Improving pressure drops estimation of fresh cemented paste backfill slurry using a hybrid machine learning method

Abstract Estimation of pressure drops of fresh cemented paste backfill slurry is a novel idea with great potentials. This paper presented a hybrid machine learning (ML) method for improved pressure drops estimation using a combination of artificial neural network and differential evolution. A comprehensive parametric study was conducted on training dataset size (Nsize), ML methods, and Monte Carlo random sampling. Moreover, dependent analysis of pressure drops to each influencing variable was performed. The results indicate that 300 Monte Carlo realizations were sufficient for the converged and reliable results. The optimum Nsize was determined to be 70%, and the proposed hybrid method outperformed six individual ML methods. The estimation performance has been significantly improved compared to the methods used in the literature (R2 increased from 0.83 to 0.95 on the testing dataset). Solids content, inlet velocity, SiO2, CaO, and Fe2O3 were determined to be the most significant variables for pressure drops.

[1]  Andy Fourie,et al.  Mechanics and safety issues in tailing-based backfill: A review , 2020, International Journal of Minerals, Metallurgy and Materials.

[2]  Xin Yao,et al.  A Large Population Size Can Be Unhelpful in Evolutionary Algorithms a Large Population Size Can Be Unhelpful in Evolutionary Algorithms , 2022 .

[3]  S. Jacquet,et al.  Advances in forecasting harmful algal blooms using machine learning models: A case study with Planktothrix rubescens in Lake Geneva. , 2020, Harmful algae.

[4]  Christian Soize,et al.  Generalized stochastic approach for constitutive equation in linear elasticity: a random matrix model , 2011, International Journal for Numerical Methods in Engineering.

[5]  Chongchong Qi,et al.  A new procedure for recycling waste tailings as cemented paste backfill to underground stopes and open pits , 2018, Journal of Cleaner Production.

[6]  Zaher Mundher Yaseen,et al.  Meteorological data mining and hybrid data-intelligence models for reference evaporation simulation: A case study in Iraq , 2019, Comput. Electron. Agric..

[7]  Adam P. Piotrowski,et al.  Review of Differential Evolution population size , 2017, Swarm Evol. Comput..

[8]  M. Fall,et al.  Thermal conductivity of cemented paste backfill material and factors affecting it , 2009 .

[9]  Qiu-song Chen,et al.  Temperature variation characteristics in flocculation settlement of tailings and its mechanism , 2020, International Journal of Minerals, Metallurgy and Materials.

[10]  Binh Thai Pham,et al.  Development of artificial intelligence models for the prediction of Compression Coefficient of soil: An application of Monte Carlo sensitivity analysis. , 2019, The Science of the total environment.

[11]  B. Eren,et al.  Application of artificial neural networks to estimate wastewater treatment plant inlet biochemical oxygen demand , 2008 .

[12]  Andy Fourie,et al.  Cemented paste backfill for mineral tailings management: Review and future perspectives , 2019 .

[13]  Andy Fourie,et al.  Recycling phosphogypsum and construction demolition waste for cemented paste backfill and its environmental impact , 2018, Journal of Cleaner Production.

[14]  Christian Soize,et al.  Stochastic continuum modeling of random interphases from atomistic simulations. Application to a polymer nanocomposite , 2015 .

[15]  Angel Kuri-Morales,et al.  Closed determination of the number of neurons in the hidden layer of a multi-layered perceptron network , 2017 .

[16]  Binh Thai Pham,et al.  Prediction and Sensitivity Analysis of Bubble Dissolution Time in 3D Selective Laser Sintering Using Ensemble Decision Trees , 2019, Materials.

[17]  K. C. Ghanta,et al.  Regime identification of slurry transport in pipelines: A novel modelling approach using ANN & differential evolution , 2010 .

[18]  M. Fall,et al.  Sulphate effect on the early age strength and self-desiccation of cemented paste backfill , 2016 .

[19]  Tim Oates,et al.  Efficient progressive sampling , 1999, KDD '99.

[20]  Kenji Suzuki,et al.  Artificial Neural Networks - Methodological Advances and Biomedical Applications , 2011 .

[21]  D. Grolimund,et al.  Co speciation in hardened cement paste: a macro- and micro-spectroscopic investigation. , 2007, Environmental science & technology.

[22]  Di Wu,et al.  Pressure drop in loop pipe flow of fresh cemented coal gangue–fly ash slurry: Experiment and simulation , 2015 .

[23]  Iztok Fister,et al.  Artificial neural network regression as a local search heuristic for ensemble strategies in differential evolution , 2015, Nonlinear Dynamics.

[24]  Chongchong Qi,et al.  Experimental study on thermal and mechanical properties of cemented paste backfill with phase change material , 2020 .

[25]  Yong Wang,et al.  A systematic review of paste technology in metal mines for cleaner production in China , 2020 .

[26]  Lotfi A. Zadeh,et al.  Fuzzy logic , 1988, Computer.

[27]  Hojjat Adeli,et al.  Neural Networks in Civil Engineering: 1989–2000 , 2001 .

[28]  Zaher Mundher Yaseen,et al.  Determination of compound channel apparent shear stress: application of novel data mining models , 2019, Journal of Hydroinformatics.

[29]  Andy Fourie,et al.  Pressure drop in pipe flow of cemented paste backfill: Experimental and modeling study , 2018, Powder Technology.

[30]  Binh Thai Pham,et al.  Development of an AI Model to Measure Traffic Air Pollution from Multisensor and Weather Data , 2019, Sensors.

[31]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[32]  Hiroshi Yajima,et al.  Benefits of machine learning and sampling frequency on phytoplankton bloom forecasts in coastal areas , 2020, Ecol. Informatics.

[33]  Zaher Mundher Yaseen,et al.  Pressure drops of fresh cemented paste backfills through coupled test loop experiments and machine learning techniques , 2020 .

[34]  Alex A. Freitas,et al.  A survey of evolutionary algorithms for data mining and knowledge discovery , 2003 .

[35]  Emil Pitkin,et al.  Peeking Inside the Black Box: Visualizing Statistical Learning With Plots of Individual Conditional Expectation , 2013, 1309.6392.

[36]  Alex Alves Freitas.  A Review of evolutionary Algorithms for Data Mining , 2008, Soft Computing for Knowledge Discovery and Data Mining.

[37]  M. Mooney,et al.  White-box regression (elastic net) modeling of earth pressure balance shield machine advance rate , 2020 .

[38]  Andy Fourie,et al.  Behavior of Cemented Paste Backfill in Two Mine Stopes: Measurements and Modeling , 2011 .

[39]  Ayhan Kesimal,et al.  The effect of desliming by sedimentation on paste backfill performance , 2003 .

[40]  Xiaodong Li,et al.  Time series forecasting by evolving artificial neural networks with genetic algorithms, differential evolution and estimation of distribution algorithm , 2011, Neural Computing and Applications.

[41]  Christian Soize,et al.  Stochastic modeling and identification of a hyperelastic constitutive model for laminated composites , 2019, Computer Methods in Applied Mechanics and Engineering.

[42]  Kurt Hornik,et al.  Approximation capabilities of multilayer feedforward networks , 1991, Neural Networks.

[43]  Weidong Song,et al.  Influence of structural factors on uniaxial compressive strength of cemented tailings backfill , 2018, Construction and Building Materials.

[44]  Ozgur Kisi,et al.  River suspended sediment concentration modeling using a neural differential evolution approach , 2010 .

[45]  K. Gnana Sheela,et al.  Review on Methods to Fix Number of Hidden Neurons in Neural Networks , 2013 .

[46]  A. Wu,et al.  Coupled effects of cement type and water quality on the properties of cemented paste backfill , 2015 .

[47]  A. Kesimal,et al.  Effect of sodium-silicate activated slag at different silicate modulus on the strength and microstructural properties of full and coarse sulphidic tailings paste backfill , 2018, Construction and Building Materials.