Prediction of specific resistance for yeast suspensions in dead-end microfiltration - 文章详情页

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In　order　to　optimize　the　operating　conditions　in　dead-end　microfiltration,　a　predicting　model　relating　the　specific　resistance　to　the　operating　conditions　based　on　the　BP　neural　network　was　firstly　developed　in　this　paper,　in　which　the　experimental　data　of　an　orthonormal　design　table　(53)　were　used　as　the　input　sample　data.　Then,　by　using　the　average　relative　absolute　error　of　the　testing　sample　as　the　criterion,　a　comparison　experiment　of　the　predicting　for　specific　resistance　of　yeast　suspensions　by　using　the　BP　neural　network　method　and　the　multiple　linear　regression　method　had　been　made.　Finally,　the　predicting　precisions　of　two　models　had　been　given.　The　results　showed　that　the　BP　neural　network　method　was　better　than　the　multiple　linear　regression　method　and　the　average　relative　absolute　errors　were　3.55%　and　5.16%　for　the　BP　neural　network　method　and　the　multiple　linear　regression　method,　respectively.

关键词：

Backpropagation Forecasting Linear regression Microfiltration Neural networks Yeast

作者机构：

[ 1 ] [Yao, Jinmiao]Department of Chemistry and Chemical Engineering, School of Environmental and Energy Engineering, Beijing University of Technology, Beijing 100022, China
[ 2 ] [Wang, Zhan]Department of Chemistry and Chemical Engineering, School of Environmental and Energy Engineering, Beijing University of Technology, Beijing 100022, China
[ 3 ] [Sun, Guangmin]School of Electronics Information and Control Engineering, Beijing University of Technology, Beijing 100022, China
[ 4 ] [Chu, Jinshu]Department of Chemistry and Chemical Engineering, School of Environmental and Energy Engineering, Beijing University of Technology, Beijing 100022, China
[ 5 ] [Chen, Deming]School of Electronics Information and Control Engineering, Beijing University of Technology, Beijing 100022, China
[ 6 ] [Zhang, Hu]Beijing Fluid Filtration and Separation Technology Research Center, Beijing 101312, China
[ 7 ] [Li, Zhaohui]Beijing Fluid Filtration and Separation Technology Research Center, Beijing 101312, China

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