Modelling of Biomass Gasification Integrated with a Solid Oxide Fuel Cell System

Biomass is of major interest as a renewable energy source in the context of climate change and energy security. Traditional biomass conversion technologies achieve low electrical efficiencies. Biomass gasification (BG) coupled with fuel cells offer higher efficiencies. Gasification is a process in which a carbonaceous fuel is converted to a combustible gas. It occurs when a controlled amount of oxidant is reacted at high temperatures with available carbon in a fuel within a gasifier. Two technologies (circulating fluidised bed air gasification and dual fluidised bed steam gasification) were modelled. Solid oxide fuel cells (SOFCs) are well suited to integration with gasification due to their high operating temperature and fuel flexibility. They convert the chemical energy contained in a fuel directly to electrical energy via electrochemical reactions, making them highly efficient. The tubular SOFC configuration was selected. The main aim of the research work was to investigate the feasibility of BG-SOFC systems through thermodynamic modelling and economic analyses. Standalone models of the gasification technologies and the SOFC were developed and validated. These models were integrated considering gas cleaning, heat recovery and balance of plant. An engineering economic model was developed and applied to determine the commercial viability of the BG-SOFC systems. The results indicated that these systems are attractive with regard to their operating efficiency; however, they are not yet commercially viable. Capital costs and biomass fuel prices must fall dramatically if these systems are to become competitive. A cathode recycle or electric heater for syngas preheating is not attractive. Thermal integration between the gasifier and fuel cell is desirable. Lowering the syngas preheat temperature is highly recommended. High temperature syngas cleaning reduces plant complexity and improves performance. Gasification air preheating is more attractive than gasification steam superheating

Simulation and parametric analyses of a tubular solid oxide fuel cell stack using aspen plus

In the context of climate change and increasing energy conversion efficiency solid oxide fuel cells (SOFCs) are likely to play an important role in the production of electricity. The tubular SOFC configuration is considered to be the most advanced and is approaching commercialisation. A major advantage of SOFCs over other types of fuel cells is that they can utilise a wide spectrum of fuels (natural gas, coal and biomass syn-gas). This is due to its high operating temperature, which also makes them suitable for integration with gas turbines and for cogeneration. A R&D project is underway to develop a computer simulation model of a tubular SOFC that can accurately predict performance under various conditions and using a range of fuels. A model is developed using the process simulator aspen plus. The software uses unit operation blocks, which are models of process operations. The user places these blocks on a flowsheet, specifying material and energy streams. There is no built in model that can represent a SOFC, however it is possible to construct one using the built in unit operation blocks. This method is an alternative to developing a fuel cell model using programming languages. The model is based on Gibbs free energy minimisation. Data available in the literature on the Siemens Power Generation tubular SOFC was used to validate the model. The model predicts thermodynamic condition and chemical composition of the stack exhaust gases, heat generated, voltage, current, and electrical efficiency. Fuel composition and operating parameters were varied over a wide range. Operating parameters such as fuel utilisation factor, current density, and steam to carbon ratio were found to have significant influence. In a future study this SOFC stack model will be integrated with a biomass gasifier model and balance of plant models all developed in aspen plus. From examination of the sensitivity analyses’ results optimum conditions are established

Modelling and Simulation of a Biomass Gasification-solid Oxide Fuel Cell Combined Heat and Power Plant using Aspen Plus

In this paper the operation and performance of a high temperature solid oxide fuel cell (SOFC) stack on biomass syn-gas from a demonstration biomass gasification combined heat and power (CHP) plant is investigated. The objective of this work is to develop a computer simulation model of a biomass-SOFC CHP system, flexible enough for use in industry, capable of predicting system performance under various operating conditions and using diverse fuels. The biomass gasifier is of the dual fluidised bed (DFB) type with steam as the gasifying agent and is operated at atmospheric pressure. The tubular SOFC configuration, developed by Siemens Power Generation Inc (SPGI), is selected. It is considered to be the most advanced design and is approaching commercialisation. The SOFC stack model, developed using the chemical process flowsheet simulator Aspen Plus, is of equilibrium type and is based on Gibbs free energy minimisation. The SOFC model performs heat and mass balances and considers the ohmic, activation and concentration losses for the voltage calculation. Data available in the literature on the SPGI SOFC operating on natural gas is used to validate the model. The system model predicts thermodynamic condition and composition of all internal flow streams, the heat generated by the SOFC stack, voltage (V), current (I) and efficiency. Operating parameters are varied over a wide range, parameters such as fuel utilisation factor (Uf), current density (j) and steam to carbon ratio (STCR) have significant influence. The results indicate that there must be a trade-off between voltage, efficiency and power with respect to j and the SOFC stack should be operated at low STCR and high Uf, within certain limits. SOFC stack operation on biomass syn-gas is compared to operation on natural gas and as expected there is a drop in performance, which is attributed to increased input fuel and air flow due to the lower quality of the fuel gas. The optimum realistic operating conditions with regard to SOFC stack performance are identified. High electrical efficiencies are predicted making these systems very attractive for CHP applications

Aspen Plus Simulation of Biomass Gasification in a Steam Blown Dual Fluidised Bed

The efficient utilisation of biomass resources is of utmost importance. Biomass gasification offers much higher efficiencies than combustion. Gasification is a process in which a fuel is converted to a combustible gas (syngas). A dual fluidised bed gasifier known as the fast internally circulating fluidised bed (FICFB) was selected. It has been demonstrated at industrial scale and data is readily available for model validation. An Aspen Plus model was developed to simulate the FICFB gasifier. The model is based on Gibbs free energy minimisation and the restricted equilibrium method was used to calibrate it. The model has been validated and predicts syngas composition, heating value and cold gas efficiency (CGE) in very good agreement with published data. Important operating parameters such as gasification temperature (Tg), biomass moisture, steam to biomass ratio (STBR), air-fuel ratio and air and steam temperature were varied. Tg and STBR were found to have very strong influence on syngas composition and heating value. Biomass moisture had the most significant impact on CGE. The other parameters, although less important, were found to have substantial effect on CGE

FisheyeMultiNet: Real-time Multi-task Learning Architecture for Surround-view Automated Parking System.

Automated Parking is a low speed manoeuvring scenario which is quite unstructured and complex, requiring full 360° near-field sensing around the vehicle. In this paper, we discuss the design and implementation of an automated parking system from the perspective of camera based deep learning algorithms. We provide a holistic overview of an industrial system covering the embedded system, use cases and the deep learning architecture. We demonstrate a real-time multi-task deep learning network called FisheyeMultiNet, which detects all the necessary objects for parking on a low-power embedded system. FisheyeMultiNet runs at 15 fps for 4 cameras and it has three tasks namely object detection, semantic segmentation and soiling detection. To encourage further research, we release a partial dataset of 5,000 images containing semantic segmentation and bounding box detection ground truth via WoodScape project [Yogamani et al., 2019]

IMOS national reference stations: A continental-wide physical, chemical and biological coastal observing system

Sustained observations allow for the tracking of change in oceanography and ecosystems, however, these are rare, particularly for the Southern Hemisphere. To address this in part, the Australian Integrated Marine Observing System (IMOS) implemented a network of nine National Reference Stations (NRS). The network builds on one long-term location, where monthly water sampling has been sustained since the 1940s and two others that commenced in the 1950s. In-situ continuously moored sensors and an enhanced monthly water sampling regime now collect more than 50 data streams. Building on sampling for temperature, salinity and nutrients, the network now observes dissolved oxygen, carbon, turbidity, currents, chlorophyll a and both phytoplankton and zooplankton. Additional parameters for studies of ocean acidification and bio-optics are collected at a sub-set of sites and all data is made freely and publically available. Our preliminary results demonstrate increased utility to observe extreme events, such as marine heat waves and coastal flooding; rare events, such as plankton blooms; and have, for the first time, allowed for consistent continental scale sampling and analysis of coastal zooplankton and phytoplankton communities. Independent water sampling allows for cross validation of the deployed sensors for quality control of data that now continuously tracks daily, seasonal and annual variation. The NRS will provide multi-decadal time series, against which more spatially replicated short-term studies can be referenced, models and remote sensing products validated, and improvements made to our understanding of how large-scale, long-term change and variability in the global ocean are affecting Australia's coastal seas and ecosystems. The NRS network provides an example of how a continental scaled observing systems can be developed to collect observations that integrate across physics, chemistry and biology

Isoniazid prophylaxis differently modulates T-cell responses to RD1-epitopes in contacts recently exposed to Mycobacterium tuberculosis: a pilot study

RATIONALE: Existing data on the effect of treatment of latent tuberculosis infection (LTBI) on T-cell responses to Mycobacterium tuberculosis (MTB)-specific antigens are contradictory. Differences in technical aspects of the assays used to detect this response and populations studied might explain some of these discrepancies. In an attempt to find surrogate markers of the effect of LTBI treatment, it would be important to determine whether, among contacts of patients with contagious tuberculosis, therapy for LTBI could cause changes in MTB-specific immune responses to a variety of RD1-antigens. METHODS AND RESULTS: In a longitudinal study, 44 tuberculin skin test(+ )recent contacts were followed over a 6-month period and divided according to previous exposure to MTB and LTBI treatment. The following tests which evaluate IFN-gamma responses to RD1 antigens were performed: QuantiFERON TB Gold, RD1 intact protein- and selected peptide-based assays. Among the 24 contacts without previous exposure that completed therapy, we showed a significant decrease of IFN-gamma response in all tests employed. The response to RD1 selected peptides was found to be more markedly decreased compared to that to other RD1 antigens. Conversely, no significant changes in the response to RD1 reagents were found in 9 treated subjects with a known previous exposure to MTB and in 11 untreated controls. CONCLUSION: These data suggest that the effect of INH prophylaxis on RD1-specific T-cell responses may be different based on the population of subjects enrolled (recent infection versus re-infection) and, to a minor extent, on the reagents used