EES Matab

Python code:

```python

# Import necessary libraries

import numpy as np

# Define the number of effects

num_effects = 3

# Define the parameters

feed_mass_flow_rate = 100 # kg/h

feed_salt_concentration = 0.2 # kg/kg

feed_temperature = 25 # °C

feed_pressure = 1 # bar

steam_temperature = 120 # °C

# Define the properties and constants

latent_heat = 2260 # kJ/kg

heat_capacity = 4.18 # kJ/kg°C

# Initialize arrays to store the results

distillate_production = np.zeros(num_effects)

steam_consumption = np.zeros(num_effects)

# Perform calculations for each effect

for i in range(num_effects):

# Calculate the distillate production in effect i

distillate_production[i] = feed_mass_flow_rate * feed_salt_concentration

# Calculate the steam consumption in effect i

steam_consumption[i] = distillate_production[i] * latent_heat / (steam_temperature - feed_temperature)

# Update the feed properties for the next effect

feed_mass_flow_rate -= distillate_production[i]

feed_salt_concentration = feed_mass_flow_rate * feed_salt_concentration / (feed_mass_flow_rate - distillate_production[i])

feed_temperature = steam_temperature

# Print the results for each effect

for i in range(num_effects):

print(f"Effect {i+1}: Distillate Production = {distillate_production[i]:.2f} kg/h, Steam Consumption = {steam_consumption[i]:.2f} kg/h")

```

Steps:

general outline of the steps you can follow to develop such a model:

1. Define the system components: Identify the key components of the multi-effect distillation system, such as the evaporators, condensers, heat exchangers, pumps, and the associated streams of feedwater, vapor, and distillate.

2. Set up the mass and energy balances: Develop the mass and energy balance equations for each component and the overall system. Consider factors such as heat transfer, mass transfer, pressure drop, phase change, and vapor-liquid equilibrium.

3. Determine the thermodynamic properties: Incorporate the necessary thermodynamic models and data to calculate properties like enthalpy, entropy, temperature, pressure, and compositions of the streams at different stages of the process.

4. Implement the heat transfer equations: Include the appropriate heat transfer equations to account for the transfer of heat between the hot and cold streams in the evaporators, condensers, and heat exchangers.

5. Consider the phase change phenomena: Account for the phase changes that occur during the evaporation and condensation processes. Use appropriate correlations or models to estimate the heat transfer coefficients and the amount of vapor generated or condensed.

6. Incorporate the energy consumption: Include the energy requirements of the pumps and other auxiliary equipment in the model.

7. Validate and optimize the model: Validate the model by comparing its predictions with available experimental or real-world data. Optimize the model by adjusting parameters or configurations to achieve desired performance objectives, such as maximizing distillate production or minimizing energy consumption.

8. Visualize and analyze the results: Use appropriate plotting and visualization techniques to analyze the model's results, such as temperature profiles, pressure profiles, and composition profiles at different stages of the multi-effect distillation process.

Hope it helps