Sewer and oil pipeline spillage issues have become major causes of pollution in urban and rural areas usually caused by blockages in the water storage and drainage system, and oil spillage of underground oil pipelines. An effective way of avoiding this problem will be by deploying some mechanism to monitor these installations at each point in time and reporting unusual liquid activity to the relevant authorities for prompt action to avoid a flooding or spillage occurrence. This research work presents a low cost energy efficient liquid level monitoring technique using Radio Frequency Identification Technology. Passive UHF RFID tags have been designed, modelled and optimized. A simple rectangular tag, the P-shaped tag and S-shaped tag with UHF band frequency of operation (850-950 MHz) has been designed and modelled. Detailed parametric analysis of the rectangular tag is made and the optimised design results analysed and presented in HFSS and Matlab. The optimised rectangular tag designs are then deployed as level sensors in a gully pot. Identical tags were deployed to detect 4 distinct levels in alternate positions and a few inches in seperation distance within the gully pot height (Low, Mid, High and Ultra high). The radiation characteristic of tag sensors in deployment as modelled on HFSS is observed to show consistent performance with application requirements. An in-manhole chamber antenna for an underground communication system is analysed, designed, deployed and measured. The antenna covers dual-band impedance bandwidths (i.e. 824 to 960 MHz, and 1710 to 2170 MHz). The results show that the antenna prototype exhibits sufficient impedance bandwidth, suitable radiation characteristics, and adequate gains for the required underground wireless sensor applications. Finally, a Linearly Shifted Quadrifilar Helical Antenna (LSQHA) designed using Genetic Algorithm optimisation technique for adoption as an RFID reader antenna is proposed and investigated. The new antenna confirms coverage of the RFID bandwidth 860-960 MHz with acceptable power gain of 13.1 dBi.

Sewer and oil pipeline spillage issues have become major causes of pollution in urban and rural areas usually caused by blockages in the water storage and drainage system, and oil spillage of underground oil pipelines. An effective way of avoiding this problem will be by deploying some mechanism to monitor these installations at each point in time and reporting unusual liquid activity to the relevant authorities for prompt action to avoid a flooding or spillage occurrence. This research work presents a low cost energy efficient liquid level monitoring technique using Radio Frequency Identification Technology. Passive UHF RFID tags have been designed, modelled and optimized. A simple rectangular tag, the P-shaped tag and S-shaped tag with UHF band frequency of operation (850-950 MHz) has been designed and modelled. Detailed parametric analysis of the rectangular tag is made and the optimised design results analysed and presented in HFSS and Matlab. The optimised rectangular tag designs are then deployed as level sensors in a gully pot. Identical tags were deployed to detect 4 distinct levels in alternate positions and a few inches in seperation distance within the gully pot height (Low, Mid, High and Ultra high). The radiation characteristic of tag sensors in deployment as modelled on HFSS is observed to show consistent performance with application requirements. An in-manhole chamber antenna for an underground communication system is analysed, designed, deployed and measured. The antenna covers dual-band impedance bandwidths (i.e. 824 to 960 MHz, and 1710 to 2170 MHz). The results show that the antenna prototype exhibits sufficient impedance bandwidth, suitable radiation characteristics, and adequate gains for the required underground wireless sensor applications. Finally, a Linearly Shifted Quadrifilar Helical Antenna (LSQHA) designed using Genetic Algorithm optimisation technique for adoption as an RFID reader antenna is proposed and investigated. The new antenna confirms coverage of the RFID bandwidth 860-960 MHz with acceptable power gain of 13.1 dBi. / Petroleum Technology Development Fund (PTDF) and National Space Research and Development Agency (NASRDA).

<p>  </p> <p>Advanced high temperature fluid reactors (AR), such as sodium fast reactors (SFR) and molten salt cooled reactors (MSCR) are promising nuclear energy options, which offer lower levelized electricity costs compared to existing light water reactors (LWR). Increasing economic competitiveness of ARs in the open market involves developing strategies for reducing operation and maintenance (O&M) costs. Digitization of AR’s allows to implement continuous on-line monitoring paradigm to achieve early detection of incipient problems, and thus reduce O&M costs. Machine learning (ML) algorithms offer a number of advantages for reactor monitoring through anticipation of key performance variables using data-driven process models. ML model does not require detailed knowledge of the system, which could be difficult to obtain or unavailable because of commercial privacy restrictions. In addition, any data obtained from sensors or through various ML models need to be securely transmitted under all possible conditions, including those of cyber-attacks. Quantum information processing offers promising solutions to these threats by establishing secure communications, due to unique properties of entanglement and superposition in quantum physics. More specifically, quantum key distribution (QKD) algorithms can be used to generate and transmit keys between the reactor and a remote user. In one of popular QKD communication protocols, BB84, the symmetric keys are paired with an advanced encryption standard (AES) protocol protecting the information. Another challenge in sensor measurements is the noise, which can affect the accuracy and reliability of the measured values. The presence of noise in sensor measurements can lead to incorrect interpretations of the data, and therefore, it is crucial to develop effective signal processing techniques to improve the quality of measurements. </p> <p>In this study, we develop several variations of Recurrent Neural Networks (RNN) and test their ability to predict future values of thermocouple measurements. Data obtained by a heat-up experiment conducted in a liquid sodium experimental facility is used for training and testing the RNNs. The method of extrapolation is also explored using measurements of different sensors to train and test a network. We then examine through computer simulations the potential of secure real-time communication of monitoring information using the BB84 protocol. Finally, signal analysis is performed with Discrete Fourier Transform (DFT) sensor signals to analyze and correlate the prediction results with the results obtained by the analysis of the time series in the frequency domain. Using information from the frequency analysis, we apply cutoff filters in the original time series and test again the performance of the networks. Results show that the ML models developed in this work can be efficiently used for forecasting of thermocouple measurements, as they provide Root Mean Square Error (RMSE) values lower than the measurement uncertainty of the thermocouples. Extrapolation produces good results, with performance related to the Euclidean distance between the sets of time series. Moreover, the results from the utilization of the BB84 protocol to securely transmit the measurements prove the feasibility of secure real-time communication of monitoring information. The application of the cutoff filters provided more accurate predictions of the thermocouple measurements than in the case of the unfiltered signals.</p> <p>The suit of computational tools developed in this work is shown to be efficient and promises to have a positive impact on improving performance of an AR.</p>