AFCI Quarterly Input – UNLV October through December, 2003

Quarterly report highlighting research projects and objectives of the Transmutation Research Program at the Nuclear Science & Technology Division, Harry Reid Research Center. The University of Nevada, Las Vegas supports the AFCI through research and development of technologies for economic and environmentally sound refinement of spent nuclear fuel. The UNLV program has four components: infrastructure, international collaboration, student-based research, and management and program support

Techno-Economic Potential of Enhanced Coal Recovery through Middlings Liberation and Re-Processing

The typical preparation plant producing coal for the utility market targets a relative separation density in the plant of around 1.60 whereas plants generating metallurgical coal use relative cut point density values approaching 1.50. In some cases, achieving the specified coal quality requires operating at lower cut point values, which results in a significant loss of valuable coal. In these situations, a middlings stream can be produced using a secondary separator or a three-product unit, which would allow crushing of the middlings for liberation purposes and re-introduction into the plant feed. In this manner, higher quality coal can be produced while maximizing plant yield. A detailed laboratory analysis was conducted to study the liberation characteristics resulting from the crushing of middlings at different top sizes. The experimental data were later used as input for modeling and simulation of plant flowsheet in LIMN. Simulations were run for several regrinding cases. The results of the current study investigating the economic benefits of middlings liberation and re-treatment are presented and discussed in this thesis. Improvement up to 6% in plant yield with 16-21% reduction in ash and 14-18% sulfur reductions can be achieved by crushing the +1/2 inch middlings to a ½-inch top size

A Computational Model of Networked Small-Scale Fuel Synthesis Demonstrating Greater Production Flexibility and Specificity

The rapid pace of industrial change over the past hundred years has led to any number of paradigm shifts in the way business is conducted and technologies are applied, but economies of large scale have persisted in the energy sector. In an age of automation and mass-production of small units, however, complex networking of many small energy systems can permit novel application of established technologies. This dissertation explores how established fuel synthesis technologies might behave in an automated network in which familiar units are arranged in unfamiliar ways. The flexibility afforded by automation and small scale operation allows for potentially complementary means of exploiting the fungible nature of hydrocarbon resources. Beyond any benefits of small-scale incurred from mass production and learning, fuel synthesis is a process with sensitivities to input streams that a network could exploit in a nuanced way. The completed work demonstrates that a network of small-scale fuel synthesis reactors and thermal crackers, based on current industrial practices at large monolithic scale, can be networked to dramatically sharpen the chemical spectrum they produce. In order to study the behavior of such a network in ways that are unavailable in current software, a hierarchical numerical modeling code was developed to offer greater flexibility to nest and optimize network configurations within network configurations, reflecting the modularity of the networks it is meant to simulate. This new code is capable of simulating aggressively numerically constrained networks, dynamically substituting various configurations while optimizing them across user-specified variables. Various weighting schemes were developed to facilitate more rapid convergence to a numerical solution so that highly constrained recycling schemes could be reconciled to a steady state that would produce the specified output spectrum. Modular units were coded to simulate the essential properties of real processes and technologies, with close attention paid to the sensitivity of these processes to input conditions, so that these units could be assembled in various configurations and subjected to user-specified constraints. Coded modules were designed under the principle that these individual units need not be custom-made or technologically ahead of their time; the benefits explored by network simulations are incurred not by dramatically upgrading the processes being simulated, but rather by directing and redirecting the chemical streams which are subject to those processes to tailor the outcome to the desired product. This principle was applied to chemical separation in an analytical framework in order to derive how unremarkable separators might be networked to produce remarkable precision of separation. Such precision is important because the direction and redirection of chemical streams is predicated on the ability to select the destination of a particular chemical. The effect of networking fuel synthesis reactors and thermal crackers was studied for unidirectional flows in order to understand how repeated applications of these units at smaller scale sharpen the spectrum relative to single large scale application. These fuel synthesis reactors and thermal crackers were also configured in aggressively recycled networks, imposing more severe constraints on the output spectrum. This work demonstrated that fuel synthesis at industrial output scales need not operate in monolithic units and can benefit dramatically from judicious networking, to the point that a network of units that would otherwise have produced a broad spectrum of chemical flavors can be configured to produce only a single user-specified output chemical

The role of Computer Aided Process Engineering in physiology and clinical medicine

This paper discusses the potential role for Computer Aided Process Engineering (CAPE) in developing engineering analysis and design approaches to biological systems across multiple levels—cell signalling networks, gene, protein and metabolic networks, cellular systems, through to physiological systems. The 21st Century challenge in the Life Sciences is to bring together widely dispersed models and knowledge in order to enable a system-wide understanding of these complex systems. This systems level understanding should have broad clinical benefits. Computer Aided Process Engineering can bring systems approaches to (i) improving understanding of these complex chemical and physical (particularly molecular transport in complex flow regimes) interactions at multiple scales in living systems, (ii) analysis of these models to help to identify critical missing information and to explore the consequences on major output variables resulting from disturbances to the system, and (iii) ‘design’ potential interventions in in vivo systems which can have significant beneficial, or potentially harmful, effects which need to be understood. This paper develops these three themes drawing on recent projects at UCL. The first project has modeled the effects of blood flow on endothelial cells lining arteries, taking into account cell shape change resulting in changes in the cell skeleton which cause consequent chemical changes. A second is a project which is building an in silico model of the human liver, tieing together models from the molecular level to the liver. The composite model models glucose regulation in the liver and associated organs. Both projects involve molecular transport, chemical reactions, and complex multiscale systems, tackled by approaches from CAPE. Chemical Engineers solve multiple scale problems in manufacturing processes – from molecular scale through unit operations scale to plant-wide and enterprise wide systems – so have an appropriate skill set for tackling problems in physiology and clinical medicine, in collaboration with life and clinical scientists

Simulating of Biofuel Production from Rice Husks

In the context of energy crisis and environmental damage due to rapid depletion and overuse of fossil fuel, alternative renewable energy resources such as biomass have been being significantly studied recently. In Southeast Asia countries like Malaysia, one of the abundant biomass feed stocks is rice husk which is a residue from rice production process. Rice husk can be transformed into gasoline through a series of fast pyrolysis and catalytic cracking processes. However, there is limited work on simulating the whole process. The objective of this project is to develop a mathematical simulation for the production of gasoline from rice husk using MATLAB. From the developed model, parametric studies have been conducted to identify the operating conditions which give the highest yield of product. The mathematic model was based on kinetic equations for the two main processes together with basic mass and energy balance for other subprocesses in the flowsheet. As a result, the model has shown that from 1000kg of rice husk, 191 liters of gasoline would be obtained. Within the studied range, the operating conditions at temperature of 783K and residence time of 5s for pyrolysis and at 723K in 1.25h for catalytic cracking are proposed to get the highest gasoline yield. The developed model can be considered as a basis for further research on simulating the production process of biofuel from rice husk

Ontology based model framework for conceptual design of treatment flow sheets

The primary objective of wastewater treatment is the removal of pollutants to meet given legal effluent standards. To further reduce operators costs additional recovery of resources and energy is desired by industrial and municipal wastewater treatment. Hence the objective in early stage of planning of treatment facilities lies in the identification and evaluation of promising configurations of treatment units. Obviously this early stage of planning may best be supported by software tools to be able to deal with a variety of different treatment configurations. In chemical process engineering various design tools are available that automatically identify feasible process configurations for the purpose to obtain desired products from given educts. In contrast, the adaptation of these design tools for the automatic generation of treatment unit configurations (process chains) to achieve preset effluent standards is hampered by the following three reasons. First, pollutants in wastewater are usually not defined as chemical substances but by compound parameters according to equal properties (e.g. all particulate matter). Consequently the variation of a single compound parameter leads to a change of related parameters (e.g. relation between Chemical Oxygen Demand and Total Suspended Solids). Furthermore, mathematical process models of treatment processes are tailored towards fractions of compound parameters. This hampers the generic representation of these process models which in turn is essential for automatic identification of treatment configurations. Second, treatment technologies for wastewater treatment rely on a variety of chemical, biological, and physical phenomena. Approaches to mathematically describe these phenomena cover a wide range of modeling techniques including stochastic, conceptual or deterministic approaches. Even more the consideration of temporal and spatial resolutions differ. This again hampers a generic representation of process models. Third, the automatic identification of treatment configurations may either be achieved by the use of design rules or by permutation of all possible combinations of units stored within a database of treatment units. The first approach depends on past experience translated into design rules. Hence, no innovative new treatment configurations can be identified. The second approach to identify all possible configurations collapses by extremely high numbers of treatment configurations that cannot be mastered. This is due to the phenomena of combinatorial explosion. It follows therefrom that an appropriate planning algorithm should function without the need of additional design rules and should be able to identify directly feasible configurations while discarding those impractical. This work presents a planning tool for the identification and evaluation of treatment configurations that tackles the before addressed problems. The planning tool comprises two major parts. An external declarative knowledge base and the actual planning tool that includes a goal oriented planning algorithm. The knowledge base describes parameters for wastewater characterization (i.e. material model) and a set of treatment units represented by process models (i.e. process model). The formalization of the knowledge base is achieved by the Web Ontology Language (OWL). The developed data model being the organization structure of the knowledge base describes relations between wastewater parameters and process models to enable for generic representation of process models. Through these parameters for wastewater characterization as well as treatment units can be altered or added to the knowledge base without the requirement to synchronize already included parameter representations or process models. Furthermore the knowledge base describes relations between parameters and properties of water constituents. This allows to track changes of all wastewater parameters which result from modeling of removal efficiency of applied treatment units. So far two generic treatment units have been represented within the knowledge base. These are separation and conversion units. These two raw types have been applied to represent different types of clarifiers and biological treatment units. The developed planning algorithm is based on a Means-Ends Analysis (MEA). This is a goal oriented search algorithm that posts goals from wastewater state and limit value restrictions to select those treatment units only that are likely to solve the treatment problem. Regarding this, all treatment units are qualified according to postconditions that describe the effect of each unit. In addition, units are also characterized by preconditions that state the application range of each unit. The developed planning algorithm furthermore allows for the identification of simple cycles to account for moving bed reactor systems (e.g. functional unit of aeration tank and clarifier). The evaluation of identified treatment configurations is achieved by total estimated cost of each configuration. The planning tool has been tested on five use cases. Some use cases contained multiple sources and sinks. This showed the possibility to identify water reuse capabilities as well as to identify solutions that go beyond end of pipe solutions. Beyond the originated area of application, the planning tool may be used for advanced interrogations. Thereby the knowledge base and planning algorithm may be further developed to address the objectives to identify configurations for any type of material and energy recovery

Flowsheet Design Of CO2 Adsorption System With Aminated Resin At Natural Gas Reserves

Natural gas is efficient, convenient and relatively clean energy source and its global use is growing rapidly. It burns to form carbon dioxide (C02) and water (H20) without or with minimal smoke subject to composition. The presence of carbon dioxide in natural gas prior to combustionwould lower the heating value of the gas, increase the volume of gas that must be transported and increase the environmental impact. Most of the existing acid gas treatment systems in gas plants are limited in C02 removal capacity of 30 mol% to 40 mol%. Hence, this project aims to investigate the potential of an onsite application of adsorption column with aminated resin to capture C02 at the natural gas reserves using flowsheet simulation based approach. The simulation of this C02 removal plant that reduces the C02 content down to 30 mol%, i.e. the gas processing plant's limitation, is done. The effects of temperature, pressure, adsorbent concentration and its flow rate on performance of C02 removalare investigated using the model

Flowsheet Model and Simulation of Produced Slag in Electric Steelmaking to Improve Resource Management and Circular Production

The steel industry is one of the most energy-intensive sectors, as it requires a great amount of resources and produces a considerable quantity of by-products, with not negligible environmental impact. Therefore, the main challenge of steelworks consists in improving sustainability and reducing carbon footprint of the production process, by ensuring the required quality of final products. In this context, the reuse and recycling of by-products can play a key role in preventing their landfilling and waste of valuable products, reducing the exploitation of primary raw materials, decreasing CO2 emissions, and supporting the implementation of the Circular Economy concept. In particular, one of the main by-products is slag, which can be used as a potentially valuable source of secondary raw materials, leading to a substantial reduction of natural resources usage and related costs. This paper concerns part of the work developed inside the EU-funded project entitled “Optimising slag reuse and recycling in electric steelmaking at optimum metallurgical performance through on-line characterization devices and intelligent decision support system – iSlag”. The main focus of this project is the valorisation of slags produced in the electric steelmaking route, by defining good practices, investigating new recycling paths, and promoting industrial symbiosis solutions. In this paper, the adaptation and the improvement of a previously developed Aspen Plus® simulation model are presented to obtain an accurate prediction of slag features. In particular, the model estimates amount and composition of slags produced in the primary and the secondary steelmaking processes, and it allows simulating different case scenarios including usual and unusual conditions, for instance, process operating conditions, raw materials compositions, steel families to be produced. In addition to slag features, product compositions and environmental and energy impacts can be monitored with the model

Dynamic simulator for a grinding circuit

Thesis (M.S.) University of Alaska Fairbanks, 2017The grinding circuit is a primary and indispensable unit of a mineral processing plant. The product from a grinding circuit affects the recovery rate of minerals in subsequent downstream processes and governs the amount of concentrate produced. Because of the huge amount of energy required during the grinding operation, they contribute to a major portion of the concentrator cost. This makes grinding a crucial process to be considered for optimization and control. There are numerous process variables that are monitored and controlled during a grinding operation. The variables in a grinding circuit are highly inter-related and the intricate interaction among them makes the process difficult to understand from an operational viewpoint. Modeling and simulation of grinding circuits have been used by past researchers for circuit design and pre-flowsheet optimization in terms of processing capacity, recovery rate, and product size distribution. However, these models were solved under steady approximation and did not provide any information on the system in real time. Hence, they cannot be used for real time optimization and control purposes. Therefore, this research focuses on developing a dynamic simulator for a grinding circuit. The Matlab/Simulink environment was used to program the models of the process units that were interlinked to produce the flowsheet of a grinding circuit of a local gold mine operating in Alaska. The flowsheet was simulated under different operating conditions to understand the behavior of the circuit. The explanation for such changes has also been discussed. The dynamic simulator was then used in designing a neural network based controller for the semi-autogenous mill (SAG). A two-layer non-linear autoregressive (NARX) neural network with feed to the mill as exogenous input was designed using data generated by the simulator for a range of operating conditions. Levenberg-Marquardt (LM) and Bayesian Regularization (BR) training algorithms were used to train the network. Comparison of both algorithms showed LM performed better provided the number of parameters in the network were chosen in a prudent manner. Finally, the implementation of the controller for maintaining SAG mill power to a reference point is discussed