Course Description: Theory, methods, and applications of molecular simulations. Elementary statistical mechanics. Molecular modeling. Basic Monte Carlo and molecular dynamics techniques and ensemble averaging. Evaluation of free energies, phase equilibria, interfacial properties, and transport and rate coefficients. Coarse-graining approaches and mesoscale models. Applications to simple and complex fluids and solids. Modeling and predicting the properties of real materials.
Course Description: Introduction to various mass transfer operations in Chemical Engineering. Diffusional mass transfer. Mass transfer coefficients, Vapour-Liquid equilibria. Steady and unsteady state operations. Batch and continuous processes. Absorption, distillation, crystallization, solvent extraction, leaching, adsorption, humidification and drying. Filtration and membrane separation. Process design and performance evaluation of mass transfer units.
Course Description: Introduction to optimization and its scope in chemical processes. Single variable and multi variable optimization with and without constraints. Linear programming, graphical, algebraic and Simplex methods. Duality. Numerical search methods. Elimination and interpolation methods. Direct search methods: Random search, grid search, Nelder-Mead Simplex. Indirect search methods: Steepest descent and conjugate gradient methods.
Course Description: Introduction to Units and Dimensions. Material balance with and without chemical reactions. Recycle, bypass, and purge. Vapor-liquid equilibrium: Bubble point, dew point, and phase envelop calculations. Energy balances with and without chemical reactions, psychometric calculations, adiabatic flame temperature. Numerical techniques and computer based calculations for solving material & energy balance equations.
Course Description: Fundamentals of process modeling and simulations. Analytical and numerical techniques. Macroscopic and microscopic mass, momentum and energy balances. Steady state and unsteady state modeling of chemical processes. Flow systems, separators, reactors and heat exchangers. Fundamentals of stochastic modeling. Empirical modeling techniques: Principal component analysis, partial least square and artificial neural network.