scholarly journals A model tool for assessing real-time mixing of mineral and anthropogenic pollutants in East Asia: a case study of April 2005

2008 ◽  
Vol 8 (13) ◽  
pp. 3603-3622 ◽  
Author(s):  
F. Lasserre ◽  
G. Cautenet ◽  
C. Bouet ◽  
X. Dong ◽  
Y. J. Kim ◽  
...  
Keyword(s):  
East Asia ◽  
Real Time ◽  
Anthropogenic Pollution ◽  
Atmospheric Modeling ◽  
Time Step ◽  
Anthropogenic Pollutants ◽  
Vertical Distributions ◽  
Dust Events ◽  
Horizontal And Vertical Distributions

Abstract. In order to assess the complex mixing of atmospheric anthropogenic and natural pollutants over the East Asian region, we present a modelling tool which takes into account the main aerosols which are to be found simultaneously over China, Korea and Japan during springtime. Using the mesoscale RAMS (Regional Atmospheric Modeling System) tool, we present a simulation of natural (desert) dust events along with some of the most critical anthropogenic pollutants over East Asia, sulphur elements (SO2 and SO2-4) and Black Carbon (BC). As regards a one-week case study of dust events which occurred during late April 2005 over an area extending from the Gobi deserts to the Japan surroundings, we satisfactorily model the behaviours of the different aerosol plumes. We focus on possible dust mixing with the anthropogenic pollutants from megacities. For both natural and anthropogenic pollution, the model results are in fairly good agreement with the horizontal and vertical distributions of concentrations as measured by in situ LIDAR, and as observed in remote data, PM10 data and literature. In particular, we show that a simplified chemistry approach of this complex issue is sufficient to model this event, with a real-time step of 3 h. The model reproduces the main patterns and orders of magnitude for Aerosol Optical Thickness (AOT) and species contributions (via the Angström Exponent) when compared with the AErosol RObotic NETwork (AERONET) data.

scholarly journals A comprehensive modelling way for assessing real-time mixings of mineral and anthropogenic pollutants in East Asia

2007 ◽  
Vol 7 (4) ◽  
pp. 11895-11971 ◽  
Author(s):  
F. Lasserre ◽  
G. Cautenet ◽  
C. Bouet ◽  
X. Dong ◽  
Y. J. Kim ◽  
...  
Keyword(s):  
East Asia ◽  
Real Time ◽  
Anthropogenic Pollution ◽  
Atmospheric Modeling ◽  
Time Step ◽  
Anthropogenic Pollutants ◽  
Vertical Distributions ◽  
Dust Events ◽  
Horizontal And Vertical Distributions ◽  
Regional Atmospheric Modeling

Abstract. In order to assess the complex mixing of atmospheric anthropogenic and natural pollutants over the East Asian region, we propose to take into account the main aerosols simultaneously present over China, Korea and Japan during the spring season. With the mesoscale RAMS (Regional Atmospheric Modeling System) tool, we present a simulation of natural (desert) dust events along with some of the most critical anthropogenic pollutants over East Asia: sulphur elements (SO2 and SO42−) and Black Carbon (BC). During a 2-week case study of dust events which occurred in April 2005 over an area extending from the Gobi deserts to the Japan surroundings, we retrieve the behaviours of the different aerosols plumes. We focus on possible dust mixing with the anthropogenic pollutants from megalopolis. For both natural and anthropogenic pollution, the model results are in general agreement with the horizontal and vertical distributions of concentrations as measured by remote data, in situ LIDAR, PM10 data and literature. In particular, we show that a simplified chemistry approach of this complex issue can be efficient enough to model this event, with a real-time step of 3 h. The model provides the good shapes and orders of magnitude for the Aerosol Optical Thickness (AOT) and species contributions (via the Angström Exponent) when compared with the AERONET data.

scholarly journals Ramping of Demand Response Event with Deploying Distinct Programs by an Aggregator

Energies ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 1389 ◽  
Author(s):  
Omid Abrishambaf ◽  
Pedro Faria ◽  
Zita Vale
Keyword(s):  
Real Time ◽  
Demand Response ◽  
Small Time ◽  
Time Step ◽  
Strategic Plans ◽  
Time Simulation ◽  
Demand Reduction ◽  
Resistive Loads ◽  
Demand Response Programs

System operators have moved towards the integration of renewable resources. However, these resources make network management unstable as they have variations in produced energy. Thus, some strategic plans, like demand response programs, are required to overcome these concerns. This paper develops an aggregator model with a precise vision of the demand response timeline. The model at first discusses the role of an aggregator, and thereafter is presented an innovative approach to how the aggregator deals with short and real-time demand response programs. A case study is developed for the model using real-time simulator and laboratory resources to survey the performance of the model under practical challenges. The real-time simulation uses an OP5600 machine that controls six laboratory resistive loads. Furthermore, the actual consumption profiles are adapted from the loads with a small-time step to precisely survey the behavior of each load. Also, remuneration costs of the event during the case study have been calculated and compared using both actual and simulated demand reduction profiles in the periods prior to event, such as the ramp period.

Real-Time Hybrid Testing for Marine Structures: Challenges and Strategies

2013 ◽  
Author(s):  
Valentin Chabaud ◽  
Sverre Steen ◽  
Roger Skjetne
Keyword(s):  
Real Time ◽  
Scale Model ◽  
Force Estimation ◽  
Time Step ◽  
Hybrid Testing ◽  
Data Measurement ◽  
Main Challenge ◽  
Set Up ◽  
The One

Within the field of hydrodynamics, it is fairly easy to find examples of model tests whose performance is impaired by only a subpart of the whole system, which may not be the one of interest. Real-time hybrid testing (RTHT) overcomes this issue by performing scale model testing only on a subpart of the whole structure, the remainder being simulated numerically. The loads acting on the virtual substructure are calculated from online-measured motions of the physical substructure and actuated back on the latter in real-time. RTHT involves data measurement, filtering, force estimation, motion observing and force actuation. The main challenge is to fit all of those items into one time step. A simple case study is suggested. It consists in a linearized one degree of freedom floating wind turbine, whose floating substructure is physically tested while wind loads are numerically simulated and actuated. Design rules to build the corresponding RTHT set up are then presented.

Pollution based real time control strategies for combined sewer systems

1997 ◽  
Vol 36 (8-9) ◽  
pp. 331-336 ◽  
Author(s):  
Gabriela Weinreich ◽  
Wolfgang Schilling ◽  
Ane Birkely ◽  
Tallak Moland
Keyword(s):  
Real Time ◽  
Control Strategies ◽  
Ammonia Nitrogen ◽  
Simple Extension ◽  
Real Time Control ◽  
Time Control ◽  
Sewer Systems ◽  
Receiving Waters ◽  
Tunnel System

This paper presents results from an application of a newly developed simulation tool for pollution based real time control (PBRTC) of urban drainage systems. The Oslo interceptor tunnel is used as a case study. The paper focuses on the reduction of total phosphorus Ptot and ammonia-nitrogen NH4-N overflow loads into the receiving waters by means of optimized operation of the tunnel system. With PBRTC the total reduction of the Ptot load is 48% and of the NH4-N load 51%. Compared to the volume based RTC scenario the reductions are 11% and 15%, respectively. These further reductions could be achieved with a relatively simple extension of the operation strategy.

scholarly journals A Computer Vision-Based Occupancy and Equipment Usage Detection Approach for Reducing Building Energy Demand

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 156
Author(s):  
Paige Wenbin Tien ◽  
Shuangyu Wei ◽  
John Calautit
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Energy Demand ◽  
Energy Savings ◽  
Internal Heat ◽  
Electrical Equipment ◽  
Building Energy ◽  
Hvac Systems ◽  
Equipment Usage

Because of extensive variations in occupancy patterns around office space environments and their use of electrical equipment, accurate occupants’ behaviour detection is valuable for reducing the building energy demand and carbon emissions. Using the collected occupancy information, building energy management system can automatically adjust the operation of heating, ventilation and air-conditioning (HVAC) systems to meet the actual demands in different conditioned spaces in real-time. Existing and commonly used ‘fixed’ schedules for HVAC systems are not sufficient and cannot adjust based on the dynamic changes in building environments. This study proposes a vision-based occupancy and equipment usage detection method based on deep learning for demand-driven control systems. A model based on region-based convolutional neural network (R-CNN) was developed, trained and deployed to a camera for real-time detection of occupancy activities and equipment usage. Experiments tests within a case study office room suggested an overall accuracy of 97.32% and 80.80%. In order to predict the energy savings that can be attained using the proposed approach, the case study building was simulated. The simulation results revealed that the heat gains could be over or under predicted when using static or fixed profiles. Based on the set conditions, the equipment and occupancy gains were 65.75% and 32.74% lower when using the deep learning approach. Overall, the study showed the capabilities of the proposed approach in detecting and recognising multiple occupants’ activities and equipment usage and providing an alternative to estimate the internal heat emissions.

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