Modelling of airborne particulate matter concentration in underground stations using a two size-class conservation model

Walther, Édouard; Bogdan, Mateusz; Cohen, Ryan

doi:10.1016/j.scitotenv.2017.07.090

Cited by 4 publications

(7 citation statements)

References 24 publications

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“…We observe that both algorithms decrease the consumption of the ventilation while maintaining a similar air quality. 5…”

Section: E Numerical Resultsmentioning

confidence: 99%

“…Moreover, we assume that trains arriving in station produce particles by wearing brake pads and wheels, but also by resuspending particles from the floor. We use the model presented in [5] to model the relation between trains arrivals and particles generation in the air.…”

Section: Air Quality Modelmentioning

confidence: 99%

“…2) Air quality in buildings and subway stations: an ANSES report [4] about air quality of underground subway stations states that the concentration of particulate matter whose size is inferior to 10µm (PM10) can be unhealthy for the workers and maybe users. This is mainly due to ferrous PM10 that are generated during braking of the trains as stated in [5] and [6]. Subway stations operators in Paris took measures to monitor the concentration of PM10 [6] that are openly available online.…”

Section: B Literaturementioning

confidence: 99%

“…These methods are computationally very expensive and could hardly be integrated in an optimization problem without using reduced basis methods [8] that are challenging to implement in dynamic environments such as subway stations. The methods presented in [5] and [9] use zonal models to compute an estimation of the global indoor air quality. These models are much more computationally efficient but require many approximations.…”

Section: B Literaturementioning

confidence: 99%

“…In [9], the authors use a bi-compartiment model in an office building in order to model the deposition/resuspension phenomenon. Due to the lack of data in subway stations to calibrate the surface dynamic model, we consider a simple air mass conservation model, as presented in [5], to model the dynamics of the particulate matters concentration in the subway station air. As in [5], we assume that the floor is always saturated in dust particles so as to ignore the particles surface dynamics.…”

Section: Air Quality Modelmentioning

confidence: 99%

See 4 more Smart Citations

Stochastic Optimization of Braking Energy Storage and Ventilation in a Subway Station

Rigaut

Carpentier

Chancelier

et al. 2019

IEEE Trans. Power Syst.

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In the Paris subway system, stations represent about one third of the overall energy consumption. Within stations, ventilation is among the top consuming devices; it is operated at maximum airflow all day long, for air quality reasons. In this paper, we present a concept of energy system that displays comparable air quality while consuming much less energy. The system comprises a battery that makes it possible to recover the trains braking energy, arriving under the form of erratic and strong peaks. We propose an energy management system (EMS) that, at short time scale, controls energy flows and ventilation airflow. By using proper optimization algorithms, we manage to match supply with demand, while minimizing energy daily costs. For this purpose, we have designed algorithms that take into account the braking variability. They are based on the so-called Stochastic Dynamic Programming (SDP) mathematical framework. We fairly compare SDP based algorithms with the widespread Model Predictive Control (MPC) ones. First, both SDP and MPC yield energy/money operating savings of the order of one third, compared to the current management without battery (our figure does not include the cost of the battery). Second, depending on the specific design, we observe that SDP outperforms MPC by a few percent, with an easier online numerical implementation.

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“…We observe that both algorithms decrease the consumption of the ventilation while maintaining a similar air quality. 5…”

Section: E Numerical Resultsmentioning

confidence: 99%

Section: Air Quality Modelmentioning

confidence: 99%

Section: B Literaturementioning

confidence: 99%

Section: B Literaturementioning

confidence: 99%