Spaces:
Sleeping
Sleeping
File size: 3,657 Bytes
451b738 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 |
import streamlit as st
import pandas as pd
import numpy as np
import io
# Load steam density CSV
@st.cache_data
def load_density_data():
return pd.read_csv("steam_density.csv").round()
density_df = load_density_data()
# List of commercially available pipe sizes
commercial_pipes = [15, 20, 25, 40, 50, 65, 80, 100, 125, 150]
def get_nearest_commercial_size(size):
"""Find the nearest upper commercially available pipe size."""
for pipe in commercial_pipes:
if size <= pipe:
return pipe
return "Not Available" # If size exceeds the largest available pipe
# Function to calculate new pipe size
def calculate_new_line_size(flow_kg_hr, pressure_bar, density_df):
pressure_bar_new = round(float(pressure_bar))
# Interpolate density from CSV data
density = np.interp(pressure_bar_new, density_df["Pressure (bar)"], density_df["Density Saturated (kg/m³)"])
# Convert flow to kg/s
flow_kg_s = flow_kg_hr / 3600
# Velocity range for saturated steam
velocity = 30 # Mid-range velocity in m/s
# Calculate required pipe diameter (m)
diameter_m = np.sqrt((4 * flow_kg_s) / (np.pi * velocity * density))
# Convert to mm
return round(diameter_m * 1000, 1)
# Streamlit UI
st.title("Steam Pipe Line Size Validator")
num_lines = st.number_input("Number of Lines to Validate", min_value=1, step=1)
data = []
if num_lines:
for i in range(num_lines):
st.subheader(f"Line {i+1}")
name = st.text_input(f"Line {i+1} Name", key=f"name_{i}")
flow = st.number_input(f"Flow (Kg/hr) for Line {i+1}", min_value=1.0, step=0.1, key=f"flow_{i}")
pressure = st.number_input(f"Pressure (bar) for Line {i+1}", min_value=1.0, step=0.1, key=f"pressure_{i}")
present_size = st.number_input(f"Present Line Size (mm) for Line {i+1}", min_value=1.0, step=0.1, key=f"size_{i}")
data.append([name, round(flow), round(pressure), round(present_size)])
if st.button("Validate All Lines"):
validated_data = []
for entry in data:
name, flow, pressure, present_size = entry
new_size = calculate_new_line_size(flow, pressure, density_df)
nearest_commercial_size = get_nearest_commercial_size(new_size)
# Allow minor tolerance
tolerance = 5 # ±5 mm
status = "Yes" if abs(new_size - present_size) <= tolerance else "No"
validated_data.append([name, flow, pressure, present_size, new_size, nearest_commercial_size, status])
df_result = pd.DataFrame(validated_data, columns=[
"Line Name", "Flow (Kg/hr)", "Pressure (bar)", "Present Size (mm)",
"New Size (mm)", "Commercial Size (mm)", "Valid"
])
# Apply styling
def highlight_status(val):
return 'background-color: green; color: white;' if val == "Yes" else 'background-color: red; color: white;'
st.dataframe(df_result.style.applymap(highlight_status, subset=["Valid"]))
# Convert DataFrame to Excel file in-memory
output = io.BytesIO()
with pd.ExcelWriter(output, engine="xlsxwriter") as writer:
df_result.to_excel(writer, index=False, sheet_name="Validated Lines")
# Download button
st.download_button(
"Download Excel File",
data=output.getvalue(),
file_name="validated_steam_lines.xlsx",
mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
)
|