Four Months, One Process: What pH Trends Reveal About System Consistency
- Tim Neill
- May 19
- 3 min read
Updated: Jun 19
What can a single number, pH, tell you about the rhythm of a massive industrial system? As it turns out, a lot.
In simple terms, pH measures how acidic or basic a substance is. In mineral processing, it is a quiet signal that can reflect everything from chemical balance to mechanical stability.
Why This Project?
I chose this project because I wanted to dive deeper into time-series analysis while applying my skills in a real-world setting. The idea of using pH data to uncover insights around system performance felt impactful. I aimed to address a significant business question:
"How consistent is the mineral processing system over time, and what can pH trends reveal about operational stability?"
This project allowed me to merge data storytelling with practical decision-making, identifying subtle shifts that might indicate equipment issues or opportunities for optimization.
Why This Matters:
Whether you are in operations, analytics, or just curious about how data reflects the real world, this project shows how a single process metric can uncover routine habits, spot inefficiencies, and improve decision-making without complex modeling.
Key Takeaways:
pH levels gradually increased over the four months.
Higher pH levels were consistently recorded on weekends.
April displayed the most stable pH control.
Air flow patterns indicated structured operational control.
Dataset Details:
The dataset, sourced from Kaggle, contains over 730,000 time-stamped records from a flotation plant, collected between March and September 2017. It includes continuous measurements of variables such as:
Ore Pulp pH
Ore Pulp Flow
Air Flow and Levels (7 flotation columns)
% Iron and % Silica concentrations (Feed and Concentrate)
Reagent flows (Starch and Amina)
Each record offered a real-time snapshot of plant operation, making it ideal for time-series analysis.
Analysis Process:
Data Cleaning: Removed errors, standardized decimal formatting, and converted Excel-style date stamps.
Visualization: Created bubble plots, histograms, line charts, and box plots to visualize pH patterns.
Statistical Review: Identified trends and variability across weekdays and months.
What Surprised Me Most:
What surprised me the most was how much I could infer about operational behavior from pH trends alone. I had not expected a single variable to offer such a clear window into process rhythm, control habits, and even staffing patterns.
Visuals and Insights:
(Average pH By Weekday): pH tends to rise on weekends, suggesting possible behavioral or operational changes.
(Box Plot Of pH By Month): April had the tightest control. May and June showed more variability, hinting at process challenges.
What This Means for Industry:
pH patterns can act as early indicators of system drift or upstream material changes.
April’s consistency could serve as a benchmark for process control.
Weekend variability may highlight areas for automation or shift protocol adjustments.
Main Takeaway:
Even basic data, like pH measurements, can uncover meaningful patterns in system behavior. Through structured analysis and thoughtful visualization, we can surface insights that drive smarter, more consistent operations.
Looking Ahead:
This project opened my eyes to how small data can make a big impact. As I grow in the analytics field, I want to keep uncovering practical insights from overlooked metrics and help teams turn those into smarter, more resilient operations.
If you are interested in discussing this further or if you know someone looking for a data analyst who loves turning raw numbers into actionable stories, let’s connect on LinkedIn!
