pina 0.1.1

A lightweight pinch analysis package

pina

Installation

To install this package, run

python -m pip install pina

Basic example

This is a basic example using the four stream problem from Pinch Analysis and Process Integration: A User Guide On Process Integration for the Efficient Use of Energy, Second edition, Ian C. Kemp, p. 4. Its data looks as follows:

Stream number and type	Heat flow [kW]	Supply Temperature [°C]	Target Temperature [°C]
1. Cold	-230	20	135
2. Hot	330	170	60
3. Cold	-240	80	140
4. Hot	180	150	30

ΔT_min = 10 K

We can input the data with the following code:

from pina import PinchAnalyzer, make_stream

# Arguments: heat flow, supply temperature, target temperature
cold_1 = make_stream(-230, 20, 135)
hot_1 = make_stream(330, 170, 60)
cold_2 = make_stream(-240, 80, 140)
hot_2 = make_stream(180, 150, 30)

min_temp_diff = 10
temp_shift = min_temp_diff / 2

analyzer = PinchAnalyzer(temp_shift)
analyzer.add_streams(cold_1, hot_1, cold_2, hot_2)

The PinchAnalyzer class calculates the energy targets, the pinch temperature(s) and the coordinates of the composite curves. The following properties can be requested from it:

• heating_demand
• cooling_demand
• hot_utility_target
• cold_utility_target
• heat_recovery_target
• pinch_temps
• cold_composite_curve
• hot_composite_curve
• shifted_cold_composite_curve
• shifted_hot_composite_curve
• grand_composite_curve

For example:

print(
    "Heating demand:       {}\n"
    "Cooling demand:       {}\n"
    "Hot utility target:   {}\n"
    "Cold utility target:  {}\n"
    "Heat recovery target: {}\n"
    "Pinch temperature(s): {}".format(
        analyzer.heating_demand,
        analyzer.cooling_demand,
        analyzer.hot_utility_target,
        analyzer.cold_utility_target,
        analyzer.heat_recovery_target,
        analyzer.pinch_temps,
    )
)

Output:

Heating demand:       470.0
Cooling demand:       510.0
Hot utility target:   20.0
Cold utility target:  60.0
Heat recovery target: 450.0
Pinch temperature(s): [85.0]

Plotting

This package deliberately does not include any plotting functions, but using it together with plotting libraries is simple. Here is an example that uses matplotlib to create a figure with two subplots: the hot and cold composite curves on the left and the grand composite curve on the right:

from matplotlib import pyplot as plt

fig, ax = plt.subplots(1, 2, figsize=(16, 7))

ax[0].plot(*analyzer.hot_composite_curve, color="tab:red", linestyle="--", label="HCC")
ax[0].plot(*analyzer.cold_composite_curve, color="tab:blue", linestyle="-", label="CCC")
ax[0].legend()
ax[0].set_title("Hot and cold composite curves")
ax[0].set_xlabel("Heat flow [kW]")
ax[0].set_ylabel("Actual temperature [\u2103]")

ax[1].plot(*analyzer.grand_composite_curve, color="k", linestyle="-", label="GCC")
ax[1].legend()
ax[1].set_title("Grand composite curve")
ax[1].set_xlabel("Net heat flow [kW]")
ax[1].set_ylabel("Shifted temperature [\u2103]")

# Make the y-axis equal in both plots
ylims = (*ax[0].get_ylim(), *ax[1].get_ylim())
minmax_ylims = (min(ylims), max(ylims))
ax[0].set_ylim(minmax_ylims)
ax[1].set_ylim(minmax_ylims)

plt.show()

Output:

More examples

More examples can be found in the examples folder. They show some additional features:

• Streams carrying latent heat (e.g. evaporation and condensation)
• Streams consisting of multiple segments
• Streams with individual temperature shifts

Anything else?

Nope, that's all. Enjoy :-)