Chemical Engineering Scientific Computing

CHEME 375 covers Excel, Python, and ASPEN skills needed for chemical engineering applications. Applied scientific computing and numerical methods are covered. Taken in Sp21 with Professor Jim Pfaendtner.

Optimization

Topic	ChemE Applications	Python Skills	Jupyter Notebook	Online
Curve fitting	Fitting experimental data to functional forms (e.g. Clausius-Clapeyron equation)	scipy.optimize.curve_fit() scipy.optimize.minimize()	ipynb	html
Solving linear systems	Balancing chemical equations	scipy.linalg.inv() scipy.linalg.solve()	ipynb	html
Solving nonlinear systems	Solving binary vapor liquid equilibrium (VLE) problems	scipy.optimize.fsolve()	ipynb	html

Differential Equations

Topic	ChemE Applications	Python Skills	Jupyter Notebook	Online
Solving systems of ODEs	Chemical kinetics of one reaction and reaction networks	Euler’s method scipy.integrate.solve_ivp()	ipynb	html
Solving time-independent PDEs	Time-independent 2D heat transfer of thin metal slab	scipy.linalg.solve()	ipynb	html
Solving time-dependent PDEs	Time-dependent 1D heat transfer of thin rod	Finite difference method	ipynb	html

Applications

ChemE Applications	Python Skills	Jupyter Notebook	Online
Solving recycle streams	scipy.linalg.solve()	ipynb	html
Constructing VLE diagram using Raoult’s law	scipy.optimize.fsolve()	ipynb	html
Determining equilibrium compositions using equation of state (EOS) methods	numpy.polynomial .polynomial.polyroots()	ipynb	html
Constructing VLE diagram using equation of state (EOS) methods	numpy.polynomial .polynomial.polyroots()	ipynb	html
Determining Antoine’s coefficients	scipy.optimize.fsolve()	ipynb	html