In this talk, we will discuss the solution methods for two classes of mixed-integer nonlinear programming (MINLP) problems that usually occur in the design and operations of process systems, namely the mixed-integer linear fractional programs and the separable concave minimization problems. To solve these problems effectively, we proposed global optimization algorithms that rely on solving only a sequence of mixed-integer linear programming (MILP) subproblems, without the need of handling any nonlinear program. We will prove the optimality of these algorithms and discuss their convergence properties. Computational results will be presented to show that orders of magnitude reduction in CPU times can be achieved when using these algorithms compared to solving the large-scale problems with commercial MINLP solvers. Real world applications of these methods in the chemical, pharmaceutical, industrial gas, paper and pulp industries will also be addressed.