Natural gas as a sustainable transport fuel

Environmental concerns relating to gaseous emissions from transport have led to growth in the use of compressed natural gas vehicles worldwide with an estimated 13 million Natural Gas Vehicles (NGVs) currently in operation. Across Europe, many countries are replacing traditional diesel oil in captive fleets such as buses used for public transport and heavy and light goods vehicles used for freight and logistics with CNG vehicles. Initially this was to reduce localised air pollution in urban environments. However, with the need to reduce greenhouse gas emissions CNG is seen as a cleaner more energy efficient and environmental friendly alternative. This paper briefly examines the growth of NGVs in Europe and worldwide. Then a case study on CNG the introduction in Spain and Italy is presented. As part of the case study, policy interventions are examined. Finally, a statistical analysis of private and public refuelling stations in both countries is also provided. CNG can also be mixed with biogas. This study and the role of CNG is relevant because of the existing European Union Directive 2009/28/EC target, requiring that 10% of transport energy come from renewable sources, not alone biofuels such as biogas. CNG offers another alternative transport fuel.

The technical merits of turbogenerating shown through the design, validation and implementation of a 1 dimensional engine model

Turbocompounding is the process of recovering a proportion of an engine’s fuel energy that would otherwise be lost in the exhaust process and adding it to the output power. This was first seen in the 1930s and is carried out by coupling an exhaust gas turbine to the crankshaft of a reciprocating engine. It has since been recognised that coupling the power turbine to an electrical generator instead of the crankshaft has the potential to reduce the fuel consumption further with the added flexibility of being able to decide how this recovered energy is used. The electricity generated can be used in automotive applications to assist the crankshaft using a flywheel motor generator or to power ancillaries that would otherwise have run off the crankshaft. In the case of stationary power plants, it can assist the electrical power output. Decoupling the power turbine from the crankshaft and coupling it to a generator allows the power electronics to control the turbine speed independently in order to optimise the specific fuel consumption for different engine operating conditions. This method of energy recapture is termed ‘turbogenerating’.

This paper gives a brief history of turbocompounding and its thermodynamic merits. It then moves on to give an account of the validation of a turbogenerated engine model. The model is then used to investigate what needs to be done to an engine when a turbogenerator is installed. The engine being modelled is used for stationary power generation and is fuelled by an induced biogas with a small portion of palm oil being injected into the cylinder to initiate combustion by compression ignition. From these investigations, optimum settings were found that result in a 10.90% improvement in overall efficiency. These savings relate to the same engine without a turbogenerator installed operating with fixed fuelling.

Predicting organic acid concentration from UV/vis spectrometry measurements - A comparison of machine learning techniques

The concentration of organic acids in anaerobic digesters is one of the most critical parameters for monitoring and advanced control of anaerobic digestion processes. Thus, a reliable online-measurement system is absolutely necessary. A novel approach to obtaining these measurements indirectly and online using UV/vis spectroscopic probes, in conjunction with powerful pattern recognition methods, is presented in this paper. An UV/vis spectroscopic probe from S::CAN is used in combination with a custom-built dilution system to monitor the absorption of fully fermented sludge at a spectrum from 200 to 750 nm. Advanced pattern recognition methods are then used to map the non-linear relationship between measured absorption spectra to laboratory measurements of organic acid concentrations. Linear discriminant analysis, generalized discriminant analysis (GerDA), support vector machines (SVM), relevance vector machines, random forest and neural networks are investigated for this purpose and their performance compared. To validate the approach, online measurements have been taken at a full-scale 1.3-MW industrial biogas plant. Results show that whereas some of the methods considered do not yield satisfactory results, accurate prediction of organic acid concentration ranges can be obtained with both GerDA and SVM-based classifiers, with classification rates in excess of 87% achieved on test data.

Determining the regional potential for a grass biomethane industry

Grass biogas/biomethane has been put forward as a renewable energy solution and it has been shown to perform well in terms of energy balance, greenhouse gas emissions and policy constraints. Biofuel and energy crop solutions are country-specific and grass biomethane has strong potential in countries with temperate climates and a high proportion of grassland, such as Ireland. For a grass biomethane industry to develop in a country, suitable regions (i.e. those with the highest potential) must be identified. In this paper, factors specifically related to the assessment of the potential of a grass biogas/biomethane industry are identified and analysed. The potential for grass biogas and grass biomethane is determined on a county-by-county basis using multi-criteria decision analysis. Values are assigned to each county and ratings and weightings applied to determine the overall county potential. The potential for grass biomethane with co-digestion of slaughter waste (belly grass) is also determined. The county with the highest potential (Limerick) is analysed in detail and is shown to have ready potential for production of gaseous biofuel to meet either 50% of the vehicle fleet or 130% of the domestic natural gas demand, through 25 facilities at a scale of ca. 30ktyr of feedstock. The assessment factors developed in this paper can be used in other resource studies into grass biomethane or other energy crops. © 2010 Elsevier Ltd.

Can grass biomethane be an economically viable biofuel for the farmer and the consumer?

Farm incomes in Ireland are in decline and many farmers would operate at a loss in the absence of subsidies. Agriculture is responsible for 27% of Ireland's greenhouse gas emissions and is the largest contributing sector. Penetration of renewable energy in the heat and transport sectors is falling short of targets, and there is no clear plan for achieving them. The anaerobic digestion of grass to produce biogas or biomethane is put forward as a multifaceted solution, which could help meet energy and emissions targets, reduce dependence on imported energy, and provide additional farm income. This paper addresses the economic viability of such a system. Grass biogas/biomethane fares poorly under the current combined heat and power tariff structure, which is geared toward feedstock that attracts a gate fee. Tariff structures similar to those used in other countries are necessary for the industry to develop. Equally, regulation should be implemented to allow injection of biomethane into the gas grid in Ireland. Blends of natural gas and biomethane can be sold, offering a cost-competitive green fuel. Sale as a renewable transport fuel could allow profitability for the farmer and savings for the consumer, but suffers due to the lack of a market. Under current conditions, the most economically viable outlet for grass biomethane is sale as a renewable heating fuel. The key to competitiveness is the existing natural gas infrastructure that enables distribution of grass biomethane, and the renewable energy targets that allow renewable fuels to compete against each other. © 2010 Society of Chemical Industry and John Wiley & Sons, Ltd.

A biofuel strategy for Ireland with an emphasis on production of biomethane and minimization of land-take

Increasing energy consumption has exerted great pressure on natural resources; this has led to a move towards sustainable energy resources to improve security of supply and to reduce greenhouse gas emissions. However, the rush to the cure may have been made in haste. Biofuels in particular, have a bad press both in terms of competition with good agricultural land for food, and also in terms of the associated energy balance with the whole life cycle analysis of the biofuel system. The emphasis is now very much on sustainable biofuel production; biofuels from wastes and lignocellulosic material are now seen as good sustainable biofuels that affect significantly better greenhouse gas balances as compared with first generation biofuels. Ireland has a significant resource of organic waste that could be a potential source of energy through anaerobic digestion. Ireland has 8% of the cattle population of the EU with less than 1% of the human population; as a result 91% of agricultural land in Ireland is under grass. Residues such as slurries and slaughter waste together with energy crops such as grass have an excellent potential to produce biogas that may be upgraded to biomethane. This biomethane may be used as a natural gas substitute; bio-compressed natural gas may then be an avenue for a biofuel strategy. It is estimated that a maximum potential of 33% of natural gas may be substituted by 2020 with a practical obtainable level of 7.5% estimated. Together with biodiesel from residues the practical obtainable level of this strategy may effect greater than a 5% substitution by energy of transport. The residues considered in this strategy to produce biofuel (excluding grass) have the potential to save 93,000 ha of agricultural land (23% of Irish arable land) when compared to a rapeseed biodiesel strategy. © 2009 Elsevier Ltd. All rights reserved.

State Estimation for Anaerobic Digesters using the ADM1

The optimization of full-scale biogas plant operation is of great importance to make biomass a competitive source of renewable energy. The implementation of innovative control and optimization algorithms, such as Nonlinear Model Predictive Control, requires an online estimation of operating states of biogas plants. This state estimation allows for optimal control and operating decisions according to the actual state of a plant. In this paper such a state estimator is developed using a calibrated simulation model of a full-scale biogas plant, which is based on the Anaerobic Digestion Model No.1. The use of advanced pattern recognition methods shows that model states can be predicted from basic online measurements such as biogas production, CH4 and CO2 content in the biogas, pH value and substrate feed volume of known substrates. The machine learning methods used are trained and evaluated using synthetic data created with the biogas plant model simulating over a wide range of possible plant operating regions. Results show that the operating state vector of the modelled anaerobic digestion process can be predicted with an overall accuracy of about 90%. This facilitates the application of state-based optimization and control algorithms on full-scale biogas plants and therefore fosters the production of eco-friendly energy from biomass.

Self-cleaning perovskite type catalysts for the dry reforming of methane

Gas-to-liquid processes are generally used to convert natural gas or other gaseous hydrocarbons into liquid fuels via an intermediate syngas stream. This includes the production of liquid fuels from biomass-derived sources such as biogas. For example, the dry reforming of methane is done by reacting CH4 and CO2, the two main components of natural biogas, into more valuable products, i.e., CO and H2. Nickel containing perovskite type catalysts can promote this reaction, yielding good conversions and selectivities; however, they are prone to coke laydown under certain operating conditions. We investigated the addition of high oxygen mobility dopants such as CeO2, ZrO2, or YSZ to reduce carbon laydown, particularly using reaction conditions that normally result in rapid coking. While doping with YSZ, YDC, GDC, and SDC did not result in any improvement, we show that a Ni perovskite catalyst (Na0.5La0.5Ni0.3Al0.7O2.5) doped with 80.9 ZrO2 15.2 CeO2 gave the lowest amount of carbon formation at 800 °C and activity was maintained over the operating time.