Back-propagation neural network was applied to predict and optimize the synthetic technology of 2-chloro-4,6-dinitroresorcinol. A model was established based on back-propagation neural network using the experimental d...Back-propagation neural network was applied to predict and optimize the synthetic technology of 2-chloro-4,6-dinitroresorcinol. A model was established based on back-propagation neural network using the experimental data of homogeneous design as the training sample set and the technological parameters were optimized by it. The optimal technological parameters are as follows： the reaction time is 4h, the reaction temperature is 80℃, the molar ratio of NaOH to 4,6-dinitro-1,2,3-trichlorobenzene is 5.5：1, the molar ratio of methanol to 4,6-dinitro-1,2,3- trichlorobenzene is 11：1, and the molar ratio of water to 4,6-dinitro-1,2,3-trichlorobenzene is 70：1. Under the optimal conditions, three groups of experiments were performed and the average yield of 2-chloro-4,6-dinitroresorcinol is 96.64%, the absolute error of it with the predicted value is -1.07%.展开更多
2-chloro-4,6-dinitroresorcinol was synthesized from 4,6-dinitro-1,2,3-trichlorobenzene by hydrolysis.The ultraviolet spectrophotometry was used to measure the absorbency of the hydrolysis mixture under different tempe...2-chloro-4,6-dinitroresorcinol was synthesized from 4,6-dinitro-1,2,3-trichlorobenzene by hydrolysis.The ultraviolet spectrophotometry was used to measure the absorbency of the hydrolysis mixture under different temperatures and periods of time.By kinetic calculation,it is shown that when the reaction temperature is in the range of 343.15 K to 358.15 K,the reaction is consistent with the secondary apparent dynamic model,the apparent activation energy is 200.11 kJ/mol and the expression of kinetic parameter k is 3.761×1028exp(-2.001×105/RT).The reaction is controlled by the course of chemical reactions.展开更多
文摘Back-propagation neural network was applied to predict and optimize the synthetic technology of 2-chloro-4,6-dinitroresorcinol. A model was established based on back-propagation neural network using the experimental data of homogeneous design as the training sample set and the technological parameters were optimized by it. The optimal technological parameters are as follows： the reaction time is 4h, the reaction temperature is 80℃, the molar ratio of NaOH to 4,6-dinitro-1,2,3-trichlorobenzene is 5.5：1, the molar ratio of methanol to 4,6-dinitro-1,2,3- trichlorobenzene is 11：1, and the molar ratio of water to 4,6-dinitro-1,2,3-trichlorobenzene is 70：1. Under the optimal conditions, three groups of experiments were performed and the average yield of 2-chloro-4,6-dinitroresorcinol is 96.64%, the absolute error of it with the predicted value is -1.07%.
基金the National Natural Science Foundations of China(Grant No.50333030)the Outstanding Youth Foundation of Heilongjiang Province of China(Grant No.JC04-12)
文摘2-chloro-4,6-dinitroresorcinol was synthesized from 4,6-dinitro-1,2,3-trichlorobenzene by hydrolysis.The ultraviolet spectrophotometry was used to measure the absorbency of the hydrolysis mixture under different temperatures and periods of time.By kinetic calculation,it is shown that when the reaction temperature is in the range of 343.15 K to 358.15 K,the reaction is consistent with the secondary apparent dynamic model,the apparent activation energy is 200.11 kJ/mol and the expression of kinetic parameter k is 3.761×1028exp(-2.001×105/RT).The reaction is controlled by the course of chemical reactions.
