Nothing
#' @title Freundlich Isotherm Linear Analysis
#' @name freundlich.LM
#' @description This isotherm model is an empirical model applicable to diluted
#' solutions adsorption processes. Furthermore, this model gives an equation which
#' defines the surface heterogeneity and the exponential distribution of active sites.
#' @param Ce the numerical value for the equilibrium capacity
#' @param Qe the numerical value for the adsorbed capacity
#' @import Metrics
#' @import stats
#' @import ggplot2
#' @return the linear regression, parameters for Freundlich isotherm, and model
#' error analysis
#' @examples Ce <- c(0.01353, 0.04648, 0.13239, 0.27714, 0.41600, 0.63607, 0.80435, 1.10327, 1.58223)
#' @examples Qe <- c(0.03409, 0.06025, 0.10622, 0.12842, 0.15299, 0.15379, 0.15735, 0.15735, 0.16607)
#' @examples freundlich.LM(Ce,Qe)
#' @author Jemimah Christine L. Mesias
#' @author Chester C. Deocaris
#' @references Freundlich, H. 1907. Ueber die adsorption in loesungen. Z.
#' Phys. Chem.57:385-470
#' @references Foo, K. Y., and Hameed, B. H. (2009, September 13).
#' <doi:10.1016/j.cej.2009.09.013> Insights into the modeling of adsorption isotherm
#' systems. Chemical Engineering Journal.
#' @export
#'
# Building the Freundlich isotherm linear form
freundlich.LM <- function(Ce, Qe){
x <- log10(Ce)
y <- log10(Qe)
data <- data.frame(x, y)
# Fitting of the Freundlich isotherm linear form
rhs <- function(x, Kf, n){
log(Qe) ~ logKF + (1/n)*log(Ce)
}
fit1 <- lm(y~x)
print("Freundlich Isotherm Linear Analysis")
print(summary(fit1))
### y = a+bx
c <- summary(fit1)
a <- c$coefficients[1]
b <- c$coefficients[2]
### Parameter values calculation
Kf <- 10^(a)
print("Kf")
print(Kf)
n <- 1/b
print("n")
print(n)
# ---------------------------------
print("Akaike Information Criterion")
print(AIC(fit1))
print("Bayesian Information Criterion")
print(BIC(fit1))
# Error analysis of the Freundlich isotherm model
errors <- function(y) {
rmse <- Metrics::rmse(y, predict(fit1))
mae <- Metrics::mae(y, predict(fit1))
mse <- Metrics::mse(y, predict(fit1))
rae <- Metrics::rae(y, predict(fit1))
N <- nrow(na.omit(data))
SE <- sqrt((sum(y-predict(fit1))^2)/(N-2))
colnames(y) <- rownames(y) <- colnames(y)
list("Root Mean Squared Error" = rmse,
"Mean Absolute Error" = mae,
"Mean Squared Error" = mse,
"Root Absolute Error" = rae,
"Standard Error for the Regression S" = SE)
}
a <- errors(y)
print(a)
# Graphical representation of the Freundlich isotherm linear model
#### Plot details
ggplot2::theme_set(ggplot2::theme_bw(10))
ggplot2::ggplot(data, ggplot2::aes(x = x, y = y)) + ggplot2::geom_point(color ="#3498DB" ) +
ggplot2::geom_smooth(formula = y ~ x, method = "lm", se = F, color = "#D35400" ) +
ggplot2::labs(x = "log(Ce)",
y = "log(Qe)",
title = "Freundlich Isotherm Linear Model",
caption = "PUPAIM") +
ggplot2::theme(plot.title=ggplot2::element_text(hjust = 0.5))
}
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.