Name: Quality Prediction In A Mining Process
Creator: Kaggle
License: https://creativecommons.org/publicdomain/zero/1.0/

About this Dataset

Quality Prediction In A Mining Process

Context

It is not always easy to find databases from real world manufacturing plants, specially mining plants. So, I would like to share this database with the community, which comes from one of the most important parts of a mining process: a flotation plant!

PLEASE HELP ME GET MORE DATASETS LIKE THIS FILLING A 30s SURVEY:

The main goal is to use this data to predict how much impurity is in the ore concentrate. As this impurity is measured every hour, if we can predict how much silica (impurity) is in the ore concentrate, we can help the engineers, giving them early information to take actions (empowering!). Hence, they will be able to take corrective actions in advance (reduce impurity, if it is the case) and also help the environment (reducing the amount of ore that goes to tailings as you reduce silica in the ore concentrate).

Content

The first column shows time and date range (from march of 2017 until september of 2017). Some columns were sampled every 20 second. Others were sampled on a hourly base.

The second and third columns are quality measures of the iron ore pulp right before it is fed into the flotation plant. Column 4 until column 8 are the most important variables that impact in the ore quality in the end of the process. From column 9 until column 22, we can see process data (level and air flow inside the flotation columns, which also impact in ore quality. The last two columns are the final iron ore pulp quality measurement from the lab.
Target is to predict the last column, which is the % of silica in the iron ore concentrate.

Inspiration

I have been working in this dataset for at least six months and would like to see if the community can help to answer the following questions:

Is it possible to predict % Silica Concentrate every minute?
How many steps (hours) ahead can we predict % Silica in Concentrate? This would help engineers to act in predictive and optimized way, mitigatin the % of iron that could have gone to tailings.
Is it possible to predict % Silica in Concentrate whitout using % Iron Concentrate column (as they are highly correlated)?

Related research using this dataset

Research/Conference Papers and Master Thesis:
- Purities prediction in a manufacturing froth flotation plant: the deep learning techniques link
- Soft Sensor: Traditional Machine Learning or Deep Learning link
- Machine Learning-based Quality Prediction in the Froth Flotation Process of Mining link

Tables

Miningprocess Flotation Plant Database

@kaggle.edumagalhaes_quality_prediction_in_a_mining_process.miningprocess_flotation_plant_database

53.07 MB
737453 rows
24 columns


CREATE TABLE miningprocess_flotation_plant_database (
  "date" TIMESTAMP,
  "n__iron_feed" BIGINT,
  "n__silica_feed" BIGINT,
  "starch_flow" BIGINT,
  "amina_flow" BIGINT,
  "ore_pulp_flow" BIGINT,
  "ore_pulp_ph" BIGINT,
  "ore_pulp_density" BIGINT,
  "flotation_column_01_air_flow" BIGINT,
  "flotation_column_02_air_flow" BIGINT,
  "flotation_column_03_air_flow" BIGINT,
  "flotation_column_04_air_flow" BIGINT,
  "flotation_column_05_air_flow" BIGINT,
  "flotation_column_06_air_flow" BIGINT,
  "flotation_column_07_air_flow" BIGINT,
  "flotation_column_01_level" BIGINT,
  "flotation_column_02_level" BIGINT,
  "flotation_column_03_level" BIGINT,
  "flotation_column_04_level" BIGINT,
  "flotation_column_05_level" BIGINT,
  "flotation_column_06_level" BIGINT,
  "flotation_column_07_level" BIGINT,
  "n__iron_concentrate" BIGINT,
  "n__silica_concentrate" BIGINT
);