The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
David M Konisky - One of the best experts on this subject based on the ideXlab platform.
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understanding excess emissions from Industrial Facilities evidence from texas
Social Science Research Network, 2019Co-Authors: Nikolaos Zirogiannis, Alex Hollingsworth, David M KoniskyAbstract:We analyze excess emissions from Industrial Facilities in Texas using data from the Texas Commission on Environmental Quality. Emissions are characterized as excess if they are beyond a facility’s permitted levels and if they occur during startups, shutdowns, or malfunctions. We provide summary data on both the pollutants most often emitted as excess emissions and the Industrial sectors and Facilities responsible for those emissions. Excess emissions often represent a substantial share of a facility’s routine (or permitted) emissions. We find that while excess emissions events are frequent, the majority of excess emissions are emitted by the largest events. That is, the sum of emissions in the 96th-100th percentile is often several orders of magnitude larger than the remaining excess emissions (i.e. the sum of emissions below the 95th percentile). Thus, the majority of events emit a small amount of pollution relative to the total amount emitted. In addition, a small group of high emitting Facilities in the most polluting Industrial sectors are responsible for the vast majority of excess emissions. Using an integrated assessment model, we estimate that the health damages in Texas from excess emissions are approximately $150 million annually.
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understanding excess emissions from Industrial Facilities evidence from texas
Environmental Science & Technology, 2018Co-Authors: Nikolaos Zirogiannis, Alex Hollingsworth, David M KoniskyAbstract:We analyze excess emissions from Industrial Facilities in Texas using data from the Texas Commission on Environmental Quality. Emissions are characterized as excess if they are beyond a facility’s permitted levels and if they occur during startups, shutdowns, or malfunctions. We provide summary data on both the pollutants most often emitted as excess emissions and the Industrial sectors and Facilities responsible for those emissions. Excess emissions often represent a substantial share of a facility’s routine (or permitted) emissions. We find that while excess emissions events are frequent, the majority of excess emissions are emitted by the largest events. That is, the sum of emissions in the 96–100th percentile is often several orders of magnitude larger than the remaining excess emissions (i.e., the sum of emissions below the 95th percentile). Thus, the majority of events emit a small amount of pollution relative to the total amount emitted. In addition, a small group of high emitting Facilities in the...
Nikolaos Zirogiannis - One of the best experts on this subject based on the ideXlab platform.
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understanding excess emissions from Industrial Facilities evidence from texas
Social Science Research Network, 2019Co-Authors: Nikolaos Zirogiannis, Alex Hollingsworth, David M KoniskyAbstract:We analyze excess emissions from Industrial Facilities in Texas using data from the Texas Commission on Environmental Quality. Emissions are characterized as excess if they are beyond a facility’s permitted levels and if they occur during startups, shutdowns, or malfunctions. We provide summary data on both the pollutants most often emitted as excess emissions and the Industrial sectors and Facilities responsible for those emissions. Excess emissions often represent a substantial share of a facility’s routine (or permitted) emissions. We find that while excess emissions events are frequent, the majority of excess emissions are emitted by the largest events. That is, the sum of emissions in the 96th-100th percentile is often several orders of magnitude larger than the remaining excess emissions (i.e. the sum of emissions below the 95th percentile). Thus, the majority of events emit a small amount of pollution relative to the total amount emitted. In addition, a small group of high emitting Facilities in the most polluting Industrial sectors are responsible for the vast majority of excess emissions. Using an integrated assessment model, we estimate that the health damages in Texas from excess emissions are approximately $150 million annually.
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understanding excess emissions from Industrial Facilities evidence from texas
Environmental Science & Technology, 2018Co-Authors: Nikolaos Zirogiannis, Alex Hollingsworth, David M KoniskyAbstract:We analyze excess emissions from Industrial Facilities in Texas using data from the Texas Commission on Environmental Quality. Emissions are characterized as excess if they are beyond a facility’s permitted levels and if they occur during startups, shutdowns, or malfunctions. We provide summary data on both the pollutants most often emitted as excess emissions and the Industrial sectors and Facilities responsible for those emissions. Excess emissions often represent a substantial share of a facility’s routine (or permitted) emissions. We find that while excess emissions events are frequent, the majority of excess emissions are emitted by the largest events. That is, the sum of emissions in the 96–100th percentile is often several orders of magnitude larger than the remaining excess emissions (i.e., the sum of emissions below the 95th percentile). Thus, the majority of events emit a small amount of pollution relative to the total amount emitted. In addition, a small group of high emitting Facilities in the...
Seung Ho Hong - One of the best experts on this subject based on the ideXlab platform.
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Demand Response Management for Industrial Facilities: A Deep Reinforcement Learning Approach
IEEE Access, 2019Co-Authors: Xuefei Huang, Seung Ho Hong, Yuemin Ding, Junhui JiangAbstract:As a major consumer of energy, the Industrial sector must assume the responsibility for improving energy efficiency and reducing carbon emissions. However, most existing studies on Industrial energy management are suffering from modeling complex Industrial processes. To address this issue, a model-free demand response (DR) scheme for Industrial Facilities was developed. In practical terms, we first formulated the Markov decision process (MDP) for Industrial DR, which presents the composition of the state, action, and reward function in detail. Then, we designed an actor-critic-based deep reinforcement learning algorithm to determine the optimal energy management policy, where both the actor (Policy) and the critic (Value function) are implemented by the deep neural network. We then confirmed the validity of our scheme by applying it to a real-world industry. Our algorithm identified an optimal energy consumption schedule, reducing energy costs without compromising production.
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hour ahead price based energy management scheme for Industrial Facilities
IEEE Transactions on Industrial Informatics, 2017Co-Authors: Xuefei Huang, Seung Ho HongAbstract:Price-based demand response (PBDR) offers a significant opportunity for electricity consumers to dynamically balance their energy demand in response to time-varying electricity prices, and therefore ease the burden on the grid during peak times. However, despite being the primary energy consumers, there is little research carried out on implementing PBDR in Industrial Facilities, especially on real-time price (RTP) based DR. In this study, we propose a DR scheme based on hour-ahead RTP for Industrial Facilities. The scheme implements an artificial neural network based price forecasting model to forecast unknown future prices to support global time horizon optimization. Based on the forecasting price, the energy cost minimization problem is formulated by mixed integer linear programming. This paper includes a practical case study of the whole process of steel powder manufacturing for performance analysis. The results show that the proposed scheme is capable of balancing the energy demand and reducing energy costs while satisfying production targets.
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an iot based energy management platform for Industrial Facilities
Applied Energy, 2016Co-Authors: Min Wei, Seung Ho Hong, Musharraf AlamAbstract:Interconnectivity and interoperability are very important features in the development of integrated energy management systems for Industrial Facilities. A simple and common strategy for exchanging energy-related information among the entities in a facility is currently lacking. To this end, the purpose of this study is to present an IoT-based communication framework with a common information model to facilitate the development of a demand response (DR) energy management system for Industrial customers. Additionally, we developed and implemented an IoT-based energy-management platform based on a common information model and open communication protocols, which takes advantage of integrated energy supply networks to deploy DR energy management in an Industrial facility. The experimental results of this study demonstrate that the proposed platform can not only improve the interconnectivity of the entities in Industrial energy management systems but also reduce the energy costs of Industrial Facilities.
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a demand response energy management scheme for Industrial Facilities in smart grid
IEEE Transactions on Industrial Informatics, 2014Co-Authors: Yuemin Ding, Seung Ho HongAbstract:Demand response (DR) smart grid technology provides an opportunity for electricity consumers to actively participate in the management of power systems. Industry is one of the major consumers of electric power. In this study, we propose a DR energy management scheme for Industrial Facilities based on the state task network (STN) and mixed integer linear programming (MILP). The scheme divides the processing tasks in Industrial Facilities into nonschedulable tasks (NSTs) and schedulable tasks (STs), and takes advantage of distributed energy resources (DERs) to implement DR. Based on day-ahead hourly electricity prices, the scheme determines the scheduling of STs and DERs in order to shift the demand from peak periods (with high electricity prices) to off-peak periods (with low electricity prices), which not only improves the reliability of the electric power system, but also reduces energy costs for Industrial Facilities.
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a model of demand response energy management system in Industrial Facilities
International Conference on Smart Grid Communications, 2013Co-Authors: Yuemin Ding, Seung Ho HongAbstract:Demand response energy management improves the reliability of electrical grids and reduces electricity cost of consumers by shifting part of the demand from peak to off-peak demand periods. On the demand side, Industrial Facilities consume huge amounts of electricity, highlighting the urgent need to implement demand response energy management. In this study, we propose a general model of demand response energy management systems for Industrial Facilities. The model consists of model elements, model architecture, and approaches to Industrial demand response. The proposed model provides a straightforward means of designing and analyzing demand response systems in Industrial Facilities and assists in developing standards for such systems. We also present an example of this model applied to a steel manufacturing facility.
L Buchaillot - One of the best experts on this subject based on the ideXlab platform.
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fabrication of 24 mhz disk resonators with silicon passive integration technology
IEEE Electron Device Letters, 2010Co-Authors: M Sworowski, F Neuilly, B Legrand, A Summanwar, P Philippe, L BuchaillotAbstract:A new approach for the fabrication of large contour-mode single-crystal silicon resonators has been demonstrated without the use of SOI substrates. Twenty-four-megahertz disk resonators have been built thanks to Industrial Facilities dedicated to the integration of passive components on silicon and exhibit a good compromise between the quality factor higher than 50 000 and the motional resistance of a few kiloohms.
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high q and low r m 24 mhz radial contour mode disk resonators fabricated with silicon passive integration technology
International Conference on Solid-State Sensors Actuators and Microsystems, 2009Co-Authors: M Sworowski, F Neuilly, B Legrand, A Summanwar, F Lallemand, P Philippe, L BuchaillotAbstract:A new approach for the fabrication of large contour-mode single-crystal silicon resonators has been demonstrated without the use of SOI substrates. 24-MHz-disk resonators have been built thanks to Industrial Facilities dedicated to the integration of passive components on silicon. They exhibit a good compromise between the quality factor Q higher than 50000 and the motional resistance of a few kilo-ohms.
Ernesto Salzano - One of the best experts on this subject based on the ideXlab platform.
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vulnerability of Industrial Facilities to attacks with improvised explosive devices aimed at triggering domino scenarios
Reliability Engineering & System Safety, 2015Co-Authors: Gabriele Landucci, Valerio Cozzani, Genserik Reniers, Ernesto SalzanoAbstract:Process- and chemical plants may constitute a critical target for a terrorist attack. In the present study, the analysis of Industrial accidents induced by intentional acts of interference is carried out focusing on accident chains triggered by attacks with home-made (improvised) explosives. The effects of blast waves caused by improvised explosive devices are compared with those expected from a net equivalent charge of TNT by using a specific methodology for the assessment of stand-off distances. It is demonstrated that a home-made explosive device has a TNT efficiency comprised between 0.2 and 0.5. The model was applied to a case study, demonstrating the potentiality of improvised explosives in causing accident escalation sequences and severe effects on population and assets. The analysis of the case-study also allowed obtaining suggestions for an adequate security management.
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risk assessment and early warning systems for Industrial Facilities in seismic zones
Reliability Engineering & System Safety, 2009Co-Authors: Ernesto Salzano, Anita Garcia Agreda, Antonio Di Carluccio, Giovanni FabbrocinoAbstract:Abstract Industrial equipments and systems can suffer structural damage when hit by earthquakes, so that accidental scenarios as fire, explosion and dispersion of toxic substances can take place. As a result, overall damage to people, environment and properties increases. The present paper deals with seismic risk analysis of Industrial Facilities where atmospheric storage tanks (anchored or unanchored to ground), horizontal pressurised tanks, reactors and pumps are installed. Simplified procedures and methodologies based on historical database and literature data on natural-technological (Na-Tech) accidents for seismic risk assessment are discussed. Equipment-specific fragility curves have been thus derived depending on a single earthquake measure, peak ground acceleration (PGA). Fragility parameters have been then transformed to linear probit coefficients in order to obtain reliable threshold values for earthquake intensity measure, both for structural damage and loss of containment. These threshold values are of great interest when development of active and passive mitigation actions and systems, safety management, and the implementation of early warning system are concerned. The approach is general and can be implemented in any available code or procedure for risk assessment. Some results of seismic analysis of atmospheric storage tanks are also presented for validation.