_pars = list(storage_depth = 4, area = 1000, floor_area = 0,
slurry_mass = slurry_mass_dat, evap = 0, rain = 0),
approx_method = c(temp = 'linear', pH = 'linear', slurry_mass = 'late'))
And mid
out3 <- abm(30, delta_t = 0.1, man_pars = man_pars1.0, grp_pars = grp_pars1.0,
add_pars = list(storage_depth = 4, area = 1000, floor_area = 0,
slurry_mass = slurry_mass_dat, evap = 0, rain = 0),
approx_method = c(temp = 'linear', pH = 'linear', slurry_mass = 'mid'))
plot(out1$time, out1$slurry_mass, type = 'l')
lines(out2$time, out2$slurry_mass, type = 'l', col = 'blue')
lines(out3$time, out3$slurry_mass, type = 'l', col = 'orange')
points(slurry_mass_dat$time, slurry_mass_dat$slurry_mass, col = 'red', pch = 19)
Expect that all three approaches hit the red points (input level) exactly.
Methane production rate:
plot(out1$time, out1$CH4_emis_rate, type = 'l', ylim = c(0, 400))
lines(out2$time, out2$CH4_emis_rate, type = 'l', col = 'blue')
lines(out3$time, out3$CH4_emis_rate, type = 'l', col = 'orange')
Case 2, slurry removal at the beginning
slurry_mass_dat <- data.frame(time = c(0, 1, 10, 15, 30),
slurry_mass = c(1000, 500, 2000, 1500, 3000))
out4 <- abm(30, delta_t = 0.1, man_pars = man_pars1.0, grp_pars = grp_pars1.0,
add_pars = list(storage_depth = 4, area = 1000, floor_area = 0,
slurry_mass = slurry_mass_dat, evap = 0, rain = 0))
out5 <- abm(30, delta_t = 0.1, man_pars = man_pars1.0, grp_pars = grp_pars1.0,
add_pars = list(storage_depth = 4, area = 1000, floor_area = 0,
slurry_mass = slurry_mass_dat, evap = 0, rain = 0),
approx_method = c(temp = 'linear', pH = 'linear', slurry_mass = 'late'))
out6 <- abm(30, delta_t = 0.1, man_pars = man_pars1.0, grp_pars = grp_pars1.0,
add_pars = list(storage_depth = 4, area = 1000, floor_area = 0,
slurry_mass = slurry_mass_dat, evap = 0, rain = 0),
approx_method = c(temp = 'linear', pH = 'linear', slurry_mass = 'mid'))
plot(out4$time, out4$slurry_mass, type = 'l', ylim = c(0, 3500))
lines(out5$time, out5$slurry_mass, type = 'l', col = 'blue')
lines(out6$time, out6$slurry_mass, type = 'l', col = 'orange')
points(slurry_mass_dat$time, slurry_mass_dat$slurry_mass, col = 'red', pch = 19)
Case 3, slurry removal at end
slurry_mass_dat <- data.frame(time = c(0, 1, 10, 15, 30),
slurry_mass = c(1000, 2000, 4000, 4500, 1000))
out7 <- abm(30, delta_t = 0.1, man_pars = man_pars1.0, grp_pars = grp_pars1.0,
add_pars = list(storage_depth = 4, area = 1000, floor_area = 0,
slurry_mass = slurry_mass_dat, evap = 0, rain = 0))
out8 <- abm(30, delta_t = 0.1, man_pars = man_pars1.0, grp_pars = grp_pars1.0,
add_pars = list(storage_depth = 4, area = 1000, floor_area = 0,
slurry_mass = slurry_mass_dat, evap = 0, rain = 0),
approx_method = c(temp = 'linear', pH = 'linear', slurry_mass = 'late'))
out9 <- abm(30, delta_t = 0.1, man_pars = man_pars1.0, grp_pars = grp_pars1.0,
add_pars = list(storage_depth = 4, area = 1000, floor_area = 0,
slurry_mass = slurry_mass_dat, evap = 0, rain = 0),
approx_method = c(temp = 'linear', pH = 'linear', slurry_mass = 'mid'))
plot(out7$time, out7$slurry_mass, type = 'l', ylim = c(0, 5500))
lines(out8$time, out8$slurry_mass, type = 'l', col = 'blue')
lines(out9$time, out9$slurry_mass, type = 'l', col = 'orange')
points(slurry_mass_dat$time, slurry_mass_dat$slurry_mass, col = 'red', pch = 19)
Note that last input point is ignored for 'late' method.
plot(out7$time, out7$CH4_emis_rate, type = 'l')
lines(out8$time, out8$CH4_emis_rate, type = 'l', col = 'blue')
lines(out9$time, out9$CH4_emis_rate, type = 'l', col = 'orange')
Case 4, rain/evaporation correction
slurry_mass_dat <- read.csv('slurry_mass.csv')
Should get an error if adjusted slurry level is negative
out_a0 <- abm(days = 4*365, add_pars = list(slurry_mass = slurry_mass_dat))
out_a1 <- abm(days = 4*365, add_pars = list(slurry_mass = slurry_mass_dat, area = 100))
Does it also work with different emptying alignment?
out_a2 <- abm(days = 4*365, add_pars = list(slurry_mass = slurry_mass_dat, area = 100),
approx_method = c(temp = 'linear', pH = 'linear', slurry_mass = 'mid'))
out_a3 <- abm(days = 4*365, add_pars = list(slurry_mass = slurry_mass_dat, area = 100),
approx_method = c(temp = 'linear', pH = 'linear', slurry_mass = 'late'))
out_b <- abm(days = 4*365, add_pars = list(slurry_mass = slurry_mass_dat, rain = 0, evap = 0))
Check for mismatch between input slurry_mass and output slurry_mass
plot(out_a1$time, out_a1$slurry_mass, col = 'red', type = 'l')
lines(out_a2$time, out_a2$slurry_mass, col = 'orange', lty = 2)
lines(out_a3$time, out_a3$slurry_mass, col = 'purple', lty = 3)
lines(out_b$time, out_b$slurry_mass, col = 'blue', lty = '3131')
These different simulations should have different CH4 production.
With no net rain, abm() interprets input as having more loading, so more methane.
plot(out_a1$time, out_a1$CH4_emis_rate, col = 'red', type = 'l')
lines(out_a2$time, out_a2$CH4_emis_rate, col = 'orange', lty = 2)
lines(out_a3$time, out_a3$CH4_emis_rate, col = 'purple', lty = 3)
lines(out_b$time, out_b$CH4_emis_rate, col = 'blue', lty = '3131')
plot(out_a1$time, out_a1$CH4_emis_cum, col = 'red', type = 'l', ylim = c(0, 2E7))
lines(out_a2$time, out_a2$CH4_emis_cum, col = 'orange', lty = 2, lwd = 3)
lines(out_a3$time, out_a3$CH4_emis_cum, col = 'purple', lty = 3, lwd = 3)
lines(out_b$time, out_b$CH4_emis_cum, col = 'blue', lty = '3131')
sashahafner/ATM99 documentation built on June 14, 2025, 5:34 p.m.
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Overview
Point of these tests is to check behavior of abm() when slurry mass is variable.
Development of the relevant code has been challenging, so it is important to check behavior of multiple scenarios after an update.
Prep
devtools::load_all()
Case 1, consecutive removals
Slurry mass data.
slurry_mass_dat <- data.frame(time = c(0, 1, 10, 15, 30), slurry_mass = c(1000, 7000, 2000, 1500, 3000)) plot(slurry_mass ~ time, data = slurry_mass_dat, type = 'o')
Default, with "early" behavior.
out1 <- abm(30, delta_t = 0.1, man_pars = man_pars1.0, grp_pars = grp_pars1.0, add_pars = list(storage_depth = 4, area = 1000, floor_area = 0, slurry_mass = slurry_mass_dat, evap = 0, rain = 0))
Late
out2 <- abm(30, delta_t = 0.1, man_pars = man_pars1.0, grp_pars = grp_pars1.0, add_pars = list(storage_depth = 4, area = 1000, floor_area = 0, slurry_mass = slurry_mass_dat, evap = 0, rain = 0), approx_method = c(temp = 'linear', pH = 'linear', slurry_mass = 'late'))
And mid
out3 <- abm(30, delta_t = 0.1, man_pars = man_pars1.0, grp_pars = grp_pars1.0, add_pars = list(storage_depth = 4, area = 1000, floor_area = 0, slurry_mass = slurry_mass_dat, evap = 0, rain = 0), approx_method = c(temp = 'linear', pH = 'linear', slurry_mass = 'mid'))
plot(out1$time, out1$slurry_mass, type = 'l') lines(out2$time, out2$slurry_mass, type = 'l', col = 'blue') lines(out3$time, out3$slurry_mass, type = 'l', col = 'orange') points(slurry_mass_dat$time, slurry_mass_dat$slurry_mass, col = 'red', pch = 19)
Expect that all three approaches hit the red points (input level) exactly.
Methane production rate:
plot(out1$time, out1$CH4_emis_rate, type = 'l', ylim = c(0, 400)) lines(out2$time, out2$CH4_emis_rate, type = 'l', col = 'blue') lines(out3$time, out3$CH4_emis_rate, type = 'l', col = 'orange')
Case 2, slurry removal at the beginning
slurry_mass_dat <- data.frame(time = c(0, 1, 10, 15, 30), slurry_mass = c(1000, 500, 2000, 1500, 3000)) out4 <- abm(30, delta_t = 0.1, man_pars = man_pars1.0, grp_pars = grp_pars1.0, add_pars = list(storage_depth = 4, area = 1000, floor_area = 0, slurry_mass = slurry_mass_dat, evap = 0, rain = 0)) out5 <- abm(30, delta_t = 0.1, man_pars = man_pars1.0, grp_pars = grp_pars1.0, add_pars = list(storage_depth = 4, area = 1000, floor_area = 0, slurry_mass = slurry_mass_dat, evap = 0, rain = 0), approx_method = c(temp = 'linear', pH = 'linear', slurry_mass = 'late')) out6 <- abm(30, delta_t = 0.1, man_pars = man_pars1.0, grp_pars = grp_pars1.0, add_pars = list(storage_depth = 4, area = 1000, floor_area = 0, slurry_mass = slurry_mass_dat, evap = 0, rain = 0), approx_method = c(temp = 'linear', pH = 'linear', slurry_mass = 'mid'))
plot(out4$time, out4$slurry_mass, type = 'l', ylim = c(0, 3500)) lines(out5$time, out5$slurry_mass, type = 'l', col = 'blue') lines(out6$time, out6$slurry_mass, type = 'l', col = 'orange') points(slurry_mass_dat$time, slurry_mass_dat$slurry_mass, col = 'red', pch = 19)
Case 3, slurry removal at end
slurry_mass_dat <- data.frame(time = c(0, 1, 10, 15, 30), slurry_mass = c(1000, 2000, 4000, 4500, 1000)) out7 <- abm(30, delta_t = 0.1, man_pars = man_pars1.0, grp_pars = grp_pars1.0, add_pars = list(storage_depth = 4, area = 1000, floor_area = 0, slurry_mass = slurry_mass_dat, evap = 0, rain = 0)) out8 <- abm(30, delta_t = 0.1, man_pars = man_pars1.0, grp_pars = grp_pars1.0, add_pars = list(storage_depth = 4, area = 1000, floor_area = 0, slurry_mass = slurry_mass_dat, evap = 0, rain = 0), approx_method = c(temp = 'linear', pH = 'linear', slurry_mass = 'late')) out9 <- abm(30, delta_t = 0.1, man_pars = man_pars1.0, grp_pars = grp_pars1.0, add_pars = list(storage_depth = 4, area = 1000, floor_area = 0, slurry_mass = slurry_mass_dat, evap = 0, rain = 0), approx_method = c(temp = 'linear', pH = 'linear', slurry_mass = 'mid'))
plot(out7$time, out7$slurry_mass, type = 'l', ylim = c(0, 5500)) lines(out8$time, out8$slurry_mass, type = 'l', col = 'blue') lines(out9$time, out9$slurry_mass, type = 'l', col = 'orange') points(slurry_mass_dat$time, slurry_mass_dat$slurry_mass, col = 'red', pch = 19)
Note that last input point is ignored for 'late' method.
plot(out7$time, out7$CH4_emis_rate, type = 'l') lines(out8$time, out8$CH4_emis_rate, type = 'l', col = 'blue') lines(out9$time, out9$CH4_emis_rate, type = 'l', col = 'orange')
Case 4, rain/evaporation correction
slurry_mass_dat <- read.csv('slurry_mass.csv')
Should get an error if adjusted slurry level is negative
out_a0 <- abm(days = 4*365, add_pars = list(slurry_mass = slurry_mass_dat))
out_a1 <- abm(days = 4*365, add_pars = list(slurry_mass = slurry_mass_dat, area = 100))
Does it also work with different emptying alignment?
out_a2 <- abm(days = 4*365, add_pars = list(slurry_mass = slurry_mass_dat, area = 100), approx_method = c(temp = 'linear', pH = 'linear', slurry_mass = 'mid'))
out_a3 <- abm(days = 4*365, add_pars = list(slurry_mass = slurry_mass_dat, area = 100), approx_method = c(temp = 'linear', pH = 'linear', slurry_mass = 'late'))
out_b <- abm(days = 4*365, add_pars = list(slurry_mass = slurry_mass_dat, rain = 0, evap = 0))
Check for mismatch between input slurry_mass and output slurry_mass
plot(out_a1$time, out_a1$slurry_mass, col = 'red', type = 'l') lines(out_a2$time, out_a2$slurry_mass, col = 'orange', lty = 2) lines(out_a3$time, out_a3$slurry_mass, col = 'purple', lty = 3) lines(out_b$time, out_b$slurry_mass, col = 'blue', lty = '3131')
These different simulations should have different CH4 production.
With no net rain, abm() interprets input as having more loading, so more methane.
plot(out_a1$time, out_a1$CH4_emis_rate, col = 'red', type = 'l') lines(out_a2$time, out_a2$CH4_emis_rate, col = 'orange', lty = 2) lines(out_a3$time, out_a3$CH4_emis_rate, col = 'purple', lty = 3) lines(out_b$time, out_b$CH4_emis_rate, col = 'blue', lty = '3131')
plot(out_a1$time, out_a1$CH4_emis_cum, col = 'red', type = 'l', ylim = c(0, 2E7)) lines(out_a2$time, out_a2$CH4_emis_cum, col = 'orange', lty = 2, lwd = 3) lines(out_a3$time, out_a3$CH4_emis_cum, col = 'purple', lty = 3, lwd = 3) lines(out_b$time, out_b$CH4_emis_cum, col = 'blue', lty = '3131')
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