Rigorous Design of Complex Distillation Columns Using Process Simulators and the Particle Swarm Optimization Algorithm

Abstract

We present a derivative-free optimization algorithm coupled with a chemical process simulator for the optimal design of individual and complex distillation processes using a rigorous tray-by-tray model. The optimal synthesis of complex distillation columns is a non-trivial problem due to the discrete nature of the tray-by-tray column model, and also because of the high degree of non-linearity and non-convexity of the underlying MESH equations

Optimization-Based Design of Crude Oil Distillation Units Using Rigorous Simulation Models

“…The optimization methods have been applied to design several chemical processess (other than crude oil distillation units) [20][21][22][23][24] .…”

Design of dividing wall columns for butanol recovery in a thermochemical biomass to butanol process

“…This is due to the presence of discrete variables such as feed location, and number of trays in different column sections, as well as the nonconvexity of the MESH equations. Javaloyes-Antón et al [25], reviewed the application of MINLP formulations for the solutions of complex distillation columns (including DWCs), and concluded that based on the high nonlinearities of these formulations, as well as sophisticated initialization techniques needed to obtain feasible solutions (only local optima are guaranteed as the solutions are highly dependent on the initialization points), these methods are complex and suited only for those skilled enough to adapt them for their own requirements.…”

“…However, they are not able to guarantee that the solutions found are optimal. Though derivative-based search methods can theoretically offer local optimality guarantees, they are not easily amenable to highly complex real world problems and might be unable to find solutions which are as good as those obtained by derivative-free algorithms [25,26]. Examples of these derivative-free algorithms include genetic algorithms (GA), simulated annealing, particle swarm optimization (PSO) among others.…”

“…As a result of these advantages of derivative-free optimization algorithms over classical derivative search methods, the use of derivative-free optimization algorithms coupled with process simulators has found wide use in the literature for optimizing complicated process systems [25]. Among many examples in literature, Leboreiro and Acevedo [26] successfully demonstrated the use of a modified GA interconnected with the Aspen Plus process simulator to optimize complex distillation sequences including a Petlyuk column.…”

“…In that work PSO was used to optimize the controller tuning parameters of the system. The PSO algorithm coupled with Aspen Hysys was used by Javaloyes-Antón et al [25] for the optimal design of conventional and complex distillation processes. In their work, the PSO algorithm implemented in MATLAB handles all the discrete variables such as the feed location and the number Vapor flow at the bottom of the column (kmol/hr) V T Vapor flow in the top section of the column (kmol/hr) z Composition of trays in column sections, while continuous variables (reflux ratio, boilup ratio, etc.)…”