A new scheme for process simulation by optimization: distillation as an example

Hoffmann, Anna; Bortz, Michael; Burger, J.; Hasse, Hans; Küfer, Karl‐Heinz

doi:10.1016/b978-0-444-63428-3.50039-4

Cited by 3 publications

(1 citation statement)

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“…Contrary, recycles are rather challenges for the simulation environment. Processes with recycles remain hard to simulate in automated fashion, although novel simulation methods let us be optimistic [32,33]. Besides robustness, another challenge arises from recycles.…”

Section: Discussionmentioning

confidence: 99%

Automated synthesis of steady-state continuous processes using reinforcement learning

Göttl

Grimm

Burger

2021

Front. Chem. Sci. Eng.

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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 or 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 build 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.

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Section: Discussionmentioning

confidence: 99%