Dr. Singh, Abhinandan Kumar
M.Sc. Abhinandan Kumar Singh
Research topic
Spray fluidized bed agglomeration: Incorporating agglomerate structure in stochastic process models.
Supervisor
Brief resume
Born 1991 in Deoghar, India
Academic background
| Since June 2017 | PhD Student, Chair of Thermal Process Engineering, Member of the Research Training Group 1554 "Micro-Macro Interactions in Structured Media and Particle Systems" |
| October 2014 - May 2017 | MSc. Chemical and Energy Engineering, Otto-von-Guericke-University, Magdeburg, Germany Thesis: Influence of drag models on CFD-DEM simulations of a polydisperse fluidized bed. |
| September 2009 - June 2013 | B.E. Mechanical Engineering, J.S.S Academy of Technical Education, Bangalore, India Thesis: Development of fluid based stabilizing and leveling system. |
Research Project
Many branches of science and engineering deals with the particulate processes such as crystallization, agglomeration, polymerization, aerosol formation etc. Spray fluidized bed agglomeration is one of this and is widely used in industries because the product formed by the spray fluidized bed have suitable properties such as excellent solubility and wetting behavior, good flowing behavior and low dust content. Spray fluidized bed agglomeration enhances the size of the particles by providing compact design of the apparatus, high heat and mass transfer rate, intensive mixing and simultaneous wetting and drying of the particles. Various application of the process can be seen in the fields of medicines, fertilizers and other food industries.
Agglomeration is the process in which the particles are joined together in the random way leading to the change in the physical properties like size, shape, density or porosity. Monte Carlo method is a stochastic process model which is used to describe the growth of agglomerate in a spray fluidized bed agglomeration. This methodology overcomes the difficulties of solving multivariate population balance equations and includes continuous addition of binder and drying. Due to the discrete nature of this approach, various micro-mechanisms can be individually analyzed. The aim of this project is to incorporate agglomerate structure in this stochastic model.