Renewable natural gas | EPFL Graph Search

Renewable natural gas (RNG), also known as biomethane or sustainable natural gas (SNG), is a biogas which has been upgraded to a quality similar to fossil natural gas and has a methane concentration of 90% or greater. By removing CO2 and other impurities from biogas, and increasing the concentration of methane to a level similar to fossil natural gas, it becomes possible to distribute RNG to customers via existing gas pipeline networks. RNG can be used in existing appliances, including vehicles with natural gas burning engines (natural gas vehicles). Renewable natural gas is a subset of synthetic natural gas or substitute natural gas (SNG). The most common way of collecting biogas with which to produce biomethane is through the process of anaerobic digestion. Multiple ways of methanizing carbon dioxide/monoxide and hydrogen also exist, including biomethanation, the Sabatier process and a new electrochemical process pioneered in the United States currently undergoing trials. Advan

Supported ruthenium nanoparticles for supercritical water gasification - synthesis, activity and stability

Christopher Marc Hunston

Supercritical water gasification (SCWG) is a promising and versatile technology for the conversion of a variety of wet biomass streams into renewable natural gas. In this work, the focus was set on methane production with the help of an active and stable methanation catalyst. The supercritical water (SCW) environment can lead to rapid catalyst deactivation through different mechanisms, namely poisoning, coking, sintering or leaching. Although addressed, it remains challenging to disentangle them in SCWG. The goal of this thesis is to get a better understanding of the aforementioned deactivations pathways by trying to study them individually, in order to tailor an active and robust SCWG catalyst.The intrinsic gasification activity of carbon supports exhibiting different properties was assessed by feeding glycerol at 400°C and 30MPa. The negative effect of micropores was shown by significant coke deposition and surface area loss around 90%, compared to mesoporous carbons that only lost 20%. Carbon nanofibers (CNF) proved to be an interesting support showing very good stability and inertness. The synthesis of 5%Ru catalysts on selected supports confirmed these trends. Highly microporous Ru/C catalysts were less stable than the ones supported on carbon frameworks of more open pore structure. Ru/CNF seemed to be a promising SCWG catalyst, with an enhanced resistance towards coke formation. The completely open pore structure of CNF was found to be a crucial parameter.Ruthenium loss was investigated by measuring the its content in the process waters with ICP-MS. Ru was successfully quantified at levels close to thermodynamic models for Ru/AC and Ru/CNF of different loadings (0.01-0.2 µg L-1). Furthermore, the process parameters had no effect on the quantified Ru, showing that the steady-state loss represents a thermodynamically-driven leaching. Fluctuations in feed rate and pressure damaged the catalyst through friction and led higher Ru losses, mainly through the release of carbon domains containing Ru. Metal oxides were compared to AC by synthesising 2%Ru catalysts on alpha-Al2O3, ZrO2 and TiO2. They yielded 20-40 times higher Ru loss rates than for 2%Ru/AC and a significantly lower activity, confirming once again the superiority of Ru/C as SCWG catalyst. Ru losses were similar on larger rigs during glycerol and sewage sludge gasification, with values around 0.14-0.29 µg gRu-1 h-1.With Ru/CNF proving to be an interesting SCWG catalyst, more in-depth studies were performed on this system. Catalysts of different loadings (1-30%) were successfullysynthesised yielding very small Ru NPs (0.9-2.2 nm) with an easy synthesis method. Ru/CNF catalysts exhibited high gasification activities and improved stability compared to Ru/AC. The initial activity correlated with the Ru dispersion. However, 1%Ru/CNF catalysts underwent rapid deactivation compared to 5%Ru/CNF, showing the importance of site density to avoid rapid deactivation. The effect of surface Ru atom density was shown with a optimum in turnover frequency in the range 0.4-0.7 atomRu,sfc nm-2. For the first time, a particle size effect and more importantly a surface atom density effect was shown during SCWG of glycerol. Coke deposition was also investigated and observed at very high space velocities. The coke deposits could partially be removed by extraction in a mix of solvents, opening a potential new route for the regeneration of Ru/C catalysts.

EPFL2021

Integrating Rate Based Models into a Multi-Objective Process Design & Optimisation Framework using Surrogate Models

Levent Sinan Teske

In the development of energy and chemical processes, the process engineers extensively apply computer aided methods to design & optimise these processes and corresponding process units. Such applications are multi-scale modelling and multi-objective optimisation methods. Multi-objective optimisation of super-structured process designs are expensive in CPU-time due to the high number of potential configurations and operation conditions to be calculated. Thus single process units are generally represented by simple models like equilibrium based (chemical or phase equilibrium) or specific short cut models. In the development of new processes, kinetic effects or mass transport limitations in certain process units may play an important role, especially in multiphase chemical reactors. Therefore, it is desirable to represent such process units by experimentally derived rate based models (i.e. reaction rates and mass transport rates) in the process flowsheet simulators used for the extensive multi-objective optimisation. This increases the trust engineers have in the results and allows enriching the process simulations with newest experimental findings. As most rate based models are iteratively solved, a direct incorporation would cause higher CPU-time that penalises the use of multi-objective optimisation. A global surrogate model (SUMO) of a rate based model was successfully generated to allow its incorporation into a process design & optimisation tool which makes use of an evolutionary multi-objective optimisation. The methodology was applied to a fluidised bed methanation reactor in the process chain from wood to Synthetic Natural Gas (SNG). Two types of surrogate model, an ordinary Kriging interpolation and an artificial neural network, were generated and compared to its underlying rate based model and the chemical equilibrium model. The analysis showed that kinetic limitations have significant influence on the result already for standard bulk gas chemical components. A case study applying the previous version of the process design model and the revised version (with rate based model introduced as a set of five surrogate models) will demonstrate that the prediction uncertainties of the process design & optimisation methodology are reduced due to the integration of the rate based model of the fluidised bed methanation reactor. It will be shown that the different process design models predict considerably different optimal operating conditions of the Wood-to-SNG process. This emphasises the importance of the integration of rate based models into the process design models. The presented approach has been developed for the fluidised bed methanation reactor, however, it is a generic approach which can be applied to other process unit technologies as well. Future investigations will target other technologies to further improve the process design & optimisation predictions and support project development.

EPFL2014