Machine Learning-Assisted Surrogate Modeling with Multi-Objective Optimization and Decision-Making of a Steam Methane Reforming Reactor

• URL: http://arxiv.org/abs/2507.07641v1
• Date: Thu, 10 Jul 2025 11:10:16 GMT
• Title: Machine Learning-Assisted Surrogate Modeling with Multi-Objective Optimization and Decision-Making of a Steam Methane Reforming Reactor
• Authors: Seyed Reza Nabavi, Zonglin Guo, Zhiyuan Wang,
• Abstract summary: This study presents an integrated modeling and optimization framework for a steam methane reforming (SMR) reactor.<n>A one-dimensional fixed-bed reactor model accounting for internal mass transfer resistance was employed to simulate reactor performance.<n>To reduce the high computational cost of the mathematical model, a hybrid ANN surrogate was constructed, achieving a 93.8% reduction in average simulation time.
• Score: 10.704938459679978
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This study presents an integrated modeling and optimization framework for a steam methane reforming (SMR) reactor, combining a mathematical model, artificial neural network (ANN)-based hybrid modeling, advanced multi-objective optimization (MOO) and multi-criteria decision-making (MCDM) techniques. A one-dimensional fixed-bed reactor model accounting for internal mass transfer resistance was employed to simulate reactor performance. To reduce the high computational cost of the mathematical model, a hybrid ANN surrogate was constructed, achieving a 93.8% reduction in average simulation time while maintaining high predictive accuracy. The hybrid model was then embedded into three MOO scenarios using the non-dominated sorting genetic algorithm II (NSGA-II) solver: 1) maximizing methane conversion and hydrogen output; 2) maximizing hydrogen output while minimizing carbon dioxide emissions; and 3) a combined three-objective case. The optimal trade-off solutions were further ranked and selected using two MCDM methods: technique for order of preference by similarity to ideal solution (TOPSIS) and simplified preference ranking on the basis of ideal-average distance (sPROBID). Optimal results include a methane conversion of 0.863 with 4.556 mol/s hydrogen output in the first case, and 0.988 methane conversion with 3.335 mol/s hydrogen and 0.781 mol/s carbon dioxide in the third. This comprehensive methodology offers a scalable and effective strategy for optimizing complex catalytic reactor systems with multiple, often conflicting, objectives.

Related papers

• Simultaneous Optimization of Efficiency and Degradation in Tunable HTL-Free Perovskite Solar Cells with MWCNT-Integrated Back Contact Using a Machine Learning-Derived Polynomial Regressor [0.8739101659113155]
  Perovskite solar cells (PSCs) without a hole transport layer (HTL) offer a cost-effective and stable alternative to conventional architectures.<n>This study presents a machine learning (ML)-driven framework to optimize the efficiency and stability of HTL-free PSCs.
  arXiv  Detail & Related papers  (2025-05-24T13:37:48Z)
• Flow-GRPO: Training Flow Matching Models via Online RL [75.70017261794422]
  We propose Flow-GRPO, the first method integrating online reinforcement learning (RL) into flow matching models.<n>Our approach uses two key strategies: (1) an ODE-to-SDE conversion that transforms a deterministic Ordinary Equation (ODE) into an equivalent Differential Equation (SDE) that matches the original model's marginal distribution at all timesteps; and (2) a Denoising Reduction strategy that reduces training denoising steps while retaining the original inference timestep number.
  arXiv  Detail & Related papers  (2025-05-08T17:58:45Z)
• Bridging Equilibrium and Kinetics Prediction with a Data-Weighted Neural Network Model of Methane Steam Reforming [0.0]
  We show a surrogate model capable of unifying both kinetic and equilibrium regimes.<n>An artificial neural network trained on a comprehensive dataset that includes experimental data from kinetic and equilibrium experiments.<n>The network's ability to provide continuous derivatives of its predictions makes it particularly useful for process modeling and optimization.
  arXiv  Detail & Related papers  (2025-04-15T14:55:06Z)
• PearSAN: A Machine Learning Method for Inverse Design using Pearson Correlated Surrogate Annealing [66.27103948750306]
  PearSAN is a machine learning-assisted optimization algorithm applicable to inverse design problems with large design spaces.<n>It uses a Pearson correlated surrogate model to predict the figure of merit of the true design metric.<n>It achieves a state-of-the-art maximum design efficiency of 97%, and is at least an order of magnitude faster than previous methods.
  arXiv  Detail & Related papers  (2024-12-26T17:02:19Z)
• Synergistic Development of Perovskite Memristors and Algorithms for Robust Analog Computing [53.77822620185878]
  We propose a synergistic methodology to concurrently optimize perovskite memristor fabrication and develop robust analog DNNs.<n>We develop "BayesMulti", a training strategy utilizing BO-guided noise injection to improve the resistance of analog DNNs to memristor imperfections.<n>Our integrated approach enables use of analog computing in much deeper and wider networks, achieving up to 100-fold improvements.
  arXiv  Detail & Related papers  (2024-12-03T19:20:08Z)
• EM Distillation for One-step Diffusion Models [65.57766773137068]
  We propose a maximum likelihood-based approach that distills a diffusion model to a one-step generator model with minimal loss of quality.<n>We develop a reparametrized sampling scheme and a noise cancellation technique that together stabilizes the distillation process.
  arXiv  Detail & Related papers  (2024-05-27T05:55:22Z)
• A Deep Unrolling Model with Hybrid Optimization Structure for Hyperspectral Image Deconvolution [50.13564338607482]
  We propose a novel optimization framework for the hyperspectral deconvolution problem, called DeepMix.<n>It consists of three distinct modules, namely, a data consistency module, a module that enforces the effect of the handcrafted regularizers, and a denoising module.<n>This work proposes a context aware denoising module designed to sustain the advancements achieved by the cooperative efforts of the other modules.
  arXiv  Detail & Related papers  (2023-06-10T08:25:16Z)
• Using evolutionary machine learning to characterize and optimize co-pyrolysis of biomass feedstocks and polymeric wastes [14.894507238371768]
  Co-pyrolysis is a promising strategy for improving the quantity and quality parameters of the resulting liquid fuel. Machine learning (ML) provides capabilities to cope with such issues by leveraging on existing data. This work aims to introduce an evolutionary ML approach to quantify the (by)products of the biomass-polymer co-pyrolysis process.
  arXiv  Detail & Related papers  (2023-05-24T19:59:21Z)
• Machine-Learning-Optimized Perovskite Nanoplatelet Synthesis [55.41644538483948]
  We develop an algorithm to improve the quality of CsPbBr3 nanoplatelets (NPLs) using only 200 total syntheses. The algorithm can predict the resulting PL emission maxima of the NPL dispersions based on the precursor ratios.
  arXiv  Detail & Related papers  (2022-10-18T11:54:11Z)
• Toward Development of Machine Learned Techniques for Production of Compact Kinetic Models [0.0]
  Chemical kinetic models are an essential component in the development and optimisation of combustion devices. We present a novel automated compute intensification methodology to produce overly-reduced and optimised chemical kinetic models.
  arXiv  Detail & Related papers  (2022-02-16T12:31:24Z)
• Quaternion Factorization Machines: A Lightweight Solution to Intricate Feature Interaction Modelling [76.89779231460193]
  factorization machine (FM) is capable of automatically learning high-order interactions among features to make predictions without the need for manual feature engineering. We propose the quaternion factorization machine (QFM) and quaternion neural factorization machine (QNFM) for sparse predictive analytics.
  arXiv  Detail & Related papers  (2021-04-05T00:02:36Z)
• Toward Machine Learned Highly Reduce Kinetic Models For Methane/Air Combustion [0.0]
  Kinetic models are used to test the effect of operating conditions, fuel composition and combustor design compared to physical experiments. We propose a novel data orientated three-step methodology to produce compact models that replicate a target set of detailed model properties to a high fidelity. The strategy is demonstrated through the production of a 19 species and a 15 species compact model for methane/air combustion.
  arXiv  Detail & Related papers  (2021-03-15T13:29:08Z)

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.