WaterOptim 1.6.2

Water-Pinch tool

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Water Pinch Analysis

A powerful pure-Python interface for optimizing industrial water networks

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Read the docs

Installation

WaterOptim runs under Python 3.6+. To install it with pip, run the following:

pip install WaterOptim

To upgrade it with pip, run the following:

pip install --upgrade WaterOptim

Basic usage

WaterOptim proposes water networks with optimized water recovery schemes to preserve freshwater and minimize wastewater production. The tool supports water networks with one or more pollutants. The optimization strategy includes 3 steps:

1. Inventory
2. Minimization of Freshwater and Wastewater
3. Design of Water-network

The inventory can be carried out on:

• Water-using process involving pollution transfer. The pollution comes from the product or the process. The inventory includes:

	Parameter	Unit
mc	Mass flowrate of contaminant	kg/h
Cin_max	Maximum inlet concentration	ppm
Cout_max	Maximum outlet concentration	ppm

Example of Water-using process inventory:

posts = [
          {"name":"process 1","cin_max":0,"cout_max":100,"mc":2},
          {"name":"process 2","cin_max":50,"cout_max":100,"mc":5},
          {"name":"process 3","cin_max":50,"cout_max":800,"mc":30},
          {"name":"process 4","cin_max":400,"cout_max":800,"mc":4}
        ]

• Source water flow, available for the REUSE. The inventory includes:

	Parameter	Unit
m	Volume flowrate of water	m3/h
c	Outlet concentration	ppm

Example of Sources inventory:

sources = [
            {'name':'Distillation bottoms','c':0,'m':.8*3600/1000},
            {'name':'Off-gas condensate','c':14,'m':5*3600/1000},
            {'name':'Aqueous layer','c':25,'m':5.9*3600/1000},
            {'name':'Ejector condensate','c':34,'m':1.4*3600/1000}]

• Sink water requirement. The inventory includes:

	Parameter	Unit
m	Volume flowrate of water	m**3/h**
cin_max	Maximum inlet concentration	ppm

Example of Sinks inventory:

demands = [
            {'name':'BFW0','cin_max':0,'m':1.2*3600/1000},
            {'name':'BFW','cin_max':10,'m':5.8*3600/1000},
            {'name':'BFW1','cin_max':1,'m':19.8*3600/1000}]

Basic compilation

Import this module with the following command:

import WaterOptim.wpinch as wp

Compilation of water-using processes

r= wp.__pinch__(posts=posts,verbose=True,design=True)  

Using the cascade attribute you can access the optimization details:

+---------+------------+-------------------+----------+----------+-----------+--------+--------+
|  C ppm  |   Purity   | Purity Difference |   NWSD   |   CWSD   |    PWF    |  CPWF  |  FFW   |
+---------+------------+-------------------+----------+----------+-----------+--------+--------+
|    -    |     -      |         -         |    -     | fw=90.00 |           |        |        |
|    0    |  1.000000  |                   |  -20.00  |          |           |        |        |
|         |            |      0.000050     |          |  70.00   |  0.003500 |        |        |
|    50   |  0.999950  |                   | -140.00  |          |           |  0.00  | 70.00  |
|         |            |      0.000050     |          |  -70.00  | -0.003500 |        |        |
|  {100}  | {0.999900} |         {}        | {120.00} |    {}    |     {}    | {0.00} | {0.00} |
|         |            |      0.000300     |          |  50.00   |  0.015000 |        |        |
|   400   |  0.999600  |                   |  -10.00  |          |           |  0.01  | 37.50  |
|         |            |      0.000400     |          |  40.00   |  0.016000 |        |        |
|   800   |  0.999200  |                   |  50.00   |          |           |  0.03  | 38.75  |
|         |            |      0.999200     |          |  90.00   | 89.928000 |        |        |
| 1000000 |  0.000000  |                   |   0.00   |          |           | 89.96  | 89.96  |
|    -    |     -      |         -         |    -     | ww=90.00 |           |        |        |
+---------+------------+-------------------+----------+----------+-----------+--------+--------+

Dependencies

• scipy
• numpy
• matplotlib
• graphviz

Acknowledgments

The authors wish to thank the French National Research Agency ANR for their funding, and the partners of the project MINIMEAU led by AgroParisTech (French higher education and public research institute), in collaboration with ProSim (Expert in process simulation) ACTALIA, CRITT, CTCPA, IFV, ITERG (Centers of expertise for the food industry), and INRAE ELSA (French institute for agriculture, food and environment)