Application of RSM and ANN Modeling in Industrial Whey Treatment
Applying RSM and ANN techniques for chemical process optimization in dairy whey treatment
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Coagulation flocculation is well established waste water treatment process since many decades. In this present study, experimental investigations using jar tests were validated with response surface methodology (RSM) and Artificial Neural Network (ANN). RSM was applied to optimize the effects of processing parameters on the yield of BOD and COD removal and a computer-stimulated artificial neural network (ANN) model was developed to get a good correlation between the input variables and the output parameter. This book, therefore, provides a novel approach for optimizing the process parameters d...
Coagulation flocculation is well established waste water treatment process since many decades. In this present study, experimental investigations using jar tests were validated with response surface methodology (RSM) and Artificial Neural Network (ANN). RSM was applied to optimize the effects of processing parameters on the yield of BOD and COD removal and a computer-stimulated artificial neural network (ANN) model was developed to get a good correlation between the input variables and the output parameter. This book, therefore, provides a novel approach for optimizing the process parameters during coagulation-flocculation process using the statistical tools where dairy whey treatment has been achieved in a user friendly way. Moreover this particular approach can be implemented across the globe for all dairy industries (irrespective of their sizes) as that will definitely help the engineers/users in the dairy waste water treatment by optimizing the process variables of the experimental conditions while minimizing the number of required experiments.
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