H
Hicham AMAR
Guest
1. Introduction
Mining waste (MW) generally contains a non-valuable sulphide mineral (Jamieson et al., 2015). The oxidation of sulphide minerals in the presence of water and oxygen triggers acid mine drainage (AMD) formation if the neutralisation potential (NP) is insufficient to buffer the produced acid (Blowes et al., 2014; Evangelou, 2018; Paktunc 1999). AMD causes water acidification, the release of harmful metal(oid)s to the surrounding environment, and the precipitation of secondary minerals such as sulphate, Fe-oxides, and Fe-oxyhydroxides (Blowes et al., 2014; Dold, 2017; Elghali et al., 2019a; Elghali et al., 2019b; Nordstrom, 2000). Therefore, prediction of AMD becomes a necessity not only to evaluate the environmental impact of mine waste, but also to design the best reclamation scenario (Demers et al., 2017). Under laboratory conditions, prediction of the geochemical behaviour of MW can be ensured by static and kinetic tests (Dold, 2017; Jambor et al., 2007).
2. Methods
Acid base accounting (ABA) is the most common static tests for short, rapid, and low-cost prediction of AMD formation (Smart et al., 2002; Sobek, 1978; Stewart et al., 2006). In contrast, kinetic tests are designed to evaluate mineral reactivity and leaching rates of various chemical species (Erguler and Erguler Kalyoncu, 2015; Paktunc and Davé, 2000).
The main objectives of this article are: i) to evaluate the Acid generation potential (AGP) using ABA static tests, and ii) to study the long-term geochemical behaviour two mining waste using weathering cells as kinetic test.
The net neutralisation potential (NNP = NP – AP) and neutralisation potential ratio (NPR=NP/AP) criteria were used to classify the acid generation potential of the studied samples (S1 and S2), where acidification potential AP=31.25 × wt.% Ssulphides (kg CaCO3/t) and neutralisation potential NP=83.3 × wt.% C (kg CaCO3/t). The sample is classified non-acid-generating if its NNP is higher than 20 kg CaCO3/t, uncertain if its NNP value is in the interval [−20 and 20] kg CaCO3/t, and acid generating if its NNP is lower than −20 kg CaCO3/t (Miller et al., 1991). For NPR, a sample is classified non-acid generating if NPR > 2.5, uncertain if 1 < NPR < 2.5, and acid generating with NPR < 1 (Benzaazoua et al., 2004b).
The geochemical behaviour of the studied samples was evaluated using weathering cells. The WCs consisted of subjecting 67 g of dry sample to weathering twice per week using 50 mL of deionised water (Cruz et al., 2001). Two WCs were set up and run for 84 days. The collected leachates were analysed for physicochemical parameters such as pH, electrical conductivity (EC), acidity, alkalinity, and chemical composition.
Structure of Data inputs ; Kinetic-analysis and initial masses
Inputs files for data analysis
3. Results
3.1. ABA Static test
Fig.1 shows the results of ABA tests; NPs and APs of the studied samples were, respectively, calculated based on the total carbon and sulphur-sulphide (Bouzahzah et al, 2014).
Code R for AGP calculation
Fig 1: Statistic of the AGP
Results of ABA tests show that the S2 is non-acid generating except for the S1 sample that is uncertain according to it NNP values that range between -20 and 20 kg CaCO3/t (Bouzahzah et al., 2014b). The NPR value of S2 was higher than 2.5 (non-acid generating), except the fraction S1 with NRP between 1 and 2.5.
R code to plot the AGP of the studied samples.
Fig.2. Graph representing results of acid generation potential by ABA test
3.2. Kinitec test
The Physicochemical parameters of the leachates from the WCs is shown in Fig.3. The pH evolution showed similar behaviour for the two samples, stabilising around circumneutral values of 7.4-8.9. The electrical conductivity tended to decrease from 472 μS/cm to 42 μS/cm. The alkalinity was around ~45 mg/L CaCO3 and tended to increase to reach 56 mg/L CaCO3 and 58 mg/L CaCO3 for S1 and S2 materials, respectively (Fig. 3); the acidity of the leachates was less than 12 mg/L for all the studied samples.
Code to plot the Physicochemical parameters :
Fig.3 : Physicochemical parameters of the studied samples
Code to plot the ponctual concentrations of the chemical elements :
The chemical composition of the leachates are presented in Fig.4 as cumulative and mass-normalised releases (mg/kg). Ca, Mg, and Mn were chosen to evaluate the neutralising mineral dissolution (mainly carbonates) and sulphate to indicate pyrite oxidation (Benzaazoua et al., 2004b; Elghali et al., 2019a).
Code to calculate the Cumulative concentrations of the chemical elements :
Code to plot the Cumulative concentrations of the chemical elements :
Fig.4 : Cumulative concentrations of the chemical elements of the studied samples
Code to plot the Normalised Cumulative concentrations of the chemical elements :
Sulphide and carbonate depletion times within the studied samples are presented in Fig.5. The depletion time calculated for sulphur in S1 was approximately two times longer than that calculated for carbonates. Therefore, complete depletion of sulphur within the S2 materials will be achieved, as compared to the S1, due to their decreased sulphur content (0.54% for S1 and 0.15% for S2).
Code for sulfide and carbonates depletion
Code for sulfide and carbonates calculation of the initial masses
Code R for sulfide and carbonates Depletion calculation
Code R to plot the carbonates and sulfides depletion
Fig.5: Carbonates and sulfide depletion of the studied samples
Oxidation–neutralisation curves for the two WCs showed a linear relationship. Slopes and correlation coefficients (R2) for the linear regressions are presented in Fig.6.
Code for oxidation/neutralisation curves
Fig.6: Oxido-neutralisation curves of the studied samples
These oxidation–neutralisation curves in Fig.6 were used to predict the long-term geochemical behaviour by extrapolating the curves and projecting the sulphate and [Ca+Mg+Mn] concentrations within the initial solid samples.
The sample is considered to be acid-generating if it is located on the sulphate side of the curve and non-acid generating if it is located on the [Ca+Mg+Mn] side of the curve, as shown in Fig.7.The samples S1 and S2 are located under their curves, which indicates that the neutralising minerals will be depleted before the sulphide (Fig. 7). The S1 and S2 sample classified as acid generating over the long term.
Code R to plot the long term prediction
Fig.4: Long-term prediction of the studied samples
You find more details in Amar et al, 2021.
Hicham AMAR, Ing in Geomatic Sciences, Co-founder of Geoinfo4all.com
References
Benzaazoua, M., Fall, M., Belem, T., 2004. A contribution to understanding the hardening process of cemented pastefill. Minerals engineering, 17(2), 141-152.
Blowes, D. W., Ptacek, C. J., Jambor, J. L., Weisener, C. G., Paktunc, D., Gould, W. D., Johnson, D. B., 2014. The geochemistry of acid mine drainage. 131-190. doi:10.1016/b978-0-08-095975-7.00905-0
Bouzahzah, H., Benzaazoua, M., Bussière, B., Plante, B., 2014. Revue de littérature détaillée sur les tests statiques et les essais cinétiques comme outils de prédiction du drainage minier acide. Déchets Sciences et Techniques Techniques, 66, 14-31.
Demers, I., Mbonimpa, M., Benzaazoua, M., Bouda, M., Awoh, S., Lortie, S., Gagnon, M., 2017. Use of acid mine drainage treatment sludge by combination with a natural soil as an oxygen barrier cover for mine waste reclamation: Laboratory column tests and intermediate scale field tests. Minerals engineering, 107, 43-52.
Dold, B., 2017. Acid rock drainage prediction: A critical review. Journal of Geochemical Exploration, 172, 120-132.
Elghali, A., Benzaazoua, M., Bussière, B., Bouzahzah, H., 2019a. Determination of the available acid-generating potential of waste rock, part ii: Waste management involvement. Applied Geochemistry, 100, 316-325. doi:https://doi.org/10.1016/j.apgeochem.2018.12.010
Elghali, A., Benzaazoua, M., Bussière, B., Genty, T., 2019b. Spatial mapping of acidity and geochemical properties of oxidized tailings within the former eagle/telbel mine site. Minerals, 9(3), 180.
Elghali, A., Benzaazoua, M., Bussière, B., Kennedy, C., Parwani, R., Graham, S., 2019c. The role of hardpan formation on the reactivity of sulfidic mine tailings: A case study at joutel mine (québec). Science of the total environment, 654, 118-128. doi:https://doi.org/10.1016/j.scitotenv.2018.11.066
Erguler, Z. A., Erguler Kalyoncu, G., 2015. The effect of particle size on acid mine drainage generation: Kinetic column tests. Minerals engineering, 76, 154-167.
Evangelou, V., 2018. Pyrite oxidation and its control: CRC press.
Jambor, J., Dutrizac, J., Raudsepp, M., 2007. Measured and computed neutralization potentials from static tests of diverse rock types. Environmental Geology, 52(6), 1019-1031.
Jamieson, H. E., Walker, S. R., Parsons, M. B., 2015. Mineralogical characterization of mine waste. Applied Geochemistry, 57, 85-105. doi:10.1016/j.apgeochem.2014.12.014
Nordstrom, D. K., 2000. Advances in the hydrogeochemistry and microbiology of acid mine waters. International Geology Review, 42(6), 499-515.
Paktunc , A., 1999. Mineralogical constraints on the determination of neutralization potential and prediction of acid mine drainage. Environmental Geology, 39(2), 103-112.
Paktunc, A., Davé, N., 2000. Mineralogy of pyritic waste rock leached by column experiments and prediction of acid mine drainage. Paper presented at the Applied Mineralogy in Research, Economy, Technology, Ecology and Culture, Proceedings of the 6th International Congress on Applied Mineralogy, ICAM.
Smart, R., Skinner, W., Levay, G., Gerson, A., Thomas, J., Sobieraj, H., Schumann, R., Weisener, C., Weber, P., Miller, S., 2002. Ard test handbook: Project p387a, prediction and kinetic control of acid mine drainage. AMIRA, International Ltd, Ian Wark Research Institute, Melbourne, Australia, http://fliphtml5.com/cumy/jsbb acces on 08 2018.
Sobek, A. A., 1978. Field and laboratory methods applicable to overburdens and minesoils: Industrial Environmental Research Laboratory, Office of Research and Development, US Environmental Protection Agency.
Stewart, W. A., Miller, S. D., Smart, R., 2006. Advances in acid rock drainage (ard) characterisation of mine wastes. Asmr.
متابعة القراءة...
Mining waste (MW) generally contains a non-valuable sulphide mineral (Jamieson et al., 2015). The oxidation of sulphide minerals in the presence of water and oxygen triggers acid mine drainage (AMD) formation if the neutralisation potential (NP) is insufficient to buffer the produced acid (Blowes et al., 2014; Evangelou, 2018; Paktunc 1999). AMD causes water acidification, the release of harmful metal(oid)s to the surrounding environment, and the precipitation of secondary minerals such as sulphate, Fe-oxides, and Fe-oxyhydroxides (Blowes et al., 2014; Dold, 2017; Elghali et al., 2019a; Elghali et al., 2019b; Nordstrom, 2000). Therefore, prediction of AMD becomes a necessity not only to evaluate the environmental impact of mine waste, but also to design the best reclamation scenario (Demers et al., 2017). Under laboratory conditions, prediction of the geochemical behaviour of MW can be ensured by static and kinetic tests (Dold, 2017; Jambor et al., 2007).
2. Methods
Acid base accounting (ABA) is the most common static tests for short, rapid, and low-cost prediction of AMD formation (Smart et al., 2002; Sobek, 1978; Stewart et al., 2006). In contrast, kinetic tests are designed to evaluate mineral reactivity and leaching rates of various chemical species (Erguler and Erguler Kalyoncu, 2015; Paktunc and Davé, 2000).
The main objectives of this article are: i) to evaluate the Acid generation potential (AGP) using ABA static tests, and ii) to study the long-term geochemical behaviour two mining waste using weathering cells as kinetic test.
The net neutralisation potential (NNP = NP – AP) and neutralisation potential ratio (NPR=NP/AP) criteria were used to classify the acid generation potential of the studied samples (S1 and S2), where acidification potential AP=31.25 × wt.% Ssulphides (kg CaCO3/t) and neutralisation potential NP=83.3 × wt.% C (kg CaCO3/t). The sample is classified non-acid-generating if its NNP is higher than 20 kg CaCO3/t, uncertain if its NNP value is in the interval [−20 and 20] kg CaCO3/t, and acid generating if its NNP is lower than −20 kg CaCO3/t (Miller et al., 1991). For NPR, a sample is classified non-acid generating if NPR > 2.5, uncertain if 1 < NPR < 2.5, and acid generating with NPR < 1 (Benzaazoua et al., 2004b).
The geochemical behaviour of the studied samples was evaluated using weathering cells. The WCs consisted of subjecting 67 g of dry sample to weathering twice per week using 50 mL of deionised water (Cruz et al., 2001). Two WCs were set up and run for 84 days. The collected leachates were analysed for physicochemical parameters such as pH, electrical conductivity (EC), acidity, alkalinity, and chemical composition.
Structure of Data inputs ; Kinetic-analysis and initial masses
Inputs files for data analysis
3. Results
3.1. ABA Static test
Fig.1 shows the results of ABA tests; NPs and APs of the studied samples were, respectively, calculated based on the total carbon and sulphur-sulphide (Bouzahzah et al, 2014).
Code R for AGP calculation
كود:
##########################################################################################
###########Static test using ABA test
##########################################################################################
# load ggplot2
library(ggplot2)
library(reshape)
library(hrbrthemes)
library(ggrepel)
#set the directory
setwd("E:/Kintec_Static_test-R")
#Data for S and C content
sample <- c("S1","S2","")
S <-c(0.54,0.15,0)
C <-c(0.42,0.43,0)
aba_test <- data.frame(sample,S,C)
## statistiques des % soufre-carbone
summary(aba_test)
## calclul de NP et AP
aba_test$NP <- 83.3*aba_test$C
aba_test$AP <- 31.25*aba_test$S
summary(aba_test)
##calcl de NNP et NRP
aba_test$NNP <- aba_test$NP- aba_test$AP
aba_test$RNP <- aba_test$NP/ aba_test$AP
View(aba_test)
## test pour PAG_RNP
for(i in 1:nrow(aba_test))
{
if (aba_test$NNP[i] > 20)
aba_test$PAG_NNP[i] <- 'Non_PAG'
else if (aba_test$NNP[i] < -20)
aba_test$PAG_NNP[i] <- 'Oui_PAG'
else
aba_test$PAG_NNP[i] <- 'Incertain'
}
## test for PAG_RNP
for(i in 1:nrow(aba_test))
{
if (aba_test$RNP[i] > 3)
aba_test$PAG_RNP[i] <- 'Non_PAG'
else if (aba_test$RNP[i] < 1)
aba_test$PAG_RNP[i] <- 'Oui_PAG'
else if (aba_test$RNP[i] < 2.9 | aba_test$RNP[i] >1.1)
aba_test$PAG_RNP[i] <- 'Incertain'
}
View(aba_test)
for(i in 1:nrow(aba_test))
{
if (aba_test$PAG_RNP[i] == 'Incertain' | aba_test$PAG_NNP[i]== 'Incertain')
aba_test$Kintic_test[i] <- 'Yes'
else if (print(aba_test$PAG_RNP[i] != aba_test$PAG_NNP[i]) == TRUE)
aba_test$Kintic_test[i] <- 'Yes'
else
aba_test$Kintic_test[i] <- 'NO'
}
aba_testFig 1: Statistic of the AGP
Results of ABA tests show that the S2 is non-acid generating except for the S1 sample that is uncertain according to it NNP values that range between -20 and 20 kg CaCO3/t (Bouzahzah et al., 2014b). The NPR value of S2 was higher than 2.5 (non-acid generating), except the fraction S1 with NRP between 1 and 2.5.
R code to plot the AGP of the studied samples.
كود:
aba_test
typeof(aba_test)
aba_test = as.data.frame(aba_test)
# plot of the AGP
AGP <- ggplot(aba_test, aes(x=AP, y=NP, label=sample )) +
geom_ribbon(aes(x = c(20,20,300),ymin= 0, ymax=c(0,0,280)), alpha= 0.1, fill = "red")+
geom_ribbon(aes(x =c(0,12,120) ,ymin= c(20,30,300), ymax=300), alpha= 0.1, fill = "green")+
geom_point(size=3) +
geom_text_repel(size= 3, box.padding= 1.5, point.padding = 0.5, force = 100, segment.size = 0.1,segment.color = "grey50",direction = "y") +
theme_bw() +
scale_x_continuous(expand = c(0, 0),name="AP (kg CaCo3/t)") +
scale_y_continuous(expand = c(0, 0),name="NP (kg CaCo3/t)") +
#slope =1
geom_abline(intercept=0, slope=1, color = "blue", size = 0.5) +
annotate(geom="text", x=175, y=180, label="PN/PA=1",color="blue", angle = 40)+
#slope =2.5
geom_abline(intercept=0, slope=2.5, color = "red", size = 0.5) +
annotate(geom="text", x=70, y=160, label="PN/PA=2.5",color="red", angle = 70)+
#slope =1 and in = 20
geom_abline(intercept = 20, slope = 1, color = "green", size = 0.5) +
annotate(geom="text", x=150, y=180, label="PN-PA=20",color="green", angle = 40)+
#slope =1 and in = -20
geom_abline(intercept = -20, slope = 1, color = "red", size = 0.5)+
annotate(geom="text", x=210, y=180, label="PN-PA=-20",color="red", angle = 40)+
geom_text( aes(x = 150, y = 250,label="Uncertain_Zone"), parse = TRUE, inherit.aes=FALSE)+
geom_text( aes(x = 200, y = 100,label="Acid_Generating_Zone"), parse = TRUE, inherit.aes=FALSE)+
geom_text( aes(x = 50, y = 250,label="Non_Acid_Generating_Zone"), parse = TRUE, inherit.aes=FALSE)
AGPFig.2. Graph representing results of acid generation potential by ABA test
3.2. Kinitec test
- Physicochemical parameters of the studied samples
The Physicochemical parameters of the leachates from the WCs is shown in Fig.3. The pH evolution showed similar behaviour for the two samples, stabilising around circumneutral values of 7.4-8.9. The electrical conductivity tended to decrease from 472 μS/cm to 42 μS/cm. The alkalinity was around ~45 mg/L CaCO3 and tended to increase to reach 56 mg/L CaCO3 and 58 mg/L CaCO3 for S1 and S2 materials, respectively (Fig. 3); the acidity of the leachates was less than 12 mg/L for all the studied samples.
Code to plot the Physicochemical parameters :
كود:
##################################################################################
##Prediction test : kietec test in WC
##################################################################################
Element_geochimique <- read.csv("kinetec_analysis.csv", sep=";",dec = ".")
View(Element_geochimique)
##Static calculation for the physicochemical paramteres
tapply(Element_geochimique$pH, Element_geochimique$WC, summary)
tapply(Element_geochimique$Eh, Element_geochimique$WC, summary)
tapply(Element_geochimique$Cond , Element_geochimique$WC, summary)
tapply(Element_geochimique$alkalinity , Element_geochimique$WC, summary)
tapply(Element_geochimique$Acidity , Element_geochimique$WC, summary)
## charge ponctuelle
nrow(Element_geochimique)
ncol(Element_geochimique)
#Element_geochimique[1,]
colnames(Element_geochimique)
Element_geochimique$Days
############################ploting the physicochemical and ponctual varaibles in facet
df_gch_Par <- melt(Element_geochimique[,c(1,2,4:8)], id.vars=c('WC',"Days"))
df_gch_Par
ggplot(df_gch_Par, aes(x=Days, y=value, color = WC))+
geom_point()+
geom_line()+
facet_wrap(~variable , scales = "free_y")Fig.3 : Physicochemical parameters of the studied samples
- Cumulative concentrations of the chemical elements
Code to plot the ponctual concentrations of the chemical elements :
كود:
####################################################################################
## Calculation of ponctuel charges
####################################################################################
## calcul des charges pontuelles = chimecal element * leachate.volume /1000
for (j in 9:34)
{
for (i in 1:nrow (Element_geochimique))
{
Element_geochimique[[paste(colnames(Element_geochimique[j]),'pon')]][i]<- ((Element_geochimique[,j])[i]*Element_geochimique$leachate.volume[i])/1000
}
}
View(Element_geochimique)- Cumulative concentrations of the chemical elements
The chemical composition of the leachates are presented in Fig.4 as cumulative and mass-normalised releases (mg/kg). Ca, Mg, and Mn were chosen to evaluate the neutralising mineral dissolution (mainly carbonates) and sulphate to indicate pyrite oxidation (Benzaazoua et al., 2004b; Elghali et al., 2019a).
Code to calculate the Cumulative concentrations of the chemical elements :
كود:
####################################################################################
## Calculation of cunulated ponctuel charges
####################################################################################
## calcul des charges pontuelles cumulées using cumsum function
###################test ##############################
el_gq <- list(Element_geochimique[c(1:16),], Element_geochimique[c(17:32),])
el_gq[[1]]
for (t in 1:2) {
for (j in 35:60)
{
for (i in 1:16){
el_gq[[t]][[paste(colnames(el_gq[[t]][j]),'cuml')]][i]<- cumsum(el_gq[[t]][,j])[i]
}
}
}
Element_geochimique12 = rbind(el_gq[[1]], el_gq[[2]])
typeof(Element_geochimique12)
Element_geochimique = as.data.frame(Element_geochimique12)
View(Element_geochimique)Code to plot the Cumulative concentrations of the chemical elements :
كود:
############################ploting the cunulated ponctual varaibles in facet
df_gch_cml <- melt(Element_geochimique[,c(1,2,61:86)], id.vars=c('WC',"Days"))
df_gch_cml
ggplot(df_gch_cml, aes(x=Days, y=value,color = WC))+
geom_point()+
scale_y_continuous(name="Cumulative charges in mg cuml/Kg") +
geom_line()+
facet_wrap(~variable , scales = "free_y")Fig.4 : Cumulative concentrations of the chemical elements of the studied samples
Code to plot the Normalised Cumulative concentrations of the chemical elements :
كود:
####################################################################################
## Calculation of normalised cunulated ponctuel charges : cum /initial mass
####################################################################################
initalmass <- 0.067 # for WC
for (j in 61:86)
{
for (i in 1:nrow (Element_geochimique))
{
Element_geochimique[[paste(colnames(Element_geochimique[j]),'nrml')]][i]<- (Element_geochimique[,j])[i]/initalmass
}
}
summary(Element_geochimique)
colnames(Element_geochimique)- Sulphur-carbonate depletion
Sulphide and carbonate depletion times within the studied samples are presented in Fig.5. The depletion time calculated for sulphur in S1 was approximately two times longer than that calculated for carbonates. Therefore, complete depletion of sulphur within the S2 materials will be achieved, as compared to the S1, due to their decreased sulphur content (0.54% for S1 and 0.15% for S2).
Code for sulfide and carbonates depletion
كود:
####################################################################################
## Depletion of sulfates, sulfides and carbonates depletion from cumulataed ponctuel charges
####################################################################################
################sum of carbonates Ca, Mn et Mg pon_clmul
Element_geochimique[,c(66,74,75)]
for (i in 1:nrow (Element_geochimique))
{
Element_geochimique$crb[i] <- rowSums(Element_geochimique[,c(66,74,75)])[i]
}
View(Element_geochimique)
colnames(Element_geochimique)
################ Calculation of sulfates from sulfides
summary(Element_geochimique)
#colnames(Element_geochimique)
Element_geochimique[,80]# soufre total pon cml
Element_geochimique$soufr <- Element_geochimique[,80]
Element_geochimique$sulfate <- Element_geochimique[,80]*3Code for sulfide and carbonates calculation of the initial masses
كود:
################Calculation of the initial mass of Carbonates and sulfates
portion_initiale <- read.csv("Samples_Inmass.csv", sep=";",dec = ".")
portion_initiale
portion_initiale$mass <- (portion_initiale$Portion.*initalmass)*1000000/100
portion_initiale$fctstc <- c(1,1,1,3,1,1,1,3 )
portion_initiale$mass_fnl <- portion_initiale$fctstc*portion_initiale$mass
portion_initiale
mass_initail_carbnt_S1 <- sum(portion_initiale$mass_fnl[c(1:3)])
mass_initail_carbnt_S1
##############
mass_initail_carbnt_S2<- sum(portion_initiale$mass_fnl[c(5:7)])
mass_initail_carbnt_S2
##############
mass_initail_sulfate_S1 <-portion_initiale$mass_fnl[4]
mass_initail_sulfate_S1
##############
mass_initail_sulfate_S2<-portion_initiale$mass_fnl[8]
mass_initail_sulfate_S2
##############
mass_initail_sulfide_S1 <-portion_initiale$mass[4]
mass_initail_sulfide_S1
##############
mass_initail_sulfide_S2<-portion_initiale$mass[8]
mass_initail_sulfide_S2Code R for sulfide and carbonates Depletion calculation
كود:
#######################################################################################
#################depletion calculation using lists
######################################################################################
elp_dplt <- list(Element_geochimique[c(1:16),], Element_geochimique[c(17:32),])
mass_initail_carbnt <- c(mass_initail_carbnt_S1,mass_initail_carbnt_S2)
mass_initail_carbnt[1]
mass_initail_sulfate <- c(mass_initail_sulfate_S1,mass_initail_sulfate_S2)
mass_initail_sulfide <- c(mass_initail_sulfide_S1,mass_initail_sulfide_S2)
elp_dplt[[1]]
for (t in 1:2) {
for (i in 1:16){
elp_dplt[[t]]$epuis_carb[i] <- (mass_initail_carbnt[t] - elp_dplt[[t]]$crb[i])/mass_initail_carbnt[t] *100
elp_dplt[[t]]$epuis_slft[i] <- (mass_initail_sulfate[t] - elp_dplt[[t]]$sulfate[i] )/mass_initail_sulfate[t]*100
elp_dplt[[t]]$epuis_slfd[i] <- (mass_initail_sulfide[t] - elp_dplt[[t]]$soufr[i] )/mass_initail_sulfide[t]*100
}
}
elp_dplt[[1]]
elp_dplt[[2]]
Element_geochimique_dplt = rbind(elp_dplt[[1]], elp_dplt[[2]])
typeof(Element_geochimique_dplt)
Element_geochimique = as.data.frame(Element_geochimique_dplt)
View(Element_geochimique)
colnames(Element_geochimique)Code R to plot the carbonates and sulfides depletion
كود:
x <- Element_geochimique$Days
y1 <- Element_geochimique$epuis_carb
y2 <- Element_geochimique$epuis_slft
y3 <-Element_geochimique$epuis_slfd
t <- Element_geochimique$WC
df2 <- data.frame(t,x, y1,y2, y3)
colnames(df2) <- c('WC','Days','Carbonates_Depletion','Sulfates_Depletion','Sulfide_Depletion')
df2
####fuction for correlation cof and equation of line smoth
lm_eqn <- function(df,y, x){
m <- lm(y ~ x, df);
eq <- substitute(italic(y) == a + b %.% italic(x)*","~~italic(r)^2~"="~r2,
list(a = format(unname(coef(m)[1]), digits = 3),
b = format(unname(coef(m)[2]), digits = 3),
r2 = format(summary(m)$r.squared, digits = 3)))
as.character(as.expression(eq));
}
##############plot of depletion
serie <- list(df2[c(1:16),], df2[c(17:32),])
#serie[[1]][,2]
#colnames(serie[[1]])[1]
for (j in 1:2)
{
for (i in c(3,5))
{
ff<- print(lm_eqn(serie[[j]], round(serie[[j]][,i]/100,4),serie[[j]][,2] ))
dpt <- (as.numeric(substring(ff,15,18))/as.numeric(substring(ff,26,33)))
p <- ggplot(serie[[j]], aes(x=Days, y=serie[[j]][,i], color= WC )) +
geom_point()+
geom_smooth(method = "lm", se=FALSE, color="black") +
geom_text(data=serie[[j]],aes(x = 40, y = 98,label=lm_eqn(serie[[j]], round(serie[[j]][,i]/100,4),Days )), parse = TRUE, inherit.aes=FALSE)+
geom_text(aes(x = 40, y = 97.5,label=paste0(colnames(serie[[j]])[i],"__in__",round(dpt, digits = 1),"__Cycle" )), parse = TRUE, inherit.aes=FALSE,size=4)+
ggtitle(paste0(colnames(serie[[j]])[i], ' of ',factor(serie[[j]][,1]) )) +
xlab("Days") + ylab(paste(colnames(serie[[j]])[i],"in_%") )+
theme_classic()
print(paste0(j,i))
print(p)
}
}Fig.5: Carbonates and sulfide depletion of the studied samples
- Oxidation/neutralisation curves
Oxidation–neutralisation curves for the two WCs showed a linear relationship. Slopes and correlation coefficients (R2) for the linear regressions are presented in Fig.6.
Code for oxidation/neutralisation curves
كود:
#########################################################################################
## oxydo neutralisation curves from charge pon clmul nrml
#########################################################################################
View(Element_geochimique)
colnames(Element_geochimique)
#sommaion des carbonates Ca, Mn et Mg charge pon clmul nrml
Element_geochimique[,c(92,100,101)]
for (i in 1:nrow (Element_geochimique))
{
Element_geochimique$carbcmlnrml[i] <- rowSums(Element_geochimique[,c(92,100,101)])[i]
}
#sommaion des carbonates S charge pon clmul nrml
Element_geochimique$sulfatecmlnrml <- Element_geochimique[,106]*3
dt_plotoxyntr <- data.frame(Element_geochimique[,c(1,2,119, 120)])
dt_plotoxyntr[c(4:16),]
serie_oxy <- list(dt_plotoxyntr[c(1:16),], dt_plotoxyntr[c(17:32),])
serie_oxy[[1]]
for(i in 1:2)
{
Data_oxy <-serie_oxy[[i]][c(4:16),]
pp <-ggplot(Data_oxy, aes(x=Data_oxy[,4], y=Data_oxy[,3] )) +
geom_point()+
geom_smooth(method = "lm", se=FALSE, color="black") +
scale_x_continuous(expand = c(0, 0),name="Sulfates (mg cuml/Kg)") +
scale_y_continuous(expand = c(0, 0),name="Carbonates Ca+Mg+Mn (mg cuml/Kg)") +
geom_text(data=Data_oxy,aes(x = min(Data_oxy[,4])+5, y = 125,label=lm_eqn(Data_oxy, Data_oxy[,3],Data_oxy[,4] )), parse = TRUE, inherit.aes=FALSE)+
ggtitle(paste0("Oxido neutralisation curve of ", Data_oxy[,1]))
print(pp)
}Fig.6: Oxido-neutralisation curves of the studied samples
- Long-term prediction
These oxidation–neutralisation curves in Fig.6 were used to predict the long-term geochemical behaviour by extrapolating the curves and projecting the sulphate and [Ca+Mg+Mn] concentrations within the initial solid samples.
The sample is considered to be acid-generating if it is located on the sulphate side of the curve and non-acid generating if it is located on the [Ca+Mg+Mn] side of the curve, as shown in Fig.7.The samples S1 and S2 are located under their curves, which indicates that the neutralising minerals will be depleted before the sulphide (Fig. 7). The S1 and S2 sample classified as acid generating over the long term.
Code R to plot the long term prediction
كود:
#########################################################################################
## projected curves for carbonates and sulfates ---ok---
#########################################################################################
SLFT <- seq(0,2000,500)
# The equation used for carbonates are in the figures above
dt_prjct_S1 <- data.frame(c(1:5),'S1',SLFT, (7.5*SLFT)-226)
colnames(dt_prjct_S1) <- c('num','WC','SLFT','CRBNT')
dt_prjct_S1
dt_prjct_S2<- data.frame(c(1:5),'S2',SLFT,(9.9*SLFT)-235)
colnames(dt_prjct_S2) <- c('num','WC','SLFT','CRBNT')
dt_prjct = rbind(dt_prjct_S1,dt_prjct_S2)
ggplot(dt_prjct, aes(x = SLFT , y = CRBNT, color = factor(WC))) +
geom_point() +
scale_x_continuous(expand = c(0, 0),name="Sulfates (mg cuml/Kg)") +
scale_y_continuous(expand = c(0, 0),name="Carbonates Ca+Mg+Mn (mg cuml/Kg)") +
stat_smooth(method = "lm", formula = y ~ x, se = FALSE)+
geom_point(aes(x=mass_initail_sulfate_S2, y=mass_initail_carbnt_S2), colour="cyan3",fill = "white", size = 2, stroke = 2 )+
geom_point(aes(x=mass_initail_sulfate_S1, y=mass_initail_carbnt_S1), colour="salmon",fill = "white", size = 2, stroke = 2 )+
ggtitle(paste0("Projection curve : Long-term prediction of the environmental behaviour of S1 and S2" )) +
theme_bw()+
geom_ribbon(aes(x = SLFT,ymin=0, ymax=CRBNT), alpha=0.05, fill = "green")+
geom_text( aes(x = 1500, y = 2000,label="PAG: Potentially_Acid_Generating_Zone"), size= 6, parse = TRUE, inherit.aes=FALSE)+
geom_text( aes(x = 800, y = 15000,label="NPAG: Non_potentially_Acid_Generating_Zone"),size= 6, parse = TRUE, inherit.aes=FALSE)Fig.4: Long-term prediction of the studied samples
You find more details in Amar et al, 2021.
Hicham AMAR, Ing in Geomatic Sciences, Co-founder of Geoinfo4all.com
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