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Random forest model predictive control for paste thickening

  • Published:2021-03 Issue: Volume:163 Page:106760
  • ISSN:0892-6875
  • Container-title:Minerals Engineering
  • language:en
  • Short-container-title:Minerals Engineering

Author:

Diaz Pablo,Salas Juan C.,Cipriano Aldo,Núñez Felipe

Funder

Comisión Nacional de Investigación Científica y Tecnológica

Publisher

Elsevier BV

Subject

Mechanical Engineering,Geotechnical Engineering and Engineering Geology,General Chemistry,Control and Systems Engineering

Reference40 articles.

1. Particle swarm optimization;Bansal;Stud. Comput. Intell.,2019

2. Solar radiation forecasting using artificial neural network and random forest methods: Application to normal beam, horizontal diffuse and global components;Benali;Renew. Energy,2019

3. Modeling and controlling clarifier-thickeners fed by suspensions with time-dependent properties;Betancourt;Miner. Eng.,2014

4. Machine Learning Strategies for Time Series Forecasting;Bontempi,2013

5. Random forests;Breiman;Mach. Learn.,2001
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