Since it is often difficult to build differential algebraic equations (DAEs) for chemical processes, a new data-based modeling approach is proposed using ARX (AutoRegressive with eXogenous inputs) combined with neural...Since it is often difficult to build differential algebraic equations (DAEs) for chemical processes, a new data-based modeling approach is proposed using ARX (AutoRegressive with eXogenous inputs) combined with neural network under partial least squares framework (ARX-NNPLS), in which less specific knowledge of the process is required but the input and output data. To represent the dynamic and nonlinear behavior of the process, the ARX combined with neural network is used in the partial least squares (PLS) inner model between input and output latent variables. In the proposed dynamic optimization strategy based on the ARX-NNPLS model, neither parameterization nor iterative solving process for DAEs is needed as the ARX-NNPLS model gives a proper representation for the dynamic behavior of the process, and the computing time is greatly reduced compared to conventional control vector parameterization method. To demonstrate the ARX-NNPLS model based optimization strategy, the polyethylene grade transition in gas phase fluidized-bed reactor is taken into account. The optimization results show that the final optimal trajectory of quality index determined by the new approach moves faster to the target values and the computing time is much less.展开更多
The preparation and characterization of an immobilized L-glutamic decarboxylase (GDC) were studied. This work is to develop a sensitive method for the determination of L-glutamate using a new biosensor, which consists...The preparation and characterization of an immobilized L-glutamic decarboxylase (GDC) were studied. This work is to develop a sensitive method for the determination of L-glutamate using a new biosensor, which consists of an enzyme column reactor of GDC immobilized on a novel ion exchange resin (carboxymethyl-copolymer of allyl dextran and N.N?methylene-bisacrylamide CM-CADB) and ion analyzer coupled with a CO2 electrode. The conditions for the enzyme immobilization were optimized by the parameters: buffer composition and concentration, adsorption equilibration time, amount of enzyme, temperature, ionic strength and pH. The properties of the immobilized enzyme on CM-CADB were studied by investigating the initial rate of the enzyme reaction, the effect of various parameters on the immobilized GDC activity and its stability. An immobilized GDC enzyme column reactor matched with a flow injection system-ion analyzer coupled with CO2 electrode-data collection system made up the original form of the apparatus of biosensor for determining of L-glutamate acid. The limit of detection is 1.0×10-5 M. The linearity response is in the range of 5×10 -2-5×10 -5 M . The equation of linear regression of the calibration curve is y= 43.3x + 181.6 (y is the milli-volt of electrical potential response, x is the logarithm of the concentration of the substrate of L-glutamate acid). The correlation coefficient equals 0.99. The coefficient of variation equals 2.7%.展开更多
基金Supported by the National Natural Science Foundation of China (61174114)the National High Technology Research and Development Program of China (2007AA04Z168, 2009AA04Z154)the Research Fund for the Doctoral Program of Higher Education in China (20050335018)
文摘Since it is often difficult to build differential algebraic equations (DAEs) for chemical processes, a new data-based modeling approach is proposed using ARX (AutoRegressive with eXogenous inputs) combined with neural network under partial least squares framework (ARX-NNPLS), in which less specific knowledge of the process is required but the input and output data. To represent the dynamic and nonlinear behavior of the process, the ARX combined with neural network is used in the partial least squares (PLS) inner model between input and output latent variables. In the proposed dynamic optimization strategy based on the ARX-NNPLS model, neither parameterization nor iterative solving process for DAEs is needed as the ARX-NNPLS model gives a proper representation for the dynamic behavior of the process, and the computing time is greatly reduced compared to conventional control vector parameterization method. To demonstrate the ARX-NNPLS model based optimization strategy, the polyethylene grade transition in gas phase fluidized-bed reactor is taken into account. The optimization results show that the final optimal trajectory of quality index determined by the new approach moves faster to the target values and the computing time is much less.
基金The Applied Fundamental Foundation of Jiangsu province P. R. China. Contract No BJ98041.
文摘The preparation and characterization of an immobilized L-glutamic decarboxylase (GDC) were studied. This work is to develop a sensitive method for the determination of L-glutamate using a new biosensor, which consists of an enzyme column reactor of GDC immobilized on a novel ion exchange resin (carboxymethyl-copolymer of allyl dextran and N.N?methylene-bisacrylamide CM-CADB) and ion analyzer coupled with a CO2 electrode. The conditions for the enzyme immobilization were optimized by the parameters: buffer composition and concentration, adsorption equilibration time, amount of enzyme, temperature, ionic strength and pH. The properties of the immobilized enzyme on CM-CADB were studied by investigating the initial rate of the enzyme reaction, the effect of various parameters on the immobilized GDC activity and its stability. An immobilized GDC enzyme column reactor matched with a flow injection system-ion analyzer coupled with CO2 electrode-data collection system made up the original form of the apparatus of biosensor for determining of L-glutamate acid. The limit of detection is 1.0×10-5 M. The linearity response is in the range of 5×10 -2-5×10 -5 M . The equation of linear regression of the calibration curve is y= 43.3x + 181.6 (y is the milli-volt of electrical potential response, x is the logarithm of the concentration of the substrate of L-glutamate acid). The correlation coefficient equals 0.99. The coefficient of variation equals 2.7%.
