AI faces slow grind in mineral processing

• Autores: Industrial Minerals
• Localización: Industrial Minerals, ISSN 0019-8544, Nº. 603, 2018 (Ejemplar dedicado a: Julio-Agosto)
• Idioma: inglés
• Texto completo no disponible (Saber más ...)
• Resumen

  • According to Bergh, the production of any mineral of commercial value stands to benefit from the development of AI in mineral processing. According to Kurilyak, AI-driven systems can improve themselves over time because machines are “taught” by humans how to make better decisions that increase throughput and recovery. “The reliable power supply [generated by the kinetic energy] helps to bring a new level of sophistication to conveyors, allowing designers to equip their systems with devices such as weight scales, proximity switches, moisture sensors, pressure switches, solenoids and relays, as well as timers, lights and even additional safety mechanisms,” the company told Industrial Minerals. Improving operational efficiencyOptimising process controls, without violating the operating constraints by combining artificial neural networks (ANNs), statistics and multivariable modelling – improving the collaboration of human and robotic systems in a mineral processing operation. 3.