Separation and purification of dodecanedioic acid (DDDA) from its homologous compounds were studied experimentally by falling film crystallization (FFC). The influences of various operation parameters, including cryst...Separation and purification of dodecanedioic acid (DDDA) from its homologous compounds were studied experimentally by falling film crystallization (FFC). The influences of various operation parameters, including crystallizing time, flow rate of melt and temperature of glycerine bath, on purity of DDDA and crystallizing rate were investigated. Over 99% (by mole) DDDA was obtained for a feed composition of 96% (by mole). The main factors affecting the separation efficiency are flow rate of melt and temperature of glycerine bath. The crystallizing layer of DDDA was further purified by sweating and blasting. A set of optimized operation data are provided for better understanding the mechanism of heat and mass transfer in FFC, and for further industrial application of DDDA purification process.展开更多
Particle Filter （PF） is a data assimilation method to solve recursive state estimation problem which does not depend on the assumption of Gaussian noise, and is able to be applied for various systems even with non-l...Particle Filter （PF） is a data assimilation method to solve recursive state estimation problem which does not depend on the assumption of Gaussian noise, and is able to be applied for various systems even with non-linear and non-Gaussian noise. However, while applying PF in dynamic systems, PF undergoes particle degeneracy, sample impoverishment, and problems of high computational complexity. Rapidly developing sensing technologies are providing highly convenient availability of real-time big traffic data from the system under study like never before. Moreover, some sensors can even receive control commands to adjust their monitoring parameters. To address these problems, a bidirectional dynamic data-driven improvement framework for PF （B3DPF） is proposed. The B3DPF enhances feedback between the simulation model and the big traffic data collected by the sensors, which means the execution strategies （sensor data management, parameters used in the weight computation, resampling） of B3DPF can be optimized based on the simulation results and the types and dimensions of traffic data injected into B3DPF can be adjusted dynamically. The first experiment indicates that the B3DPF overcomes particle degeneracy and sample impoverishment problems and accurately estimates the state at a faster speed than the normal PF. More importantly, the new method has higher accuracy for multidimensional random systems. In the rest of experiments, the proposed framework is applied to estimate the traffic state on a real road network and obtains satisfactory results. More experiments can be designed to validate the universal properties of B3DPF.展开更多
文摘Separation and purification of dodecanedioic acid (DDDA) from its homologous compounds were studied experimentally by falling film crystallization (FFC). The influences of various operation parameters, including crystallizing time, flow rate of melt and temperature of glycerine bath, on purity of DDDA and crystallizing rate were investigated. Over 99% (by mole) DDDA was obtained for a feed composition of 96% (by mole). The main factors affecting the separation efficiency are flow rate of melt and temperature of glycerine bath. The crystallizing layer of DDDA was further purified by sweating and blasting. A set of optimized operation data are provided for better understanding the mechanism of heat and mass transfer in FFC, and for further industrial application of DDDA purification process.
基金supported by the State Basic Scientific Research of National Defense (No. c0420110005)13th Five-Year Key Basic Research Project (No. JCKY2016206B001)the Six talent peaks project in Jiangsu Province (No. XXRJ-004)
文摘Particle Filter （PF） is a data assimilation method to solve recursive state estimation problem which does not depend on the assumption of Gaussian noise, and is able to be applied for various systems even with non-linear and non-Gaussian noise. However, while applying PF in dynamic systems, PF undergoes particle degeneracy, sample impoverishment, and problems of high computational complexity. Rapidly developing sensing technologies are providing highly convenient availability of real-time big traffic data from the system under study like never before. Moreover, some sensors can even receive control commands to adjust their monitoring parameters. To address these problems, a bidirectional dynamic data-driven improvement framework for PF （B3DPF） is proposed. The B3DPF enhances feedback between the simulation model and the big traffic data collected by the sensors, which means the execution strategies （sensor data management, parameters used in the weight computation, resampling） of B3DPF can be optimized based on the simulation results and the types and dimensions of traffic data injected into B3DPF can be adjusted dynamically. The first experiment indicates that the B3DPF overcomes particle degeneracy and sample impoverishment problems and accurately estimates the state at a faster speed than the normal PF. More importantly, the new method has higher accuracy for multidimensional random systems. In the rest of experiments, the proposed framework is applied to estimate the traffic state on a real road network and obtains satisfactory results. More experiments can be designed to validate the universal properties of B3DPF.
