From 6ef69393abdc657a3e1ab09153a3efca53d2dcc2 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Batuhan=20Berk=20Ba=C5=9Fo=C4=9Flu?= Date: Sun, 25 May 2025 23:18:20 -0400 Subject: [PATCH] Updated the formulas to be more proper. --- Grade_Calculator.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/Grade_Calculator.py b/Grade_Calculator.py index ee33302..fe89be0 100644 --- a/Grade_Calculator.py +++ b/Grade_Calculator.py @@ -16,16 +16,16 @@ df = pd.DataFrame(data) # Recovery (%) = R_max / (1 + exp(-k * (Grade - x0))) # Models increasing recovery efficiency with grade using an S-curve R_max = 100 # Maximum theoretical recovery (%) -k = 5 # Steepness of the logistic curve -x_0 = 0.8 # Grade at which recovery rate reaches ~50% of R_max +k = 1.5 # Steepness of the logistic curve +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 ($/t) = A + B * Grade # Reflects increasing cost with higher grade due to more intensive processing A = 12 # Fixed base processing cost ($/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 (%)"] # === Cut-off Grade Calculation ===