Settling and rheological studies of solid liquid suspensions

Abstract

In the present thesis the settling and rheological behaviour of magnetite ore suspensions newlineare studied in aqueous CMC and guar gum solutions to investigate the effects of different parameters on them The data obtained from experimentation are suitably tailored to build data driven mathematical models for drag coefficient particle Reynolds number relationship CD ReP suspension rheological behaviour, concentration effects on suspension viscosity and artificial neural network ANN models to approximate shear stress shear rate relationship and the relative viscosity of the suspensions This thesis is structured as follows newlineChapter 1 provides a brief overview of the solid liquid suspensions their settling and newlinerheological behaviours and parameters which govern them Introduction of ANN is given and objectives of the thesis and thesis outline are stated in this chapter newlineThe chapter 2 is the discussion of the previous work by other researchers In this chapter newlineearlier studies of the effects of solids concentration and wall effects on the settling newlinebehaviour of suspensions are described The CD ReP relationships proposed by earlier newlineworkers are presented Investigations of other workers and their recommendations are newlinediscussed This chapter includes discussion on rheological properties of the suspensions, newlinethe rheological behaviours and different rheological models Further effects of system newlineparameters like solids concentration particle size and temperature on suspension newlineviscosity are discussed Different dispersants are also discussed The chapter concludes newlinewith the discussion on applications of artificial neural networks in various fields of newlinechemical engineering The third chapter gives the overview of the theory of different forces acting the particles settling velocity and the CD ReP relationships for the suspensions The rheological behaviour of suspensions and various mathematical equations used to describe the suspension behaviour are presented in this chapter Here ANN structure and its development main components, training and practical aspects of neural computing are introduced newlineChapter 4 gives detail information of the materials and methods used for experimental newlinework of this thesis Here the properties of the material experimental setup and procedure are discussed newlineChapter 5 is the discussion on findings of experimental investigations its analysis and newlinevarious models used to fit the experimental data Effects of dispersant concentration newlinesolids concentration and particle size on the settling of the suspensions are discussed newlinehere The mathematical formulation to calculate particle Reynolds number and drag newlinecoefficient of settling particles and the CD ReP relationship obtained are presented The newlinerheological nature of the suspensions under consideration effect of particle size and newlinesolids concentration on suspension rheology is discussed and effect of temperature on newlinesuspension viscosity is also presented in this chapter newlineModel fitting for suspension rheology and concentration effects on suspension viscosity newlineis presented in the last part of the chapter Reported constants of different models used to fit the data are obtained by curve fitting and using the Matjematica41 software The newlinedevelopment of artificial neural network model for the prediction of the suspension newlinerheology and relative viscosity their results and comparison with other models is newlinediscussed in last section of this chapter newlineFinally the sixth chapter of this thesis provides a summary of important results and newlineconclusions drawn from the experimental investigations and model fitting newline newline