Updated the formulas and readme text.

.idea/vcs.xml

@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="" vcs="Git" />
+  </component>
+</project>

Formulas.txt

@@ -1,25 +1,32 @@
-Use Average Weighted Grade to calculate Recovery and Processing cost then use these two to calculate cut off grade using the formula cof(g) = processing cost / ((net price) * recovery). Use inputs from a table. Make graphs in Python for the Cutoff Grade, Recovery, Processing Cost.
+# Description
+
+The process began with utilizing average weighted grade data from a table to determine the recovery rate and processing costs.  These values were then applied in the cut-off grade calculation: Cut-Off Grade = Processing Cost / ((Net Price) * Recovery). The average weighted grade and net price were sourced directly from the table. Finally, Python was employed to create graphs illustrating the cut-off grade, recovery rate, and processing cost trends.
+
+Cut-Off Grade = Processing Cost / ((Net Price) * Recovery)
 
 
-cof(g) = processing cost / ((net price) * recovery)
+Recovery = Rmax​ * (1 − e^(−k*G))
+
+Processing Cost = a + b*G
 
 
-recovery = Rmax​ x (1−e^(−k⋅G))
+# Processing Cost Formulas
-
-processing cost = C0​ + C1/G​​
 
 1. Fixed + Grade-Dependent Cost Model  
 processing cost = C0​ + C1/G​​
 
-2. Tabulated Cost Based on Grade
+2. Regression from Real Cost Data (Realist)
-
-Table equivalent for the given grade
-
-3. Regression from Real Cost Data (Realist)
 
 Linear: C = a + bG
 
-Inverse or logarithmic: C = a + b/G or C = alog⁡(G) + b
+Inverse or Logarithmic: C = a + b/G or C = a*log⁡(G) + b
+
+3. Tabulated Cost Based on Grade
+
+Table equivalent for the given grade
+
+
+# Recovery Formulas
 
 1. Empirical Linear Model
 

Grade_Calculator.py

@@ -21,7 +21,7 @@ x_0 = 0.8     # Grade at which recovery rate reaches ~50% of R_max
 
 df["Recovery (%)"] = (R_max / (1 + np.exp(-k * (df["Weighted Grade (%)"] - x_0)))) / 100
 
-# === Processing Cost Estimation (Regression from Real Cost Model) ===
+# === Processing Cost Estimation (Linear Regression Model) ===
 # Processing Cost ($/t) = A + B * Grade
 # Reflects increasing cost with higher grade due to more intensive processing
 A = 12    # Fixed base processing cost ($/t)

README.md

@@ -2,6 +2,12 @@
 
 This Python script is designed to calculate the cut-off grade for use in Mining Economics. It begins by calculating the recovery using a logistic model based on the weighted average grade. Next, it estimates the processing cost through a regression model derived from actual cost data. Using the calculated recovery, processing cost, and net price, the script determines the cut-off grade. Finally, all calculations are visualized through a graph for better interpretation.
 
+Cut-Off Grade = Processing Cost / ((Net Price) * Recovery)
+
+Recovery = Rmax​ * (1 − e^(−k*G))
+
+Processing Cost = a + b*G
+
 
 # Author