Development of an empirical model of human sweating and a semi-empirical model of human thermoregulation

Walker, Jennifer J.

doi:10.31274/rtd-180813-13457

Cited by 1 publication

(1 citation statement)

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“…H-BEST applications have not been limited to chemical processes. Walker 15 used it successfully in SISO modeling of a surrogate human's (i.e., a mathematical human model) thermoregulatory response to changes in ambient conditions.…”

Section: Introductionmentioning

confidence: 99%

A Continuous-Time Nonlinear Dynamic Predictive Modeling Method for Hammerstein Processes

Rollins

Bhandari

Bassily

et al. 2003

Ind. Eng. Chem. Res.

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This paper extends the method introduced by Rollins et al. (ISA Trans. 1998, 36, 293) to multiple-input, multiple-output systems that give an exact closed-form solution to continuous-time Hammerstein processes written in terms of differential equations and nonlinear inputs. This ability is demonstrated on a theoretical nonlinear Hammerstein process of complex dynamics where perfect identification of the closed-form model is assumed. This paper then demonstrates the simplicity of the proposed identification procedure to obtain an accurate estimate of the exact model using a theoretical Hammerstein model. A powerful attribute of this methodology is the ability to make full use of the statistical design of experiments for optimal data collection and accurate parameter estimation. Application of the proposed method is demonstrated on a household clothes dryer with four input and five output variables. Only 27 trials (input changes) of a central composite design were needed for accurate model development of all five outputs over the input space, and the accurate predictive performance is demonstrated. This paper extends the method introduced by Rollins et al. (ISA Trans. 1998, 36, 293) to multipleinput, multiple-output systems that give an exact closed-form solution to continuous-time Hammerstein processes written in terms of differential equations and nonlinear inputs. This ability is demonstrated on a theoretical nonlinear Hammerstein process of complex dynamics where perfect identification of the closed-form model is assumed. This paper then demonstrates the simplicity of the proposed identification procedure to obtain an accurate estimate of the exact model using a theoretical Hammerstein model. A powerful attribute of this methodology is the ability to make full use of the statistical design of experiments for optimal data collection and accurate parameter estimation. Application of the proposed method is demonstrated on a household clothes dryer with four input and five output variables. Only 27 trials (input changes) of a central composite design were needed for accurate model development of all five outputs over the input space, and the accurate predictive performance is demonstrated.

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Section: Introductionmentioning

confidence: 99%