Flowsheet synthesis through hierarchical reinforcement learning and graph neural networks

• URL: http://arxiv.org/abs/2207.12051v1
• Date: Mon, 25 Jul 2022 10:42:15 GMT
• Title: Flowsheet synthesis through hierarchical reinforcement learning and graph neural networks
• Authors: Laura Stops, Roel Leenhouts, Qinghe Gao, Artur M. Schweidtmann
• Abstract summary: We propose a reinforcement learning algorithm for chemical process design based on actor-critic logic. Our proposed algorithm represents chemical processes as graphs and uses graph convolutional neural networks to learn from process graphs.
• Score: 0.4588028371034406
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 actor-critic logic. Our proposed algorithm represents chemical processes as graphs and uses graph convolutional neural networks to learn from process graphs. In particular, the graph neural networks are implemented within the agent architecture to process the states and make decisions. Moreover, we implement a hierarchical and hybrid decision-making process to generate flowsheets, where unit operations are placed iteratively as discrete decisions and corresponding design variables are selected as continuous decisions. We demonstrate the potential of our method to design economically viable flowsheets in an illustrative case study comprising equilibrium reactions, azeotropic separation, and recycles. The results show quick learning in discrete, continuous, and hybrid action spaces. Due to the flexible architecture of the proposed reinforcement learning agent, the method is predestined to include large action-state spaces and an interface to process simulators in future research.

Related papers

• Learning reduced-order Quadratic-Linear models in Process Engineering using Operator Inference [7.471096682644106]
  This work addresses the challenge of efficiently modeling dynamical systems in process engineering. We use reduced-order model learning, specifically operator inference. The application in our study is carbon dioxide methanation, an important reaction within the Power-to-X framework.
  arXiv  Detail & Related papers  (2024-02-27T17:21:10Z)
• Amortized Network Intervention to Steer the Excitatory Point Processes [8.15558505134853]
  Excitatory point processes (i.e., event flows) occurring over dynamic graphs provide a fine-grained model to capture how discrete events may spread over time and space. How to effectively steer the event flows by modifying the dynamic graph structures presents an interesting problem, motivated by curbing the spread of infectious diseases. We design an Amortized Network Interventions framework, allowing for the pooling of optimal policies from history and other contexts.
  arXiv  Detail & Related papers  (2023-10-06T11:17:28Z)
• Understanding Activation Patterns in Artificial Neural Networks by Exploring Stochastic Processes [0.0]
  We propose utilizing the framework of processes, which has been underutilized thus far. We focus solely on activation frequency, leveraging neuroscience techniques used for real neuron spike trains. We derive parameters describing activation patterns in each network, revealing consistent differences across architectures and training sets.
  arXiv  Detail & Related papers  (2023-08-01T22:12:30Z)
• Online Network Source Optimization with Graph-Kernel MAB [62.6067511147939]
  We propose Grab-UCB, a graph- kernel multi-arms bandit algorithm to learn online the optimal source placement in large scale networks. We describe the network processes with an adaptive graph dictionary model, which typically leads to sparse spectral representations. We derive the performance guarantees that depend on network parameters, which further influence the learning curve of the sequential decision strategy.
  arXiv  Detail & Related papers  (2023-07-07T15:03:42Z)
• Contrastive-Signal-Dependent Plasticity: Self-Supervised Learning in Spiking Neural Circuits [61.94533459151743]
  This work addresses the challenge of designing neurobiologically-motivated schemes for adjusting the synapses of spiking networks. Our experimental simulations demonstrate a consistent advantage over other biologically-plausible approaches when training recurrent spiking networks.
  arXiv  Detail & Related papers  (2023-03-30T02:40:28Z)
• The Predictive Forward-Forward Algorithm [79.07468367923619]
  We propose the predictive forward-forward (PFF) algorithm for conducting credit assignment in neural systems. We design a novel, dynamic recurrent neural system that learns a directed generative circuit jointly and simultaneously with a representation circuit. PFF efficiently learns to propagate learning signals and updates synapses with forward passes only.
  arXiv  Detail & Related papers  (2023-01-04T05:34:48Z)
• Simulation Paths for Quantum Circuit Simulation with Decision Diagrams [72.03286471602073]
  We study the importance of the path that is chosen when simulating quantum circuits using decision diagrams. We propose an open-source framework that allows to investigate dedicated simulation paths.
  arXiv  Detail & Related papers  (2022-03-01T19:00:11Z)
• Dynamically Grown Generative Adversarial Networks [111.43128389995341]
  We propose a method to dynamically grow a GAN during training, optimizing the network architecture and its parameters together with automation. The method embeds architecture search techniques as an interleaving step with gradient-based training to periodically seek the optimal architecture-growing strategy for the generator and discriminator.
  arXiv  Detail & Related papers  (2021-06-16T01:25:51Z)
• Bottom-up and top-down approaches for the design of neuromorphic processing systems: Tradeoffs and synergies between natural and artificial intelligence [3.874729481138221]
  Moore's law has driven exponential computing power expectations, its nearing end calls for new avenues for improving the overall system performance. One of these avenues is the exploration of alternative brain-inspired computing architectures that aim at achieving the flexibility and computational efficiency of biological neural processing systems. We provide a comprehensive overview of the field, highlighting the different levels of granularity at which this paradigm shift is realized.
  arXiv  Detail & Related papers  (2021-06-02T16:51:45Z)
• Progressive Graph Convolutional Networks for Semi-Supervised Node Classification [97.14064057840089]
  Graph convolutional networks have been successful in addressing graph-based tasks such as semi-supervised node classification. We propose a method to automatically build compact and task-specific graph convolutional networks.
  arXiv  Detail & Related papers  (2020-03-27T08:32:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.