pyIsotherm 0.0.1

Parameter estimation of monocomponent-isotherms

pyMono: A Comprehensive Python Library for Adsorption Isotherm Parameter Estimation

Overview

The pyMono library is a powerful Python tool designed to facilitate the estimation of adsorption isotherm parameters using Particle Swarm Optimization (PSO). It supports seven isotherm models: Langmuir, Langmuir-Freundlich (Sips), Toth, BET, BET-Aranovich, GAB, and Langmuir Multisite. This library is ideal for researchers working on adsorption studies, offering a simple, fast, and free method to obtain fundamental parameters.

Features

• Support for Multiple Isotherm Models: Langmuir, Sips, Toth, BET, BET-Aranovich, GAB, and Langmuir Multisite.
• Particle Swarm Optimization (PSO): Efficient parameter estimation using PSO.
• Data Loading: Load experimental data from .xlsx or .csv files.
• Plotting Capabilities: Visualize experimental and simulated isotherms.
• Error Analysis: Comprehensive error analysis, including absolute, quadratic, mean, and standard deviation of errors.
• Kruskal-Wallis Test: Statistical test to compare medians of experimental and simulated data.

Installation

Install the pyMono library via pip:

pip install pyIsotherm

Usage

Loading Experimental Data

Load data from a .csv or .xlsx file:

from pyMono.Load import load

isotherm = load('data.xlsx')

Estimating Isotherm Parameters

Estimate parameters using the estimate function:

from pyMono.Estimation import estimate

result = estimate(p=[1, 2, 3], qe=[0.1, 0.2, 0.3], model='langmuir')

Plotting Isotherms

Plot the experimental and simulated isotherms:

result.plot()

Error Analysis

Print various error analyses:

result.error_all()

Isotherm Models

Classes and Methods

Isotherm

The Isotherm class represents the isotherm data:

class Isotherm:
    def __init__(self, p, q):
        # Initialize with pressure and quantity adsorbed lists.
        
    def plot(self):
        # Plot the isotherm data.

Particle

The Particle class is used in PSO for parameter estimation:

class Particle:
    def __init__(self, param):
        # Initialize with parameter ranges.
        
    def update_velocity(self, swarm_best_position):
        # Update particle velocity.
        
    def update_position(self):
        # Update particle position.

Result

The Result class encapsulates the estimation results:

class Result:
    def __init__(self, parameters, fitness, exp_isotherm, sim_isotherm):
        # Initialize with parameters, fitness, and isotherms.
        
    def plot(self, export=False, extension='png', only_exp=False, only_sim=False, legend=False):
        # Plot the isotherms.
        
    def error_all(self):
        # Print all error analyses.
        
    def kruskal(self):
        # Perform Kruskal-Wallis test.

Example

from pyMono.Load import load
from pyMono.Estimation import estimate

# Load experimental data
isotherm = load('data.xlsx')

# Estimate parameters
result = estimate(p=isotherm.p, qe=isotherm.q, model='langmuir')

# Plot the isotherms
result.plot()

# Print error analysis
result.error_all()

# Perform Kruskal-Wallis test
result.kruskal()

References

The theoretical background and model equations implemented in the pyMono library are based on the extensive research and publications in the field of adsorption, as detailed in the article "Artigo Adsorção - Novo.docx".

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contributing

Please read CONTRIBUTING.md for details on the code of conduct, and the process for submitting pull requests.

---

With pyMono, streamline your adsorption isotherm studies with robust parameter estimation and comprehensive data analysis tools. Happy researching!