Improving multivariate data streams clustering.

Clustering data streams is an important task in data mining research. Recently, some algorithms have been proposed to cluster data streams as a whole, but just few of them deal with multivariate data streams. Even so, these algorithms merely aggregate the attributes without touching upon the correlation among them. In order to overcome this issue, we propose a new framework to cluster multivariate data streams based on their evolving behavior over time, exploring the correlations among their attributes by computing the fractal dimension. Experimental results with climate data streams show that the clusters' quality and compactness can be improved compared to the competing method, leading to the thoughtfulness that attributes correlations cannot be put aside. In fact, the clusters' compactness are 7 to 25 times better using our method. Our framework also proves to be an useful tool to assist meteorologists in understanding the climate behavior along a period of time.

Assessment of hydrogen sulfide removal from biogas using a field scale anoxic biotrickling filter.

Hydrogen sulphide is one of the most toxic and corrosive compound present in swine-derived biogas streams.In this study, afield scale biotrickling filter for the removal of hydrogen sulfide was investigated.A Biofilter packed with supporting biofilm materials was fed continuously with a proprietary nutrient solution and operatedfor over 73days. The system has been operating with a H2S inlet concentrations ranging from 1,000to 3,000 ppm.Significant removal efficiencies >95% was demonstrated. pH of the stock feeding solution decreased from 6.2 to as low as 3.5within couple days.The resulting drop in pH provided circumstantial evidence to support biological H2 Soxidation to sulphuric acid by sulfide-oxidizers. Sulfur precipitation was also observed to occur. The results suggested that H2S removal from biogas stream can be efficiently achieved using portable, low cost and maintenance free biotrickling filters.