Artificial Neural Network Modeling of Thermo-catalytic Methane Decomposition for Hydrogen Production

Predictive modeling of membrane reactor efficiency using advanced artificial neural networks for green hydrogen production

The imperative to decarbonize the energy sector has prompted substantial advancements in clean electricity generation, with hydrogen emerging as a promising low-carbon energy carrier. While hydrogen synthesis from renewable sources is crucial, challenges persist, necessitating innovative approaches for efficient and sustainable production. This study leverages diverse artificial neural network (ANN) models to assess and predict system efficiency based on key operational variables in membrane reactor systems. The multilayered perceptron (MLP) and radial basis function (RBF) methodologies are employed, with the MLP models optimized across twelve training algorithms and eight activation functions, exploring up to three hidden layers with variable neuron counts. The MLP model, utilizing the Levenberg-Marquard training algorithm and Tangent-Sigmoid activation function, achieved a high correlation coefficient (R2) of 0.9975 for training and 0.9962 for testing, and a mean squared error (MSE) of 0.00425 for training and 0.23951 for testing, indicating precise and accurate efficiency predictions. The Log-Sigmoid activation function also performed well, with R² values of 0.9971 (training) and 0.9961 (testing), and MSE values of 0.004086 (training) and 0.17694 (testing). Optimization of the RBF network identified the best performance with a spread parameter of 1 and 35 neurons, although the MLP model demonstrated superior accuracy and reduced computational time. Statistical analysis, encompassing correlation coefficient, mean squared error, Root Mean Squared error, absolute average deviation, absolute average relative deviation, and runtime, confirms the network's consistent and accurate estimation of system efficiency across various input variables. The study highlights that applying tansig and logsig activation functions, configured with neuron counts of 20, 17, 6 and 23, 20, 2 at the first, second and third hidden layers, respectively, offers enhanced accuracy and reliability. The MLP model's high performance underscores its potential to identify optimal conditions for H2 generation based on system efficiency, thereby advancing membrane reactor technology for hydrogen production.

Thermocatalytic Decomposition of Methane: A Review on Carbon-Based Catalysts

The global initiatives on sustainable and green energy resources as well as large methane reserves have encouraged more research to convert methane to hydrogen. Catalytic decomposition of methane (CDM) is one optimistic route to generate clean hydrogen and value-added carbon without the emission of harmful greenhouse gases, typically known as blue hydrogen. This Review begins with an attempt to understand fundamentals of a CDM process in terms of thermodynamics and the prerequisite characteristics of the catalyst materials. In-depth understanding of rate-determining steps of the heterogeneous catalytic reaction taking place over the catalyst surfaces is crucial for the development of novel catalysts and process conditions for a successful CDM process. The design of state-of-the-art catalysts through both computational and experimental optimizations is the need of hour, as it largely governs the economy of the process. Recent mono- and bimetallic supported and unsupported materials used in CDM process have been highlighted and classified based on their performances under specific reaction conditions, with an understanding of their advantages and limitations. Metal oxides and zeolites have shown interesting performance as support materials for Fe- and Ni-based catalysts, especially in the presence of promoters, by developing strong metal-support interactions or by enhancing the carbon diffusion rates. Carbonaceous catalysts exhibit lower conversions without metal active species and largely result in the formation of amorphous carbon. However, the stability of carbon catalysts is better than that of metal oxides at higher temperatures, and the overall performance depends on the operating conditions, catalyst properties, and reactor configurations. Although efforts to summarize the state-of-art have been reported in literature, they lack systematic analysis on the development of stable and commercially appealing CDM technology. In this work, carbon catalysts are seen as promising futuristic pathways for sustained H2 production and high yields of value-added carbon nanomaterials. The influence of the carbon source, particle size, surface area, and active sites on the activity of carbon materials as catalysts and support templates has been demonstrated. Additionally, the catalyst deactivation process has been discussed, and different regeneration techniques have been evaluated. Recent studies on theoretical models towards better performance have been summarized, and future prospects for novel CDM catalyst development have been recommended.

Catalytic Methane Decomposition to Carbon Nanostructures and COx-Free Hydrogen: A Mini-Review

Catalytic methane decomposition (CMD) is a highly promising approach for the rational production of relatively CO_x-free hydrogen and carbon nanostructures, which are both important in multidisciplinary catalytic applications, electronics, fuel cells, etc. Research on CMD has been expanding in recent years with more than 2000 studies in the last five years alone. It is therefore a daunting task to provide a timely update on recent advances in the CMD process, related catalysis, kinetics, and reaction products. This mini-review emphasizes recent studies on the CMD process investigating self-standing/supported metal-based catalysts (e.g., Fe, Ni, Co, and Cu), metal oxide supports (e.g., SiO₂, Al₂O₃, and TiO₂), and carbon-based catalysts (e.g., carbon blacks, carbon nanotubes, and activated carbons) alongside their parameters supported with various examples, schematics, and comparison tables. In addition, the review examines the effect of a catalyst’s shape and composition on CMD activity, stability, and products. It also attempts to bridge the gap between research and practical utilization of the CMD process and its future prospects.