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Haimanot Habte Lemji Hartmut Eckstädt 《Journal of Zhejiang University. Science. B》2013,14(10):924-933
Evaluating the performance of a biotrickling filter for the treatment of wastewaters produced by a company manufacturing beer was the aim of this study. A pilot scale trickling filter filled with gravel was used as the experimental biofilter. Pilot scale plant experiments were made to evaluate the performance of the trickling filter aerobic and anaerobic biofilm systems for removal of chemical oxygen demand (COD) and nutrients from synthetic brewery wastewater. Performance evaluation data of the trickling filter were generated under different experimental conditions. The trickling filter had an average efficiency of (86.81±6.95)% as the hydraulic loading rate increased from 4.0 to 6.4 m3/(m2·d). Various COD concentrations were used to adjust organic loading rates from 1.5 to 4.5 kg COD/(m3·d). An average COD removal efficiency of (85.10±6.40)% was achieved in all wastewater concentrations at a hydraulic loading of 6.4 m3/(m2·d). The results lead to a design organic load of 1.5 kg COD/(m3·d) to reach an effluent COD in the range of 50–120 mg/L. As can be concluded from the results of this study, organic substances in brewery wastewater can be handled in a cost-effective and environmentally friendly manner using the gravel-filled trickling filter. 相似文献
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Classifying Amharic webnews 总被引：1，自引：1，他引：0
Lars Asker Atelach Alemu Argaw Björn Gambäck Samuel Eyassu Asfeha Lemma Nigussie Habte 《Information Retrieval》2009,12(3):416-435
We present work aimed at compiling an Amharic corpus from the Web and automatically categorizing the texts. Amharic is the
second most spoken Semitic language in the World (after Arabic) and used for countrywide communication in Ethiopia. It is
highly inflectional and quite dialectally diversified. We discuss the issues of compiling and annotating a corpus of Amharic
news articles from the Web. This corpus was then used in three sets of text classification experiments. Working with a less-researched
language highlights a number of practical issues that might otherwise receive less attention or go unnoticed. The purpose
of the experiments has not primarily been to develop a cutting-edge text classification system for Amharic, but rather to
put the spotlight on some of these issues. The first two sets of experiments investigated the use of Self-Organizing Maps
(SOMs) for document classification. Testing on small datasets, we first looked at classifying unseen data into 10 predefined
categories of news items, and then at clustering it around query content, when taking 16 queries as class labels. The second
set of experiments investigated the effect of operations such as stemming and part-of-speech tagging on text classification
performance. We compared three representations while constructing classification models based on bagging of decision trees
for the 10 predefined news categories. The best accuracy was achieved using the full text as representation. A representation
using only the nouns performed almost equally well, confirming the assumption that most of the information required for distinguishing
between various categories actually is contained in the nouns, while stemming did not have much effect on the performance
of the classifier.
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| Lemma Nigussie HabteEmail: |
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