Desalsim Tutorial

1. Introduction

This tutorial provides a comprehensive overview and guide to utilizing a simulation package tailored for analyzing desalination and brine treatment technologies. Here’s what you’ll find:

2. Usage: Instructions on how to use the simulation models, including input parameters and result interpretation.

3. Technical Process Models: Detailed descriptions of each technology model, including input-output relationships and simulation steps.

4. Economic Models: Explanation of economic models for evaluating operating and investment costs.

5. Treatment trains Comparison: Guidance on comparing different treatment trains using provided tools.

2. Installation

The easiest way is through pip, in command-line interface:

pip install DesalSim

You can find the last version: https://pypi.org/project/desalsim/

If you want to install the latest GitHub version:

• Download the repository to your local machine:

https://github.com/rodoulak/Desalination-and-Brine-Treatment-Simulation-

• Install the required dependencies:

pip install -r requirements.txt

3. Usage

Each simulation model serves as a standalone tool for analyzing the performance of a specific desalination or brine treatment technology. Before running the simulation, ensure that you have provided the necessary input parameters, such as feed flow rates, salinity levels, membrane properties, heat sources, and operating conditions.

The simulation results, including salt concentration profiles, ion fluxes, energy consumption, chemical consumption, and operational costs, will be generated based on the specified inputs and displayed in the console output or saved to output files for further analysis.

However, simulation models of more than one technology can be combined to simulate and evaluate the performance of a treatment train (desalination and brine treatment system). In this case, the output flow rates and stream concentrate are the input data for the next technology.

Additionally, two example files are provided to demonstrate the usage of the simulation suite (see Example 1 and Example 2). These examples simulate and evaluate two different treatment trains, showcasing the integration of multiple technologies. The economic evaluation of the treatment train is given in Example 1 and in the Economic Tutorial. Furthermore, a comparison file is included, where the results of the two examples are compared in terms of various parameters. Users can extend this comparison by adding more indicators as needed.

Followed steps:

Step 1: Import required functions for process units in the treatment train.

Step 2: Set input data like feed flow rate, ion concentration, relevant ions for the feed solution.

Step 3: Set input parameters for each process unit as shown in Table 1 and for economic model as shown in Table 2 and Table 3.

Step 4: Call function of each process unit, create objects for each calculation.

Step 5: Results interpretation.

3.1. Documentation

You can find Tutorials and documents at:

• Tutorial for Example 1

• Economic Tutorial

• The mathematical description of each technology is given in Mathematical description

• Example 1

• Example 2

• Scenarios comparison

4. Technical process models

For more detailed steps and instructions see Tutorial for Example 1. The mathematical description of each technology is given in Mathematical description. Table 1 gives an overview of the main inputs and outputs for each process unit in Desalsim.

Table 1. Overview of Inputs and Outputs for each process unit in Desalsim.

Process	Input	Output
Nanofiltration	Feed flow rate [m³/h]	Permeate flow rate and composition [g/L]
	Ion concentration [g/L]	Concentrate flow rate and composition [g/L]
	Osmotic pressure [bar]	Electrical requirements [kWhel]
	Water recovery [%]
	Ion rejection [-]
Multi-effect distillation	Feed flow rate [m³/h]	Flow rate of water [m³/h]
	Ion concentration [g/L]	Effluent flow rate and composition [g/L]
	Feed temperature [°C]	Electrical [kWhel] and thermal [kWhth] requirements
	Steam temperature [°C]	Cooling water flow rate [m³/h]
Thermal crystallizer	Feed flow rate [m³/h]	Flow rate of water [kg/h]
	Ion concentration [g/L]	Flow rate of NaCl [kg/h]
	Feed temperature [°C]	Cooling water flow rate [m³/h]
	Steam temperature [°C]	Electrical [kWhel] and thermal [kWhth] requirements
Multi-plug flow reactor	Feed flow rate [m³/h]	Alkaline solution flow rate [L/h]
	Ion concentration [g/L]	Flow rate of Mg(OH)₂ [kg/h]
	Concentration of the alkaline solution (NaOH) [M]	Flow rate of Ca(OH)₂ [kg/h]
	Concentration of the acid solution (HCl) [M]	Acid solution flow rate [L/h]
		Effluent flow rate [m³/h] and composition [g/L]
		Electricity requirements [kWhel]
Eutectic freeze crystallizer	Feed flow rate [m³/h]	Flow rate of Na2SO4 [kg/h]
	Ion concentration [g/L]	Flow rate of ice [kg/h]
	Feed temperature [°C]	Effluent flow rate [m³/h] and composition [g/L]
		Electricity requirements [kWhel]
Electrodialysis with bipolar membranes	Feed flow rate [m³/h]	Flow rate of acid [m³/h] and composition [g/L]
	Ion concentration [g/L]	Flow rate of base [m³/h] and composition [g/L]
	Current Density [A/m2]	Flow rate of salt [m³/h] and composition [g/L]
		Electricity requirements [kWhel]
Electrodialysis	Feed flow rate [m³/h]	Flow rate of diluted stream [m³/h] and composition [g/L]
	Ion concentration [g/L]	Flow rate of concentrate stream [m³/h] and composition [g/L]
	Current Density [A/m2]	Electricity requirements [kWhel]

5. Economic models

For more detailed steps and instructions see Economic Tutorial. The mathematical description of economic model is given also in Mathematical description.

Table 2 gives an overview of the main inputs and outputs of economic model (economic_f.py).

Table 2. Overview of Inputs and Outputs of economic models.

Input	Output
Selling price for products [€/ton] or [€/m³]	Operating cost (OPEX) [€/year]
Prices for energy [€/KWh], input chemicals [€/m³], cooling water [€/m³]	Investment cost (CAPEX) [€]
Operating hours, lifetime	Revenues from selling products [€/year]
Interest rate, Inflation rate
Equipment cost [€]
Assumptions on CAPEX and OPEX calculations

For the economic analysis of a full-scale desalination plant, the equipment costs of pilot-scale units are scaled-up to a capacity of 30000 m³/d. The equipment (material) costs of the full-scale plant are derived from the cost of the same equipment in the pilot plant with known capacity using function scaleup.py.

Table 3 gives an overview of the main assumptions made to calculate the CAPEX and OPEX.

Table 3. Overview of main assumptions for CAPEX and Annual OPEX calculations.

CAPEX	Annual OPEX
Installation: 25% of purchased equipment cost	Maintenance: 3% of the fixed-capital investment
Buildings, process, and auxiliary: 20% of purchased equipment cost	Operating Supplies: 5% of maintenance
Land: 6% of purchased equipment cost	Operating Labor: 15% of annual OPEX
Indirect costs: 15% of direct cost	Direct supervisory and clerical labor: 15% of operating labor
Working capital: 20% of total investment cost	Laboratory charges: 15% of operating labor
	Patents and royalties: 3% of annual OPEX
	Fixed charges: 5% of annual OPEX
	Plant overhead costs: 5% of annual OPEX

Note

Note that the assumptions listed in Table 3 can be modified to suit different case studies.

6. Treatment trains comparison

In comparison file, results from different treatment trains are summarised. Indicators are formulated to compare the treatment trains.

Import results

First, import the results from the two examples.

# Import results
import example_1 as sc1
import example_2 as sc2

Import required functions:

import numpy as np
import pandas as pd

Create lists with results

X = ['Example 1', 'Example 2']
X_axis = np.arange(len(X))

Electrical consumption Vs Thermal consumption

For instance, the two examples are compared based on their electrical and thermal energy requirements.

# Create lists for OPEX and assigned results for Electrical consumption and thermal consumption
Eel = [ sc1.sum_el_en, sc2.sum_el_en]
Eth = [sc1.sum_th_en,   sc2.sum_th_en]
# Yearly calculation
Eel = [i * hr/1e6 for i in Eel] # Total electrical energy consumption
Eth = [i * hr/1e6 for i in Eth] # Total thermal energy consumption

Visualization

For the visualization, a bar figure is created and saved.

# Create Figure 1: Electrical consumption Vs thermal consumption
plt.bar(X_axis - 0.2, Eel, 0.4, color="#00516a", label = 'Electrical (GWel)')
plt.bar(X_axis + 0.2, Eth, 0.4, color="sandybrown", label = 'Thermal (GWth)')
plt.xticks(X_axis, X)
plt.ticklabel_format(style='sci', axis='y', scilimits=(0,0))
plt.xlabel("Scenarios")
plt.ylabel("Energy consumption (GW)")
plt.legend()
plt.savefig('electricVSthermal.png')
plt.show()

Operating costs (OPEX)

For instance, the two examples are compared based on the operating costs (OPEX).

# Create lists for OPEX and asigned reuslts
OPEX = [sc1.OPEX, sc2.OPEX]
# Yearly calculation
OPEX = [i/1e6 for i in OPEX]

Visualization

For the visualization, a bar figure is created and saved.