Digital Twin Implementation in Lead Oxide Production Unit

Project 2

Challenges in Lead Oxide Production

Frequent machine failures and higher Mean Time to Failure (MTF) created production bottlenecks, halting throughout and disrupting schedules.

Repeated breakdowns led to accelerated wear and tear, reducing the lifecycle of critical Ball Mill components.

Emergency repairs, unplanned maintenance, and production delays significantly increased operating expenses.

Reliance on manual floor reports and operator inputs resulted in delayed decision-making and inconsistent insights into machine health.

Fragmented data from multiple sources made it difficult to monitor production in real time or gain a holistic view of asset performance.

Unforeseen hazards and safety threats remained undetected until damage had already occurred

Creating a virtual replica of the Ball Mill to simulate and predict failures before they happen.

Aggregating data from all relevant sources (vibration, temperature, load, wear rate) into one centralized platform.

Triggering real-time alerts when critical parameters approach failure thresholds.

Enabling remote monitoring and automated reporting for operational and maintenance teams.

Delivering actionable insights through predictive analytics and historical trend visualization.

25%

Predictive alerts and condition-based maintenance strategies drastically cut down emergency repairs.

Real-time monitoring allowed early detection of anomalies, reducing unplanned downtime and improving Mean Time Between Failures (MTBF).

Stakeholders gained full transparency into machine status, performance KPIs, and risk indicators—anytime, anywhere.

Reduced dependency on individual operator knowledge with system-generated insights and process memory.