Preparation of high purity ruthenium nitrosyl nitrate using spent Ru-Zn/ZrO_(2)catalyst was studied,including melting and leaching to obtain potassium ruthenate solution,reduction,dissolving,concentrating and drying t...Preparation of high purity ruthenium nitrosyl nitrate using spent Ru-Zn/ZrO_(2)catalyst was studied,including melting and leaching to obtain potassium ruthenate solution,reduction,dissolving,concentrating and drying to obtain ruthenium trichloride,nitrosation and hydrolysis to obtain ruthenium nitrosyl hydroxide,removing of K^(+)and Cl^(-),and neutralization with nitric acid.The effects of temperature,concentration,time and pH on the yield and purity of intermediates and final product were studied,and the optimum process conditions were obtained.The yield of ruthenium nitrosyl nitrate is 92%,the content of ruthenium in high purity product is 32.16%,and the content of Cl^(-)and K^(+)are much less than 0.005%.The reaction kinetics of ruthenium nitrosyl chloride to ruthenium nitrosyl hydroxide was studied.The reaction orders of Ru(NO)Cl_(3)at 40,55 and 70℃are 0.39,0.37 and 0.39,respectively,while those of KOH are 0.16,0.15 and 0.17,respectively.The activation energy is-2.33 k J/mol.展开更多
In order to solve the non-linear and high-dimensional optimization problems more effectively, an improved self-adaptive membrane computing(ISMC) optimization algorithm was proposed. The proposed ISMC algorithm applied...In order to solve the non-linear and high-dimensional optimization problems more effectively, an improved self-adaptive membrane computing(ISMC) optimization algorithm was proposed. The proposed ISMC algorithm applied improved self-adaptive crossover and mutation formulae that can provide appropriate crossover operator and mutation operator based on different functions of the objects and the number of iterations. The performance of ISMC was tested by the benchmark functions. The simulation results for residue hydrogenating kinetics model parameter estimation show that the proposed method is superior to the traditional intelligent algorithms in terms of convergence accuracy and stability in solving the complex parameter optimization problems.展开更多
In this work, focusing on the demerit of AEA (Alopex-based evolutionary algorithm) algorithm, an improved AEA algorithm (AEA-C) which was fused AEA with clonal selection algorithm was proposed. Considering the irratio...In this work, focusing on the demerit of AEA (Alopex-based evolutionary algorithm) algorithm, an improved AEA algorithm (AEA-C) which was fused AEA with clonal selection algorithm was proposed. Considering the irrationality of the method that generated candidate solutions at each iteration of AEA, clonal selection algorithm could be applied to improve the method. The performance of the proposed new algorithm was studied by using 22 benchmark functions and was compared with original AEA given the same conditions. The experimental results show that the AEA-C clearly outperforms the original AEA for almost all the 22 benchmark functions with 10, 30, 50 dimensions in success rates, solution quality and stability. Furthermore, AEA-C was applied to estimate 6 kinetics parameters of the fermentation dynamics models. The standard deviation of the objective function calculated by the AEA-C is 41.46 and is far less than that of other literatures' results, and the fitting curves obtained by AEA-C are more in line with the actual fermentation process curves.展开更多
基金Project(22178392)supported by the National Natural Science Foundation of China。
文摘Preparation of high purity ruthenium nitrosyl nitrate using spent Ru-Zn/ZrO_(2)catalyst was studied,including melting and leaching to obtain potassium ruthenate solution,reduction,dissolving,concentrating and drying to obtain ruthenium trichloride,nitrosation and hydrolysis to obtain ruthenium nitrosyl hydroxide,removing of K^(+)and Cl^(-),and neutralization with nitric acid.The effects of temperature,concentration,time and pH on the yield and purity of intermediates and final product were studied,and the optimum process conditions were obtained.The yield of ruthenium nitrosyl nitrate is 92%,the content of ruthenium in high purity product is 32.16%,and the content of Cl^(-)and K^(+)are much less than 0.005%.The reaction kinetics of ruthenium nitrosyl chloride to ruthenium nitrosyl hydroxide was studied.The reaction orders of Ru(NO)Cl_(3)at 40,55 and 70℃are 0.39,0.37 and 0.39,respectively,while those of KOH are 0.16,0.15 and 0.17,respectively.The activation energy is-2.33 k J/mol.
基金Projects(61203020,61403190)supported by the National Natural Science Foundation of ChinaProject(BK20141461)supported by the Jiangsu Province Natural Science Foundation,China
文摘In order to solve the non-linear and high-dimensional optimization problems more effectively, an improved self-adaptive membrane computing(ISMC) optimization algorithm was proposed. The proposed ISMC algorithm applied improved self-adaptive crossover and mutation formulae that can provide appropriate crossover operator and mutation operator based on different functions of the objects and the number of iterations. The performance of ISMC was tested by the benchmark functions. The simulation results for residue hydrogenating kinetics model parameter estimation show that the proposed method is superior to the traditional intelligent algorithms in terms of convergence accuracy and stability in solving the complex parameter optimization problems.
基金Projects(20976048, 21176072) supported by the National Natural Science Foundation of ChinaProject provided by the Fundamental Research Fund for Central Universities
文摘In this work, focusing on the demerit of AEA (Alopex-based evolutionary algorithm) algorithm, an improved AEA algorithm (AEA-C) which was fused AEA with clonal selection algorithm was proposed. Considering the irrationality of the method that generated candidate solutions at each iteration of AEA, clonal selection algorithm could be applied to improve the method. The performance of the proposed new algorithm was studied by using 22 benchmark functions and was compared with original AEA given the same conditions. The experimental results show that the AEA-C clearly outperforms the original AEA for almost all the 22 benchmark functions with 10, 30, 50 dimensions in success rates, solution quality and stability. Furthermore, AEA-C was applied to estimate 6 kinetics parameters of the fermentation dynamics models. The standard deviation of the objective function calculated by the AEA-C is 41.46 and is far less than that of other literatures' results, and the fitting curves obtained by AEA-C are more in line with the actual fermentation process curves.
