Integrating compressed CO2 energy storage in an oxy-coal combustion power plant with CO2 capture

To compensate for the high cost of CO2 capture, this study proposes a novel solution that integrates a compressed CO2 energy storage (CCES) system into an oxy-coal combustion power plant with CO2 capture (Oxy_CCES). The integration of energy storage has the potential to create arbitrage from variations in electricity prices. The proposed Oxy_CCES system can achieve a higher net efficiency of 34.1%, and a higher exergy efficiency of 57.5%, than that of a liquified oxygen storage-integrated oxy-coal combustion power plant (Oxy_O2). Two scenarios, i.e., retrofitting an existing oxy-coal combustion power plant (S–I) and building a new plant (S-II), were established to compare the Oxy_CCES and Oxy_O2. In S–I, the payback time of the Oxy_CCES is one year and in the S-II the levelized cost of electricity (LCOE) of the Oxy_CCES increases by 1.8%, which is lower than that of the Oxy_O2. The sensitivity analysis shows that, when the difference between the peak and the valley electricity prices and the capacities of the energy storage systems increase by 50%, the net present value (NPV) and LCOE of the Oxy_CCES system increase by 113.4% and 1.7% respectively, which are lower than the NPV and LCOE increase of the Oxy_O2

data-sims.tar.gz

<b>Simulation data</b><p>The BOLD timeseries data and underlying ground-truth network matrices are available below. We welcome any feedback on our evaluation paper or the simulation datasets. The data from all 28 simulations is available in MATLAB format. The variables are:</p><ul><li><i>Nsubjects</i> is the number of "subjects" in the simulation.</li><li><i>Ntimepoints</i> is the number of timepoints for each subject.</li><li><i>Nnodes</i> is the number of nodes in the network.</li><li><i>ts</i> contains all subjects' timeseries concatenated. Please note that all methods only ever process one "subject" at a time, in order to evaluate on realistic session durations, and then characterise variability by comparing results across subjects. There are <i>Nnodes</i> columns, one for each network node. Each vertical chunk of <i>Ntimepoints</i> X <i>Nnodes</i> is a different subject's dataset.</li><li><i>net</i> contains the ground truth networks (dimensions <i>Nsubjects</i> X <i>Nnodes</i> X <i>Nnodes</i>), which in general are slightly different for each of the subjects.</li></ul

Learning Causal Biological Networks with Parallel Ant Colony Optimization Algorithm

A wealth of causal relationships exists in biological systems, both causal brain networks and causal protein signaling networks are very classical causal biological networks (CBNs). Learning CBNs from biological signal data reliably is a critical problem today. However, most of the existing methods are not excellent enough in terms of accuracy and time performance, and tend to fall into local optima because they do not take full advantage of global information. In this paper, we propose a parallel ant colony optimization algorithm to learn causal biological networks from biological signal data, called PACO. Specifically, PACO first maps the construction of CBNs to ants, then searches for CBNs in parallel by simulating multiple groups of ants foraging, and finally obtains the optimal CBN through pheromone fusion and CBNs fusion between different ant colonies. Extensive experimental results on simulation data sets as well as two real-world data sets, the fMRI signal data set and the Single-cell data set, show that PACO can accurately and efficiently learn CBNs from biological signal data

Malaria control and fever management in Henan Province, China, 1992

Henan Province, which once had the highest malaria prevalence in China, had only 318 reported cases in 1992. Our purpose was to investigate this late 'consolidation phase' of malaria control in Henan with reference to malaria surveillance. We conducted a questionnaire survey of village doctors in Shang Shi Qiao Township during the transmission period of 1992. Of the 732 recorded fever cases, 16 were probable malaria cases by clinical and treatment response criteria, but only one received a full course of antimalarials. Of the 732 patients, 61% had fever every day, 37% went for treatment the first day, 52% waited 2-3 days and 10% waited longer. One hundred and twenty-eight patients took self-medication before seeing the doctor. Blood examination was carried out in 526 (71%) fever cases but only four were positive, all for Plasmodium vivax. Our findings highlight problems relating to patient behaviour and motivation of village doctors, malaria treatment, surveillance and microscopy, rural migration, economic development and malaria transmission. All need to be considered for reforming the malaria control strategy in Henan Province

A method for economic evaluation of predictive maintenance technologies by integrating system dynamics and evolutionary game modelling

International audiencePredictive maintenance technologies can be employed for failure prediction and system health management. Nevertheless, the additional cost involved in establishing the predictive maintenance system can be an obstacle to its widespread application. The decision on the predictive maintenance technology adoption can be made through the computation of the return on investment. To investigate the mechanisms of dynamic game between stakeholders involved in predictive maintenance, we establish the SD-EGT model from the perspective of systems engineering. This paper aims to propose an integrated method for the economic evaluation of predictive maintenance technologies by considering the incremental costs and benefits associated with its deployment. As an exemplary case, we take the Lithium-ion batteries whose failures have led to unexpected safety accidents. Firstly, we construct a quantitative relationship model between the failure modes and the predictive benefits of Lithium-ion battery systems to quantify the incremental benefits. Then, we establish a cost-benefit analysis (CBA) model by using system dynamics (SD) to make decisions about cost-effectiveness. Secondly, to optimize the cost investment strategy for the predictive maintenance technology, we develop an enterprise-government evolutionary game model, considering the information asymmetry between players. Eventually, we conduct a sensitivity analysis of the static subsidy strategy. The proposed methodology is serviceable to optimize the decision-making of predictive maintenance technology investment, which is a difficult yet very important task in industrial practice

Evolution of springy organisms

A springy organism is an extremely simpli ed model of a moving structure consisting of springs contracting in a simple harmonic motion (SHM). Several projects are devoted to design and simulation of these artifficial models. Automatic tools which search for an appropriate control system of a given springy organism are also available, but for two-dimensional versions only. The main contribution of the present work is to provide a set of tools for designing, simulating and optimizing three-dimensional springy organisms. They include an interactive editor, a run-time graphical simulator and an automatic tool based on genetic algorithms, which optimizes parameters of SHMs. The tools are integrated into a framework for evolutionary experiments and distributed computations called ERO. Optimization mechanism is tested using a set of experiments. Analysis of the experiments led to improvement of the original con guration of the mechanism

Learning Effective Connectivity Network Structure from fMRI Data Based on Artificial Immune Algorithm.

Many approaches have been designed to extract brain effective connectivity from functional magnetic resonance imaging (fMRI) data. However, few of them can effectively identify the connectivity network structure due to different defects. In this paper, a new algorithm is developed to infer the effective connectivity between different brain regions by combining artificial immune algorithm (AIA) with the Bayes net method, named as AIAEC. In the proposed algorithm, a brain effective connectivity network is mapped onto an antibody, and four immune operators are employed to perform the optimization process of antibodies, including clonal selection operator, crossover operator, mutation operator and suppression operator, and finally gets an antibody with the highest K2 score as the solution. AIAEC is then tested on Smith's simulated datasets, and the effect of the different factors on AIAEC is evaluated, including the node number, session length, as well as the other potential confounding factors of the blood oxygen level dependent (BOLD) signal. It was revealed that, as contrast to other existing methods, AIAEC got the best performance on the majority of the datasets. It was also found that AIAEC could attain a relative better solution under the influence of many factors, although AIAEC was differently affected by the aforementioned factors. AIAEC is thus demonstrated to be an effective method for detecting the brain effective connectivity

Experimental research on degradation performance of magnesium alloy affected by mechanical environment

Objective To research the mechanical properties of magnesium alloy degradation affected by mechanical environment. Methods: After being divided into 4 groups randomly such as No.1, 2, 3 and 4,12 AZ31 magnesium alloy screws need suspending in the vessels containing 35ml simulated body fluid. No.1 was control group. Group 1 was under no load condition, experimental group 2, 3, and 4 were discontinuously applied fixed axial compression load of 30N, 60N, 90N respectively on time. The weight loss of magnesium alloy screws and the amount of gas generated were measured regularly. Results: From the cumulative amount of hydrogen production, group 1 and group 4 were relatively large, whereas group 2 and group 3 were relatively small. From the weight loss of magnesium alloy screws, group 3 > group 4 > group 2 > group 1. Conclusions: The general trend is the greater the load, the faster the degradation

Experimental research on degradation performance of magnesium alloy affected by mechanical environment

Objective To research the mechanical properties of magnesium alloy degradation affected by mechanical environment. Methods: After being divided into 4 groups randomly such as No.1, 2, 3 and 4,12 AZ31 magnesium alloy screws need suspending in the vessels containing 35ml simulated body fluid. No.1 was control group. Group 1 was under no load condition, experimental group 2, 3, and 4 were discontinuously applied fixed axial compression load of 30N, 60N, 90N respectively on time. The weight loss of magnesium alloy screws and the amount of gas generated were measured regularly. Results: From the cumulative amount of hydrogen production, group 1 and group 4 were relatively large, whereas group 2 and group 3 were relatively small. From the weight loss of magnesium alloy screws, group 3 > group 4 > group 2 > group 1. Conclusions: The general trend is the greater the load, the faster the degradation

Social Services for Homeless in Pardubice

There is actual conditon of providing social services for homeless people in Pardubice in this graduation work, confront with the needs of these people that are expressed in the questionnaire. Social work and its distinction according the structure of service purpose in the institutions of social services for homeless people in Pardubice is described very properly in this graduation work. There is sollution of disproportion between offer and demand for individual social instituons in Pardubice suggested in the end of this graduation work. The main change would be in the origin of new institutions such as Lodging house for women, Subliminal institution for homeless people, Social and sanitary institution for homeless people and Housing for seniors and deseased homeless people in the end of this graduation work. Powered by TCPDF (www.tcpdf.org