Constraint handling in stochastic optimization algorithms for natural gas liquefaction processes

Liquefaction of natural gas requires energy intensive refrigeration. A fair comparison of different process concepts and energy efficient designs requires some use of optimization. In near optimal designs, the driving forces in heat transfer are small. Thus, rigorous thermodynamics providing accurate and reliable temperature profiles must be applied for the solution to have a practical value. Owing to the characteristics of the process and the thermodynamics, the optimization problem is non-convex. Furthermore, the optimal solution is expected to be located close to the boundary of the feasible region, suggesting the importance of constraint handling. In this paper, a single-mixed refrigerant process (PRICO®) has been optimized using adaptive simulated annealing. A constraint handling method utilizing process characteristics is proposed and compared with static penalty function formulations. The results indicate the importance of constraint handling, and the best solution found exceeds previously published results. Copyright © 2013 Elsevier B.V. All rights reserved