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The sequence of implementation of investment projects adopted at industrial enterprises is discussed, in the light of their financial and production resources. Hierarchy analysis and integral linear programming are essential components of the proposed algorithm. Practical calculations for an industrial enterprise show that this approach is practically expedient.
Bovine viral vaccines contain both live or inactivated/killed formulations, but few studies have evaluated the impact of vaccinating with either live or killed antigens and re-vaccinating with the reciprocal. Commercial dairy heifers were utilized for the study and randomly assigned to three treatment groups. Treatment groups received a commercially available modified-live viral (MLV) vaccine containing BVDV and were revaccinated with a commercially available killed viral (KV) vaccine containing BVDV, another group received the same KV vaccine and was revaccinated with the same MLV vaccine, and yet another group served as negative controls and did not receive any viral vaccines. Heifers in KV/MLV had higher virus neutralizing titers (VNT) at the end of the vaccination period than heifers in MLV/KV and control groups. The frequency of IFN-γ mRNA positive CD4+, CD8+, and CD335+ populations, as well as increased mean fluorescent intensity of CD25+ cells was increased for the MLV/KV heifers as compared to KV/MLV and controls. The data from this study would suggest that differences in initial antigen presentation such as live versus killed could augment CMI and humoral responses and could be useful in determining vaccination programs for optimizing protective responses, which is critical for promoting lifetime immunity.
GO WEST is an open-source power grid modeling framework for U.S. Western Interconnection, which allows users to tailor the model depending on their research study and science questions. It covers 28 balancing authorities (BA) and 12 states in U.S. Western Interconnection. GO WEST allows users to select different number of nodes and come up with a simplified network by utilizing 10,000 nodal topology of U.S. Western Interconnection created by Texas A&M University. Users can try and select different number of nodes, mathematical formulations (linear programming vs. mixed-integer linear programming), transmission line limit scaling factors, and hurdle rate scaling factors. GO WEST offers a unit commitment and economic dispatch (UC/ED) module to simulate grid operations on an hourly scale. In this sense, users can calibrate and validate their model versions by comparing model outputs to historical datasets. TEP is an open-source transmission capacity expansion model, built on GO WEST framework. It utilizes linear programming to optimize transmission capacity addition investment on existing lines within GO WEST framework. In this sense, TEP model only increases the thermal capacity of existing transmission lines and does not add new lines to the system, which leaves the topology preserved. TEP minimizes the total cost of the system which comprises the operational cost of satisfying electricity demand (i.e., generation cost), cost of loss of load (i.e., unserved energy), cost of power flow, and cost of new transmission capacity additions (i.e., investment cost). In order to use TEP model, users need to create scenarios with GO WEST framework. In this analysis, outputs from several models are used to create future inputs to GO WEST and TEP models, including GCAM-USA, TELL, CERF and reV. This dataset includes experiment inputs and outputs from three different transmission expansion scenarios (cooperative, intermediate, and individual) for 2019 and 2059. For 2019, a base scenario to illustrate the default (i.e., historical) power grid operations is also included. This study utilizes rcp45hotter_ssp3 scenario from a previous version of GCAM-USA simulations. Sources of the shapefiles in supplementary data are HIFLD Open and U.S. Energy Atlas. Please see the README file for a detailed description of the main and supplementary data.
This dataset provides projected grid stress and reliability results (including all model inputs and outputs from GO WEST and TEP) for Integrated Multisector, Multiscale Modeling (IM3) Phase 2 simulations across eight different scenarios for the U.S. Western Interconnection through 2055. The scenarios include combinations of two Shared Socioeconomic Pathways (SSP3 and SSP5) with four high-resolution climate projections specific to the United States from a set of Thermodynamic Global Warming (TGW) simulations. These climate projections include "hotter" and "cooler" variants for two Representative Concentration Pathways (RCP4.5 and RCP8.5). The resulting eight simulations are: rcp45cooler_ssp3 rcp45cooler_ssp5 rcp45hotter_ssp3 rcp45hotter_ssp5 rcp85cooler_ssp3 rcp85cooler_ssp5 rcp85hotter_ssp3 rcp85hotter_ssp5 GO WEST is an open-source power grid modeling framework for the U.S. Western Interconnection, which allows users to tailor the model depending on their research study and science questions. It covers 28 balancing authorities (BAs) and 12 states in U.S. Western Interconnection. GO WEST allows users to select different number of nodes and come up with a simplified network by utilizing 10,000 nodal topology of the U.S. Western Interconnection (ACTIVSg10k). Users can select different number of nodes, mathematical formulations (linear programming vs. mixed-integer linear programming), transmission line limit scaling factors, and hurdle rate scaling factors. GO WEST offers a unit commitment and economic dispatch (UC/ED) module to simulate grid operations on an hourly scale. In this sense, users can calibrate and validate their model versions by comparing model outputs to historical datasets. TEP is an open-source transmission capacity expansion model, built on the GO WEST framework. It utilizes linear programming to optimize transmission capacity addition investment on existing lines within the GO WEST framework. The TEP model only increases the thermal capacity of existing transmission lines and does not add new lines to the system, which leaves the topology preserved. In order to use TEP model, users need to create scenarios with the GO WEST framework. Please refer to README file for a detailed description of the dataset including individual files and references.
At ICALEPCS 09, we introduced a source code generator that allows processes to communicate safely using data types native to each host language. In this paper, we discuss further development that has occurred since the conference in Kobe, Japan, including the addition of three more client languages, an optimization in network packet size and the addition of a new protocol data type. The protocol compiler is continuing to prove itself as an easy and robust way to get applications written in different languages hosted on different computer architectures to communicate. We have two active Erlang projects that are using the protocol compiler to access ACNET data at high data rates. We also used the protocol compiler output to deliver ACNET data to an iPhone/iPad application. Since it takes an average of two weeks to support a new language, we're willing to expand the protocol compiler to support new languages that our community uses.
PyRAT is a radiography analysis tool used to reconstruction images of unknown 1-0 objects. The tool is written in Python and developed for use on LINUX and Windows platforms. The tool is capable of performing nonlinear inversions of the images with minimal manual interaction in the optimization process. The tool utilizes the NOMAD mixed variable optimization tool to perform the optimization.
Provided are an apparatus and method for load-balancing of a three-phase electric power distribution system having a multi-phase feeder, including obtaining topology information of the feeder identifying supply points for customer loads and feeder sections between the supply points, obtaining customer information that includes peak customer load at each of the points between each of the feeder sections, performing a phase balancing analysis, and recommending phase assignment at the customer load supply points.
Abstract not provided.
With the transportation sector being a major source of air pollutant emissions, this study seeks to evaluate criteria pollutant emissions associated with the use of petroleum fuels in vehicles using the GREET® (Greenhouse gases, Regulated Emissions, and Energy use in Transportation) well-to-wheel model. The life cycle of petroleum fuels includes the major stages of crude recovery, crude transportation, crude refining, fuel distribution and fuel combustion (vehicle operation). This study focuses on quantifying refinery emissions, in terms of both refinery throughput and various refinery fuel products, by matching a refinery emissions inventory dataset with refinery operation information, at various levels (national, regional, facility, and process unit). The refinery emissions in terms refinery throughput (per refinery crude input, in g/mmbtu crude) are developed at a regional level for various Petroleum Administration for Defense Districts (PADD), and also aggregated at the national level. To allocate refinery emissions to refinery products, a methodology is developed using a refinery operation optimization Linear Programming (LP) model for individual refineries. The results from individual refinery facilities are then aggregated to obtain statistical national average (capacity weighted) emissions for each product. Because refineries vary by complexity of operation, configuration, crude quality, and product slate, the derived emissions demonstrate large variation for each product and per unit of crude input. Relative to previous estimates used in the GREET model, the present research shows reductions in refinery (and life cycle) emissions for most criteria pollutants. The reduction is especially significant for sulfur dioxide (SOx) emissions.
Abstract not provided.
Abstract not provided.
Presentation describes research into use of LightTools, an illumination design program, to optimize time and sensitivity performance in Liquid-VI gamma detectors for neutron-diagnosed experiments.
The example embodiments are directed to a system and method for forecasting load flexibility of a power grid. In one example, the method includes receiving temperature values associated with temperature set points of a plurality of loads that are included on a power grid, forecasting a flexibility of the plurality of loads using a polynomial-time mixed-integer non-linear programming (MINLP) optimization based on the received temperature values for the plurality of loads, and outputting information about the forecasted flexibility for display to a display device. The MINLP optimization performs the forecasting of the load flexibility on a fine-grained basis in comparison to conventional methods and is still fast enough that it can be computed in real-time.
We describe the development and application of stochastic optimization models and algorithms to address an issue of critical importance in the strategic allocation of resources; namely, the selection of a portfolio of capital investment projects under the constraints of a limited and uncertain budget. This issue is significant and one that faces decision-makers across all industries. The objective of this strategic decision process is generally self evident - to maximize the value obtained from the portfolio of selected projects (with value usually measured in terms of the portfolio's net present value). However, heretofore, many organizations have developed processes to make these investment decisions using simplistic rule-based rank-ordering schemes. This approach has the significant limitation of not accounting for the (often large) uncertainties in the costs or economic benefits associated with the candidate projects or in the uncertainties in the actual funds available to be expended over the projected period of time. As a result, the simple heuristic approaches that typically are employed in industrial practice generate outcomes that are non-optimal and do not achieve the level of benefits intended. In this paper we describe the results of research performed to utilize stochastic optimization models and algorithms to address this limitation by explicitly incorporating the evaluation of uncertainties in the analysis and decision making process. (authors)
This panel discussed the needs and importance of developing risk-based approaches to environmental remediation that will enable complex sites to be adaptively managed by applying cost-effective remedies that are protective of human health and the environment. The discussion emphasized risk-based approaches within decision processes and subsequent incorporation into regulatory decision documents. Remedy approaches, remediation exit strategies, and long-term monitoring plans that support risk-based end-states for difficult-to-remediate sites were discussed Panelists with presentations: Risk-Informed Approaches to Environmental Remediation and Adaptive Site Management (Vicky Freedman); ITRC Adaptive Site Management Perspective (Michael Truex); Optimization During Operations and Maintenance Phase at the Hanford Site 200-ZP-1 Groundwater Operable Unit (Emerald Laija, Kate Amrhein, Michael Truex); Adaptive Management: EPA Overview (Benjamin Simes); Decommissioning and Environmental Remediation Section Division of Nuclear Fuel Cycle and Waste Technology (Horst Monken Fernandes)
French Andra's currently operational LILW repository, Aube disposal facility, is almost 30 years old. From the beginning, the repository has benefited from the lessons learned from the first disposal facility, the CSM. It has nevertheless continued to evolve, to improve and to adapt, seeking to gain in efficiency, safety and cost optimization. This is a look at what has changed at the CSA since 1992 - and the effect these changes had on its operating practices, construction principles and the entire management organization. (authors)
In AREVA NP's recent projects of new plants or control room modernizations, the engineering of human factors was guided by NUREG 0711 and IEC 60964 and applied throughout the project phases. Each of the projects had a group of stakeholders, whose early mutual agreement on HFE matters was a prerequisite for successful HFE work. The group of main stakeholders comprises the utility's plant managers and members of the plant operating team, AREVA NP's project managers and members of the various technical competence centers, and representatives of the local safety authority. The objectives of each of these stakeholders and our advanced yet practice-proven HFE approach to successfully meet these objectives are described in this paper. It describes both our unique attention to the project's reference plant and its human-centered aspects thus inherited as well as our focus on those issues of the current plant which significantly depart from referenced practices. Deviations' significance and nature determine and grade the resulting HFE effort and its interdisciplinary conductance. (authors)
Herein we present a new modeling paradigm for optimization that we call random field optimization. Random fields are a powerful modeling abstraction that aims to capture the behavior of random variables that live on infinite-dimensional spaces (e.g., space and time) such as stochastic processes (e.g., time series, Gaussian processes, and Markov processes), random matrices, and random spatial fields. This paradigm involves sophisticated mathematical objects (e.g., stochastic differential equations and space-time kernel functions) and has been widely used in neuroscience, geoscience, physics, civil engineering, and computer graphics. Despite of this, however, random fields have seen limited use in optimization; specifically, existing optimization paradigms that involve uncertainty (e.g., stochastic programming and robust optimization) mostly focus on the use of finite random variables. This trend is rapidly changing with the advent of statistical optimization (e.g., Bayesian optimization) and multi-scale optimization (e.g., integration of molecular sciences and process engineering). Our work extends a recently-proposed abstraction for infinite-dimensional optimization problems by capturing more general uncertainty representations. Moreover, we discuss solution paradigms for this new class of problems based on finite transformations and sampling, and identify open questions and challenges.