Modeling of the Complex Behavior through an Improved Response Surface Methodology
暂无分享,去创建一个
Edelmira D. Gálvez | Luis A. Cisternas | Mauricio Sales-Cruz | Freddy A. Lucay | F. Lucay | L. Cisternas | E. Gálvez | M. Sales-Cruz
[1] Wenyin Gong,et al. Adaptive Ranking Mutation Operator Based Differential Evolution for Constrained Optimization , 2015, IEEE Transactions on Cybernetics.
[2] Sospeter Pastory Maganga,et al. Application of Response Surface Methodology for Optimization of Vat Leaching Parameters in Small Scale Mines: Case Study of Tanzania , 2015 .
[3] De-Shuang Huang,et al. A mended hybrid learning algorithm for radial basis function neural networks to improve generalization capability , 2007 .
[4] Liguang Wang,et al. Correlation of air recovery with froth stability and separation efficiency in coal flotation , 2013 .
[5] A. Setianto,et al. COMPARISON OF KRIGING AND INVERSE DISTANCE WEIGHTED (IDW) INTERPOLATION METHODS IN LINEAMENT EXTRACTION AND ANALYSIS , 2015 .
[6] Jan J. Cilliers,et al. Dynamic froth stability in froth flotation , 2003 .
[7] Mohsen Karimi,et al. Estimation of flotation rate constant and particle-bubble interactions considering key hydrodynamic parameters and their interrelations , 2019, Minerals Engineering.
[8] Savaş Bayram,et al. Comparison of multi layer perceptron (MLP) and radial basis function (RBF) for construction cost estimation: the case of Turkey , 2015 .
[9] Sue Vink,et al. The joint action of saline water and flotation reagents in stabilizing froth in coal flotation , 2016 .
[10] Jon C. Helton,et al. Alternative representations of epistemic uncertainty , 2004, Reliab. Eng. Syst. Saf..
[11] Francisco Charte,et al. Comparative analysis of data mining and response surface methodology predictive models for enzymatic hydrolysis of pretreated olive tree biomass , 2017, Comput. Chem. Eng..
[12] Dipak Sarkar,et al. Evaluation and comparison of ordinary kriging and inverse distance weighting methods for prediction of spatial variability of some chemical parameters of Dhalai district, Tripura , 2010 .
[13] Liqiang Ma,et al. Application of Box–Behnken design and response surface methodology for modeling and optimization of batch flotation of coal , 2020 .
[14] Daricha Sutivong,et al. Avoiding Local Minima in Feedforward Neural Networks by Simultaneous Learning , 2007, Australian Conference on Artificial Intelligence.
[15] Yanhui Yang,et al. Response surface methodology using Gaussian processes: Towards optimizing the trans-stilbene epoxidation over Co2+-NaX catalysts , 2010 .
[16] Sunil Kumar Tripathy,et al. Particle Classification Optimization of a Circulating Air Classifier , 2019, Mineral Processing and Extractive Metallurgy Review.
[17] A. OHagan,et al. Bayesian analysis of computer code outputs: A tutorial , 2006, Reliab. Eng. Syst. Saf..
[18] Dionissios T. Hristopulos,et al. Improving kriging of groundwater level data using nonlinear normalizing transformations—a field application , 2012 .
[19] Gerónimo Quiñónez Barraza,et al. ESTIMACIÓN DEL DIÁMETRO, ALTURA Y VOLUMEN A PARTIR DEL TOCÓN PARA ESPECIES FORESTALES DE DURANGO , 2012, Revista Mexicana de Ciencias Forestales.
[20] Ponisseril Somasundaran,et al. Reversal of Bubble Charge in Multivalent Inorganic Salt Solutions -Effect of Aluminum , 1992 .
[21] Václav Snásel,et al. Metaheuristic design of feedforward neural networks: A review of two decades of research , 2017, Eng. Appl. Artif. Intell..
[22] Jon C. Helton,et al. Guest editorial: treatment of aleatory and epistemic uncertainty in performance assessments for complex systems , 1996 .
[23] Sied Ziaedin Shafaei,et al. Application of Response Surface Method and Central Composite Design for Modeling and Optimization of Gold and Silver Recovery in Cyanidation Process , 2010 .
[24] Pierre Baldi,et al. A theory of local learning, the learning channel, and the optimality of backpropagation , 2015, Neural Networks.
[25] Saltelli Andrea,et al. Global Sensitivity Analysis: The Primer , 2008 .
[26] Mark S. Klima,et al. Application of Statistical and Machine Learning Techniques for Laboratory-Scale Pressure Filtration: Modeling and Analysis of Cake Moisture , 2018, Mineral Processing and Extractive Metallurgy Review.
[27] N. K. Nanda,et al. Beneficiation of Low-grade Iron Ore Fines by Using a Circulating-type Air Classifier , 2019, Mineral Processing and Extractive Metallurgy Review.
[28] Dian-Qing Li,et al. Response surface methods for slope reliability analysis: Review and comparison , 2016 .
[29] P. D. Kondos,et al. Process optimization studies in gold cyanidation , 1995 .
[30] Nicolaos B. Karayiannis,et al. Reformulated radial basis neural networks trained by gradient descent , 1999, IEEE Trans. Neural Networks.
[31] Farshad Rahimpour,et al. A modeling study by response surface methodology (RSM) and artificial neural network (ANN) on Cu2+ adsorption optimization using light expended clay aggregate (LECA) , 2014 .
[32] Cemal Ozer Yigit,et al. Performance evaluation of IDW, Kriging and multiquadric interpolation methods in producing noise mapping: A case study at the city of Isparta, Turkey , 2016 .
[33] Amir Ahmad Dehghani,et al. Optimizing Rougher Flotation Parameters of the Esfordi Phosphate Ore , 2012 .
[34] Stefano Tarantola,et al. Sensitivity analysis of spatial models , 2009, Int. J. Geogr. Inf. Sci..
[35] Ali Ahmadi,et al. Optimization of continuous air-assisted solvent extraction for treating dilute Cu leach solutions using response surface methodology , 2019, Minerals Engineering.
[36] Luis A. Cisternas,et al. Technical–economic feasibility study of the installation of biodiesel from microalgae crops in the Atacama Desert of Chile , 2014 .
[37] R Core Team,et al. R: A language and environment for statistical computing. , 2014 .
[38] Luís Marcelo Tavares,et al. Improvement in Roller Screening of Green Iron Ore Pellets by Statistical Analysis and Discrete Element Simulations , 2019, Mineral Processing and Extractive Metallurgy Review.
[39] Barón Marco Aurelio Azpúrua Auyanet,et al. A Comparison of Spatial Interpolation Methods for Estimation of Average Electromagnetic Field Magnitude , 2010 .
[40] Maximo Cobos,et al. Spatio-Temporal Analysis of Urban Acoustic Environments with Binaural Psycho-Acoustical Considerations for IoT-Based Applications , 2018, Sensors.
[41] Kenny Q. Ye,et al. Algorithmic construction of optimal symmetric Latin hypercube designs , 2000 .
[42] Bahram Asiabanpour,et al. Optimization of Solar Energy Harvesting: An Empirical Approach , 2018 .
[43] Jim W. Hall,et al. Sensitivity analysis of environmental models: A systematic review with practical workflow , 2014, Environ. Model. Softw..
[44] Huseyin Koca,et al. Modelling and optimization of the pyrolysis of low-rank lignite by central composite design (CCD) method , 2020, International Journal of Coal Preparation and Utilization.
[45] Clayton V. Deutsch,et al. GSLIB: Geostatistical Software Library and User's Guide , 1993 .
[46] Bryan A. Tolson,et al. Review of surrogate modeling in water resources , 2012 .
[47] Erkan Besdok,et al. A Comparison of RBF Neural Network Training Algorithms for Inertial Sensor Based Terrain Classification , 2009, Sensors.
[48] R. Storn,et al. Differential Evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces , 2004 .
[49] Longfei Tang,et al. Flotation Characteristics and Particle Size Distribution of Micro-fine Low Rank Coal☆ , 2015 .
[50] A. Saltelli,et al. Importance measures in global sensitivity analysis of nonlinear models , 1996 .
[51] Ernst C. Nienaber,et al. Experimental modelling and plant simulation of spiral concentrators: Comparing response surface methodology and extended Holland-Batt models , 2019, Minerals Engineering.
[52] Rainer Storn,et al. Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..
[53] Paola Annoni,et al. Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index , 2010, Comput. Phys. Commun..
[54] Yang Wang,et al. Repairing the crossover rate in adaptive differential evolution , 2014, Appl. Soft Comput..
[55] Peng Zhang,et al. Molding Process Design for Asphalt Mixture Based on Response Surface Methodology , 2016 .
[56] Richard J. Beckman,et al. A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output From a Computer Code , 2000, Technometrics.
[57] M. M. Ali,et al. Differential evolution with preferential crossover , 2007, Eur. J. Oper. Res..
[58] Carlos Eduardo,et al. Análisis Geoestadístico Espacio Tiempo Basado en Distancias y Splines con Aplicaciones , 2012 .
[59] Przemyslaw B. Kowalczuk,et al. Classification of Flotation Frothers , 2018 .
[60] Friedhelm Schwenker,et al. Three learning phases for radial-basis-function networks , 2001, Neural Networks.
[61] Saeed Farrokhpay,et al. An investigation into the effect of water quality on froth stability , 2012 .
[62] Mercedes Fernández-Redondo,et al. Training Radial Basis Functions by Gradient Descent , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.
[63] Luis A. Cisternas,et al. Modeling, Design and Optimization of Multiphase Systems in Minerals Processing , 2019, Minerals.
[64] Gurinder Singh,et al. Response surface methodology and process optimization of sustained release pellets using Taguchi orthogonal array design and central composite design , 2012, Journal of advanced pharmaceutical technology & research.
[65] Feridun Boylu,et al. Ultrafine coal flotation and dewatering: Selecting the surfactants of proper hydrophilic–lipophilic balance (HLB) , 2020 .
[66] Daryl Henwood,et al. The effect of conditioning on froth flotation , 1995 .
[67] Robert A. Jacobs,et al. Increased rates of convergence through learning rate adaptation , 1987, Neural Networks.
[68] João Lourenço,et al. Gaussian Process Model – An Exploratory Study in the Response Surface Methodology , 2016, Qual. Reliab. Eng. Int..
[69] Geoffrey E. Hinton,et al. Learning representations by back-propagating errors , 1986, Nature.
[70] Janusz S. Laskowski,et al. Effect of frothers on bubble coalescence and foaming in electrolyte solutions and seawater , 2013 .
[71] AbrahamAjith,et al. Metaheuristic design of feedforward neural networks , 2017 .
[72] Freddy A. Lucay,et al. Modeling the effect of air flow, impeller speed, frother dosages, and salt concentrations on the bubbles size using response surface methodology , 2019, Minerals Engineering.
[73] Pablo Moscato,et al. On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts : Towards Memetic Algorithms , 1989 .
[74] Weiguo Xie,et al. Optimisation of a Multi-Gravity Separator with Novel Modifications for the Recovery of Ferberite , 2018 .
[75] G. Akdogan,et al. Maximizing REE Enrichment by Froth Flotation of Alaskan Coal Using Box-Behnken Design , 2019, Mining, Metallurgy & Exploration.
[76] Michael S. Eldred,et al. OVERVIEW OF MODERN DESIGN OF EXPERIMENTS METHODS FOR COMPUTATIONAL SIMULATIONS , 2003 .
[77] Iftekhar A. Karimi,et al. Design of computer experiments: A review , 2017, Comput. Chem. Eng..
[78] Edelmira D. Gálvez,et al. A Posteriori Analysis of Analytical Models for Heap Leaching Using Uncertainty and Global Sensitivity Analyses , 2018 .
[79] G. Senanayake,et al. The effects of dissolved oxygen and cyanide dosage on gold extraction from a pyrrhotite-rich ore , 2004 .
[80] S. Chakraborty,et al. Application of response surface methodology (RSM) for optimization of leaching parameters for ash reduction from low-grade coal , 2018, International Journal of Mining Science and Technology.
[81] Erdal Kiliç,et al. Comparison of the results of response surface methodology and artificial neural network for the biosorption of lead using black cumin. , 2012, Bioresource technology.
[82] P. Werbos,et al. Beyond Regression : "New Tools for Prediction and Analysis in the Behavioral Sciences , 1974 .
[83] Jian Liu,et al. Response Surface Methodology for Optimization of Copper Leaching from Refractory Flotation Tailings , 2018 .
[84] Mario R. Eden,et al. Efficient Surrogate Model Development: Impact of Sample Size and Underlying Model Dimensions , 2018 .
[85] D. Shahsavani,et al. Variance-based sensitivity analysis of model outputs using surrogate models , 2011, Environ. Model. Softw..
[86] Gürkan Sin,et al. Systematic framework development for the construction of surrogate models for wastewater treatment plants , 2018 .
[87] L. Hua,et al. A hybrid of back propagation neural network and genetic algorithm for optimization of injection molding process parameters , 2011 .
[88] T. Jiang,et al. Extraction Behavior of Vanadium and Chromium by Calcification Roasting-Acid Leaching from High Chromium Vanadium Slag: Optimization Using Response Surface Methodology , 2018, Mineral Processing and Extractive Metallurgy Review.
[89] Emily M. Ryan,et al. Verification, validation, and uncertainty quantification of a sub-grid model for heat transfer in gas-particle flows with immersed horizontal cylinders , 2018 .
[90] J. Aldrich. R.A. Fisher and the making of maximum likelihood 1912-1922 , 1997 .
[91] Mostafa Khajeh,et al. Application of PSO-artificial neural network and response surface methodology for removal of methylene blue using silver nanoparticles from water samples , 2013 .
[92] Steven G Gilmour,et al. Response Surface Designs for Experiments in Bioprocessing , 2006, Biometrics.
[93] Atul Kumar Varma,et al. Kinetic Studies on Petrographic Components of Coal in Batch Flotation Operation , 2019 .
[94] J. Chilès,et al. Geostatistics: Modeling Spatial Uncertainty , 1999 .
[95] Carlos Henríquez,et al. Comparación de los interpoladores IDW y Kriging en la variación espacial de pH, Ca, CICE y P del suelo , 2008 .
[96] Wencheng Xia,et al. Biodiesel as a renewable collector for coal flotation in the future , 2016 .
[97] M. Bezerra,et al. Response surface methodology (RSM) as a tool for optimization in analytical chemistry. , 2008, Talanta.
[98] S. Farrokhpay. The significance of froth stability in mineral flotation--a review. , 2011, Advances in colloid and interface science.
[99] Kurt Hornik,et al. Approximation capabilities of multilayer feedforward networks , 1991, Neural Networks.
[100] J. Eksteen,et al. Extraction of Gold and Copper from a Gold Ore Thiosulfate Leachate by Use of Functionalized Magnetic Nanoparticles , 2020, Mineral Processing and Extractive Metallurgy Review.
[101] Ali Akbar Daya,et al. ORDINARY KRIGING FOR THE ESTIMATION OF VEIN TYPE COPPER DEPOSIT: A CASE STUDY OF THE CHELKUREH, IRAN , 2015 .
[102] Margaret J. Robertson,et al. Design and Analysis of Experiments , 2006, Handbook of statistics.
[103] Cüneyt Arslan,et al. Mechano-chemical conversion of celestite in highly concentrated sodium carbonate media , 2018 .
[104] Ali Idri,et al. Design of Radial Basis Function Neural Networks for Software Effort Estimation , 2010 .
[105] Christian F. Ihle,et al. Chemometric Optimisation of a Copper Sulphide Tailings Flocculation Process in the Presence of Clays , 2019, Minerals.
[106] Jonathan Berger,et al. Analysis of Pitch Perception of Inharmonicity in Pipa Strings Using Response Surface Methodology , 2010 .
[107] Huiyu Zhou,et al. Using deep neural network with small dataset to predict material defects , 2019, Materials & Design.
[108] Hua Chen,et al. Optimal Color Design of Psychological Counseling Room by Design of Experiments and Response Surface Methodology , 2014, PloS one.
[109] Kristopher J Preacher,et al. Testing Multilevel Mediation Using Hierarchical Linear Models , 2008 .
[110] Xinwei Liu,et al. Response Surface Optimization of Reductive Leaching Manganese from Low-Grade Pyrolusite Using Biogas Residual as Reductant , 2015 .
[111] G. Box,et al. On the Experimental Attainment of Optimum Conditions , 1951 .
[112] Anthony Banford,et al. Dynamic froth stability: Particle size, airflow rate and conditioning time effects , 2008 .
[113] Wenyin Gong,et al. Engineering optimization by means of an improved constrained differential evolution , 2014 .
[114] A. Pasini,et al. Neural network modelling for the analysis of forcings/temperatures relationships at different scales in the climate system , 2006 .
[115] Zheng Tang,et al. A modified error function for the backpropagation algorithm , 2004, Neurocomputing.
[116] Andrea Saltelli,et al. Sensitivity Analysis for Importance Assessment , 2002, Risk analysis : an official publication of the Society for Risk Analysis.
[117] Ajith Abraham,et al. Global Optimisation of Neural Networks Using a Deterministic Hybrid Approach , 2001, HIS.
[118] D. Shepard. A two-dimensional interpolation function for irregularly-spaced data , 1968, ACM National Conference.
[119] Mari Lundström,et al. Circulation of Sodium Sulfate Solution Produced During NiMH Battery Waste Processing , 2019, Mining, Metallurgy & Exploration.
[120] Okan Ozgonenel,et al. Artificial neural network (ANN) approach for modeling Zn(II) adsorption from leachate using a new biosorbent , 2011 .