(91a) Lifetime Value of Simulation: From Point Improvement to Comprehensive Optimization

Chemicals and materials industry is undergoing transition due to two reasons: responding to the energy transition & sustainability requirements and demand for new innovative products to stay competitive.

Designing equipment and processes to satisfy these multiple objectives and constraints by modifying a large number of design parameters is a challenging task since the relationships of these multiple inputs and outputs can be highly non-linear. By analyzing data on multiple variables, engineers can identify the most sensitive factors affecting energy efficiency and performance and select a design or operating condition that meets the process or product requirements. Traditional methods, such as parametric studies can be too time consuming or fraught with computational limitations.

Rigorous optimization algorithms lend themselves well to being applied to solve such multi-input, multi-output problems. An optimization algorithm iteratively runs evaluations and understands the sensitivities between all inputs and combination of inputs to all outputs. The algorithm successively guesses a better design until a converged design is reached that optimizes all the required objectives while satisfying all constraints. This study details the implementation of this optimization methodology to design optimal processes for example cases. We will then introduce how the outcomes can be captured in a way that it can be used throughout the lifetime of the operation.

References

1. Optimization of a divided wall distillation column
2. Optimization of chemical reaction mechanism with HEEDS
3. Optimizing pump designs for process applications
4. CFD based rigorous optimization in the chemical industry