This paper considers the problem of simulating the humidity distributions of a grain storage system. The distributions are described by partial differential equations(PDE). It is quite difficult to obtain the humidity...This paper considers the problem of simulating the humidity distributions of a grain storage system. The distributions are described by partial differential equations(PDE). It is quite difficult to obtain the humidity profiles from the PDE model. Hence, a discretization method is applied to obtain an equivalent ordinary differential equation model. However, after applying the discretization technique, the cost of solving the system increases as the size increases to a few thousands. It may be noted that after discretization,the degree of freedom of the system remain the same while the order increases. The large dynamic model is reduced using a proper orthogonal decomposition based technique and an equivalent model but of much reduced size is obtained. A controller based on optimal control theory is designed to obtain an input such that the output humidity reaches a desired profile and also its stability is analyzed.Numerical results are presented to show the validity of the reduced model and possible further extensions are identified.展开更多
Near infrared reflectance spectra (NIRS) was collected from Arachis hypogaea seed samples and used in predictive models to rapidly identify varieties with high oleic acid. The method was developed for shelled peanut s...Near infrared reflectance spectra (NIRS) was collected from Arachis hypogaea seed samples and used in predictive models to rapidly identify varieties with high oleic acid. The method was developed for shelled peanut seeds with intact testa. Spectra was evaluated initially by principal component analysis (PCA) followed by partial least squares (PLS). PCA performed with full spectra and reduced spectra with one principal component accounted for 97% to 99% variability, respectively. The PLS model generated from first derivative spectra provided a standard error of prediction (SEP) of 7.7204808. This technique provides a non-destructive method to rapidly identify high oleic peanut seeds to support the selection and cultivation of high oleic acid peanut varieties. The method can also be useful at peanut processing facilities for screening and quality assessments.展开更多
文摘This paper considers the problem of simulating the humidity distributions of a grain storage system. The distributions are described by partial differential equations(PDE). It is quite difficult to obtain the humidity profiles from the PDE model. Hence, a discretization method is applied to obtain an equivalent ordinary differential equation model. However, after applying the discretization technique, the cost of solving the system increases as the size increases to a few thousands. It may be noted that after discretization,the degree of freedom of the system remain the same while the order increases. The large dynamic model is reduced using a proper orthogonal decomposition based technique and an equivalent model but of much reduced size is obtained. A controller based on optimal control theory is designed to obtain an input such that the output humidity reaches a desired profile and also its stability is analyzed.Numerical results are presented to show the validity of the reduced model and possible further extensions are identified.
文摘Near infrared reflectance spectra (NIRS) was collected from Arachis hypogaea seed samples and used in predictive models to rapidly identify varieties with high oleic acid. The method was developed for shelled peanut seeds with intact testa. Spectra was evaluated initially by principal component analysis (PCA) followed by partial least squares (PLS). PCA performed with full spectra and reduced spectra with one principal component accounted for 97% to 99% variability, respectively. The PLS model generated from first derivative spectra provided a standard error of prediction (SEP) of 7.7204808. This technique provides a non-destructive method to rapidly identify high oleic peanut seeds to support the selection and cultivation of high oleic acid peanut varieties. The method can also be useful at peanut processing facilities for screening and quality assessments.
