The value of a saved tree : The lifecycle of CO2-emissions associated with the combination of printing paper and electricity production of wood in the U.S. and Sweden

The pulp- and paper production is a very energy intensive industry sector. Both Sweden and the U.S. are major pulpandpaper producers. This report examines the energy and the CO2-emission connected with the pulp- and paperindustry for the two countries from a lifecycle perspective.New technologies make it possible to increase the electricity production in the integrated pulp- andpaper mill through black liquor gasification and a combined cycle (BLGCC). That way, the mill canproduce excess electricity, which can be sold and replace electricity produced in power plants. In thisprocess the by-products that are formed at the pulp-making process is used as fuel to produce electricity.In pulp- and paper mills today the technology for generating energy from the by-product in aTomlinson boiler is not as efficient as it could be compared to the BLGCC technology. Scenarios havebeen designed to investigate the results from using the BLGCC technique using a life cycle analysis.Two scenarios are being represented by a 1994 mill in the U.S. and a 1994 mill in Sweden.The scenariosare based on the average energy intensity of pulp- and paper mills as operating in 1994 in the U.S.and Sweden respectively. The two other scenarios are constituted by a »reference mill« in the U.S. andSweden using state-of-the-art technology. We investigate the impact of varying recycling rates and totalenergy use and CO2-emissions from the production of printing and writing paper. To economize withthe wood and that way save trees, we can use the trees that are replaced by recycling in a biomassgasification combined cycle (BIGCC) to produce electricity in a power station. This produces extra electricitywith a lower CO2 intensity than electricity generated by, for example, coal-fired power plants.The lifecycle analysis in this thesis also includes the use of waste treatment in the paper lifecycle. Both Sweden and theU.S. are countries that recycle paper. Still there is a lot of paper waste, this paper is a part of the countries municipalsolid waste (MSW). A lot of the MSW is landfilled, but parts of it are incinerated to extract electricity. The thesis hasdesigned special scenarios for the use of MSW in the lifecycle analysis.This report is studying and comparing two different countries and two different efficiencies on theBLGCC in four different scenarios. This gives a wide survey and points to essential parameters to specificallyreflect on, when making assumptions in a lifecycle analysis. The report shows that there arethree key parameters that have to be carefully considered when making a lifecycle analysis of wood inan energy and CO2-emission perspective in the pulp- and paper mill in the U.S. and in Sweden. First,there is the energy efficiency in the pulp- and paper mill, then the efficiency of the BLGCC and last theCO2 intensity of the electricity displaced by BIGCC or BLGCC generatedelectricity. It also show that with the current technology that we havetoday, it is possible to produce CO2 free paper with a waste paper amountup to 30%. The thesis discusses the system boundaries and the assumptions.Further and more detailed research, including amongst others thesystem boundaries and forestry, is recommended for more specificanswers.

Classification of Parkinson’s Disease using MultiPass Lvq,Logistic Model Tree,K-Star for Audio Data set : Classification of Parkinson Disease using Audio Dataset

Parkinson's disease (PD) is a degenerative illness whose cardinal symptoms include rigidity, tremor, and slowness of movement. In addition to its widely recognized effects PD can have a profound effect on speech and voice.The speech symptoms most commonly demonstrated by patients with PD are reduced vocal loudness, monopitch, disruptions of voice quality, and abnormally fast rate of speech. This cluster of speech symptoms is often termed Hypokinetic Dysarthria.The disease can be difficult to diagnose accurately, especially in its early stages, due to this reason, automatic techniques based on Artificial Intelligence should increase the diagnosing accuracy and to help the doctors make better decisions. The aim of the thesis work is to predict the PD based on the audio files collected from various patients.Audio files are preprocessed in order to attain the features.The preprocessed data contains 23 attributes and 195 instances. On an average there are six voice recordings per person, By using data compression technique such as Discrete Cosine Transform (DCT) number of instances can be minimized, after data compression, attribute selection is done using several WEKA build in methods such as ChiSquared, GainRatio, Infogain after identifying the important attributes, we evaluate attributes one by one by using stepwise regression.Based on the selected attributes we process in WEKA by using cost sensitive classifier with various algorithms like MultiPass LVQ, Logistic Model Tree(LMT), K-Star.The classified results shows on an average 80%.By using this features 95% approximate classification of PD is acheived.This shows that using the audio dataset, PD could be predicted with a higher level of accuracy.

Predictive models for chronic renal disease using decision trees, naïve bayes and case-based methods

Data mining can be used in healthcare industry to “mine” clinical data to discover hidden information for intelligent and affective decision making. Discovery of hidden patterns and relationships often goes intact, yet advanced data mining techniques can be helpful as remedy to this scenario. This thesis mainly deals with Intelligent Prediction of Chronic Renal Disease (IPCRD). Data covers blood, urine test, and external symptoms applied to predict chronic renal disease. Data from the database is initially transformed to Weka (3.6) and Chi-Square method is used for features section. After normalizing data, three classifiers were applied and efficiency of output is evaluated. Mainly, three classifiers are analyzed: Decision Tree, Naïve Bayes, K-Nearest Neighbour algorithm. Results show that each technique has its unique strength in realizing the objectives of the defined mining goals. Efficiency of Decision Tree and KNN was almost same but Naïve Bayes proved a comparative edge over others. Further sensitivity and specificity tests are used as statistical measures to examine the performance of a binary classification. Sensitivity (also called recall rate in some fields) measures the proportion of actual positives which are correctly identified while Specificity measures the proportion of negatives which are correctly identified. CRISP-DM methodology is applied to build the mining models. It consists of six major phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment.

Multiclass Adaboost Based on an Ensemble of Binary Adaboosts

This paper presents a multi-class AdaBoost based on incorporating an ensemble of binary AdaBoosts which is organized as Binary Decision Tree (BDT). It is proved that binary AdaBoost is extremely successful in producing accurate classification but it does not perform very well for multi-class problems. To avoid this performance degradation, the multi-class problem is divided into a number of binary problems and binary AdaBoost classifiers are invoked to solve these classification problems. This approach is tested with a dataset consisting of 6500 binary images of traffic signs. Haar-like features of these images are computed and the multi-class AdaBoost classifier is invoked to classify them. A classification rate of 96.7% and 95.7% is achieved for the traffic sign boarders and pictograms, respectively. The proposed approach is also evaluated using a number of standard datasets such as Iris, Wine, Yeast, etc. The performance of the proposed BDT classifier is quite high as compared with the state of the art and it converges very fast to a solution which indicates it as a reliable classifier.