Modeling and Simulation of a Prototypical Advanced Reactor

Current online risk monitors provide a point-in-time estimate of the system risk given the current plant configuration (e.g., equipment availability, operational regime, environmental conditions). However, these risk monitors do not account for plant- specific normal, abnormal, and deteriorating states of active components and systems. The lack of operating experience with proposed advanced reactor designs limits our ability to estimate the probability of failure (POF) of key components. Incorporation of unit-specific estimates of POF into dynamic probabilistic risk assessment (PRA) has the potential to enable real-time decisions about stress relief and to support effective maintenance planning while ensuring investment protection. The enhanced risk monitor (ERM) supports the safe and economic operation goals of advanced reactor by providing a dynamic assessment of system risk with real-time estimates of POF and event probability based on equipment condition assessment. A simulation framework for a prototypical advanced reactor (PAR) was developed in this work to provide a platform to demonstrate the ERM. A Simulink model of the PAR was developed, including the primary system, intermediate heat transport loop, steam generator, and balance of plant (BOP). To ensure accuracy across a large range of operating conditions, a nonlinear model for the primary system, including reactor kinetics and heat transfer, was used. A perturbation model of the steam generator showed good performance across the range of conditions and was thus employed. The PAR power block features two independent primary systems, each with dedicated intermediate heat exchangers and steam generators. These two modules are connected to a common BOP through a steam header. To balance the power output of each unit to meet overall power demand, fuzzy control is implemented in the primary system. Degradation of the primary and intermediate sodium pumps is numerically simulated to investigate the effect on overall plant performance. The results indicate that the core power decreases as pump degradation leads to reduced flow in either primary or intermediate loops. The developed PAR model provides simulated power block performance data under component degradation, which can be used to develop and demonstrate the ERM framework

Thermal chemical conversion of plastics waste for production of carbon nanotubes

With continuous growth for more than 50 years, global plastics production increased to 336 million tonnes in 2016, and over 27 million tonnes of post-consumer plastic wastes were produced. Waste management is necessary to minimise the plastic waste in order to reduce their negative impacts. Catalytic-pyrolysis of plastic waste provides an environmental friendly and economic method to produce valuable products such as H2rich syngas and carbon nanotubes (CNTs). Recently, CNTs attract a great interest and have been widely explored due to the excellent and unique chemical, mechanical, thermal and physical properties. However, the quality and quantity of CNTs produced from waste plastics need to be improved. The quality of CNTs can significantly affect and limit their applications. The aim of this research is to improve the quantity and quantity of CNTs by developing efficient catalysts. Four groups of different catalysts (Ni/Fe-based; Ni/AAO, Ni/ceramic, and Ni/sphere) have been investigated in relation to their performance on the production of CNTs from catalytic gasification of waste polypropylene, using a two-stage fixed-bed reaction system. The influences of reaction parameters for each group of catalysts on product yields and the production of CNTs in terms of morphology have been studied using a range of techniques; gas chromatography (GC); X-ray diffraction (XRD); temperature programme oxidation (TPO); scanning electron microscopy (SEM); transmission electron microscopy (TEM). It was found reaction temperature, catalytic particle size, steam addition, and catalytic metal content have significant effect on CNTs production. The particular optimum of each parameter for different catalysts could contribute to the enhancement of the quality and quantity of CNTs. For example, the optimum reaction temperature for Ni/AAO was suggested at 700oC, because the catalyst might not be activated at 600 °C, which produced a low yield of CNTs. However, a reaction temperature of 800 °C resulted in a low yield of CNTs. In addition, the results indicated that a higher loading of Ni on AAO resulted in the formation of metal particles with various sizes, thus leading to the production of non-uniform CNTs. Carbon deposition was also found decreasing with an increase of steam injection, but the quality of CNTs formation in relation to the uniform of CNTs seemed to be improved in the presence of steam. For Fe/Ni-based catalysts study, the results show that the Fe-based catalysts, in particular with large particle size (about 80 nm), produced the highest yield of hydrogen (25.60 mmol H2g-1plastic) and the highest yield of carbons (29 wt.%), as well as the largest fraction of graphite carbons (as obtained from TPO analysis of the reacted catalyst). Both Fe-and Ni-based catalysts with larger metal particles produced higher yield of hydrogen compared with the catalysts with smaller metal particles, respectively

Healthy Designed Environments for Pre-school Children: Investigating Ways to Optimize the Restoration Experience in Nature-based Outdoor Play Environments

Current research points out that a safe, healthy, and supportive built environment is one factor that supports lifelong health (Center on the Developing Child at Harvard University, 2017). Additionally, an individual’s early childhood experiences deeply affect his/her brain development, learning capabilities, and health throughout his/her lifespan (National Scientific Council on the Developing Child, 2010). However, 21st century designs of children’s playgrounds are facing challenges in terms of their positive impact on children’s physical fitness, health, as well their cognitive development and well-being (Frost & Wortham). Attention Restoration Theory (ART) (1989) and related studies suggest that the nature or natural elements in a built environment can provide a restorative experience that helps people recover from mental fatigue and stress and improve their overall health (Berto, Baroni, Zainaghi, & Bettella, 2010; Kaplan & Kaplan, 1989; Kaplan, 1993; Kaplan, 2001; Kuo, 2011; Mårtensson et al., 2009; van den Berg, Hartig, & Staats, 2007). Although a child’s restoration experience in childcare centers is critical for healthy development, few studies have linked children’s health and their restorative experience in a designed nature-based outdoor play environment. This cross-disciplinary research intends to fill this research gap, focusing especially on preschool children (four to five-year-old age group), and investigate the inter-relationships of children’s health, nature-based outdoor play environments at childcare centers, and the children’s restorative experience. A larger goal is to contribute to children’s healthy development and overall well-being in South Carolina’s outdoor play environments at licensed childcare centers and beyond. This study proposes a comparative case study approach. Primary data and empirical evidence of the physical environment, children - nature interaction, children’s use of outdoor play environment and restorative experience were collected through assessment of the physical environment’s spatial forms, field observations, interviews, and perceived restorative experience survey. The data analysis and synthesis reveal that nature-based outdoor play environment may provide higher level of children-nature interaction and indicate the significant role of outdoor play environment and natural elements on children’s restorative experience. This research helps expand on Attention Restoration Theory (1989) and contributes to our understanding of the significance of nature-based outdoor designed environments on children’s overall health and well-being

Reasoning about Cardinal Directions between Extended Objects

Direction relations between extended spatial objects are important commonsense knowledge. Recently, Goyal and Egenhofer proposed a formal model, known as Cardinal Direction Calculus (CDC), for representing direction relations between connected plane regions. CDC is perhaps the most expressive qualitative calculus for directional information, and has attracted increasing interest from areas such as artificial intelligence, geographical information science, and image retrieval. Given a network of CDC constraints, the consistency problem is deciding if the network is realizable by connected regions in the real plane. This paper provides a cubic algorithm for checking consistency of basic CDC constraint networks, and proves that reasoning with CDC is in general an NP-Complete problem. For a consistent network of basic CDC constraints, our algorithm also returns a 'canonical' solution in cubic time. This cubic algorithm is also adapted to cope with cardinal directions between possibly disconnected regions, in which case currently the best algorithm is of time complexity O(n^5)

Bidirectional-Convolutional LSTM Based Spectral-Spatial Feature Learning for Hyperspectral Image Classification

This paper proposes a novel deep learning framework named bidirectional-convolutional long short term memory (Bi-CLSTM) network to automatically learn the spectral-spatial feature from hyperspectral images (HSIs). In the network, the issue of spectral feature extraction is considered as a sequence learning problem, and a recurrent connection operator across the spectral domain is used to address it. Meanwhile, inspired from the widely used convolutional neural network (CNN), a convolution operator across the spatial domain is incorporated into the network to extract the spatial feature. Besides, to sufficiently capture the spectral information, a bidirectional recurrent connection is proposed. In the classification phase, the learned features are concatenated into a vector and fed to a softmax classifier via a fully-connected operator. To validate the effectiveness of the proposed Bi-CLSTM framework, we compare it with several state-of-the-art methods, including the CNN framework, on three widely used HSIs. The obtained results show that Bi-CLSTM can improve the classification performance as compared to other methods