The conversion of syngas derived
from natural gas into methanol
has been considered a relatively clean and environmentally friendly
process. However, carbon dioxide is emitted as a result of using natural
gas as fuel in the reformer furnace combustion zone to supply the
heat required for endothermic reforming reactions. Carbon dioxide
is a primary greenhouse gas emitted as flue gas from the reformer
and has been contributing to global warming over the past few decades.
Thereby, environmental regulations for new and existing industrial
facilities have been enforced to mitigate the adverse effects of carbon
dioxide emission. In this research, multiobjective optimization is
applied for the operating conditions of the methanol synthesis loop
via a multistage fixed bed adiabatic reactor system with an additional
interstage CO2 quenching stream to maximize methanol production
while reducing CO2 emissions. The model prediction for
the methanol synthesis loop at steady state showed good agreement
against data from an existing commercial plant. Then, the process
flowsheet was developed and fully integrated with the Genetic Algorithms
Toolbox that generated a set of optimal operating conditions with
respect to upper and lower limits and several constraints. The results
showed methanol production was improved by injecting shots of carbon
dioxide recovered from the reformer at various reactor locations.