This is the 25 + 1 year anniversary version of the 1995 Rachford-Rice contest

Overview

Rachford-Rice Contest

This is the 25 + 1 year anniversary version of the 1995 Rachford-Rice contest. Can you solve the Rachford-Rice problem for all these cases? With the initiative of Curtis Hays Whitson, and the extensive help by Aaron Zick, the original Rachford-Rice contest offered $1000 to any student who could solve the problem for all cases. Only three people were able to develop procedures that passed all the test, and none of these were students.

Today we are happy to re-release the Rachford-Rice contest, but now for Python. There are several differences between the original contest and this version, the main one being that this version will focus mainly on passing all the tests and not that much on speed. A timer will be set for your calculations, so if you want to optimize for speed as well you are free to do so.

To access the original version of the Rachford-Rice contest, go to this link.

Table of Contents

How do you access the code?

You can either use Git to clone the repository using

git clone https://github.com/WhitsonAS/Rachford-Rice-Contest.git

If you do not want to use Git or know how to use Git, you can manually download the repository by clicking the green code button and press the option called "Download ZIP".

Code download button

How do you enter your code?

The main stucture of the code is not to be modified at all, so you are only supposed to write code in certain designated files or functions. The file that contains the function which you have to change is called rachford_rice_solution.py and the function is called rachford_rice_solver(). This is the only place where you can change anything.

The function takes in the number of components (Nc) as an integer, the composition (zi) as a numpy array, and the K-values (Ki) as a numpy array.

The output of the function must be given in the following order, as the following types. The number of iterations used (N) as an integer, the vapor molar composition (yi) as a numpy array, the liquid molar composition (xi) as a numpy array, the vapor molar fraction (V) as a float, and the liquid molar fraction (L) as a float.

For debugging purposes, there is an option in section (3) in the main.py file in the function called is_converged() where you can turn on an optinal variable called print_to_console (which is False by default) by setting it to True. This feature will print some useful information about which tests pass / fail for each case.

See video 5 for an example of how to add you code.

Note that you're not allowed to change the floating point precision to get the desired threshold! The goal is that your algorithm should be able to have a threshold equal to your floating point precision. Any solution based on floating point manipulation of this kind will be disqualified.

When you have developed a code that you want to submit, make a folder within the folder called Solutions with the date of your submission (YYYYMMDD) and your name (e.g. 20211015-Markus-Hays-Nielsen) and add a file called solution.py within this folder (the relative path should look like this: Solutions/20211015-Markus-Hays-Nielsen/solution-py). The solution file should be possible to copy and paste straight into rachford_rice_solution.py and run with no modifications needed. If there is any issue with running your solution, you will be contacted and asked to fix the issue. If no answer is recived, the solution will be removed. This is meant to make everything easier for the reviewer and anyone else who wants to try your code.

The procedure for adding your code to the repository is to first make a new branch with your name (e.g. markus-hays-nielsen) and once you are ready to submit you can create a pull request with your file(s) in the structure detailed above. Once your code has been reviewed, we will add your code to the main branch and it will become public.

If you want to submit you solution privately, please contact us by mail at [email protected].

Basic theory about Rachford-Rice problem

The Rachford-Rice equation is a combination of (1) the material balance equation, (2) the assumption that the vapor (yi) and liquid (xi) compositions are defined by a constant set of K-values (Ki) and (3) that the sum of vapor and liquid molar fractions sum to one.

The equation is given by

equation

where zi is the total molar composition of component i, and V is the vapor molar fraction defined by

equation

where nV is the total molar amount of the vapor phase and nL is the total molar amount of the liquid phase.

The constraints for passing the contest are given by a set of test functions (R) and are given by

Vapor composition test

equation

Liquid composition test

equation

Vapor and liquid fraction test

equation

Material balance test

equation

K-value test

equation

where the threshold value (εt) is set to be 10-15.

The tests will be judged based on their severity which is given by

equation

For more information about the Rachford-Rice solution, watch the following videos:

List of participants who have successfully completed

  • Aaron Zick
  • Michael Michelsen
  • Kim Knudsen
A Python package implementing a new model for text classification with visualization tools for Explainable AI :octocat:

A Python package implementing a new model for text classification with visualization tools for Explainable AI 🍣 Online live demos: http://tworld.io/s

Sergio Burdisso 285 Jan 02, 2023
In this workshop we will be exploring NLP state of the art transformers, with SOTA models like T5 and BERT, then build a model using HugginFace transformers framework.

Transformers are all you need In this workshop we will be exploring NLP state of the art transformers, with SOTA models like T5 and BERT, then build a

Aymen Berriche 8 Apr 13, 2022
Python powered crossword generator with database with 20k+ polish words

crossword_generator Generate simple crossword puzzle from words and definitions fetched from krzyżowki.edu.pl endpoints -/ string:word - returns js

0 Jan 04, 2022
Türkçe küfürlü içerikleri bulan bir yapay zeka kütüphanesi / An ML library for profanity detection in Turkish sentences

"Kötü söz sahibine aittir." -Anonim Nedir? sinkaf uygunsuz yorumların bulunmasını sağlayan bir python kütüphanesidir. Farkı nedir? Diğer algoritmalard

KaraGoz 4 Feb 18, 2022
Shared, streaming Python dict

UltraDict Sychronized, streaming Python dictionary that uses shared memory as a backend Warning: This is an early hack. There are only few unit tests

Ronny Rentner 192 Dec 23, 2022
BERT-based Financial Question Answering System

BERT-based Financial Question Answering System In this example, we use Jina, PyTorch, and Hugging Face transformers to build a production-ready BERT-b

Bithiah Yuan 61 Sep 18, 2022
T‘rex Park is a Youzan sponsored project. Offering Chinese NLP and image models pretrained from E-commerce datasets

T‘rex Park is a Youzan sponsored project. Offering Chinese NLP and image models pretrained from E-commerce datasets (product titles, images, comments, etc.).

55 Nov 22, 2022
Random-Word-Generator - Generates meaningful words from dictionary with given no. of letters and words.

Random Word Generator Generates meaningful words from dictionary with given no. of letters and words. This might be useful for generating short links

Mohammed Rabil 1 Jan 01, 2022
BERT, LDA, and TFIDF based keyword extraction in Python

BERT, LDA, and TFIDF based keyword extraction in Python kwx is a toolkit for multilingual keyword extraction based on Google's BERT and Latent Dirichl

Andrew Tavis McAllister 41 Dec 27, 2022
PyTorch original implementation of Cross-lingual Language Model Pretraining.

XLM NEW: Added XLM-R model. PyTorch original implementation of Cross-lingual Language Model Pretraining. Includes: Monolingual language model pretrain

Facebook Research 2.7k Dec 27, 2022
🤗 Transformers: State-of-the-art Natural Language Processing for Pytorch, TensorFlow, and JAX.

English | 简体中文 | 繁體中文 State-of-the-art Natural Language Processing for Jax, PyTorch and TensorFlow 🤗 Transformers provides thousands of pretrained mo

Hugging Face 77.2k Jan 03, 2023
Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing

Introduction Funnel-Transformer is a new self-attention model that gradually compresses the sequence of hidden states to a shorter one and hence reduc

GUOKUN LAI 197 Dec 11, 2022
Vad-sli-asr - A Python scripts for a speech processing pipeline with Voice Activity Detection (VAD)

VAD-SLI-ASR Python scripts for a speech processing pipeline with Voice Activity

Dynamics of Language 14 Dec 09, 2022
Accurately generate all possible forms of an English word e.g "election" --> "elect", "electoral", "electorate" etc.

Accurately generate all possible forms of an English word Word forms can accurately generate all possible forms of an English word. It can conjugate v

Dibya Chakravorty 570 Dec 31, 2022
Simple Text-To-Speech Bot For Discord

Simple Text-To-Speech Bot For Discord This is a very simple TTS bot for discord made with python. For this bot you need FFMPEG, see installation to se

1 Sep 26, 2022
LSTM based Sentiment Classification using Tensorflow - Amazon Reviews Rating

LSTM based Sentiment Classification using Tensorflow - Amazon Reviews Rating (Dataset) The dataset is from Amazon Review Data (2018)

Immanuvel Prathap S 1 Jan 16, 2022
CrossNER: Evaluating Cross-Domain Named Entity Recognition (AAAI-2021)

CrossNER is a fully-labeled collected of named entity recognition (NER) data spanning over five diverse domains (Politics, Natural Science, Music, Literature, and Artificial Intelligence) with specia

Zihan Liu 89 Nov 10, 2022
TEACh is a dataset of human-human interactive dialogues to complete tasks in a simulated household environment.

TEACh is a dataset of human-human interactive dialogues to complete tasks in a simulated household environment.

Alexa 98 Dec 09, 2022
An open-source NLP research library, built on PyTorch.

An Apache 2.0 NLP research library, built on PyTorch, for developing state-of-the-art deep learning models on a wide variety of linguistic tasks. Quic

AI2 11.4k Jan 01, 2023
Practical Machine Learning with Python

Master the essential skills needed to recognize and solve complex real-world problems with Machine Learning and Deep Learning by leveraging the highly popular Python Machine Learning Eco-system.

Dipanjan (DJ) Sarkar 2k Jan 08, 2023