Evaluation of slagging and fouling tendency during biomass co-firing with coal in a fluidized bed

Over the last decades, several indices based on ash chemistry and ash fusibility have been used to predict the ash behaviour during coal combustion, namely, its tendency for slagging and fouling. However, due to the physicalechemical differences between coals and biomass, in this work only the applicability of an ash fusibility index (AFI) to the combustion and co-combustion of three types of biomass (straw pellets, olive cake and wood pellets) with coals was evaluated. The AFI values were compared with the behaviour of ash during combustion in a pilot fluidized bed and a close agreement was observed between them. For a better understanding of the mechanisms associated with bed ash sintering, they were evaluated by SEM/EDS and the elements present on the melted ash were identified. Evidences of different sintering mechanisms were found out for the fruit biomass and herbaceous biomass tested, depending on the relative proportions of problematic elements. The particles deposited on a fouling probe inserted in the FBC were analyzed by XRD and the differences between the compounds identified allowed concluding that the studied biomasses present different tendencies for fouling. Identification of KCl and K2SO4 in the deposits confirmed the higher tendency for fouling of fruit biomass tested rather than wood pellets

A soft computing approach to kidney diseases evaluation

Kidney renal failure means that one’s kidney have unexpectedly stopped functioning, i.e., once chronic disease is exposed, the presence or degree of kidney dysfunction and its progression must be assessed, and the underlying syndrome has to be diagnosed. Although the patient’s history and physical examination may denote good practice, some key information has to be obtained from valuation of the glomerular filtration rate, and the analysis of serum biomarkers. Indeed, chronic kidney sickness depicts anomalous kidney function and/or its makeup, i.e., there is evidence that treatment may avoid or delay its progression, either by reducing and prevent the development of some associated complications, namely hypertension, obesity, diabetes mellitus, and cardiovascular complications. Acute kidney injury appears abruptly, with a rapid deterioration of the renal function, but is often reversible if it is recognized early and treated promptly. In both situations, i.e., acute kidney injury and chronic kidney disease, an early intervention can significantly improve the prognosis.The assessment of these pathologies is therefore mandatory, although it is hard to do it with traditional methodologies and existing tools for problem solving. Hence, in this work, we will focus on the development of a hybrid decision support system, in terms of its knowledge representation and reasoning procedures based on Logic Programming, that will allow one to consider incomplete, unknown, and even contradictory information, complemented with an approach to computing centered on Artificial Neural Networks, in order to weigh the Degree-of-Confidence that one has on such a happening. The present study involved 558 patients with an age average of 51.7 years and the chronic kidney disease was observed in 175 cases. The dataset comprise twenty four variables, grouped into five main categories. The proposed model showed a good performance in the diagnosis of chronic kidney disease, since the sensitivity and the specificity exhibited values range between 93.1 and 94.9 and 91.9–94.2 %, respectively

Analysis of electroencephalogram-derived indexes for anesthetic depth monitoring in pediatric patients with intellectual disability undergoing dental surgery

Background: Patients with intellectual disability (ID) often require general anesthesia during oral procedures. Anesthetic depth monitoring in these patients can be difficult due to their already altered mental state prior to anesthesia. In this study, the utility of electroencephalographic indexes to reflect anesthetic depth was evaluated in pediatric patients with ID. Methods: Seventeen patients (mean age, 9.6 ± 2.9 years) scheduled for dental procedures were enrolled in this study. After anesthesia induction with propofol or sevoflurane, a bilateral sensor was placed on the patient's forehead and the bispectral index (BIS) was recorded. Anesthesia was maintained with sevoflurane, which was adjusted according to the clinical signs by an anesthesiologist blinded to the BIS value. The index performance was accessed by correlation (with the end-tidal sevoflurane [EtSevo] concentration) and prediction probability (with a clinical scale of anesthesia). The asymmetry of the electroencephalogram between the left and right sides was also analyzed. Results: The BIS had good correlation and prediction probabilities (above 0.5) in the majority of patients; however, BIS was not correlated with EtSevo or the clinical scale of anesthesia in patients with Lennox-Gastaut, West syndrome, cerebral palsy, and epilepsy. BIS showed better correlations than SEF95 and TP. No significant differences were observed between the left- and right-side indexes. Conclusion: BIS may be able to reflect sevoflurane anesthetic depth in patients with some types of ID; however, more research is required to better define the neurological conditions and/or degrees of disability that may allow anesthesiologists to use the BIS.Aura Silva’s work was supported by Portuguese Foundation for Science and Technology, reference SFRH/BPD /75697/2011info:eu-repo/semantics/publishedVersio

Fluidised bed combustion of two species of energy crops

The use of biomass fuels for energy production through combustion has a growing application worldwide mainly for two reasons: first, the utilization of biomass for energy contributes to mitigate emission of green house gases; second, its use decreases the dependence of imported fossil fuels in Europe. The objective of this work was to study the combustion behaviour of two endogenous biomass species: cardoon (cynara cardunculus) and arundo (arundo donax), which were specially produced in energy crops plantations. Mixtures of cardoon and a forestry biomass specie (eucalyptus) were also studied to evaluate potential benefits from synergies between both biomass fuel types. The results showed that the utilization of cardoon, in pelletized form, and loose arundo as feedstock, did not give rise to any operational problems related with the feeding system. It was verified that the mono combustion of cardoon could pose problems at industrial scale in fluidised bed systems, considering the high levels of HCl and NOX emissions obtained and tendency to sinter the bed sand material. The addition of the forestry biomass to cardoon appeared to prevent the bed agglomeration problem. Furthermore, both the NOX and SO2 emissions were found to decrease at the same time suggesting potential synergy of blending different types of biomass regarding pollutant emissions and in bed agglomeration problems

Co-firing of biomass and other wastes in fluidised bed systems

A project on co-firing in large-scale power plants burning coal is currently funded by the European Commission. It is called COPOWER. The project involves 10 organisations from 6 countries. The project involves combustion studies over the full spectrum of equipment size, ranging from small laboratory-scale reactors and pilot plants, to investigate fundamentals and operating parameters, to proving trials on a commercial power plant in Duisburg. The power plant uses a circulating fluidized bed boiler. The results to be obtained are to be compared as function of scale-up. There are two different coals, 3 types of biomass and 2 kinds of waste materials are to be used for blending with coal for co-firing tests. The baseline values are obtained during a campaign of one month at the power station and the results are used for comparison with those to be obtained in other units of various sizes. Future tests will be implemented with the objective to achieve improvement on baseline values. The fuels to be used are already characterized. There are ongoing studies to determine reactivities of fuels and chars produced from the fuels. Reactivities are determined not only for individual fuels but also for blends to be used. Presently pilot-scale combustion tests are also undertaken to study the effect of blending coal with different types of biomass and waste materials. The potential for synergy to improve combustion is investigated. Early results will be reported in the Conference. Simultaneously, studies to verify the availability of biomass and waste materials in Portugal, Turkey and Italy have been undertaken. Techno-economic barriers for the future use of biomass and other waste materials are identified. The potential of using these materials in coal fired power stations has been assessed. The conclusions will also be reported

Using data mining for prediction of hospital length of stay: an application of the CRISP-DM Methodology

Hospitals are nowadays collecting vast amounts of data related with patient records. All this data hold valuable knowledge that can be used to improve hospital decision making. Data mining techniques aim precisely at the extraction of useful knowledge from raw data. This work describes an implementation of a medical data mining project approach based on the CRISP-DM methodology. Recent real-world data, from 2000 to 2013, were collected from a Portuguese hospital and related with inpatient hospitalization. The goal was to predict generic hospital Length Of Stay based on indicators that are commonly available at the hospitalization process (e.g., gender, age, episode type, medical specialty). At the data preparation stage, the data were cleaned and variables were selected and transformed, leading to 14 inputs. Next, at the modeling stage, a regression approach was adopted, where six learning methods were compared: Average Prediction, Multiple Regression, Decision Tree, Artificial Neural Network ensemble, Support Vector Machine and Random Forest. The best learning model was obtained by the Random Forest method, which presents a high quality coefficient of determination value (0.81). This model was then opened by using a sensitivity analysis procedure that revealed three influential input attributes: the hospital episode type, the physical service where the patient is hospitalized and the associated medical specialty. Such extracted knowledge confirmed that the obtained predictive model is credible and with potential value for supporting decisions of hospital managers

Antimicrobial and Photoantimicrobial Activities of Chitosan/CNPPV Nanocomposites

Multidrug-resistant bacteria represent a global health and economic burden that urgently calls for new technologies to combat bacterial antimicrobial resistance. Here, we developed novel nanocomposites (NCPs) based on chitosan that display different degrees of acetylation (DAs), and conjugated polymer cyano-substituted poly(p-phenylene vinylene) (CNPPV) as an alternative approach to inactivate Gram-negative (E. coli) and Gram-positive (S. aureus) bacteria. Chitosan's structure was confirmed through FT-Raman spectroscopy. Bactericidal and photobactericidal activities of NCPs were tested under dark and blue-light irradiation conditions, respectively. Hydrodynamic size and aqueous stability were determined by DLS, zeta potential (ZP) and time-domain NMR. TEM micrographs of NCPs were obtained, and their capacity of generating reactive oxygen species (ROS) under blue illumination was also characterized. Meaningful variations on ZP and relaxation time T2 confirmed successful physical attachment of chitosan/CNPPV. All NCPs exhibited a similar and shrunken spherical shape according to TEM. A lower DA is responsible for driving higher bactericidal performance alongside the synergistic effect from CNPPV, lower nanosized distribution profile and higher positive charged surface. ROS production was proportionally found in NCPs with and without CNPPV by decreasing the DA, leading to a remarkable photobactericidal effect under blue-light irradiation. Overall, our findings indicate that chitosan/CNPPV NCPs may constitute a valuable asset for the development of innovative strategies for inactivation and/or photoinactivation of bacteria. Keywords: photoantimicrobial activity; blue-light irradiation; chitosan; CNPPV; nanocomposites; E. coli; S. aureu

Logic programming and artificial neural networks in breast cancer detection

About 90% of breast cancers do not cause or are capable of producing death if detected at an early stage and treated properly. Indeed, it is still not known a specific cause for the illness. It may be not only a beginning, but also a set of associations that will determine the onset of the disease. Undeniably, there are some factors that seem to be associated with the boosted risk of the malady. Pondering the present study, different breast cancer risk assessment models where considered. It is our intention to develop a hybrid decision support system under a formal framework based on Logic Programming for knowledge representation and reasoning, complemented with an approach to computing centered on Artificial Neural Networks, to evaluate the risk of developing breast cancer and the respective Degree-of-Confidence that one has on such a happening.This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the Project Scope UID/CEC/00319/2013