Development of a Real-Time Data Monitoring and Analytics Dashboard for Mineral Refinement Processes

The client currently relies on manual analysis of temperature, shape, and color changes during mineral fusion, leading to defects and inefficiencies. They lack an integrated, real-time monitoring system that provides detailed insights into material proportions and cooling effects, impeding timely decision-making and process control within an isolated, non-networked environment.

A mid to large-scale mining or mineral processing company specializing in extraction and refinement of critical alloys, seeking to optimize operational efficiency through real-time data analysis and process monitoring.

• Implement a web-based dashboard to collect, display, and analyze real-time data from mineral fusion processes.
• Enable intuitive visualization through graphical and tabular formats, with capability for targeted data extraction and comparison.
• Automate data export functions to generate structured CSV files for further analysis.
• Reduce manual intervention and defect rates, increasing operational reliability and efficiency.
• Facilitate prompt issue detection and resolution via live video streaming integration and real-time data analysis.
• Deliver a scalable, maintainable platform that supports future feature expansion and technological evolution.

• Dashboard displaying real-time temperature, shape, and color change data in graphical and tabular views.
• Interactive graphs allowing users to pin specific data points and extract detailed information for comparison.
• Automated export feature generating formatted CSV files for external analysis.
• Video streaming integration for live monitoring of mineral cooling and formation processes.
• Support for multiple database systems (e.g., document and time-series databases) to facilitate comprehensive data collection and retrieval.
• User-friendly interface with secure access controls and responsive design for operators and analysts.

• Real-time data ingestion from mineral fusion analysis algorithms
• Video streaming services for live process monitoring
• Databases: MongoDB and InfluxDB for data storage and retrieval

• System must support high-frequency data updates with minimal latency, ensuring near real-time responsiveness.
• Scalability to handle increasing volumes of sensor data and video streams as operations grow.
• Secure access controls and data protection, especially given the isolated server environment.
• High availability and fault tolerance in data collection, processing, and visualization modules.
• Compatibility with existing hardware and infrastructure constraints, including private, network-isolated servers.

The implementation aims to significantly enhance process efficiency by reducing manual analysis time, decreasing defect rates, and enabling quicker response to operational issues through real-time monitoring. The platform is expected to streamline data analysis workflows, improve operational reliability, and provide a scalable solution adaptable to future technological and process advancements.