Per Sieverts Nielsen

Pretreatment of wheat straw and conversion of xylose and xylan to ethanol by thermophilic anaerobic bacteria

Wheat straw was pretreated by wet oxidation (oxygen pressure, alkaline conditions, elevated temperature) or hydrothermal processing (without oxygen) in order to solubilize the hemicellulose, facilitating bio-conversion. The effect of oxygen pressure and sodium carbonate addition on hemicellulose solubilization was investigated. The two process parameters had little effect on the solubilization of hemicellulose. However, alkaline conditions affected the furfural formation, whereas oxygen had no effect. After pretreatment, the filtrate was used as a fermentation medium for thermophilic anaerobic bacteria. Of five different thermophilic bacteria used in this study only two strains produced ethanol with xylan as substrate, one of them being the strain A3 isolated from an Icelandic hot-spring. Probably other degradation products formed in the presence of oxygen might act as inhibitors. Adaptation of the microorganism to the wet oxidized filtrate was also examined.

The Danish incinerator dioxin study. Part 1

Abstract During the last two years an extensive series of dioxin measurements has been conducted on Danish municipal and hospital solid waste incinerators. The study was directed toward finding the total annual dioxin emissions from MSWI in Denmark, now estimated to be 3 kg. of dioxines and furanes. This sum is equivalent to 50 g. of 2,3,7,8-TCDD according to the Nordic Equivalents. Measurements were carried out according to a statistical design following a plan of pre-randomized sampling. This procedure allowed causal interpretation of the correlations found between the dioxin emissions and certain operating parameters. The statistical model obtained describes the emissions by variations between incinerators, by variation in time, and by changes in the load, the excess air and the HCl concentration in the flue gas.

A Hybrid ICT-Solution for Smart Meter Data Analytics

Smart meters are increasingly used worldwide. Smart meters are the advanced meters capable of measuring energy consumption at a fine-grained time interval, e.g., every 15 min. Smart meter data are typically bundled with social economic data in analytics, such as meter geographic locations, weather conditions and user information, which makes the data sets very sizable and the analytics complex. Data mining and emerging cloud computing technologies make collecting, processing, and analyzing the so-called big data possible. This paper proposes an innovative ICT-solution to streamline smart meter data analytics. The proposed solution offers an information integration pipeline for ingesting data from smart meters, a scalable platform for processing and mining big data sets, and a web portal for visualizing analytics results. The implemented system has a hybrid architecture of using Spark or Hive for big data processing, and using the machine learning toolkit, MADlib, for doing in-database data analytics in PostgreSQL database. This paper evaluates the key technologies of the proposed ICT-solution, and the results show the effectiveness and efficiency of using the system for both batch and online analytics.

Logistics cost analysis of rice residues for second generation bioenergy production in Ghana.

This study explores the techno-economic potential of rice residues as a bioenergy resource to meet Ghanas energy demands. Major rice growing regions of Ghana have 70-90% of residues available for bioenergy production. To ensure cost-effective biomass logistics, a thorough cost analysis was made for two bioenergy routes. Logistics costs for a 5 MWe straw combustion plant were 39.01, 47.52 and 47.89 USD/t for Northern, Ashanti and Volta regions respectively. Logistics cost for a 0.25 MWe husk gasification plant (with roundtrip distance 10 km) was 2.64 USD/t in all regions. Capital cost (66-72%) contributes significantly to total logistics costs of straw, however for husk logistics, staff (40%) and operation and maintenance costs (46%) dominate. Baling is the major processing logistic cost for straw, contributing to 46-48% of total costs. Scale of straw unit does not have a large impact on logistic costs. Transport distance of husks has considerable impact on logistic costs.

CITIESData: a smart city data management framework

Smart city data come from heterogeneous sources including various types of the Internet of Things such as traffic, weather, pollution, noise, and portable devices. They are characterized with diverse quality issues and with different types of sensitive information. This makes data processing and publishing challenging. In this paper, we propose a framework to streamline smart city data management, including data collection, cleansing, anonymization, and publishing. The paper classifies smart city data in sensitive, quasi-sensitive, and open/public levels and then suggests different strategies to process and publish the data within these categories. The paper evaluates the framework using a real-world smart city data set, and the results verify its effectiveness and efficiency. The framework can be a generic solution to manage smart city data.

Online Anomaly Energy Consumption Detection Using Lambda Architecture

With the widely use of smart meters in the energy sector, anomaly detection becomes a crucial mean to study the unusual consumption behaviors of customers, and to discover unexpected events of using energy promptly. Detecting consumption anomalies is, essentially, a real-time big data analytics problem, which does data mining on a large amount of parallel data streams from smart meters. In this paper, we propose a supervised learning and statistical-based anomaly detection method, and implement a Lambda system using the in-memory distributed computing framework, Spark and its extension Spark Streaming. The system supports not only iterative refreshing the detection models from scalable data sets, but also real-time anomaly detection on scalable live data streams. This paper empirically evaluates the system and the detection algorithm, and the results show the effectiveness and the scalability of the lambda detection system.

SciCloud: A Scientific Cloud and Management Platform for Smart City Data

The pervasive use of Internet of Things and smart meter technologies in smart cities increases the complexity of managing the data, due to their sizes, diversity, and privacy issues. This requires an innovate solution to process and manage the data effectively. This paper presents an elastic private scientific cloud, SciCloud, to tackle these grand challenges. SciCloud provides on-demand computing resource provisions, a scalable data management platform and an in-place data analytics environment to support the scientific research using smart city data.

Environmental assessment of Smart City Solutions using a coupled urban metabolism—life cycle impact assessment approach

PurposeThe purpose of the study is to quantify the environmental performance of Smart City Solutions at urban system level and thus evaluate their contribution to develop environmentally sustainable urban systems. Further, the study illustrates how this quantification is conducted.MethodsThe case city chosen in our modeling is Copenhagen, where seven Smart City Solutions are introduced: Green Roofs, Smart Windows, Pneumatic Waste Collection, Sensorized Waste Collection, Smart Water Meters, Greywater Recycling, and Smart Energy Grid. The assessment is conducted using a fused urban metabolism (UM)-life cycle assessment (LCA) approach, referred to as UM-LCA. The UM-LCA uses metabolic flows across an urban system as inputs and outputs in an LCA. All life cycle stages of the metabolic flows can be accounted for by using this approach and burden shifting from one stage to another is made quantifiable and hence transparent. The impact assessment is conducted using the ReCiPe method.Results and discussionThe results obtained for the midpoint indicator, global warming potential (GWP), show reduced environmental performance effect at 75% relative to a business as usual reference scenario by introducing Smart Windows. Furthermore, the GWP indicator shows an environmental improvement of 10% for a Smart Energy Grid solution. Introduction of Pneumatic Waste Collection or Greywater Recycling reveals a minor negative performance effect of 0.76 and 0.70%, respectively, for GWP. The performance changes in terms of GWP for the remaining solutions are so small that these are expected to be within the uncertainty of the calculations. To obtain endpoint indicators (damages), the entire palette of ReCiPe indicators is included. The results of the endpoint indicator assessment yield a tendency similar to the one observed for climate change.ConclusionsIt is found that the implementation of Smart City Solutions generally has a negative influence on the environmental sustainability performance of an urban system. The limited positive influence from the Smart City Solutions is due to burden shifting from the direct impacts of the urban system to embedded impacts which are out of sight for most policy makers. The influence of the Solutions on Copenhagen is generally small, due to a focus on reducing in areas that are not a large environmental burden in Copenhagen. The results are not sufficient to discard the idea of using Smart City Solutions to reduce environmental impacts, but highlight the importance of choosing solutions with the right focus and optimizing the design to best fit the intensions.

Scalable prediction-based online anomaly detection for smart meter data

Abstract Today smart meters are widely used in the energy sector to record energy consumption in real time. Large amounts of smart meter data have been accumulated and used for diverse analysis purposes. Anomaly detection raises the big data problem, namely the detection of abnormal events or unusual consumption behaviors. However, there is a lack of appropriate online systems that can handle anomaly detection for large-scale smart meter data effectively and efficiently. This paper proposes a lambda system for detecting anomalous consumption patterns, aiming at assisting decision makings for smart energy management. The proposed system uses a prediction-based detection method, combined with a novel lambda architecture for iterative model updates and real-time anomaly detection. This paper evaluates the system using a real-world data set and a large synthetic data set, and compares with three baselines. The results show that the proposed system has good scalability, and has a competitive advantage over others in anomaly detection.

Air Quality Monitoring System and Benchmarking

Air quality monitoring has become an integral part of smart city solutions. This paper presents an air quality monitoring system based on Internet of Things (IoT) technologies, and establishes a cloud-based platform to address the challenges related to IoT data management and processing capabilities, including data collection, storage, analysis, and visualization. In addition, this paper also benchmarks four state-of-the-art database systems to investigate the appropriate technologies for managing large-scale IoT datasets.