A Study of the Gas Phase Polymerization of Propylene: The Impact of Catalyst Treatment, Injection Conditions and the Presence of Alkanes on Polymerization and Polymer Properties

2016 ◽  
Vol 11 (1) ◽  
pp. 1600011 ◽  
Author(s):  
Ana R. Martins ◽  
Aarón J. Cancelas ◽  
Timothy F. L. McKenna
Keyword(s):  
Gas Phase ◽  
Polymer Properties ◽  
Gas Phase Polymerization ◽  
The Impact

Impact of catalyst injection conditions on the gas phase polymerization of propylene

2017 ◽  
Vol 2 (1) ◽  
pp. 75-87 ◽  
Author(s):  
Aarón J. Cancelas ◽  
Vincent Monteil ◽  
Timothy F. L. McKenna
Keyword(s):  
Gas Phase ◽  
Initial Temperature ◽  
Flow Reactor ◽  
Mineral Oil ◽  
Polymerization Kinetics ◽  
Current Work ◽  
Stopped Flow ◽  
Temperature Profiles ◽  
Polymer Properties ◽  
Gas Phase Polymerization

In the current work, gas phase propylene polymerizations were performed on ZN catalysts in a stopped flow reactor to understand the effect that the injection conditions (dry, as received, or wetted with a commercially available paraffinic mineral oil) have on initial temperature profiles, nascent polymer properties, and polymerization kinetics.

The Impact of the Aqueous Phase Chemistry of HNO4 on Gas Phase Tropospheric Chemistry

2001 ◽  
Vol 32 ◽  
pp. 269-270
Author(s):  
J.E. WILLIAMS ◽  
F.J. DENTENER ◽  
A.R. van den BERG
Keyword(s):  
Gas Phase ◽  
Aqueous Phase ◽  
Tropospheric Chemistry ◽  
Phase Chemistry ◽  
The Impact

scholarly journals Application of random forest regression to the calculation of gas-phase chemistry within the GEOS-Chem chemistry model v10

2019 ◽  
Vol 12 (3) ◽  
pp. 1209-1225 ◽  
Author(s):  
Christoph A. Keller ◽  
Mat J. Evans
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Gas Phase ◽  
Atmospheric Chemistry ◽  
Random Forest Regression ◽  
Data Set ◽  
Gas Phase Chemistry ◽  
Chemical Conditions ◽  
Phase Chemistry ◽  
The Impact

Abstract. Atmospheric chemistry models are a central tool to study the impact of chemical constituents on the environment, vegetation and human health. These models are numerically intense, and previous attempts to reduce the numerical cost of chemistry solvers have not delivered transformative change. We show here the potential of a machine learning (in this case random forest regression) replacement for the gas-phase chemistry in atmospheric chemistry transport models. Our training data consist of 1 month (July 2013) of output of chemical conditions together with the model physical state, produced from the GEOS-Chem chemistry model v10. From this data set we train random forest regression models to predict the concentration of each transported species after the integrator, based on the physical and chemical conditions before the integrator. The choice of prediction type has a strong impact on the skill of the regression model. We find best results from predicting the change in concentration for long-lived species and the absolute concentration for short-lived species. We also find improvements from a simple implementation of chemical families (NOx = NO + NO2). We then implement the trained random forest predictors back into GEOS-Chem to replace the numerical integrator. The machine-learning-driven GEOS-Chem model compares well to the standard simulation. For ozone (O3), errors from using the random forests (compared to the reference simulation) grow slowly and after 5 days the normalized mean bias (NMB), root mean square error (RMSE) and R2 are 4.2 %, 35 % and 0.9, respectively; after 30 days the errors increase to 13 %, 67 % and 0.75, respectively. The biases become largest in remote areas such as the tropical Pacific where errors in the chemistry can accumulate with little balancing influence from emissions or deposition. Over polluted regions the model error is less than 10 % and has significant fidelity in following the time series of the full model. Modelled NOx shows similar features, with the most significant errors occurring in remote locations far from recent emissions. For other species such as inorganic bromine species and short-lived nitrogen species, errors become large, with NMB, RMSE and R2 reaching >2100 % >400 % and <0.1, respectively. This proof-of-concept implementation takes 1.8 times more time than the direct integration of the differential equations, but optimization and software engineering should allow substantial increases in speed. We discuss potential improvements in the implementation, some of its advantages from both a software and hardware perspective, its limitations, and its applicability to operational air quality activities.

scholarly journals Gaseous chemistry and aerosol mechanism developments for version 3.5.1 of the online regional model, WRF-Chem

2014 ◽  
Vol 7 (6) ◽  
pp. 2557-2579 ◽  
Author(s):  
S. Archer-Nicholls ◽  
D. Lowe ◽  
S. Utembe ◽  
J. Allan ◽  
R. A. Zaveri ◽  
...  
Keyword(s):  
Gas Phase ◽  
Chemical Mechanism ◽  
Reduced Form ◽  
Sea Spray ◽  
Wind Fields ◽  
Aerosol Composition ◽  
Aerosol Emission ◽  
Sea Spray Aerosol ◽  
The Impact ◽  
Phase Chemical

Abstract. We have made a number of developments to the Weather, Research and Forecasting model coupled with Chemistry (WRF-Chem), with the aim of improving model prediction of trace atmospheric gas-phase chemical and aerosol composition, and of interactions between air quality and weather. A reduced form of the Common Reactive Intermediates gas-phase chemical mechanism (CRIv2-R5) has been added, using the Kinetic Pre-Processor (KPP) interface, to enable more explicit simulation of VOC degradation. N2O5 heterogeneous chemistry has been added to the existing sectional MOSAIC aerosol module, and coupled to both the CRIv2-R5 and existing CBM-Z gas-phase schemes. Modifications have also been made to the sea-spray aerosol emission representation, allowing the inclusion of primary organic material in sea-spray aerosol. We have worked on the European domain, with a particular focus on making the model suitable for the study of nighttime chemistry and oxidation by the nitrate radical in the UK atmosphere. Driven by appropriate emissions, wind fields and chemical boundary conditions, implementation of the different developments are illustrated, using a modified version of WRF-Chem 3.4.1, in order to demonstrate the impact that these changes have in the Northwest European domain. These developments are publicly available in WRF-Chem from version 3.5.1 onwards.

scholarly journals Nautilus multi-grain model: Importance of cosmic-ray-induced desorption in determining the chemical abundances in the ISM

2018 ◽  
Vol 615 ◽  
pp. A20 ◽  
Author(s):  
Wasim Iqbal ◽  
Valentine Wakelam
Keyword(s):  
Grain Size ◽  
Surface Temperature ◽  
Gas Phase ◽  
Numerical Models ◽  
Cosmic Ray ◽  
Grain Sizes ◽  
Small Grains ◽  
Grain Model ◽  
Grain Surface ◽  
The Impact

Context. Species abundances in the interstellar medium (ISM) strongly depend on the chemistry occurring at the surfaces of the dust grains. To describe the complexity of the chemistry, various numerical models have been constructed. In most of these models, the grains are described by a single size of 0.1 μm. Aims. We study the impact on the abundances of many species observed in the cold cores by considering several grain sizes in the Nautilus multi-grain model. Methods. We used grain sizes with radii in the range of 0.005 μm to 0.25 μm. We sampled this range in many bins. We used the previously published, MRN and WD grain size distributions to calculate the number density of grains in each bin. Other parameters such as the grain surface temperature or the cosmic-ray-induced desorption rates also vary with grain sizes. Results. We present the abundances of various molecules in the gas phase and also on the dust surface at different time intervals during the simulation. We present a comparative study of results obtained using the single grain and the multi-grain models. We also compare our results with the observed abundances in TMC-1 and L134N clouds. Conclusions. We show that the grain size, the grain size dependent surface temperature and the peak surface temperature induced by cosmic ray collisions, play key roles in determining the ice and the gas phase abundances of various molecules. We also show that the differences between the MRN and the WD models are crucial for better fitting the observed abundances in different regions in the ISM. We show that the small grains play a very important role in the enrichment of the gas phase with the species which are mainly formed on the grain surface, as non-thermal desorption induced by collisions of cosmic ray particles is very efficient on the small grains.

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