Comparison of Chemical Absorbents Used for CO2 Capture in Coal-Fired Power Plants.

Climate change conference was hold in Copenhagen in 2009, global warming became the worldwide focus once again. China as a developing country has paid more attention for this environmental problem. In China, a large part of carbon dioxide is emitted to the atmosphere from combustion of fossil fuels in power plants. How to control emission of the greenhouse gas into atmosphere is becoming an urgent concern. Among numerous methods, CO2 capture is the hope to limit the amount of CO2 emitted into the air. The well-established method for CO2 capture is to remove CO2 by absorption into solutions in conventional equipment. Absorbents used for CO2 and H2S capture are important choice for CO2 capture technology. It is related to the cost and efficiency of plant directly and is essential to investigate the proposed CO2 and H2S absorbents.

Wind intermittency and CO2 reductions: the case of the spanish power system

Renewable energy sources are believed to reduce drastically greenhouse gas emissions that would otherwise be generated from fossil fuels used to generate electricity. This implies that a unit of renewable energy will replace a unit of fossil-fuel, with its CO2 emissions, on an equivalent basis (with no other effects on the grid). But, the fuel economy and emissions in the existing power systems are not proportional with the electricity production of intermittent sources due to cycling of the fossil fuel plants that make up the balance of the grid (i.e. changing the power output makes thermal units to operate less efficiently). This study focuses in the interactions between wind generation and thermal plants cycling, by establishing the levels of extra fuel use caused by decreased efficiencies of fossil back-up for wind electricity in Spain. We analyze the production of all thermal plants in 2011, studying different scenarios where wind penetration causes major deviations in programming, while we define a procedure for quantifying the carbon reductions by using emission factors and efficiency curves from the existing installations. The objectives are to discuss the real contributions of renewable energies to the environmental targets as well as suggest alternatives that would improve the reliability of future power systems.

Co2 capture in power plants-using the oxy-combustion principle

In the C02 capture from power generation, the energy penalties for the capture are one of the main challenges. Nowadays, the post-combustion methods have energy penalties 10wer than the oxy combustion and pre-combustion technologies. One of the main disadvantages of the post combustion method is the fact that the capture ofC02at atmospheric pressure requires quite big equipment for the high flow rates of flue gas, and the 10w partial pressure of the CO2generates an important 10ss of energy. The A1lam cyc1e presented for NETPOWER gives high efficiencies in the power production and 10w energy penalties. A simulation of this cyc1e is made together with a simulation of power plants with pre-combustion and post-combustion capture and without capture for natural gas and forcoa1. The simulations give 10wer efficiencies than the proposed for NETPOWER For natural gas the efficiency is 52% instead of the 59% presented, and 33% instead of51% in the case of using coal as fuel. Are brought to light problems in the CO2compressor due the high flow ofC02that is compressed unti1300 bar to be recyc1ed into the combustor.

A cyber-physical approach to combined HW-SW monitoring for improving energy efficiency in data centers

High-Performance Computing, Cloud computing and next-generation applications such e-Health or Smart Cities have dramatically increased the computational demand of Data Centers. The huge energy consumption, increasing levels of CO2 and the economic costs of these facilities represent a challenge for industry and researchers alike. Recent research trends propose the usage of holistic optimization techniques to jointly minimize Data Center computational and cooling costs from a multilevel perspective. This paper presents an analysis on the parameters needed to integrate the Data Center in a holistic optimization framework and leverages the usage of Cyber-Physical systems to gather workload, server and environmental data via software techniques and by deploying a non-intrusive Wireless Sensor Net- work (WSN). This solution tackles data sampling, retrieval and storage from a reconfigurable perspective, reducing the amount of data generated for optimization by a 68% without information loss, doubling the lifetime of the WSN nodes and allowing runtime energy minimization techniques in a real scenario.

Investigations on the distribution of air transport traffic and CO2 emissions within the European Union

This study analyses the structure of air traffic and its distribution among the different countries in the European Union, as well as traffic with an origin or destination in non-EU countries. Data sources are Eurostat statistics and actual flight information from EUROCONTROL. Relevant variables such as the number of flights, passengers or cargo tonnes and production indicators (RPKs) are used together with fuel consumption and CO2 emissions data. The segmentation of air traffic in terms of distance permits an assessment of air transport competition with surface transport modes. The results show a clear concentration of traffic in the five larger countries (France, Germany, Italy, Spain and UK), in terms of RPKs. In terms of distance the segment between 500 and 1000 km in the EU, has more flights, passengers, RTKs and CO2 emissions than larger distances. On the environmental side, the distribution of CO2 emissions within the EU Member States is presented, together with fuel efficiency parameters. In general, a direct relationship between RPKs and CO2 emissions is observed for all countries and all distance bands. Consideration is given to the uptake of alternative fuels. Segmenting CO2 emissions per distance band and aircraft type reveals which flights contribute the most the overall EU CO2 emissions. Finally, projections for future CO2 emissions are estimated, according to three different air traffic growth and biofuel introduction scenarios.

Simulation of an Integrated Gasification Combined Cycle with Chemical-Looping Combustion and CO2 sequestration

Chemical-looping combustion allows an integration of CO2 capture in a thermal power plant without energy penalty; secondly, a less exergy destruction in the combustion chemical transformation is achieved, leading to a greater overall thermal efficiency. This paper focus on the study of the energetic performance of this concept of combustion in an integrated gasification combined cycle power plant when synthesis gas is used as fuel for the gas turbines. After thermodynamic modelling and optimization of some cycle parameters, the power plant performance is evaluated under diverse working conditions and compared to a conventional integrated gasification combined cycle with precombustion capture. Energy savings in CO2 capture and storage has been quantified. The overall efficiency increase is found to be significant and even notable, reaching values of around 7%. In order to analyze the influence of syngas composition on the results, different H2-content fuels are considered.

Utilizing the Internet of Things to promote energy awareness and efficiency at discrete production processes: Practices and methodology

El actual contexto de fabricación, con incrementos en los precios de la energía, una creciente preocupación medioambiental y cambios continuos en los comportamientos de los consumidores, fomenta que los responsables prioricen la fabricación respetuosa con el medioambiente. El paradigma del Internet de las Cosas (IoT) promete incrementar la visibilidad y la atención prestada al consumo de energía gracias tanto a sensores como a medidores inteligentes en los niveles de máquina y de línea de producción. En consecuencia es posible y sencillo obtener datos de consumo de energía en tiempo real proveniente de los procesos de fabricación, pero además es posible analizarlos para incrementar su importancia en la toma de decisiones. Esta tesis pretende investigar cómo utilizar la adopción del Internet de las Cosas en el nivel de planta de producción, en procesos discretos, para incrementar la capacidad de uso de la información proveniente tanto de la energía como de la eficiencia energética. Para alcanzar este objetivo general, la investigación se ha dividido en cuatro sub-objetivos y la misma se ha desarrollado a lo largo de cuatro fases principales (en adelante estudios). El primer estudio de esta tesis, que se apoya sobre una revisión bibliográfica comprehensiva y sobre las aportaciones de expertos, define prácticas de gestión de la producción que son energéticamente eficientes y que se apoyan de un modo preeminente en la tecnología IoT. Este primer estudio también detalla los beneficios esperables al adoptar estas prácticas de gestión. Además, propugna un marco de referencia para permitir la integración de los datos que sobre el consumo energético se obtienen en el marco de las plataformas y sistemas de información de la compañía. Esto se lleva a cabo con el objetivo último de remarcar cómo estos datos pueden ser utilizados para apalancar decisiones en los niveles de procesos tanto tácticos como operativos. Segundo, considerando los precios de la energía como variables en el mercado intradiario y la disponibilidad de información detallada sobre el estado de las máquinas desde el punto de vista de consumo energético, el segundo estudio propone un modelo matemático para minimizar los costes del consumo de energía para la programación de asignaciones de una única máquina que deba atender a varios procesos de producción. Este modelo permite la toma de decisiones en el nivel de máquina para determinar los instantes de lanzamiento de cada trabajo de producción, los tiempos muertos, cuándo la máquina debe ser puesta en un estado de apagada, el momento adecuado para rearrancar, y para pararse, etc. Así, este modelo habilita al responsable de producción de implementar el esquema de producción menos costoso para cada turno de producción. En el tercer estudio esta investigación proporciona una metodología para ayudar a los responsables a implementar IoT en el nivel de los sistemas productivos. Se incluye un análisis del estado en que se encuentran los sistemas de gestión de energía y de producción en la factoría, así como también se proporcionan recomendaciones sobre procedimientos para implementar IoT para capturar y analizar los datos de consumo. Esta metodología ha sido validada en un estudio piloto, donde algunos indicadores clave de rendimiento (KPIs) han sido empleados para determinar la eficiencia energética. En el cuarto estudio el objetivo es introducir una vía para obtener visibilidad y relevancia a diferentes niveles de la energía consumida en los procesos de producción. El método propuesto permite que las factorías con procesos de producción discretos puedan determinar la energía consumida, el CO2 emitido o el coste de la energía consumida ya sea en cualquiera de los niveles: operación, producto o la orden de fabricación completa, siempre considerando las diferentes fuentes de energía y las fluctuaciones en los precios de la misma. Los resultados muestran que decisiones y prácticas de gestión para conseguir sistemas de producción energéticamente eficientes son posibles en virtud del Internet de las Cosas. También, con los resultados de esta tesis los responsables de la gestión energética en las compañías pueden plantearse una aproximación a la utilización del IoT desde un punto de vista de la obtención de beneficios, abordando aquellas prácticas de gestión energética que se encuentran más próximas al nivel de madurez de la factoría, a sus objetivos, al tipo de producción que desarrolla, etc. Así mismo esta tesis muestra que es posible obtener reducciones significativas de coste simplemente evitando los períodos de pico diario en el precio de la misma. Además la tesis permite identificar cómo el nivel de monitorización del consumo energético (es decir al nivel de máquina), el intervalo temporal, y el nivel del análisis de los datos son factores determinantes a la hora de localizar oportunidades para mejorar la eficiencia energética. Adicionalmente, la integración de datos de consumo energético en tiempo real con datos de producción (cuando existen altos niveles de estandarización en los procesos productivos y sus datos) es esencial para permitir que las factorías detallen la energía efectivamente consumida, su coste y CO2 emitido durante la producción de un producto o componente. Esto permite obtener una valiosa información a los gestores en el nivel decisor de la factoría así como a los consumidores y reguladores. ABSTRACT In today‘s manufacturing scenario, rising energy prices, increasing ecological awareness, and changing consumer behaviors are driving decision makers to prioritize green manufacturing. The Internet of Things (IoT) paradigm promises to increase the visibility and awareness of energy consumption, thanks to smart sensors and smart meters at the machine and production line level. Consequently, real-time energy consumption data from the manufacturing processes can be easily collected and then analyzed, to improve energy-aware decision-making. This thesis aims to investigate how to utilize the adoption of the Internet of Things at shop floor level to increase energy–awareness and the energy efficiency of discrete production processes. In order to achieve the main research goal, the research is divided into four sub-objectives, and is accomplished during four main phases (i.e., studies). In the first study, by relying on a comprehensive literature review and on experts‘ insights, the thesis defines energy-efficient production management practices that are enhanced and enabled by IoT technology. The first study also explains the benefits that can be obtained by adopting such management practices. Furthermore, it presents a framework to support the integration of gathered energy data into a company‘s information technology tools and platforms, which is done with the ultimate goal of highlighting how operational and tactical decision-making processes could leverage such data in order to improve energy efficiency. Considering the variable energy prices in one day, along with the availability of detailed machine status energy data, the second study proposes a mathematical model to minimize energy consumption costs for single machine production scheduling during production processes. This model works by making decisions at the machine level to determine the launch times for job processing, idle time, when the machine must be shut down, ―turning on‖ time, and ―turning off‖ time. This model enables the operations manager to implement the least expensive production schedule during a production shift. In the third study, the research provides a methodology to help managers implement the IoT at the production system level; it includes an analysis of current energy management and production systems at the factory, and recommends procedures for implementing the IoT to collect and analyze energy data. The methodology has been validated by a pilot study, where energy KPIs have been used to evaluate energy efficiency. In the fourth study, the goal is to introduce a way to achieve multi-level awareness of the energy consumed during production processes. The proposed method enables discrete factories to specify energy consumption, CO2 emissions, and the cost of the energy consumed at operation, production and order levels, while considering energy sources and fluctuations in energy prices. The results show that energy-efficient production management practices and decisions can be enhanced and enabled by the IoT. With the outcomes of the thesis, energy managers can approach the IoT adoption in a benefit-driven way, by addressing energy management practices that are close to the maturity level of the factory, target, production type, etc. The thesis also shows that significant reductions in energy costs can be achieved by avoiding high-energy price periods in a day. Furthermore, the thesis determines the level of monitoring energy consumption (i.e., machine level), the interval time, and the level of energy data analysis, which are all important factors involved in finding opportunities to improve energy efficiency. Eventually, integrating real-time energy data with production data (when there are high levels of production process standardization data) is essential to enable factories to specify the amount and cost of energy consumed, as well as the CO2 emitted while producing a product, providing valuable information to decision makers at the factory level as well as to consumers and regulators.

Production of methanol from CO2 electroreduction at Cu2O and Cu2O/ZnO-based electrodes in aqueous solution

In this study, we examine the performance of Cu2O and Cu2O/ZnO surfaces in a filter-press electrochemical cell for the continuous electroreduction of CO2 into methanol. The electrodes are prepared by airbrushing the metal particles onto a porous carbon paper and then are electrochemically characterized by cyclic voltammetry analyses. Particular emphasis is placed on evaluating and comparing the methanol production and Faradaic efficiencies at different loadings of Cu2O particles (0.5, 1 and 1.8 mg cm−2), Cu2O/ZnO weight ratios (1:0.5, 1:1 and 1:2) and electrolyte flow rates (1, 2 and 3 ml min−1 cm−2). The electrodes including ZnO in their catalytic surface were stable after 5 h, in contrast with Cu2O-deposited carbon papers that present strong deactivation with time. The maximum methanol formation rate and Faradaic efficiency for Cu2O/ZnO (1:1)-based electrodes, at an applied potential of −1.3 V vs. Ag/AgCl, were r = 3.17 × 10−5 mol m−2 s−1 and FE = 17.7 %, respectively. Consequently, the use of Cu2O–ZnO mixtures may be of application for the continuous electrochemical formation of methanol, although further research is still required in order to develop highly active, selective and stable catalysts the electroreduction of CO2 to methanol.

Energy Efficiency: Trends and Perspectives in the Southern Mediterranean. MEDPRO Technical Report No. 21/December 2012

This paper has two objectives. First, it attempts to establish the potential of policies on energy efficiency and energy demand-side management in the southern Mediterranean region. Second, by examining past trends in energy intensity and trends up to 2030, it analyses the prospects and costs of such policies, compared with expected developments in the price of energy resources. Based on both analyses (MEDPRO WP4) and on prospects for growth (MEDPRO WP8), it seems that energy intensity in the Mediterranean should fall perceptibly by approximately 13% in the next 20 years. But given the programmed energy mix, this will not limit emissions of CO2, which are likely to increase by more than 90%. The paper first presents the rationale for demand-side management (DSM) policies. After a general discussion of concepts, it tackles the question of instruments and measures for implementing such policies, before posing the question of the cost-efficiency approach for monitoring the measures the authorities introduce. Secondly, the paper assesses energy consumption and energy efficiency in the countries of the southern Mediterranean and the ways in which their main economic sectors have changed in recent decades. The third section outlines the demand management measures introduced and, taking Tunisia and Egypt as examples, estimates the cost of such policies. The fourth and last section offers a forecast analysis of energy consumption in the Mediterranean up to 2030, highlighting probable trends in terms of final consumption, energy intensity, energy mix and emissions of CO2. The section concludes with estimates in terms of cost, comparing objectives for lower intensity, results in terms of resource savings and the types of costs this approach represents.

The investigation of energy efficiency measures in the traditional buildings in Oporto World Heritage Site

Background The improvement of energy efficiency in buildings is widely promoted as a measure to mitigate climate change through the reduction of CO2 emissions. Thermal regulations worldwide promote it, for both new and existing buildings. Among the existing stock, traditional and historic buildings pose the additional challenge of heritage conservation. Their energy efficiency upgrade raises the risk of provoking negative impacts on their significance. Aims and Methodology This research used an approach based on impact assessment methodologies, defining an inital baseline scenario for both heritage and energy, from which the appropriate improvement solutions were identified and assessed. The measures were dynamically simulated and the results for energy, CO2, cost and comfort compared with the initial scenario, and then being further assessed for their heritage impact to eventually determine the most feasible solutions. To test this method, ten case studies, representative of the identified typological variants, were selected among Oporto’s traditional buildings located in the World Heritage Site. Findings and Conclusions The fieldwork data revealed that the energy consumption of these dwellings was below the European average. Additionally, the households expressed that their home comfort sensation was overall positive. The simulations showed that the introduction of insulation and solar thermal panels were ineffective on these cases in terms of energy, cost and comfort. At the same time, these measures pose a great risk to the buildings heritage value. The most efficient solutions were obtained from behavioural changes and DHW retrofit. The study reinforced the idea that traditional buildings performed better than expected and can be retrofitted and updated at a low-cost and with passive solutions. The use of insulation and solar panels should be disregarded.

The North Pacific biological pump: Rates, efficiency and influence on ocean carbon uptake

Thesis (Ph.D.)--University of Washington, 2016-05

Optimization of pipeline transport for CO2 sequestration

Coal fired power generation will continue to provide energy to the world for the foreseeable future. However, this energy use is a significant contributor to increased atmospheric CO2 concentration and, hence, global warming. Capture and disposal Of CO2 has received increased R&D attention in the last decade as the technology promises to be the most cost effective for large scale reductions in CO2 emissions. This paper addresses CO2 transport via pipeline from capture site to disposal site, in terms of system optimization, energy efficiency and overall economics. Technically, CO2 can be transported through pipelines in the form of a gas, a supercritical. fluid or in the subcooled liquid state. Operationally, most CO2 pipelines used for enhanced oil recovery transport CO2 as a supercritical fluid. In this paper, supercritical fluid and subcooled liquid transport are examined and compared, including their impacts on energy efficiency and cost. Using a commercially available process simulator, ASPEN PLUS 10.1, the results show that subcooled liquid transport maximizes the energy efficiency and minimizes the Cost Of CO2 transport over long distances under both isothermal and adiabatic conditions. Pipeline transport of subcooled liquid CO2 can be ideally used in areas of cold climate or by burying and insulating the pipeline. In very warm climates, periodic refrigeration to cool the CO2 below its critical point of 31.1 degrees C, may prove economical. Simulations have been used to determine the maximum safe pipeline distances to subsequent booster stations as a function of inlet pressure, environmental temperature and ground level heat flux conditions. (c) 2005 Published by Elsevier Ltd.

Overcoming the problem of loss-in-capacity of calcium oxide in CO2 capture

Calcium oxide has been identified to be one of the best candidates for CO2 capture in zero-emission power-generation systems. However, it suffers a well-known problem of loss-in-capacity (i.e., its capacity of CO2 capture decreases after it undergoes cycles of carbonation/decarbonation). This problem is a potential obstacle to the adoption of the new technologies. This paper proposes a method of fabricating a CaO-based adsorbent without the problem of loss-in-capacity. An adsorbent was fabricated using the method and tested on a thermogravimetric analyzer. It was shown that the sorbent attained a utilization efficiency of more than 90% after 9 cycles of carbonation/decarbonation.

Carbon efficiency evaluation:an analytical framework using fuzzy DEA

Data Envelopment Analysis (DEA) is a powerful analytical technique for measuring the relative efficiency of alternatives based on their inputs and outputs. The alternatives can be in the form of countries who attempt to enhance their productivity and environmental efficiencies concurrently. However, when desirable outputs such as productivity increases, undesirable outputs increase as well (e.g. carbon emissions), thus making the performance evaluation questionable. In addition, traditional environmental efficiency has been typically measured by crisp input and output (desirable and undesirable). However, the input and output data, such as CO2 emissions, in real-world evaluation problems are often imprecise or ambiguous. This paper proposes a DEA-based framework where the input and output data are characterized by symmetrical and asymmetrical fuzzy numbers. The proposed method allows the environmental evaluation to be assessed at different levels of certainty. The validity of the proposed model has been tested and its usefulness is illustrated using two numerical examples. An application of energy efficiency among 23 European Union (EU) member countries is further presented to show the applicability and efficacy of the proposed approach under asymmetric fuzzy numbers.

CO2 emissions reduction of Chinese light manufacturing industries:a novel RAM-based global Malmquist-Luenberger productivity index

Climate change has become one of the most challenging issues facing the world. Chinese government has realized the importance of energy conservation and prevention of the climate changes for sustainable development of China's economy and set targets for CO2 emissions reduction in China. In China industry contributes 84.2% of the total CO2 emissions, especially manufacturing industries. Data envelopment analysis (DEA) and Malmquist productivity (MP) index are the widely used mathematical techniques to address the relative efficiency and productivity of a group of homogenous decision making units, e.g. industries or countries. However, in many real applications, especially those related to energy efficiency, there are often undesirable outputs, e.g. the pollutions, waste and CO2 emissions, which are produced inevitably with desirable outputs in the production. This paper introduces a novel Malmquist-Luenberger productivity (MLP) index based on directional distance function (DDF) to address the issue of productivity evolution of DMUs in the presence of undesirable outputs. The new RAM (Range-adjusted measure)-based global MLP index has been applied to evaluate CO2 emissions reduction in Chinese light manufacturing industries. Recommendations for policy makers have been discussed.