No abstract

This work describes the integration of data acquisition (DAQ) hardware and software for the purpose of acquiring not only data but real-time transport model parameter estimates in the context of subsurface flow and transport problems. Integrated DAQ parameter estimation systems can be used to reduce data storage requirements, trigger event recognition and more detailed sampling actions, and otherwise enhance remote monitoring capabilities. The contaminant transport problem is posed here as the analogous heat transfer problem in a three-dimensional, intermediate-scale physical aquifer model. A constant source of warm water is fed into a sandy aquifer undergoing steady, unidirectional flow. The spatial distribution of temperature in the medium is monitored over time using 17 thermocouples embedded in the medium. These sensors log temperatures via conventional analog-to-digital conversion hardware driven by commercially available DAQ software (LabVIEWä). Parameter estimation routines programmed in MATLABä-based M-files are embedded in the LabVIEW DAQ routine and access parameter estimation libraries, such as the descent method employed here, via the Internet. The integrated DAQ parameter estimation system is demonstrated for the estimation of (1) the thermal dispersion coefficients (analogous to mass dispersion coefficients), given a known heat source; and (2) the location of a heat source, given known thermal dispersion coefficients. In both cases, the parameter estimation procedure is executed repeatedly as the data are acquired. For the case of source location, the effect of the number of sensors on the parameter estimation procedure is also demonstrated. Reasonable parameter estimates are provided rapidly during both the transient and steady-state phases of the experiments, with accuracy increasing with time and with the number of observations employed.

During nuclear accidents, decision-makers need to handle considerable data to take appropriate protective actions to protect people and the environment from radioactive material release. In such scenarios, machine learning can be an essential tool in facilitating the protection action decisions that will be made by decision-makers. By feeding machines software with big data to analyze and identify nuclear accident behavior, types, and the concentrations of released radioactive materials can be predicted, thus helping in early warning and protecting people and the environment. In this study, based on the ground deposition concentration of radioactive materials at different distances offsite in an emergency planning zone (EPZ), we proposed classification and regression models for three severe accidents. The objective of the classification model is to recognize the transient situation type for taking appropriate actions, while the objective of the regression model is to estimate the concentrations of the released radioactive materials. We used the Personal Computer Transient Analyser (PCTRAN) Advanced Power Reactor (APR) 1400 to simulate three severe accident scenarios and to generate a source term released to the environment. Additionally, the Radiological Consequence Analysis Program (RCAP) was used to assess the off-site consequences of nuclear power plant accidents and to estimate the ground deposition concentrations of radionuclides. Moreover, ground deposition concentrations at different distances were used as input data for the classification and regression tree (CART) models to obtain an accident pattern and to establish a prediction model. Results showed that the ground deposition concentration at a near distance from a nuclear power plant is a more informative parameter in predicting the concentration of radioactive material release, while the ground deposition concentration at a far distance is a very informative parameter in identifying accident types. In the regression model, the R-square of the training and test data was 0.995 and 0.994, respectively, showing a mean strong linear relationship between the predicted and actual concentration of radioactive material release. The mean absolute percentage error was found to be 26.9% and 28.1% for the training and test data, respectively. In the classification model, the model predicted a scenario (1) of 99.8% and 98.9%, scenario (2) of 98.4% and 91.6%, and scenario (3) of 98.6% and 94.7% for the training and test data, respectively.

Various products containing a small number of added radionuclides are commonly available for use worldwide. However, frequent use of such products puts the public at risk of radiation exposure. In this study, dose assessments to members of the public using consumer products containing naturally occurring radioactive materials (NORMs) were conducted for various usage scenarios to evaluate the external and internal exposure dose. Data for this study were obtained from previous literature and were statistically analyzed using Boxplot to determine the input data for assessment. A normalized value of activity concentration was used for dose evaluation. In addition to other external and internal dose calculation codes, analytical calculations were used to perform age-dependent. Based on analytical calculations, the highest total effective dose equivalent (TEDE) received from necklace products at the upper whiskers with an activity concentration of 4.21 Bq/g for 238U, 24.4 Bq/g for 232Th, and 0.55 Bq/g for 40K for various age groups is 2.03 mSv/y for 1 year old, 1.24 mSv/y for 10 years old and 1.11 mSv/y for adult, which are above the international commission for radiation protection (ICRP) recommended public dose limit of 1 mSv/y. Results of external and internal exposure dose obtained using Microshield code, IMBA code and Visual Monte Carlo (VMC) code are all below the recommended public dose limit of 1 mSv/y.

The most recent assessments conducted by the International Energy Agency indicate that natural gas accounts for the majority of Nigeria’s fossil fuel-derived electricity generation, with crude oil serving mostly as a backup source. Fossil fuel-generated electricity represents 80% of the country’s total. In addition, carbon dioxide (CO2) emissions in Nigeria in 2018 (101.3014 Mtons) demonstrated a 3.83% increase from 2017. The purpose of this study is to suggest an alternate energy supply mix to meet future electrical demand and reduce CO2 emissions in Nigeria. The Model for Energy Supply Strategy Alternatives and their General Environmental Impact (MESSAGE) was used in this study to model two case situations of the energy supply systems in Nigeria to determine the best energy supply technology to meet future demand. The Simplified Approach to Estimating Electricity Generation’s External Costs and Impacts (SIMPACTS) code is also used to estimate the environmental impacts and resulting damage costs during normal operation of various electricity generation technologies. Results of the first scenario show that gas and oil power plants are the optimal choice for Nigeria to meet future energy needs with no bound on CO2 emission. If Nigeria adopts CO2 emission restrictions to comply with the Paris Agreement’s target of decreasing worldwide mean temperature rise to 1.5 °C, the best option is nuclear power plants (NPPs). The MESSAGE results demonstrate that both fossil fuels and NPPs are the optimal electricity-generating technologies to meet Nigeria’s future energy demand. The SIMPACTS code results demonstrate that NPPs have the lowest damage costs because of their low environmental impact during normal operation. Therefore, NPP technology is the most environmentally friendly technology and the best choice for the optimization of future electrical technology to meet the demand. The result from this study will serve as a reference source in modeling long-term energy mix therefore reducing CO2 emission in Nigeria.