To monitor and optimize the operation and to predict the concentration profiles of the components in commercial units for toluene disproportionation and transalkylation with C9-aromatics, a kinetics-based mathematical...
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To monitor and optimize the operation and to predict the concentration profiles of the components in commercial units for toluene disproportionation and transalkylation with C9-aromatics, a kinetics-based mathematical model was developed and solved by the widely used Runge-Kutta algorithm. Based on several sets of operation data obtained from a commercial unit at steady state, the reaction kinetic parameters involved were estimated by the Broyden-Fletcher-Goldfarb-Shanno (BFGS) method. The kinetic model was also validated by the data from the commercial-scale unit operated with different feedstock compositions and operation variables. The results' showed good agreement between the model predictions and plant observations, signifying that the proposed model could be applied to both offline simulation and online soft sensors.
Based on the reported reaction networks, a novel six components hydroisomerization reaction network with a new lumped species including C8-naphthenes and C8-paraffins was proposed and a kinetic model for commercial un...
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Based on the reported reaction networks, a novel six components hydroisomerization reaction network with a new lumped species including C8-naphthenes and C8-paraffins was proposed and a kinetic model for commercial unit was also developed. An empirical catalyst deactivation function was incorporated into the model to account the loss in activity because of coke formation on the catalyst surface during the long-term operation. The Runge-Kutta method was used to solve the ordinary differential equations of the model. The reaction kinetic parameters were benchmarked with several sets of balanced plant data and estimated by the differential variable metric optimization method (BFGS). The kinetic model was validated by an industrial unit with sets of plant data at different operation conditions and simulation results show a good agreement between the model predictions and plant observations.
Multi-kernel-based support vector machine （SVM） model structure of nonlinear systems and its specific identification method is proposed, which is composed of a SVM with linear kernel function followed in series by a...
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Multi-kernel-based support vector machine （SVM） model structure of nonlinear systems and its specific identification method is proposed, which is composed of a SVM with linear kernel function followed in series by a SVM with spline kernel function. With the help of this model, nonlinear model predictive control can be transformed to linear model predictive control, and consequently a unified analytical solution of optimal input of multi-step-ahead predictive control is possible to derive. This algorithm does not require online iterative optimization in order to be suitable for real-time control with less calculation. The simulation results of pH neutralization process and CSTR reactor show the effectiveness and advantages of the presented algorithm.
A new kinetic model for commercial unit of toluene disproportionation and C9-armatiocs transalkylation is developed based on the reported reaction scheme.A time based catalyst deactivation function taking weight hourl...
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A new kinetic model for commercial unit of toluene disproportionation and C9-armatiocs transalkylation is developed based on the reported reaction scheme.A time based catalyst deactivation function taking weight hourly space velocity(WHSV)into account is incorporated into the model,which reasonably accounts for the loss in activity because of coke deposition on the surface of catalyst during long-term *** kinetic parameters are benchmarked with several sets of balanced plant data and estimated by the differential variable metric optimiza- tion *** of plant data at different operating conditions are applied to make sure validation of the model and the results show a good agreement between the model predictions and plant *** simulation analysis of key variables such as temperature and WHSV affecting process performance is discussed in detail,giv- ing the guidance to select suitable operating conditions.
Modeling olfactory neural systems, the Kill model proposed by Freeman exhibits chaotic dynamic characteristics and has potential for pattern recognition. Fuzzy c-means clustering can classify an object to several clas...
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Modeling olfactory neural systems, the Kill model proposed by Freeman exhibits chaotic dynamic characteristics and has potential for pattern recognition. Fuzzy c-means clustering can classify an object to several classes at the same time but with different degrees based on fuzzy sets theory. Based on the Kill model, mandarin digital speech is recognized utilizing the features extracted by the fuzzy c-means clustering. Experimental results show that the Kill model can perform digital speech recognition efficiently and the fuzzy c-means clustering has better performance than the hard k-means clustering.
A new multi-step adaptive predictive control algorithm for a class of bilinear systems is presented. The structure of the bilinear system is converted into a simple linear model by using nonlinear support vector machi...
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A new multi-step adaptive predictive control algorithm for a class of bilinear systems is presented. The structure of the bilinear system is converted into a simple linear model by using nonlinear support vector machine (SVM) dynamic approximation with analytical control law derived. The method does not need on-line parameters estimation because the system’s internal model has been transformed into an off-line global model. Compared with other traditional methods, this control law reduces on-line parameter estimating burden. In addition, its overall linear behavior treating method allows an analytical control law available and avoids on-line nonlinear optimization. Simulation results are presented in the article to illustrate the efficiency of the method.
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