Abstract

Big data has transformed processing plant operations by enabling a proactive, data-driven approach to efficiency, safety, and adaptability. Through real-time monitoring and predictive analytics, big data allows for continuous optimization, where advanced data analysis models manage and adjust process variables to maintain peak performance and product quality with minimal human intervention. As the mining industry continues to evolve, these centralized intelligence and data-driven models are set to play an increasingly pivotal role in shaping the next generation of mineral processing.

Some of the key features that will be discussed of the data-driven modus operandi include:

• Data aggregation centers collect data from various sources—SCADA systems, sensors, databases, and control systems—into a single platform. By centralizing data, the system can filter, clean, and standardize information, ensuring accuracy and reliability for analysis and decision-making. Data aggregation also enables long-term storage of operational history, which is essential for identifying trends, conducting root-cause analysis, and developing performance benchmarks.
• Real-time monitoring and process control centers provide immediate visibility into the plant's operational status, allowing operators to track process variables, and equipment performance continuously, allowing for early detection of deviations and timely corrective actions. The focus for this center is to maintain operational consistency and reduce variability and maintain product quality as this allows operators to fine-tune process parameters based on current conditions.
• Predictive insights can be generated for proactive adjustments to the process plant. Through trend analysis and pattern recognition, operators can predict and adjust for upcoming fluctuations, keeping the plant running smoothly and prevent unnecessary disruptions or shutdowns.
• These remote monitoring systems reduces the need for on-site presence by centralizing analytics and control functions. With the ability to operate remotely, personnel are less exposed to potentially hazardous environments, supporting best practices in worker safety. Additionally, remote monitoring decreases the costs associated with travel and on-site staffing, making operations more cost-effective.

An example of the implementation of the data-driven approach to process plant operation is the NeuroMine Mining Insights Center, developed by Minopex. NeuroMine is a digital hub that centralizes monitoring, data analytics, and optimization solutions to enhance the performance of mineral processing facilities. The processing facilities are supported by a centralized team of data scientists, process engineers, metallurgists, and IT experts which provide both offline support, which entails deep analytical studies, and online monitoring for real-time anomaly detection and continuous performance assessment.

A case study from a gold processing operation illustrates the practical impact of NeuroMine’s application. The operation achieved a 0.5% increase in throughput and a 0.25% improvement in recovery, coupled with a 12-hour annual reduction in plant breakdowns. These improvements resulted in an estimated USD3.3M million in additional revenue per year, demonstrating the economic benefits of adhering to best practices in process monitoring, control, and maintenance.