English abstract
The optimization approach has been essential in improving many chemical processes and
providing significant improvements to many difficult processes. It often falls under two
categories which are single-objective optimization and multi-objective optimization. These
methods involve a process-derived objective function that can be maximized or minimized.
Multi-objective optimization was considered in this research to optimize the biomethane
liquefaction process. Finding a trade-off between two or more objective functions that
might have an impact on one another is the central goal of this methodology. These
conflicting objectives result in trade-offs that lead to the Pareto front solution, which is a
set of feasible solutions.
Recently, the biomethane liquefaction process became an interesting process to look at in
a time where the demand for energy from all its sources is rising significantly. Liquified
biomethane (LBM) is one type of biogas or bioenergy source that will be highly needed.
The issue is that the liquefaction of biomethane gas is considered an energy-intensive
process. In this project, multi-objective optimization is used to find a trade-off among the
selected objective functions. In LBM optimization, specific energy consumption (SEC),
total energy of the system, heat transfer coefficient (UA), exergy efficiency, and exergy
destruction are the selected objective functions with the associated design variables. The
optimization has been achieved through a simulation stage in Aspen HYSYS v12.1 and
using MATLAB as an interface platform. The selected algorithm is NSGA-II, and a few
cases have been obtained with Pareto optimal fronts based on the requirements. The
obtained results indicated a significant enhancement in the values of the optimized
objective functions in comparison with the base-case values.
