Project Description: Gold recovery optimization

In this project, we are going to build a model, which will help to optimize the production gold production and eliminate unprofitable parameters. The model should predict the amount of gold recovered from gold ore when specific parameters are used. We have been provided the data on extraction and purification stages of gold.

We need to predict two values:

• rougher concentrate recovery rougher.output.recovery
• final concentrate recovery final.output.recovery

Table of Contents:

• Load the data.
• Perform data analysis.
• Develop and train a model.

Data Description

The data is stored in three files:

• ../datasets/gold_recovery_train.csv
• ../datasets/gold_recovery_test.csv
• ../datasets/gold_recovery_full.csv

Data is indexed with the date and time of acquisition (date feature). Parameters that are next to each other in terms of time are often similar.

Some parameters are not available because they were measured and/or calculated much later. That's why, some of the features that are present in the training set may be absent from the test set. The test set also doesn't contain targets. The source dataset contains the training and test sets with all the features. You have the raw data that was only downloaded from the warehouse. Before building the model, check the correctness of the data.

Details about the data

Technological process

• Rougher feed — raw material
• Rougher additions (or reagent additions) — flotation reagents: Xanthate, Sulphate, Depressant
  • Xanthate — promoter or flotation activator;
  • Sulphate — sodium sulphide for this particular process;
  • Depressant — sodium silicate.
• Rougher process — flotation
• Rougher tails — product residues
• Float banks — flotation unit
• Cleaner process — purification
• Rougher Au — rougher gold concentrate
• Final Au — final gold concentrate

Parameters of stages

• air amount — volume of air
• fluid levels
• feed size — feed particle size
• feed rate

Feature naming Here's how you name the features: [stage].[parameter_type].[parameter_name]

Example: rougher.input.feed_ag:

Possible values for [stage]:

• rougher — flotation
• primary_cleaner — primary purification
• secondary_cleaner — secondary purification
• final — final characteristics

Possible values for [parameter_type]:

• input — raw material parameters
• output — product parameters
• state — parameters characterizing the current state of the stage
• calculation — calculation characteristics