  Automated flowsheet synthesis is an important field in computer-aided process
engineering. The present work demonstrates how reinforcement learning can be
used for automated flowsheet synthesis without any heuristics of prior
knowledge of conceptual design. The environment consists of a steady-state
flowsheet simulator that contains all physical knowledge. An agent is trained
to take discrete actions and sequentially built up flowsheets that solve a
given process problem. A novel method named SynGameZero is developed to ensure
good exploration schemes in the complex problem. Therein, flowsheet synthesis
is modelled as a game of two competing players. The agent plays this game
against itself during training and consists of an artificial neural network and
a tree search for forward planning. The method is applied successfully to a
reaction-distillation process in a quaternary system.
