Atiemo-Obeng, 2004, 543
Zwietering, 1958, Suspending of solid particles in liquid by agitators, Chem. Eng. Sci., 8, 244, 10.1016/0009-2509(58)85031-9
Tahvildarian, 2011, Using electrical resistance tomography images to characterize the mixing of micron-sized polymeric particles in a slurry reactor, Chem. Eng. J., 172, 517, 10.1016/j.cej.2011.06.056
Jafari, 2012, Characterization of minimum impeller speed for suspension of solids in liquid at high solid concentration, using gamma-ray densitometry, Int. J. Chem. Eng., 2012, 15, 10.1155/2012/945314
Kazemzadeh, 2019
McKee, 1995, Development of solid—liquid mixing models using tomographic techniques, Chem. Eng. J. Biochem. Eng. J., 56, 101, 10.1016/0923-0467(94)02904-0
Baldi, 1978, Complete suspension of particles in mechanically agitated vessels, Chem. Eng. Sci., 33, 21, 10.1016/0009-2509(78)85063-5
Ayranci, 2014, Critical analysis of Zwietering correlation for solids suspension in stirred tanks, Chem. Eng. Res. Des., 92, 413, 10.1016/j.cherd.2013.09.005
Ayranci, 2013, Prediction of just suspended speed for mixed slurries at high solids loadings, Chem. Eng. Res. Des., 91, 227, 10.1016/j.cherd.2012.08.002
Armenante, 1998, Effect of low off-bottom impeller clearance on the minimum agitation speed for complete suspension of solids in stirred tanks, Chem. Eng. Sci., 53, 1757, 10.1016/S0009-2509(98)00001-3
Myers, 2013, Effect of solids loading on agitator just-suspended speed, Can. J. Chem. Eng., 91, 1508, 10.1002/cjce.21763
Grenville, 2016, Suspension of solid particles in vessels agitated by Rushton turbine imperllers, Chem. Eng. Res. Des., 109, 730, 10.1016/j.cherd.2016.03.024
Mitchell, 2008, Solids suspension agitation in square tanks, Can. J. Chem. Eng., 86, 110, 10.1002/cjce.20004
Zheng, 2018, Investigation of cleaner sulfide mineral oxidation technology: simulation and evaluation of stirred bioreactors for gold-bioleaching process, J. Clean. Prod., 192, 364, 10.1016/j.jclepro.2018.04.172
Zheng, 2019, Experimental study and simulation of a three-phase flow stirred bioreactor, Chin. J. Chem. Eng., 27, 649, 10.1016/j.cjche.2018.06.010
Ata, 2015, Artificial neural networks applications in wind energy systems: a review, Renew. Sustain. Energy Rev., 49, 534, 10.1016/j.rser.2015.04.166
Yi-fan, 2017, Modeling of expanded granular sludge bed reactor using artificial neural network, J. Environ. Chem. Eng., 5, 2142, 10.1016/j.jece.2017.04.007
Singh, 2017, Preparation of CuO nanoparticles using Tamarindus indica pulp extract for removal of As(III): optimization of adsorption process by ANN-GA, J. Environ. Chem. Eng., 5, 1302, 10.1016/j.jece.2017.01.046
Zoveidavianpoor, 2014, A comparative study of artificial neural network and adaptive neurofuzzy inference system for prediction of compressional wave velocity, Neural Comput. Appl., 25, 1169, 10.1007/s00521-014-1604-2
Hočevar, 2005, Prediction of cavitation vortex dynamics in the draft tube of a francis turbine using radial basis neural networks, Neural Compt. Appl., 14, 229, 10.1007/s00521-004-0458-4
Ahmadloo, 2016, Prediction of thermal conductivity of various nanofluids using artificial neural network, Int. J. Heat Mass Transf., 74, 69, 10.1016/j.icheatmasstransfer.2016.03.008
Tabatabaei, 2017, A probabilistic neural network based approach for predicting the output power of wind turbines, J. Exp. Theor. Artif. Intell., 29, 273, 10.1080/0952813X.2015.1132272
Zou, 2008, An intelligent neural networks system for adaptive learning and prediction of a bioreactor benchmark process* *supported by China scholarship council grant (No. 21302095), Chin. J. Chem. Eng., 16, 62, 10.1016/S1004-9541(08)60038-5
Rumpfkeil, 2017, Using steady flow analysis for noise predictions, Comput. Fluids, 154, 347, 10.1016/j.compfluid.2017.03.003
Erzin, 2010, Artificial neural network models for predicting electrical resistivity of soils from their thermal resistivity, Int. J. Therm. Sci., 49, 118, 10.1016/j.ijthermalsci.2009.06.008
Inthachot, 2016, Artificial neural network and genetic algorithm hybrid intelligence for predicting Thai stock price index trend, Comput. Intell. Neurosci., 2016, 8, 10.1155/2016/3045254
Ibrahim, 1991
Ibrahim, 2015, Influence of geometry and slurry properties on fine particles suspension at high loadings in a stirred vessel, Chem. Eng. Res. Des., 94, 324, 10.1016/j.cherd.2014.08.008
Wong, 2015, Effect of impeller-to-tank geometry on particles distribution and just-suspension speeds for a range of solids loadings, J. Chem. Eng. Jpn., 48, 374, 10.1252/jcej.14we059
Fu, 2015, Prediction of particular matter concentrations by developed feed-forward neural network with rolling mechanism and gray model, Neural Comput. Appl., 26, 1789, 10.1007/s00521-015-1853-8
Khoshjavan, 2011, Evaluation of effect of coal chemical properties on coal swelling index using artificial neural networks, Expert Syst. Appl., 38, 12906, 10.1016/j.eswa.2011.04.084
Howard, 1994
Kocabaş, 2008, A neural network approach for prediction of critical submergence of an intake in still water and open channel flow for permeable and impermeable bottom, Comput. Fluids, 37, 1040, 10.1016/j.compfluid.2007.11.002
Chan, 2003, Improving bayesian regularization of ANN via pre-training with early-stopping, Neural Process. Lett., 18, 29, 10.1023/A:1026271406135
Nawi, 2013, The effect of data pre-processing on optimized training of artificial neural networks, Procedia Technol., 11, 32, 10.1016/j.protcy.2013.12.159
Evans, 2013, Utilizing artificial neural networks and genetic algorithms to build an algo-trading model for intra-day foreign exchange speculation, Math. Comput. Model., 58, 1249, 10.1016/j.mcm.2013.02.002
Cheng, 2009, Ratio adjustment and calibration scheme for gene-wise normalization to enhance microarray inter-study prediction, Bioinf, 25, 1655, 10.1093/bioinformatics/btp292
Sheela, 2013, Review on methods to fix number of hidden neurons in neural networks, Math. Probl. Eng., 2013, 11, 10.1155/2013/425740