Applied Immobilized algae bacteria (ABI) to remove ammonia of freshwater aquaculture wastewater. Temperature (T),PH,light intensity (I),dissolved oxygen (DO) and filling rate five factors plays important role in the p...Applied Immobilized algae bacteria (ABI) to remove ammonia of freshwater aquaculture wastewater. Temperature (T),PH,light intensity (I),dissolved oxygen (DO) and filling rate five factors plays important role in the process of ammonia nitrogen removal ,related data between ammonia removal and five factors was received through multi-factor orthogonal test,and established relations model between the five factor and nitrogen removal. The results show that five-factors had significant effect on AR,and the best combinations for removing AR was temperature 30 ℃,pH=7.0,light intensity 6 000 lux,dissolved oxygen 5.0 mg/L and the fill rate 10%. According to the experimental data,equation model was proposed and coefficient of determination R2 =0.864 8,P<0.05. Samples T-test was done between the model predictions and the actual measured values.Test results showed that the significant difference of overall mean value sig. (2-tailed) was 0.978 (P>0.05),it Shows that had no significant difference between model predictions and the actual measured value,and model had a high degree of fitting.展开更多
This article presents an Artificial Neural Network （ANN） architecture to model the Electrical Discharge Machining （EDM） process. It is aimed to develop the ANN model using an input-output pattern of raw data colle...This article presents an Artificial Neural Network （ANN） architecture to model the Electrical Discharge Machining （EDM） process. It is aimed to develop the ANN model using an input-output pattern of raw data collected from an experimental of EDM process, whereas several research objectives have been outlined such as experimenting machining material for selected gap current, identifying machining parameters for ANN variables and selecting appropriate size of data selection. The experimental data （input variables） of copper-electrode and steel-workpiece is based on a selected gap current where pulse on time, pulse off time and sparking frequency have been chosen at optimum value of Material Removal Rate （MRR）. In this paper, the result has significantly demonstrated that the ANN model is capable of predicting the MRR with low percentage prediction error when compared with the experimental result.展开更多
基金Supported by the National Natural Science Foundation of China(No.30972260)~~
文摘Applied Immobilized algae bacteria (ABI) to remove ammonia of freshwater aquaculture wastewater. Temperature (T),PH,light intensity (I),dissolved oxygen (DO) and filling rate five factors plays important role in the process of ammonia nitrogen removal ,related data between ammonia removal and five factors was received through multi-factor orthogonal test,and established relations model between the five factor and nitrogen removal. The results show that five-factors had significant effect on AR,and the best combinations for removing AR was temperature 30 ℃,pH=7.0,light intensity 6 000 lux,dissolved oxygen 5.0 mg/L and the fill rate 10%. According to the experimental data,equation model was proposed and coefficient of determination R2 =0.864 8,P<0.05. Samples T-test was done between the model predictions and the actual measured values.Test results showed that the significant difference of overall mean value sig. (2-tailed) was 0.978 (P>0.05),it Shows that had no significant difference between model predictions and the actual measured value,and model had a high degree of fitting.
文摘This article presents an Artificial Neural Network （ANN） architecture to model the Electrical Discharge Machining （EDM） process. It is aimed to develop the ANN model using an input-output pattern of raw data collected from an experimental of EDM process, whereas several research objectives have been outlined such as experimenting machining material for selected gap current, identifying machining parameters for ANN variables and selecting appropriate size of data selection. The experimental data （input variables） of copper-electrode and steel-workpiece is based on a selected gap current where pulse on time, pulse off time and sparking frequency have been chosen at optimum value of Material Removal Rate （MRR）. In this paper, the result has significantly demonstrated that the ANN model is capable of predicting the MRR with low percentage prediction error when compared with the experimental result.
