Case Study- Data analytics for providing measurable optimisation insights into complex conveyor system performance

CLIENT OVERVIEW

Our client, a leading global resource company with operations in over 90 locations worldwide, transports mining products globally. This process involves moving materials from the mine to the port, where the efficiency of material movement is significantly influenced by the bulk density and moisture content of the product. To better manage these factors, the client aims to quantify their impact and develop mathematical models to predict future effects on material movement at port. The client has requested to provide Data Science and Engineering Services to support for this project.

THE CHALLENGE

Although a large quantity of data is available, the quality of the data poses significant challenges in extracting valuable information. Additionally, the data lacks certain critical information, such as active route details, needed for further analysis. Furthermore, the nature of the coverers system, with 60 possible routes and 7 different product types, adds additional complexity to the data analytics. A poor understanding of relationship between moisture, downtime delay, and flow rate are also presents challenges in the modelling work.

OUR APPROACH

The data analytics plans were developed through collaboration with SMEs, by observing key operational challenges, and considering resource availability. Identifying technology needs, data, and information requirements facilitated the successful execution of the necessary analyses and the development of mathematical models.

SOLUTION

The multiple solutions were provided to the client on their requirements, including: 1. Metrics for data preprocessing with defined standard filters 2. Additional layers of information through supplementary calculations 3. Data visualisation for inflow movement of 60 routes and 7 product combinations 4. A flowsheet model for predicting future throughput based on critical density 5. Time series forecasting for moisture content and delay

IMPACT

The client successfully integrated the model and findings of the study into their decision-making procedure, establishing the new theoretical setpoint for inflow to test their assumptions. The flowsheet model effectively projects future flow rates by accounting observed critical density and the proportion of volumetric constraints for each route.