Unpacking June 1st Mining Trends: A Surprising Journey into Data

I never thought I would find myself delving into mining data, but a chance encounter with some intriguing figures changed that. While scrolling through datasets, I stumbled upon one related to mining operations. My curiosity was piqued, especially regarding how minerals are separated from ore. It was fascinating to learn about the processes involved, even if I didn't have a background in chemistry. This project not only challenged my understanding but also opened my eyes to a world of numbers and trends.

Why THIS Project?

The motivation for this project came from a mix of curiosity and a desire to learn something new. Though mining wasn't on my radar, the data provided an opportunity to explore how mining performance varied on a specific day, June 1, compared to average weekdays. This unique angle made the project special—a blend of numbers that told a story about a crucial industry.

What Readers Will Gain

In this article, you’ll learn about the performance of a mining operation on June 1 and how it stacks up against average weekday values. I'll share key insights, significant surprises, and what these findings could mean for future operations.

Key Takeaways

• No general relationships were found between different columns on June 1.

• A notable drop in Flotation Column 5 and Ore Pulp pH occurred at 6 PM.

• Amina flow varied throughout the week, but the % iron concentrate remained stable.

• On June 1, both amina flow and % iron concentrate were on track for a Thursday.

Dataset Details

The dataset I worked with was sourced from Kaggle, assumed to be from "Metals 'R' Us." It spanned March to September 2017 and included 737,453 rows and 24 columns. Each row represented a time point at 20-second intervals, capturing various metrics like % Iron Concentrate and Ore Pulp pH. This extensive dataset was perfect for examining trends over time.

Analysis Process

I began my analysis by cleaning the data to ensure accuracy. This included fixing formatting problems and noting there were no missing values. Next, I transformed the data to create visual representations, using line charts and pairplots to identify relationships. I was surprised to find no correlation between key metrics on June 1, which challenged my assumptions about how these elements interacted. It turned out I had to drill down further to find relationships.

Visuals and Insights

1. Lack of Relationships: The pairplot clearly indicates no significant relationships among several columns. This was unexpected and made me rethink how these factors might affect each other.

   

   

   No relevant graphs show trends

   

   Correlation values are all low

   
   

2. Sharp Drops at 6 PM: The line charts illustrated a sharp decline in both Ore Pulp pH and Flotation Column 5 around 6 PM. Falling values can also be seen in % Iron Concentrate and % Silica Concentrate. These drops raised questions about what caused the sudden change.

   

   

   Drop around 6 PM

   

   Drop around 6 PM

   

   Starts falling between 3 and 6 PM

   

   Starts falling between 12 and 3 PM

   
   

3. Spread of pH Levels: The Ore Pulp pH most often between 10 and 10.1, indicating this might be the ideal range for optimal performance.

   

   

   Mostly stays between 10 and 10.1

   
   

Amina Flow vs. Iron Concentrate: While the amina flow showed variation throughout the week, the % iron concentrate remained steady at around 65%. I found it surprising that the decreased amina levels didn't seem to affect the iron extraction as much as I expected. Also, animal levels and % iron concentrate were on target for a Thursday on June 1.

Amina Flow varies but % Iron Concentrate is steady

Values are on target for a Thursday

Main Takeaways

• Investigating the sharp drop in metrics around 6 PM could reveal underlying issues. This might warrant a closer look at the operational processes to identify any correlation between the fluctuations in pH and flotation levels and how those might affect iron ore output.

• Correcting the pH could potentially resolve issues experienced during that time. Engaging with a chemist might provide valuable insights into long-term solutions.

• Since lower amina levels didn’t seem to impact the % iron concentrate, examining how to optimize these levels could lead to cost savings for the operation.

Conclusion and Personal Reflections

This project has been a real eye-opener for me. I faced challenges, particularly in understanding the nuances of the dataset and the chemistry behind mining processes. However, through persistence and curiosity, I learned a great deal. It's fascinating how data can reveal patterns that challenge our expectations. This experience has sparked my interest in exploring more projects that blend data analysis with real-world applications.

Call to Action

I’d love to hear your thoughts! Please connect with me on LinkedIn, or leave a comment with your questions or insights. Let’s keep the conversation going!