# Optimizing Boleaching for Printed Circuit Board Copper Recovery: An AI-Driven RGB-Based Approach
## 3 Results  
The newly developed web-based digital twin integrates sensor data into a unified dashboard, accessible on both desktop and mobile devices. The intuitive interface enables technicians to monitor key parameters, such as Cu (II) and Fe (III) concentrations, pH levels, and temperature, in real time. Interactive charts and historical data visualizations facilitate trend analysis and anomaly detection, enabling prompt adjustments to maintain optimal conditions.  
Remote accessibility enhances operational flexibility, enabling technicians to monitor and control the system offsite. This is especially valuable for bioleaching processes that require constant oversight, as it reduces the need for onsite presence, lowers labor costs, and ensures continuous operations.  
Data export functionality supports process optimization by enabling the download of historical datasets for further analysis. This facilitates advanced analytics and machine learning, contributing to continuous improvements and refinements in system performance. Analyzing historical data identifies trends that inform operational adjustments.  
To ensure security and data integrity, the system employs modern protocols, including encrypted communication and role-based access control. This ensures that only authorized personnel can access or modify plant data. Additionally, the system logs all user actions, creating an audit trail that promotes transparency and accountability.  
Extensive testing was conducted during the development of the software architecture to ensure functionality, compatibility, and efficiency. The system underwent rigorous compatibility testing across various operating systems, including Windows 10, Android 12, and Ubuntu 24.04 LTS, and on different web browsers such as Chrome, Firefox, and Edge. These tests confirmed the web application's seamless operation across diverse platforms.  
To optimize performance, the system integrates a gevent-based concurrency management configuration within Flask, enabling efficient handling of multiple simultaneous connections, which is crucial for real-time monitoring in industrial environments. Additionally, a caching mechanism was implemented to reduce data loading times, enhancing page speed and providing a smoother user experience during remote monitoring.  
Preliminary testing demonstrated the system's ability to forecast process behaviors, detect anomalies, and reduce manual intervention, representing significant advancements in automation and operational efficiency. By analyzing real-time sensor data, including RGB colorimetric readings and pH values, the system accurately estimates copper recovery rates, enhancing resource efficiency, lowering costs, and promoting sustainability by minimizing non-productive runtime, preventing solution overexposure, and optimizing chemical and energy use. These capabilities, combined with substantial improvements in accessibility, usability, and scalability, position the architecture for deployment in larger-scale operations.  
The integration of a web-based interface, predictive modeling, and real-time data access establishes a robust foundation for a more autonomous, efficient, and data-driven bioleaching plant. This scalable platform supports future enhancements, such as additional predictive models or advanced sensors, making it a powerful tool for optimizing bioleaching processes and advancing sustainable metal recovery.