(384a) Multi-Objective Optimization of Crude Distillation Unit, Vacuum Distillation Unit and Hydrocracking Reactor in Diesel Production Process to Maximize Profitability and Reduce Green House Gases Using Nspso and Nsga-II Bioinspired Techniques

Multi-objective optimization of crude distillation
unit, vacuum distillation unit and hydrocracking reactor in Diesel production
process to maximize profitability and reduce greenhouse gases using NSPSO and
NSGAII bioinspired techniques.

Johana ORJUELA, ¹Camilo MONROY-PEÑA¹,

1 Universidad Nacional de Colombia Universidad de
Cundinamarca, Colombia

The present work compares the performance of two bioinspired algorithms:
Nondominated Sorting Particle Swarm Optimizer (NSPSO)
and Nondominated Sorting Genetic Algorithm II
(NSGAII), as tools for solving a multi-objective optimization problem, applied
to diesel production process, including Crude Distillation Unit (CDU), Vacuum
Distillation Unit (VDU) and hydrocracking reactor (HC). Aspen Hysys ® simulation software is used as a tool for modeling
and simulation of the process and Matlab as a tool
for optimization and programming algorithms (Figure 1). Before the multi-objective
optimization, the sensitivity analysis was performed in order to identify the independent
variables that have the greatest impact on the objective functions (maximize
the profitability of the process and minimize the emission of greenhouse
gases). The ten variables that had the greatest impact on the objective
functions were selected for optimization.

The results show that the NSPSO algorithm finds the best solution based
on the optimal Pareto front obtained, allowing to select the different
operating conditions of the three coupled units that maximize the profitability
of the process and minimize the emission of greenhouse gases (GHG), maintaining
the specifications of products, light naphtha, heavy naphtha, kerosene, gas
oils (LGO, LVGO and HVGO) as well as diesel. Through the implementation of the
multi-objective optimization was possible to increase the profitability of the process
up to 20% and reduce the emission of GHG up to 30% compared to the industrial
point of operation (Figure 2).