Noha A. Al-Shalaby, Hend A. Malhat, Saber H. Zainud-Deen
AbstractThis paper investigates a hybrid coupled plasmonic gas sensor with stacked gold-SiO2 layers for air-quality monitoring. The gas absorption characteristics of hybrid-stacked layers sensors are studied and compared with single-layer sensors. Gases with different refractive indexes changing from 1 to 1.4 are studied. The total absorption radar cross section (ACS) has increased from 1.681 × 105 nm2 for single-layer sensors to 5.021 × 105 nm2 for hybrid stacked layers sensors. A graphene monolayer is used to enhance the total gas absorption. It acts as an insulator to the dipole sensor. The lumped-element equivalent circuit is developed using a particle swarm optimization technique (PSO). The sensitivity is 788 nm/RIU for the single-layer sensor and 910 nm/RIU for the hybrid-stacked layers sensor with a graphene monolayer placed as a cover for the plasmonic dipole. A polarization-insensitive sensor concerning the direction of the electric field (single layer/hybrid-stacked layers) is constructed from 45° quad-rotated dipole arms of sensors. The total ACS is enhanced to 2.31 × 105 nm2 for the polarization-insensitive single-layer sensor and 6.76 × 105 nm2 for the polarization-insensitive hybrid-stacked layers sensor. Planar arrays of 3 × 3, 4 × 4, and 5 × 5 elements of the last sensor are investigated for absorption and sensitivity enhancement. Ethanol, acetone, nitrogen dioxide, and toluene gases are tested with a total ACS peak value of 8.7 × 106 nm2. The sensitivity is 895.5 nm/RIU for 4 × 4 array elements.
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 models. Our training data consists of one 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 O3, error from using the random forests grow slowly and after 5 days the normalised mean bias (NMB), root mean square error (RMSE) and R2 are 4.2 %, 35 %, 0.9 respectively; after 30 days the errors increase to 13 %, 67 %, 0.75. 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 %, <0.1 respectively. This proof-of-concept implementation is 85 % slower than the direct integration of the differential equations but optimisation and software engineering would 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.
Abstract There are substantial remaining reserves in most of the matured fields offshore Sarawak. Additional wells are required to access those reserves. However, in most cases there are limited remaining well slots available on the existing platforms to drill the new wells. Putting in new structures is costly and often renders the revisit projects uneconomic. After successful implementation of the Twin Wellhead Technology in its Tukau (ref. 1) and Baram fields, PETRONAS Carigali Sdn. Bhd. (PCSB), has taken a quantum leap in its pursuit for application of innovative new technology. This time it is the implementation of Triple Wellhead Technology in the Bokor field, allows 3 independent wells with 6 production strings to be drilled through 1 shared conductor. This is the first such installation in the world, and significant reduction in project cost has been achieved through reduction in additional conductors to be installed. This paper presents the development and the application of this new technology. The mechanics and methodology of the wellhead system are briefly explained. The design considerations, the actual project execution and some lessons learned from the project are also addressed.