Market-driven profit optimization for tabular chemical reactors with genetic algorithms

With the uncertainty in the prices of feedstock, energy and finished products, the profit optimization plays a critical role in making a chemical production enterprise more dynamic and flexible to adapt the changes in global marketplace. This paper presents the development of profit optimization solution with an integration of profit model and model-based genetic algorithm (GA) optimization, and its industrial case study on an ethanolamine (EA) production line. The proposed methodology and solution may also be applied to other chemical manufacturing enterprises.