Comparison of CO2 From Coal Capture Processes and Valorisation Technologies

Coal is the most plentiful and evenly distributed fossil fuel worldwide. Based on current production, it is estimated that the reserves will last approximately 130 years. Its use worldwide has been increasing, mainly due to consumption by emerging countries. CO2 emissions generated by combustion and the repercussions of such on climate change support the view that it could no longer be used. CO2 capture may be the solution to continue using it, which would cater for the growing energy demand worldwide. The aim of this study is to compare different processes concerning CO2 capture that may be economically viable, ultimately showing that coal, a fossil energy source widely distributed around the world, can, as a result of using different CO2 capture processes, be used as a clean source of electricity. Hence, in places where geological hurdles may render the costs of CO2 storage considerably higher, since it might have to travel far, coal may be used for other purposes, thus valorising CO2 within the industrial sector. This research is focused on the technical and economic comparison of the most relevant CO2 capture projects designed in Spain using different existing technologies. The oxyfuel project in Ciuden (Leon, Spain), the IGCC Elcogas, precombustion CO2-capture project (Puertollano, Spain) and the postcombustion project in Carboneras (Almeria, Spain) will be analyzed in order to assess the options available to valorise captured CO2. Valorising captured CO2 may be an adequate solution in areas where, although CO2 capture is still possible, storage is not equally so, thus generating a further benefit. The possible uses of CO2 will be assessed in vegetable growing greenhouses, harnessing CO2 in vegetable life cycles. This will also be used in growing algae for subsequent biodiesel production. Both CO2 capture and valorising will eventually lead to the clean use of coal, which will thus enhance the level of self-supply, aiding the development of electric vehicles, which require large amounts of electricity, as well as improve the level of energy autonomy in countries around the world. Another type of fuel, biodiesel, will also be obtained, without this affecting international food prices.

Clean Coal Technologies Scenario and Evaluation of Present CO2 Dwindling Initiatives to Approach Zero Emission Power Stations By Coal Combustion. Deployment Situation and Evaluation Study

In the present uncertain global context of reaching an equal social stability and steady thriving economy, power demand expected to grow and global electricity generation could nearly double from 2005 to 2030. Fossil fuels will remain a significant contribution on this energy mix up to 2050, with an expected part of around 70% of global and ca. 60% of European electricity generation. Coal will remain a key player. Hence, a direct effect on the considered CO2 emissions business-as-usual scenario is expected, forecasting three times the present CO2 concentration values up to 1,200ppm by the end of this century. Kyoto protocol was the first approach to take global responsibility onto CO2 emissions monitoring and cap targets by 2012 with reference to 1990. Some of principal CO2emitters did not ratify the reduction targets. Although USA and China spur are taking its own actions and parallel reduction measures. More efficient combustion processes comprising less fuel consuming, a significant contribution from the electricity generation sector to a CO2 dwindling concentration levels, might not be sufficient. Carbon Capture and Storage (CCS) technologies have started to gain more importance from the beginning of the decade, with research and funds coming out to drive its come in useful. After first researching projects and initial scale testing, three principal capture processes came out available today with first figures showing up to 90% CO2 removal by its standard applications in coal fired power stations. Regarding last part of CO2 reduction chain, two options could be considered worthy, reusing (EOR & EGR) and storage. The study evaluates the state of the CO2 capture technology development, availability and investment cost of the different technologies, with few operation cost analysis possible at the time. Main findings and the abatement potential for coal applications are presented. DOE, NETL, MIT, European universities and research institutions, key technology enterprises and utilities, and key technology suppliers are the main sources of this study. A vision of the technology deployment is presented.

Comparison of CO2 from coal captureprocesses and valorisation technologies

Coal is the most plentiful and evenly distributed fossil fuel worldwide. Based on current production, it is estimated that the reserves will last approximately 130 years. Its use worldwide has been increasing, mainly due to consumption by emerging countries. CO2 emissions generated by combustion and the repercussions of such on climate change support the view that it could no longer be used. CO2 capture may be the solution to continue using it, which would cater for the growing energy demand worldwide. The aim of this study is to compare different processes concerning CO2 capture that may be economically viable, ultimately showing that coal, a fossil energy source widely distributed around the world, can, as a result of using different CO2 capture processes, be used as a clean source of electricity. Hence, in places where geological hurdles may render the costs of CO2 storage considerably higher, since it might have to travel far, coal may be used for other purposes, thus valorizing CO2 within the industrial sector. This research is focused on the technical and economic comparison of the most relevant CO2 capture projects designed in Spain using different existing technologies. The oxyfuel project in Ciuden (Leon, Spain), the IGCC Elcogas, precombustion CO2-capture project (Puertollano, Spain) and the postcombustion project in Carboneras (Almeria, Spain) will be analyzed in order to assess the options available to valorizecaptured CO2. Valorizing captured CO2 may be an adequate solution in areas where, although CO2 capture is still possible, storage is not equally so, thus generating a further benefit. The possible uses of CO2 will be assessed in vegetable growing greenhouses, harnessing CO2 in vegetable life cycles. This will also be used in growing algae for subsequent biodiesel production. Both CO2capture and valorizing will eventually lead to the clean use of coal, which will thus enhance the level of self-supply, aiding the development of electric vehicles, which require large amounts of electricity, as well as improve the level of energy autonomy in countries around the world. Another type of fuel, biodiesel, will also be obtained, without this affecting international food prices.

Hybrid incremental modeling based on least squares and fuzzy K-NN for monitoring tool wear in turning processes

There is now an emerging need for an efficient modeling strategy to develop a new generation of monitoring systems. One method of approaching the modeling of complex processes is to obtain a global model. It should be able to capture the basic or general behavior of the system, by means of a linear or quadratic regression, and then superimpose a local model on it that can capture the localized nonlinearities of the system. In this paper, a novel method based on a hybrid incremental modeling approach is designed and applied for tool wear detection in turning processes. It involves a two-step iterative process that combines a global model with a local model to take advantage of their underlying, complementary capacities. Thus, the first step constructs a global model using a least squares regression. A local model using the fuzzy k-nearest-neighbors smoothing algorithm is obtained in the second step. A comparative study then demonstrates that the hybrid incremental model provides better error-based performance indices for detecting tool wear than a transductive neurofuzzy model and an inductive neurofuzzy model.

Depth sensors-based upper limb motion capture system for functional neurorehabilitation

Versatile and accurate motion capture systems, with the required properties to be integrated within both clinical and domiciliary environments, would represent a significant advance in following the progress of the patients as well as in allowing the incorporation of new data exploitation and analysis methods to enhance the functional neurorehabilitation therapeutic processes. Besides, these systems would permit the later development of new applications focused on the automatization of the therapeutic tasks in order to increase the therapist/patient ratio, thus decreasing the costs [1]. However, current motion capture systems are not still ready to work within uncontrolled environments.

Application of knowledge engineering acquisition techniques to requirements capture: a case study

In this paper we want to point out, by means of a case study, the importance of incorporating some knowledge engineering techniques to the processes of software engineering. Precisely, we are referring to the knowledge eduction techniques. We know the difficulty of requirements acquisition and its importance to minimise the risks of a software project, both in the development phase and in the maintenance phase. To capture the functional requirements use cases are generally used. However, as we will show in this paper, this technique is insufficient when the problem domain knowledge is only in the "experts? mind". In this situation, the combination of the use case with eduction techniques, in every development phase, will let us to discover the correct requirements.

Linear latent force models using Gaussian Processes

Purely data-driven approaches for machine learning present difficulties when data are scarce relative to the complexity of the model or when the model is forced to extrapolate. On the other hand, purely mechanistic approaches need to identify and specify all the interactions in the problem at hand (which may not be feasible) and still leave the issue of how to parameterize the system. In this paper, we present a hybrid approach using Gaussian processes and differential equations to combine data-driven modeling with a physical model of the system. We show how different, physically inspired, kernel functions can be developed through sensible, simple, mechanistic assumptions about the underlying system. The versatility of our approach is illustrated with three case studies from motion capture, computational biology, and geostatistics.