Synthesis of separation processes with reinforcement learning

2022-11-03 · Stephan C. P. A. van Kalmthout, Laurence I. Midgley, Meik B. Franke

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

📄 PDF Abstract BibTeX arXiv:2211.04327

Code (1)

lollcat/aspen-rl 공식 구현 jax

Tasks

reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Methods 이 논문이 사용한 방법론

Global Average Pooling Global Average Pooling is a pooling operation designed to replace fully connected layers in classical CNNs. The idea is to generate one feature map for each corresponding…

Convolution A convolution is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the…

Average Pooling 설명 없음

Dilated Convolution 설명 없음

1x1 Convolution A 1 x 1 Convolution is a convolution with some special properties in that it can be used for dimensionality reduction,…

SAC 설명 없음

Deep Reinforcement Learning for Process Synthesis

2020-09-23 · Laurence Illing Midgley

This paper demonstrates the application of reinforcement learning (RL) to process synthesis by presenting Distillation Gym, a set of RL environments in which an RL agent is tasked with designing a distillation train, giv…

Deep Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Flowsheet synthesis through hierarchical reinforcement learning and graph neural networks

2022-07-25 · Laura Stops, Roel Leenhouts, Qinghe Gao, Artur M. Schweidtmann

Process synthesis experiences a disruptive transformation accelerated by digitization and artificial intelligence. We propose a reinforcement learning algorithm for chemical process design based on a state-of-the-art act…

Chemical Process Decision Making Hierarchical Reinforcement Learning reinforcement-learning+2

Style Separation and Synthesis via Generative Adversarial Networks

2018-11-07 · Rui Zhang, Sheng Tang, Yu Li, Junbo Guo 외

Style synthesis attracts great interests recently, while few works focus on its dual problem "style separation". In this paper, we propose the Style Separation and Synthesis Generative Adversarial Network (S3-GAN) to sim…

Generative Adversarial Network Object

Test-Time Regret Minimization in Meta Reinforcement Learning

2024-06-04 · Mirco Mutti, Aviv Tamar

Meta reinforcement learning sets a distribution over a set of tasks on which the agent can train at will, then is asked to learn an optimal policy for any test task efficiently. In this paper, we consider a finite set of…

Meta Reinforcement Learning reinforcement-learning Reinforcement Learning

Automated Synthesis of Steady-State Continuous Processes using Reinforcement Learning

2021-01-12 · Quirin Göttl, Dominik G. Grimm, Jakob Burger

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 …