batuhan-basoglu/Cut-Off-Grade-Calculator
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Updated the formulas to be more proper.

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Batuhan Berk Başoğlu 2025-05-25 23:18:20 -04:00
parent 5b31267d05
commit 6ef69393ab
Signed by: batuhan-basoglu
SSH key fingerprint: SHA256:kEsnuHX+qbwhxSAXPUQ4ox535wFHu/hIRaa53FzxRpo
1 changed files with 4 additions and 4 deletions
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Grade_Calculator.py
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@ -16,16 +16,16 @@ df = pd.DataFrame(data)
# Recovery (%) = R_max / (1 + exp(-k * (Grade - x0))) # Recovery (%) = R_max / (1 + exp(-k * (Grade - x0)))
# Models increasing recovery efficiency with grade using an S-curve # Models increasing recovery efficiency with grade using an S-curve
R_max = 100 # Maximum theoretical recovery (%) R_max = 100 # Maximum theoretical recovery (%)
k = 5 # Steepness of the logistic curve k = 1.5 # Steepness of the logistic curve
x_0 = 0.8 # Grade at which recovery rate reaches ~50% of R_max x_0 = 0.2 # Grade at which recovery rate reaches ~50% of R_max
df["Recovery (%)"] = (R_max / (1 + np.exp(-k * (df["Weighted Grade (%)"] - x_0)))) / 100 df["Recovery (%)"] = (R_max / (1 + np.exp(-k * (df["Weighted Grade (%)"] - x_0))))
# === Processing Cost Estimation (Linear Regression Model) === # === Processing Cost Estimation (Linear Regression Model) ===
# Processing Cost ($/t) = A + B * Grade # Processing Cost ($/t) = A + B * Grade
# Reflects increasing cost with higher grade due to more intensive processing # Reflects increasing cost with higher grade due to more intensive processing
A = 12 # Fixed base processing cost ($/t) A = 12 # Fixed base processing cost ($/t)
B = 0.1 # Incremental cost increase per % grade B = 0.6 # Incremental cost increase per % grade
df["Processing Cost ($/t)"] = A + B * df["Weighted Grade (%)"] df["Processing Cost ($/t)"] = A + B * df["Weighted Grade (%)"]
# === Cut-off Grade Calculation === # === Cut-off Grade Calculation ===