Flotation in mechanically agitated cells has been the workhorse of the mining industry, but our quantitative understanding of the effect of microturbulence generated by agitation on flotation is still very limited. This paper aims to review the literature on quantifying the microturbulence effects on bubble-particle interactions in flotation. The particular focus is on the stochastic description of bubble-particle interactions in the turbulent flow which is a random field. We briefly review the stochastic description of microturbulence and motions of particles of micrometre sizes and bubbles of millimetre sizes in the isotropic turbulence of mechanical flotation cells. The key starting point is the generic equation of motion, which can be decomposed into the mean turbulent variables and fluctuating turbulent variables. The turbulent flow of the carrying liquid is characterised using isotropic turbulence theory. The next focus is on reviewing bubble-particle turbulent collision and detachment interactions. Bubble-particle turbulent collision is poorly quantified; no quantitative models of the bubble-particle turbulent collision efficiency relevant for flotation are available.Current theories on bubble-particle turbulent detachment face some deficiencies. In assessing the microturbulence effect on bubble-particle detachment, the majority of studies only consider the particle acceleration in the centrifugal direction but ignore the transverse acceleration of particles, which is due to turbulent shear flow. Critically, contact angle required in quantifying the detachment is not constant, single-valued as considered in the theories, but can vary from receding to advancing value during the relaxation of the triple contact line on the particle surface. The latest experiments show that multiple-valued contact angle can significant affect stability and detachment of floating particles. Finally, quantifying the microturbulence effect on flotation requires further research.
Artificial Neural Network (ANN) approaches were used to model and predict water trading prices in the Murry Irrigation area, Australia. • Prices forecast using hybrid ANN-Bayesian modelling showed greater agreement with actual water prices. • Water security allocations, cereal and meat prices were significant determinants of future water trading prices.
This paper aims to develop the long short-term memory (LSTM) network modelling strategy based on deep learning principles, tailored for the very short-term, near-real-time global solar radiation (GSR) forecasting. To build the prescribed LSTM model, the partial autocorrelation function is applied to the high resolution, 1 min scaled solar radiation dataset that generates statistically significant lagged predictor variables describing the antecedent behaviour of GSR. The LSTM algorithm is adopted to capture the short- and the long-term dependencies within the GSR data series patterns to accurately predict the future GSR at 1, 5, 10, 15, and 30 min forecasting horizons. This objective model is benchmarked at a solar energy resource rich study site (Bac-Ninh, Vietnam) against the competing counterpart methods employing other deep learning, a statistical model, a single hidden layer and a machine learning-based model. The LSTM model generates satisfactory predictions at multiple-time step horizons, achieving a correlation coefficient exceeding 0.90, outperforming all of the counterparts. In accordance with robust statistical metrics and visual analysis of all tested data, the study ascertains the practicality of the proposed LSTM approach to generate reliable GSR forecasts. The Diebold–Mariano statistic test also shows LSTM outperforms the counterparts in most cases. The study confirms the practical utility of LSTM in renewable energy studies, and broadly in energy-monitoring devices tailored for other energy variables (e.g., hydro and wind energy).
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.