optimization strategies are essential in process design, operations and control. This study provides a concise overview of recent advances in process optimization, with a particular focus on nonlinear programming. Here, derivative-free, SQP, reduced gradient, and interior point methods are described. Moreover, state of the art problem formulations and modeling environments are described, and contrasted with popular process simulation tools. Finally research directions are identified for future trends in multi-scale optimization within large-scale equation-oriented optimization environments.