An adaptive chaotic gradient descending optimization algorithm for single objective optimization was presented. A local minimum judged by two rules was obtained by an improved mutative-step gradient descending method....An adaptive chaotic gradient descending optimization algorithm for single objective optimization was presented. A local minimum judged by two rules was obtained by an improved mutative-step gradient descending method. A new optimal minimum was obtained to replace the local minimum by mutative-scale chaotic search algorithm whose scales are magnified gradually from a small scale in order to escape local minima. The global optimal value was attained by repeatedly iterating. At last, a BP (back-propagation) neural network model for forecasting slag output in matte converting was established. The algorithm was used to train the weights of the BP neural network model. The simulation results with a training data set of 400 samples show that the training process can be finished within 300 steps to obtain the global optimal value, and escape local minima effectively. An optimization system for operation parameters, which includes the forecasting model, is achieved, in which the output of converter increases by 6.0%, and the amount of the treated cool materials rises by 7.8% in the matte converting process.展开更多
Corrosion behavior of TP316L was investigated with simulated atmosphere and ash deposition for the superheater in biomass boiler.Corrosion dynamic curves were plotted by mass gain.The results showed that the corrosion...Corrosion behavior of TP316L was investigated with simulated atmosphere and ash deposition for the superheater in biomass boiler.Corrosion dynamic curves were plotted by mass gain.The results showed that the corrosion was dependent on temperature and was greatly accelerated by ash deposition.The mass gain was distinctly reduced in the presence of SO2 with and without ash deposition on the specimens.Corrosion rates with ash deposit at different temperatures were calculated.Two feasible methods were provided to avoid serious high-temperature corrosion in the biomass boiler.展开更多
文摘An adaptive chaotic gradient descending optimization algorithm for single objective optimization was presented. A local minimum judged by two rules was obtained by an improved mutative-step gradient descending method. A new optimal minimum was obtained to replace the local minimum by mutative-scale chaotic search algorithm whose scales are magnified gradually from a small scale in order to escape local minima. The global optimal value was attained by repeatedly iterating. At last, a BP (back-propagation) neural network model for forecasting slag output in matte converting was established. The algorithm was used to train the weights of the BP neural network model. The simulation results with a training data set of 400 samples show that the training process can be finished within 300 steps to obtain the global optimal value, and escape local minima effectively. An optimization system for operation parameters, which includes the forecasting model, is achieved, in which the output of converter increases by 6.0%, and the amount of the treated cool materials rises by 7.8% in the matte converting process.
文摘Corrosion behavior of TP316L was investigated with simulated atmosphere and ash deposition for the superheater in biomass boiler.Corrosion dynamic curves were plotted by mass gain.The results showed that the corrosion was dependent on temperature and was greatly accelerated by ash deposition.The mass gain was distinctly reduced in the presence of SO2 with and without ash deposition on the specimens.Corrosion rates with ash deposit at different temperatures were calculated.Two feasible methods were provided to avoid serious high-temperature corrosion in the biomass boiler.
