orcpy

orcpy: A lightweight Python module that applies thermodynamic principles, analysing a given set of heat source and heat sink data to find the optimum design parameters of an organic Rankine cycle for waste heat recovery projects.

Table of contents

• Description

  • Problem definition
  • Problem solution

• Dependencies and installation

• Examples and Tutorials

• Authors and contributors

• License

Description

orcpy is a Python package which can come to your aid to deal with Waste heat recovery (WHR) projects. This package can give you the optimum pressure level, working fluid and mass flow rate to obtain maximum power generation and heat recovery, considering the constraints of your project such as the minimum allowable temperature of the waste heat stream and condenser.

Problem definition

First step is formalization of the problem in the orcpy framework. The optimization problem which orcpy is desigend to solve is shown below:

Maximization f_work (pressure level)

sub. to:

waste heat temperature > T_dew

temperature difference in heat exchangers > $\Delta$T_min

evaporator pressure < 0.85 P_critical

condenser temperature > T_allowable

condenser pressure > 1 bar

Problem solution

After defining it, we want of course to solve such a problem. To this aim, the orcpy package uses a golden section search approach to find the optimum pressure level between the upper (0.85 P_critical) and the lower (P_condenser) boundaries. The input variables that should be given to the orcpy are as follow:

• The inlet temperature for the waste stream (ºC) _{for ORC systems it is usually less than 400}

• The outlet temperature for the waste stream (ºC) _{for flue gas it is usually more than 70}

• The power of the waste stream (kW)

• The heat capacity of the waste stream (kJ / kgºC) _{for water it is 4.186 and for air (flue gas) it is about 1}

• The minimum temperature of the condenser (ºC) _{for ORC systems it is usually more than 40}

• The minimum temperature difference (ºC) _{10 is a typical value for this parameter}

• Isentropic efficiencies of turbine and pump (%)

  knowing these parameter, orcpy will give you optimum design parameters of the ORC system.

Dependencies and installation

orcpy requires numpy, pandas, CoolProp, pina, plotly. The code is tested for Python 3, while compatibility of Python 2 is not guaranteed anymore. It can be installed using pip or directly from the source code.

Installing via PIP

To install the package just type:

> pip install orcpy

To uninstall the package:

> pip uninstall orcpy

Examples and Tutorials

To use orcpy after installation of requierd packages just type:

> from orcpy import design
Results, figure = design.ORC.cycle("all")

Next, the orcpy will ask you input variables. instead of "all" you can input a list of working fluids you want to analyze. You can also use orcpy as a function:

>
!pip install orcpy
from orcpy import design

waste_heat_temperature = 300 #C
minimum_allowable_temperature = 100 #C
waste_stream_power = 40 #kW
waste_heat_capacity = 4 #kJ/kg k
turbine_efficiency = 90 #%
pump_efficiency = 90 #%
minimum_temperature_difference = 10 #C
minimum_condenser_temperature = 40 #C

opt, fig = design.ORC.model(waste_heat_temperature,
                           minimum_allowable_temperature,
                           waste_stream_power,
                           waste_heat_capacity,
                           turbine_efficiency,
                           pump_efficiency,
                           minimum_temperature_difference,
                           minimum_condenser_temperature
                           ,"all")

Authors and contributors

orcpy is developed and mantained by

• Mehran Ahmadpour

under the supervision of Prof. Ramin Roshandel and Prof. Mohammad B. Shafii

Contact us by email for further information or questions about orcpy, or suggest pull requests. Contributions improving either the code or the documentation are welcome! You can find out more about my projects by visiting my website.

Cite this package

If you find this package helpful in your work, please consider citing our research paper: Ahmadpour, M., Roshandel, R. & Shafii, M.B. The effect of organic Rankine cycle system design on energy-based agro-industrial symbiosis. Energy Efficiency 17, 39 (2024). https://doi.org/10.1007/s12053-024-10221-0

License

See the LICENSE file for license rights and limitations (MIT).