Building a Kiln Thermal Anomaly Detector in Python: An Industrial Guide

python

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

import seaborn as sns



# 1. Simulate Kiln Shell Temperature Data (Length: 1 to 100 meters)

np.random.seed(42)

kiln_length = np.arange(1, 101)



# Base thermal profile with normal operational decay curve

base_temp = 350 - (kiln_length * 2.2) + np.random.normal(0, 15, 100)

base_temp[0:30] += 120   # Elevating burning zone limits

base_temp[30:60] += 50   # Elevating transition zone limits



# Injecting a CRITICAL HOTSPOT at meter 22 (Refractory damage simulation)

base_temp[21] = 435.0 



# Create Telemetry DataFrame

kiln_data = pd.DataFrame({

    'Meter': kiln_length,

    'Shell_Temp_C': np.clip(base_temp, 120, 450)

})



# 2. Zone Segmentation & Threshold Logic

def scan_kiln_zones(row):

    meter = row['Meter']

    temp = row['Shell_Temp_C']



    if 1 <= meter <= 30:

        zone = "Burning Zone"

        threshold = 400.0  

    elif 31 <= meter <= 60:

        zone = "Transition Zone"

        threshold = 320.0

    else:

        zone = "Calcining Zone"

        threshold = 250.0



    status = "CRITICAL HOTSPOT" if temp > threshold else "NORMAL"

    return pd.Series([zone, threshold, status])



kiln_data[['Zone', 'Threshold_Limit', 'Status']] = kiln_data.apply(scan_kiln_zones, axis=1)



# 3. Print Automated Control Room Alarms

hotspots = kiln_data[kiln_data['Status'] == 'CRITICAL HOTSPOT']

print("="*60)

print("             INDUSTRIAL COMMANDER: KILN THERMAL SCANNER       ")

print("="*60)

if not hotspots.empty:

    for idx, row in hotspots.iterrows():

        print(f"🚨 ALERT: {row['Status']} at Meter {row['Meter']} ({row['Zone']})!")

        print(f"   Current Temp: {row['Shell_Temp_C']:.1f}°C | Limit: {row['Threshold_Limit']}°C\n")

else:

    print("✅ SYSTEM STATUS: All zones within safe thermal limits.")

print("="*60)



# 4. Generate Visual Heatmap & Trend Graph

plt.figure(figsize=(14, 5))

colors = ['red' if status == 'CRITICAL HOTSPOT' else 'blue' for status in kiln_data['Status']]



plt.plot(kiln_data['Meter'], kiln_data['Shell_Temp_C'], color='black', alpha=0.5, linestyle='--')

plt.scatter(kiln_data['Meter'], kiln_data['Shell_Temp_C'], c=colors, s=40, label='Kiln Telemetry')



plt.title("Rotary Kiln Shell Temperature Profile & Hotspot Detection", fontsize=14, fontweight='bold')

plt.xlabel("Kiln Length (Meters from Discharge)", fontsize=11)

plt.ylabel("Shell Temperature (°C)", fontsize=11)

plt.axvline(x=30, color='gray', linestyle=':', label='Zone Boundaries')

plt.axvline(x=60, color='gray', linestyle=':')

plt.grid(True, alpha=0.3)

plt.legend()

plt.show()



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