Next step to ‘autonomous crushing’
SBM Mineral Processing says that the company has taken the next step to autonomous crushing with the launch of the new REMAX 600, which will be on display at Bauma Munich.
The new crusher serves as a technology platform for the first fully automatic production monitoring and control system that SBM is developing in co-operation with researchers of the Montanuniversität Leoben, Styria.
Based upon an innovative sensor system and video technology, powerful communication and IT networks, as well as technological innovations such as the automatic gap adjustment, ‘artificial intelligence’ finds its way into mobile processing technique for the very first time.
The crusher will be fully available from around the middle of 2023.
With a feed capacity of 600 t/h, the 1400-impact crusher is for material feed sizes up to 900 mm and a maximum total weight of 75 tons depending on the equipment. The plant can provide up to five fractions in one cycle.
The so-called autonomous crusher can help users and operators with all decisions, permanently support efficient operation, as well as make an important contribution to save energy and costs during crushing. The ambitious R&D project, with interdisciplinary teams consisting of colleagues of the renowned Leoben Chair of Mineral Processing and SBM research departments, has been running for more than two years.
It is the aim of this project to make the decisive step from today‘s already extensively monitored equipment to autonomous fully-automated production by means of ‘intelligent’ self-learning mobile crushers. The machine independently assesses feed material and final products via sensors and camera systems, registers even better than before the load conditions of crusher and conveyor equipment, and optimises all separation processes down to overbelt magnetic separators and wind sifters.
The sophisticated SBM control system Crush Control validates all operating conditions and material properties in real-time and sends the values to the SBM headquarters via the cloud. A ‘digital twin’ compiled there matches the real machine data with thousands of stored reference data.