  This paper shows the implementation of reinforcement learning (RL) in
commercial flowsheet simulator software (Aspen Plus V12) for designing and
optimising a distillation sequence. The aim of the SAC agent was to separate a
hydrocarbon mixture in its individual components by utilising distillation.
While doing so it tries to maximise the profit produced by the distillation
sequence. All actions of the agent were set by the SAC agent in Python and
communicated in Aspen Plus via an API. Here the distillation column was
simulated by use of the build-in RADFRAC column. With this a connection was
established for data transfer between Python and Aspen and the agent succeeded
to show learning behaviour, while increasing profit. Although results were
generated, the use of Aspen was slow (190 hours) and Aspen was found unsuitable
for parallelisation. This makes that Aspen is incompatible for solving RL
problems. Code and thesis are available at https://github.com/lollcat/Aspen-RL
