Efficient Nonlinear Programming Algorithms for Chemical Process Control and Operations

Nonlinear programming (NLP) has been a key enabling tool for model-based decision-making in the chemical industry for over 50 years. Optimization is frequently applied in numerous areas of chemical engineering including the development of process models from experimental data, design of process flowsheets and equipment, planning and scheduling of chemical process operations, and the analysis of chemical processes under uncertainty and adverse conditions. These off-line tasks frequently require the solution of NLPs formulated with detailed, laregscale process models.