Mining Technology Vol. 133, No. 3, is now available

Automation, natural language processing and machine learning models are among the topics discussed in this issue.

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Exploring the impacts of automation in the mining industry: A systematic review using natural language processing
Loreto Codoceo-Contreras, Nikodem Rybak, Maureen Hassall
pp. 191–213
DOI: 10.1177/25726668241270486

Finite element analysis & topology optimization of excavator bucket teeth for optimal performance
B. A. Madan Mohan, Mazen Ur Rahaman, Pavan G, Rohan Madhu, Mantesh B. Khot
pp. 214–225
DOI: 10.1177/25726668241270488

A modified approach for cut-off grade and production rate optimization in block caving projects
Mohammad Shami-Qalandari, Mehdi Rahmanpour, Hassan Bakhshandeh Amnieh
pp. 226–240
DOI: 10.1177/25726668241264923

Simultaneous stochastic optimisation of mining complexes with equipment uncertainty: Application at an open-pit copper mining complex
Yi Jiang, Roussos Dimitrakopoulos
pp. 241–256
DOI: 10.1177/25726668241263408

Development of an intelligent evolution algorithm for open pit mines’ long-term production scheduling using the concept of block aggregation
Nooshin Azadi, Hossein Mirzaei-Nasirabad
pp. 257–275
DOI: 10.1177/25726668241256707

Wet inrush susceptibility assessment at the Deep Ore Zone mine using a random forest machine learning model
Sahar Ghadirianniari, Scott McDougall, Erik Eberhardt, Jovian Varian, Karl Llewelyn, Ryan Campbell, Allan Moss
pp. 276–288
DOI: 10.1177/25726668241255442