Grey-box modelling and identification using physical knowledge and Bayesian techniques
Automatica (Journal of IFAC)
Mathematical Programming: Series A and B
System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
On the Convergence of Pattern Search Algorithms
SIAM Journal on Optimization
Convergence Properties of the Nelder--Mead Simplex Method in Low Dimensions
SIAM Journal on Optimization
Practical Grey-box Process Identification: Theory and Applications (Advances in Industrial Control)
Practical Grey-box Process Identification: Theory and Applications (Advances in Industrial Control)
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Work related to the tuning of the first-principle model of a feedwater heater operating in a coal-fired power unit is presented, along with discussion concerning the most efficient and accurate tuning algorithms based on direct-search, first- and second-order optimization techniques. The objective of this work is to find the most efficient and accurate algorithm to tune the model parameters, that is, heat transfer coefficients based on the algorithms' benchmarking study. The model variables (e.g. variability of the power rate of energy exchange) and estimated parameter values were used to formulate key performance indicators intended for a model-driven diagnostics approach. The computational process was organized in an iterative process of updating model parameters and indicators. The validation was successfully performed using operational data from a 225 MW coal-fired power unit.