We aim to achieve a significant step in your digital transformation with the Viravist SCADA™ system, which provides the advantage of remote and easy control by transferring all business processes of your plants and factories to an automation environment.
With the Viravist VisorLevel™ system, uniquely developed using image processing and artificial intelligence technology, automatic intervention is made to the proportional valves in facilities using the flotation method, allowing real-time level control of the cells and ensuring that production stays at the most efficient level.
With the Viravist TenorAnalysis™ system, uniquely developed using image processing and artificial intelligence technology, the camera and electronic equipment detect the grade of elements such as Copper (Cu), Zinc (Zn), Antimony (Sb), Nickel (Ni), and others, separated using the flotation method in real-time. It provides information to the operator while enabling automatic chemical dosing, thereby increasing production efficiency.
Industrial automation is the integration of systems that reduce human intervention in production processes, enhancing efficiency, safety, and quality. Thanks to sensors, control units, and software, production lines can operate faster, more accurately, and continuously.
Today, industrial automation solutions used in many sectors, such as factories and mining, not only reduce costs but also support sustainability by optimizing business processes.
It is the process of converting analog data from sensors into digital signals, which are then processed by PLCs, microcontrollers, or industrial computers. This allows systems to analyze the data, make decisions, and automatically control machines or processes.
It is the process of digitally analyzing images captured by cameras or sensors for tasks such as object recognition, quality control, guidance, and fault detection. This enables automatic decision-making on production lines without the need for human intervention.
It is a technology that uses data analysis and learning algorithms to optimize production processes, predict faults, ensure quality control, and make systems autonomous. This allows machines to learn from historical data and make smarter, more efficient decisions.