Parameter Estimation and Statistical Methods
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[1] Marcio Schwaab,et al. Optimum reference temperature for reparameterization of the Arrhenius equation. Part 2: Problems involving multiple reparameterizations , 2008 .
[2] C. D. Gelatt,et al. Optimization by Simulated Annealing , 1983, Science.
[3] J. A. González-Marcos,et al. SEQUENTIAL DESIGN OF EXPERIMENTS FOR OPTIMAL MODEL DISCRIMINATION AND PARAMETER ESTIMATION IN ISOPROPANOL DEHYDRATION , 1991 .
[4] José Carlos Pinto,et al. Sequential experimental design for parameter estimation : a different approach , 1990 .
[5] Rolf Sundberg,et al. Statistical aspects on fitting the Arrhenius equation , 1998 .
[6] S. K. Katti,et al. Hazards of linearization of Langmuir's model , 1990 .
[7] Albert Tarantola,et al. Inverse problem theory - and methods for model parameter estimation , 2004 .
[8] E. Lima,et al. Data Reconciliation and Control in Styrene-Butadiene Emulsion Polymerizations , 2011 .
[9] N. Draper,et al. Applied Regression Analysis , 1967 .
[10] J. Pinto,et al. Modelling the effects of reaction temperature and flow rate on the conversion of ethanol to 1,3-butadiene , 2017 .
[11] Michael G. Epitropakis,et al. Evolving cognitive and social experience in Particle Swarm Optimization through Differential Evolution: A hybrid approach , 2012, Inf. Sci..
[12] David Mautner Himmelblau,et al. Process analysis by statistical methods , 1970 .
[13] Marcos L. Corazza,et al. A comparison among stochastic optimization algorithms for parameter estimation of biochemical kinetic models , 2013, Appl. Soft Comput..
[14] J. Pinto,et al. Microkinetic analysis of ethanol to 1,3-butadiene reactions over MgO-SiO2 catalysts based on characterization of experimental fluctuations , 2017 .
[15] Amaro G. Barreto,et al. A new approach for sequential experimental design for model discrimination , 2006 .
[16] Douglas M. Bates,et al. Nonlinear Regression Analysis and Its Applications , 1988 .
[17] Diego Martinez Prata,et al. Nonlinear dynamic data reconciliation and parameter estimation through particle swarm optimization: Application for an industrial polypropylene reactor , 2009 .
[18] Stephen J. Wright,et al. Numerical Optimization , 2018, Fundamental Statistical Inference.
[19] W. Näther. Optimum experimental designs , 1994 .
[20] David E. Goldberg,et al. Genetic Algorithms in Search Optimization and Machine Learning , 1988 .
[21] T. Brubaker,et al. Nonlinear Parameter Estimation , 1979 .
[22] S. Sivanesan,et al. Prediction of optimum sorption isotherm: comparison of linear and non-linear method. , 2005, Journal of hazardous materials.
[23] A. Krause,et al. Kinetic analysis of temperature-programmed desorption , 2004 .
[24] G. Froment,et al. Chemical Reactor Analysis and Design , 1979 .
[25] N. Kalogerakis,et al. Applied parameter estimation for chemical engineers , 2000 .
[26] J. S. Hunter,et al. Statistics for experimenters : an introduction to design, data analysis, and model building , 1979 .
[27] H. Knözinger,et al. Kinetics of the bimolecular ether formation from alcohols over alumina , 1973 .
[28] Diego Martinez Prata,et al. Simultaneous robust data reconciliation and gross error detection through particle swarm optimization for an industrial polypropylene reactor , 2010 .
[29] E. Biscaia,et al. Nonlinear parameter estimation through particle swarm optimization , 2008 .
[30] J. Pinto,et al. Analysis of experimental errors in catalytic tests for production of synthesis gas , 2003 .
[31] N. Draper,et al. Applied Regression Analysis , 1966 .
[32] Determination of Arrhenius constants by linear and nonlinear fitting , 1992 .
[33] J. Pinto,et al. The influence of covariances during laboratory evaluation of FCC catalysts , 2001 .
[34] Amanda L. T. Brandão,et al. Modeling and parameter estimation of step‐growth polymerization of poly(ethylene‐2,5‐furandicarboxylate) , 2018 .
[35] D. Wolf,et al. Estimating rate constants of heterogeneous catalytic reactions without supposition of rate determining surface steps — an application of a genetic algorithm , 1997 .
[36] Lubomír Kubáček,et al. Statistical properties of linearization of the Arrhenius equation via the logarithmic transformation , 1997 .
[37] José Carlos Pinto,et al. Sequential experimental design for model discrimination: Taking into account the posterior covariance matrix of differences between model predictions , 2008 .
[38] J. Pinto,et al. Statistical Analysis of Linear and Non-Linear Regression for the Estimation of Adsorption Isotherm Parameters , 2013 .
[39] J. Pinto,et al. Experimental errors in kinetic tests and its influence on the precision of estimated parameters. Part I—Analysis of first-order reactions , 2009 .
[40] Irwin Guttman,et al. Confidence intervals versus regions , 1995 .
[41] G. Froment,et al. A hybrid genetic algorithm for the estimation of parameters in detailed kinetic models , 1998 .
[42] J. Pinto,et al. Design of experiments for discrimination of rival models based on the expected number of eliminated models , 2012 .
[43] José Luiz Fontes Monteiro,et al. Sequential experimental design for parameter estimation: analysis of relative deviations , 1991 .
[44] D. Himmelblau,et al. Optimization of Chemical Processes , 1987 .
[45] Erwie Zahara,et al. A hybrid genetic algorithm and particle swarm optimization for multimodal functions , 2008, Appl. Soft Comput..
[46] José Carlos Pinto,et al. Experimental design for the joint model discrimination and precise parameter estimation through information measures , 2011 .
[47] The influence of experimental errors during laboratory evaluation of FCC catalysts , 1999 .
[48] Shu-Kai S. Fan,et al. A hybrid simplex search and particle swarm optimization for unconstrained optimization , 2007, Eur. J. Oper. Res..
[49] M. Senthil Arumugam,et al. On the improved performances of the particle swarm optimization algorithms with adaptive parameters, cross-over operators and root mean square (RMS) variants for computing optimal control of a class of hybrid systems , 2008, Appl. Soft Comput..
[50] Ariane Leites Larentis,et al. Modeling and optimization of the combined carbon dioxide reforming and partial oxidation of natural gas , 2001 .
[51] Marcio Schwaab,et al. Optimum reference temperature for reparameterization of the Arrhenius equation. Part 1 : Problems involving one kinetic constant , 2007 .
[52] Robert B. Schnabel,et al. Computational Experience With Confidence Regions and Confidence Intervals for Nonlinear Least Squares , 1986 .
[53] Yonathan Bard,et al. Comparison of Gradient Methods for the Solution of Nonlinear Parameter Estimation Problems , 1970 .
[54] Rainer Storn,et al. Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..