Molycop has forged sustainable partnerships with a range of research leaders to further its goal of delivering holistic and innovative solutions for the mining industry.
Molycop understands that creating an efficient and sustainable mining future requires collaboration with future-focused organisations. We're fortunate to have partnered with major universities, technology partners, industry leaders and public sector departments in a spirit of cooperation to make major advances in product innovation and thought leadership.
Molycop places a high level of importance on our relationships with the major mining universities around the world. In addition to having access to the brightest minds, our support and partnership runs deep, either through direct support of research funding, shared access to world-class facilities, or cutting edge research across all facets of the mining industry.
Civil Chemical Engineer María José Astudillo Rodríguez has an important role to play in fostering a culture of innovation at Molycop and ensuring the company’s innovation efforts target the needs of Molycop customers.
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Molycop is proud to have brought its insights on advanced optimisation processes to an audience in Ghana, Africa.
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In this presentation, Levi Guzman, Molycop Technical Director – Applications Services, explains the methodology behind DEM modelling and how it can be used to optimise ball size, liner design, mill speed and discharge component design for improved mill performance.
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A gold mine test that conducted over 517 mill operating hours revealed impressive results for Molycop’s forged and high chrome cast grinding media.
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Observations of operations management of Minerals Processing Mills – Dr Paul Shelley
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Molycop actively participates in events – such as MetFest – that bring industry leaders, innovators, and enthusiasts together to foster knowledge exchange, networking and collective growth.
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Molycop is transforming mine operations by using digital data technologies to increase their productivity while reducing their carbon footprint.
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This paper demonstrates how convolutional neural networks (CNNs) have the potential to classify abnormal froth conditions and maintain the efficiency of the flotation process.
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