211,689 research outputs found

    Performance of electrical energy monitoring data acquisition system for plant-based microbial fuel cell

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    Plant microbial fuel cell (Plant-MFC) is an emerging technology that uses the metabolic activity of electrochemically active bacteria (EABs) to continue the production of bioelectricity. Since its invention and to date, great efforts have been made for its application both in real-time and large-scale. However, the construction of platforms or systems for automatic voltage monitoring has been insufficiently studied. Therefore, this study aimed to develop an automatic real-time voltage data acquisition system, which was coupled with an ATMEGA2560 connected to a personal computer. Before the system operation started it was calibrated to obtain accurate data. During this experiment, the power generation performance of two types of reactors i.e. (i) Plant-MFC and (ii) control microbial fuel cell (C-MFC), was evaluated for 15 days. The Plant-MFC was planted with an herbaceous perennial plant (Stevia rebaudiana), electrode system was placed close to the plant roots at the depth of 20 cm. The results of the study have indicated that the Plant-MFC, was more effective and achieved higher bioelectricity generation than C-MFC. The maximum voltage reached with Plant-MFC was 850 mV (0.85 V), whereas C-MFC achieved a maximum voltage of 762 mV (0.772 V). Furthermore, the same reactor demonstrated a maximum power generation of 66 mW m¯2 on 10 min of polarization, while a power density with C-MFC was equal to 13.64 mW m¯2. S.rebaudiana showed a great alternative for power generation. In addition, the monitoring acquisition system was suitable for obtaining data in real-time. However, more studies are recommended to enhance this type of system

    Condition monitoring of induction motors in the nuclear power station environment

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    The induction motor is a highly utilised electrical machine in industry, with the nuclear industry being no exception. A typical nuclear power station usually contains more than 1000 motors, where they are used in safety and non-safety application. The efficient and fault-free operation of this machine is critical to the safe and economical operation of any plant, including nuclear power stations. A comprehensive literature review was conducted that covered the functioning of the induction machine, its common faults and methods of detecting these faults. The Condition Based Maintenance framework was introduced in which condition monitoring of induction machines is an essential component. The main condition monitoring methods were explained with the main focus being on Motor Current Signature Analysis (MCSA) and the various methods associated with it. Three analysis methods were selected for further study, namely, Current Signature Analysis, Instantaneous Power Signature Analysis (IPSA) and Motor Square Current Signature Analysis (MSCSA). Essentially, the methodology used in this dissertation was to study the three common motor faults (bearings, stator and rotor cage) in isolation and compare the results to that of the healthy motor of the same type. The test loads as well as fault severity were varied where possible to investigate its effect on the fault detection scheme. The data was processed using an FFT based algorithm programed in MATLAB. The results of the study of the three spectral analysis techniques showed that no single technique is able to detect motor faults under all tested circumstances. The MCSA technique proved the most capable of the three techniques as it was able to detect faults under most conditions, but generally suffered poor results in inverter driven motor applications. The IPSA and MSCSA techniques performed selectively when compared to MCSA and were relatively successful when detecting the mechanical faults. The fact that the former techniques produce results at unique points in the spectrum would suggest that they are more suitable for verifying results. As part of a comprehensive condition monitoring scheme, as required by a large population of the motors on a nuclear power station, the three techniques presented in this study could readily be incorporated into the Condition Based Maintenance framework where the strengths of each could be exploited

    Self-tuning routine alarm analysis of vibration signals in steam turbine generators

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    This paper presents a self-tuning framework for knowledge-based diagnosis of routine alarms in steam turbine generators. The techniques provide a novel basis for initialising and updating time series feature extraction parameters used in the automated decision support of vibration events due to operational transients. The data-driven nature of the algorithms allows for machine specific characteristics of individual turbines to be learned and reasoned about. The paper provides a case study illustrating the routine alarm paradigm and the applicability of systems using such techniques

    Energy efficiency in discrete-manufacturing systems: insights, trends, and control strategies

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    Since the depletion of fossil energy sources, rising energy prices, and governmental regulation restrictions, the current manufacturing industry is shifting towards more efficient and sustainable systems. This transformation has promoted the identification of energy saving opportunities and the development of new technologies and strategies oriented to improve the energy efficiency of such systems. This paper outlines and discusses most of the research reported during the last decade regarding energy efficiency in manufacturing systems, the current technologies and strategies to improve that efficiency, identifying and remarking those related to the design of management/control strategies. Based on this fact, this paper aims to provide a review of strategies for reducing energy consumption and optimizing the use of resources within a plant into the context of discrete manufacturing. The review performed concerning the current context of manufacturing systems, control systems implemented, and their transformation towards Industry 4.0 might be useful in both the academic and industrial dimension to identify trends and critical points and suggest further research lines.Peer ReviewedPreprin

    Self-tuning diagnosis of routine alarms in rotating plant items

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    Condition monitoring of rotating plant items in the energy generation industry is often achieved through examination of vibration signals. Engineers use this data to monitor the operation of turbine generators, gas circulators and other key plant assets. A common approach in such monitoring is to trigger an alarm when a vibration deviates from a predefined envelope of normal operation. This limit-based approach, however, generates a large volume of alarms not indicative of system damage or concern, such as operational transients that result in temporary increases in vibration. In the nuclear generation context, all alarms on rotating plant assets must be analysed and subjected to auditable review. The analysis of these alarms is often undertaken manually, on a case- by-case basis, but recent developments in monitoring research have brought forward the use of intelligent systems techniques to automate parts of this process. A knowledge- based system (KBS) has been developed to automatically analyse routine alarms, where the underlying cause can be attributed to observable operational changes. The initialisation and ongoing calibration of such systems, however, is a problem, as normal machine state is not uniform throughout asset life due to maintenance procedures and the wear of components. In addition, different machines will exhibit differing vibro- acoustic dynamics. This paper proposes a self-tuning knowledge-driven analysis system for routine alarm diagnosis across the key rotating plant items within the nuclear context common to the UK. Such a system has the ability to automatically infer the causes of routine alarms, and provide auditable reports to the engineering staff

    Review of best management practices for aquatic vegetation control in stormwater ponds, wetlands, and lakes

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    Auckland Council (AC) is responsible for the development and operation of a stormwater network across the region to avert risks to citizens and the environment. Within this stormwater network, aquatic vegetation (including plants, unicellular and filamentous algae) can have both a positive and negative role in stormwater management and water quality treatment. The situations where management is needed to control aquatic vegetation are not always clear, and an inability to identify effective, feasible and economical control options may constrain management initiatives. AC (Infrastructure and Technical Services, Stormwater) commissioned this technical report to provide information for decision- making on aquatic vegetation management with in stormwater systems that are likely to experience vegetation-related issues. Information was collated from a comprehensive literature review, augmented by knowledge held by the authors. This review identified a wide range of management practices that could be potentially employed. It also demonstrated complexities and uncertainties relating to these options that makes the identification of a best management practice difficult. Hence, the focus of this report was to enable users to screen for potential options, and use reference material provided on each option to confirm the best practice to employ for each situation. The report identifies factors to define whether there is an aquatic vegetation problem (Section 3.0), and emphasises the need for agreed management goals for control (e.g. reduction, mitigation, containment, eradication). Resources to screen which management option(s) to employ are provided (Section 4.0), relating to the target aquatic vegetation, likely applicability of options to the system being managed, indicative cost, and ease of implementation. Initial screening allows users to shortlist potential control options for further reference (Section 5.0). Thirty-five control options are described (Section 5.0) in sufficient detail to consider applicability to individual sites and species. These options are grouped under categories of biological, chemical or physical control. Biological control options involve the use of organisms to predate, infect or control vegetation growth (e.g. classical biological control) or manipulate conditions to control algal growth (e.g. pest fish removal, microbial products). Chemical control options involve the use of pesticides and chemicals (e.g. glyphosate, diquat), or the use of flocculants and nutrient inactivation products that are used to reduce nutrient loading, thereby decreasing algal growth. Physical control options involve removing vegetation or algal biomass (e.g. mechanical or manual harvesting), or setting up barriers to their growth (e.g. shading, bottom lining, sediment capping). Preventative management options are usually the most cost effective, and these are also briefly described (Section 6.0). For example, the use of hygiene or quarantine protocols can reduce weed introductions or spread. Catchment- based practices to reduce sediment and nutrient sources to stormwater are likely to assist in the avoidance of algal and possibly aquatic plant problems. Nutrient removal may be a co-benefit where harvesting of submerged weed biomass is undertaken in stormwater systems. It should also be considered that removal of substantial amounts of submerged vegetation may result in a sudden and difficult-to-reverse s witch to a turbid, phytoplankton dominated state. Another possible solution is the conversion of systems that experience aquatic vegetation issues, to systems that are less likely to experience issues. The focus of this report is on systems that receive significant stormwater inputs, i.e. constructed bodies, including ponds, amenity lakes, wetlands, and highly-modified receiving bodies. However, some information will have application to other natural water bodies

    An overview of current status of carbon dioxide capture and storage technologies

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    AbstractGlobal warming and climate change concerns have triggered global efforts to reduce the concentration of atmospheric carbon dioxide (CO2). Carbon dioxide capture and storage (CCS) is considered a crucial strategy for meeting CO2 emission reduction targets. In this paper, various aspects of CCS are reviewed and discussed including the state of the art technologies for CO2 capture, separation, transport, storage, leakage, monitoring, and life cycle analysis. The selection of specific CO2 capture technology heavily depends on the type of CO2 generating plant and fuel used. Among those CO2 separation processes, absorption is the most mature and commonly adopted due to its higher efficiency and lower cost. Pipeline is considered to be the most viable solution for large volume of CO2 transport. Among those geological formations for CO2 storage, enhanced oil recovery is mature and has been practiced for many years but its economical viability for anthropogenic sources needs to be demonstrated. There are growing interests in CO2 storage in saline aquifers due to their enormous potential storage capacity and several projects are in the pipeline for demonstration of its viability. There are multiple hurdles to CCS deployment including the absence of a clear business case for CCS investment and the absence of robust economic incentives to support the additional high capital and operating costs of the whole CCS process

    Consistency Index-Based Sensor Fault Detection System for Nuclear Power Plant Emergency Situations Using an LSTM Network

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    A nuclear power plant (NPP) consists of an enormous number of components with complex interconnections. Various techniques to detect sensor errors have been developed to monitor the state of the sensors during normal NPP operation, but not for emergency situations. In an emergency situation with a reactor trip, all the plant parameters undergo drastic changes following the sudden decrease in core reactivity. In this paper, a machine learning model adopting a consistency index is suggested for sensor error detection during NPP emergency situations. The proposed consistency index refers to the soundness of the sensors based on their measurement accuracy. The application of consistency index labeling makes it possible to detect sensor error immediately and specify the particular sensor where the error occurred. From a compact nuclear simulator, selected plant parameters were extracted during typical emergency situations, and artificial sensor errors were injected into the raw data. The trained system successfully generated output that gave both sensor error states and error-free states

    On-line transformer condition monitoring through diagnostics and anomaly detection

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    This paper describes the end-to-end components of an on-line system for diagnostics and anomaly detection. The system provides condition monitoring capabilities for two in- service transmission transformers in the UK. These transformers are nearing the end of their design life, and it is hoped that intensive monitoring will enable them to stay in service for longer. The paper discusses the requirements on a system for interpreting data from the sensors installed on site, as well as describing the operation of specific diagnostic and anomaly detection techniques employed. The system is deployed on a substation computer, collecting and interpreting site data on-line
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