A deep-learning-based multiobjective optimization for the design of in-situ uranium leaching system under multiple uncertainties

This publication has 59 references indexed in Scilit:

• What chemical reaction dominates the CO2 and O2 in-situ uranium leaching? Insights from a three-dimensional multicomponent reactive transport model at the field scale
  Applied Geochemistry, 2022
• Quantifying the impact of mineralogical heterogeneity on reactive transport modeling of CO2 + O2 in-situ leaching of uranium
  Acta Geochimica, 2021
• CCSNet: A deep learning modeling suite for CO2 storage
  Advances in Water Resources, 2021
• Deep residual U-net convolution neural networks with autoregressive strategy for fluid flow predictions in large-scale geosystems
  Advances in Water Resources, 2021
• Uncertainty quantification for uranium production in mining exploitation by In Situ Recovery
  Computational Geosciences, 2021
• Effects of basic intrusions on REE mobility of sandstones and their geological significance: A case study from the Qianjiadian sandstone-hosted uranium deposit in the Songliao Basin
  Applied Geochemistry, 2020
• Computationally efficient stochastic optimization using multiple realizations
  Advances in Water Resources, 2008
• A comparative study of Monte Carlo simple genetic algorithm and noisy genetic algorithm for cost-effective sampling network design under uncertainty
  Advances in Water Resources, 2006
• Evolutionary Multi-objective Ranking with Uncertainty and Noise
  2001
• Transition probability-based indicator geostatistics
  Mathematical Geology, 1996