9th Annual Symposium of School of Science, Engineering and Health

This symposium continues a strong tradition of annual events designed to showcase student and faculty innovation, creativity and productivity. However, it is the first that we are holding as a newly re-organized School of Science, Engineering and Health

AN ADVISORY SYSTEM FOR THE DEVELOPMENT OF UNCONVENTIONAL GAS RESERVOIRS

With the rapidly increasing demand for energy and the increasing prices for oil and gas, the role of unconventional gas reservoirs (UGRs) as energy sources is becoming more important throughout the world. Because of high risks and uncertainties associated with UGRs, their profitable development requires experts to be involved in the most critical development stages, such as drilling, completion, stimulation, and production. However, many companies operating UGRs lack this expertise. The advisory system we developed will help them make efficient decisions by providing insight from analogous basins that can be applied to the wells drilled in target basins. In North America, UGRs have been in development for more than 50 years. The petroleum literature has thousands of papers describing best practices in management of these resources. If we can define the characteristics of the target basin anywhere in the world and find an analogous basin in North America, we should be able to study the best practices in the analogous basin or formation and provide the best practices to the operators. In this research, we have built an advisory system that we call the Unconventional Gas Reservoir (UGR) Advisor. UGR Advisor incorporates three major modules: BASIN, PRISE and Drilling & Completion (D&C) Advisor. BASIN is used to identify the reference basin and formations in North America that are the best analogs to the target basin or formation. With these data, PRISE is used to estimate the technically recoverable gas volume in the target basin. Finally, by analogy with data from the reference formation, we use D&C Advisor to find the best practice for drilling and producing the target reservoir. To create this module, we reviewed the literature and interviewed experts to gather the information required to determine best completion and stimulation practices as a function of reservoir properties. We used these best practices to build decision trees that allow the user to take an elementary data set and end up with a decision that honors the best practices. From the decision trees, we developed simple computer algorithms that streamline the process

Knowledge Extraction and Summarization for Textual Case-Based Reasoning: A Probabilistic Task Content Modeling Approach

Case-Based Reasoning (CBR) is an Artificial Intelligence (AI) technique that has been successfully used for building knowledge systems for tasks/domains where different knowledge sources are easily available, particularly in the form of problem solving situations, known as cases. Cases generally display a clear distinction between different components of problem solving, for instance, components of the problem description and of the problem solution. Thus, an existing and explicit structure of cases is presumed. However, when problem solving experiences are stored in the form of textual narratives (in natural language), there is no explicit case structure, so that CBR cannot be applied directly. This thesis presents a novel approach for authoring cases from episodic textual narratives and organizing these cases in a case base structure that permits a better support for user goals. The approach is based on the following fundamental ideas: - CBR as a problem solving technique is goal-oriented and goals are realized by means of task strategies. - Tasks have an internal structure that can be represented in terms of participating events and event components. - Episodic textual narratives are not random containers of domain concept terms. Rather, the text can be considered as generated by the underlying task structure whose content they describe. The presented case base authoring process combines task knowledge with Natural Language Processing (NLP) techniques to perform the needed knowledge extraction and summarization

Deep Learning-Based Machinery Fault Diagnostics

This book offers a compilation for experts, scholars, and researchers to present the most recent advancements, from theoretical methods to the applications of sophisticated fault diagnosis techniques. The deep learning methods for analyzing and testing complex mechanical systems are of particular interest. Special attention is given to the representation and analysis of system information, operating condition monitoring, the establishment of technical standards, and scientific support of machinery fault diagnosis

Resource selection and route generation in discrete manufacturing environment

When put to various sources, the question of which sequence of operations and machines is best for producing a particular component will often receive a wide range of answers. When the factors of optimum cutting conditions, minimum time, minimum cost, and uniform equipment utilisation are added to the equation, the range of answers becomes even more extensive. Many of these answers will be 'correct', however only one can be the best or optimum solution. When a process planner chooses a route and the accompanying machining conditions for a job, he will often rely on his experience to make the choice. Clearly, a manual generation of routes does not take all the important considerations into account. The planner may not be aware of all the factors and routes available to him. A large workshop might have hundreds of possible routes, even if he did know it all', he will never be able to go through all the routes and calculate accurately which is the most suitable for each process - to do this, something faster is required. This thesis describes the design and implementation of an Intelligent Route Generator. The aim is to provide the planner with accurate calculations of all possible production routes m a factory. This will lead up to the selection of an optimum solution according to minimum cost and time. The ultimate goal will be the generation of fast decisions based on expert information. Background knowledge of machining processes and machine tools was initially required, followed by an identification of the role of the knowledge base and the database within the system. An expert system builder. Crystal, and a database software package, DBase III Plus, were chosen for the project. Recommendations for possible expansion of and improvements to the expert system have been suggested for future development

Assessment of flagellate diversity at deep-sea hydrothermal vents using the combined approach of culture-dependent and culture-independent methods

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution May 2000Eighteen strains of flagellated protists representing 9 species were isolated and cultured from four deep-sea hydrothermal vents in the Eastern Pacific Ocean: Juan de Fuca Ridge, Guaymas Basin, and both 21°N and 9°N on the East Pacific Rise (EPR). The hydrothermal vent flagellates belonged to six different taxonomic orders: the Ancyromonadida, Bicosoecida, Cercomonadida, Choanoflagellida, Chrysomonadida, and Kinetoplastida. Molecular and ultrastructural evidence point to one of the isolates, Ancyromonas, as a plausible candidate for the closest relative to the common ancestor of Metazoans, Fungi, and Choanoflagellates (the Opisthokonta). Using l8S rDNA sequences from most of the major eukaryotic lineages, maximum likelihood, minimum evolution and maximum parsimony analyses yielded congruent phylogenies supporting this hypothesis. Deep-sea vent samples were both cultured to select for kinetoplastid flagellates and analyzed without culturing by denaturing gradient gel electrophoresis (DGGE) using PCR primers specific to the kinetoplastid clade. By comparing these two different methods of analysis, my goal was to decrease the biases and/or errors inherent in either method alone and to improve our ability to assess flagellate diversity and distribution in samples from remote vent environments. PCR and DGGE were used to specifically isolate and amplify target DNA's from all cultured kinetoplastid species in matching vent samples, thus corroborating the findings of culturing. Molecular methods had the additional ability to detect species presence where culturing did not, thereby providing a better indication of the distribution of these species. Many of the vent isolates were ubiquitous members of marine, freshwater, and terrestrial ecosystems worldwide, suggesting a global distribution of these flagellate species. This discovery advanced the hypothesis that ubiquity in distribution patterns among heterotrophic flagellates implies high tolerance and/or adaptability to a wide range of environmental conditions. Experiments under vent conditions of high pressure and high concentrations of metals and sulfide showed that some of these species are very tolerant to extreme environmental conditions.WHOI Education Office, National Science Foundation, Rhinehart Coastal Research Center, PADI Foundatio

Eleventh International Conference on the Bearing Capacity of Roads, Railways and Airfields

Innovations in Road, Railway and Airfield Bearing Capacity – Volume 1 comprises the first part of contributions to the 11th International Conference on Bearing Capacity of Roads, Railways and Airfields (2022). In anticipation of the event, it unveils state-of-the-art information and research on the latest policies, traffic loading measurements, in-situ measurements and condition surveys, functional testing, deflection measurement evaluation, structural performance prediction for pavements and tracks, new construction and rehabilitation design systems, frost affected areas, drainage and environmental effects, reinforcement, traditional and recycled materials, full scale testing and on case histories of road, railways and airfields. This edited work is intended for a global audience of road, railway and airfield engineers, researchers and consultants, as well as building and maintenance companies looking to further upgrade their practices in the field

Dust control handbook for industrial minerals mining and processing

"This handbook was written by a task force of safety and health specialists, industrial hygienists, and engineers to provide information on proven and effective control technologies that lower workers' dust exposures during all stages of mineral processing. The handbook describes both dust-generating processes and the control strategies necessary to enable mine operators to reduce worker dust exposure. Implementation of the engineering controls discussed can assist operators, health specialists, and workers in reaching the ultimate goal of eliminating pneumoconiosis and other occupational diseases caused by dust exposure in the mining industry. Designed primarily for use by industrial minerals producers, this handbook contains detailed information on control technologies to address all stages of the minerals handling process, including drilling, crushing, screening, conveyance, bagging, loadout, and transport. The handbook's aim is to empower minerals industry personnel to apply state-of-the-art dust control technology to help reduce or eliminate mine and mill worker exposure to hazardous dust concentrations - a critical component in ensuring the health of our nation's mine workers." - NIOSHTIC-2NIOSHTIC no. 20055113Suggested citation: NIOSH [2019]. Dust control handbook for industrial minerals mining and processing. Second edition. By Cecala AB, O\u2019Brien AD, Schall J, Colinet JF, Franta RJ, Schultz MJ, Haas EJ, Robinson J, Patts J, Holen BM, Stein R, Weber J, Strebel M, Wilson L, and Ellis M. Pittsburgh PA: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Institute for Occupational Safety and Health, DHHS (NIOSH) Publication No. 2019\u2013124, RI 9701. https://doi.org/10.26616/NIOSHPUB2019124201910.26616/NIOSHPUB2019124606