Peak Alignment of Gas Chromatography-Mass Spectrometry Data with Deep Learning

We present ChromAlignNet, a deep learning model for alignment of peaks in Gas Chromatography-Mass Spectrometry (GC-MS) data. In GC-MS data, a compound's retention time (RT) may not stay fixed across multiple chromatograms. To use GC-MS data for biomarker discovery requires alignment of identical analyte's RT from different samples. Current methods of alignment are all based on a set of formal, mathematical rules. We present a solution to GC-MS alignment using deep learning neural networks, which are more adept at complex, fuzzy data sets. We tested our model on several GC-MS data sets of various complexities and analysed the alignment results quantitatively. We show the model has very good performance (AUC $\sim 1$ for simple data sets and AUC $\sim 0.85$ for very complex data sets). Further, our model easily outperforms existing algorithms on complex data sets. Compared with existing methods, ChromAlignNet is very easy to use as it requires no user input of reference chromatograms and parameters. This method can easily be adapted to other similar data such as those from liquid chromatography. The source code is written in Python and available online

Charging performance of a CO₂ semi-clathrate hydrate based PCM in a lab-scale cold storage system

Cold thermal energy storage aids in the efficient deployment of thermal energy whenever there is a mismatch between energy generation and energy use. In this study, a lab-scale demonstration of cold storage system was built to investigate the performance of CO₂ semi-clathrate hydrate as a cold storage material in the charge of a realistic cold thermal storage. The experimental rig was basically a vessel equipped with an external loop and an ultrasonic crystallizer to boost the hydrate formation. The hydrate was formed from a salt solution composed of tetra-n-butyl ammonium bromide 20 wt%, tetra-n-butyl ammonium fluoride 0.25 wt% and sodium decyl sulfate 0.15 wt%. At a constant pressure condition of 5.0 bar, the hydrate formation was triggered by the heat transfer fluid at 7.5 °C. The charged cooling capacity of two control strategies, namely ‘constant pressure’ and ‘constant mass’, were compared. The repeatability and stability of hydrate formation conditions were studied. It was indicated that CO₂ semi-clathrate hydrate could serve in cold storage systems effectively. However, it was also found that under the experimental conditions, hydrate formation was hard to thoroughly complete due to the lack of sufficient driving force or heat/mass transfer

A comparison of battery and phase change coolth storage in a PV cooling system under different climates

Energy storage in PV cooling systems is desirable to supply on-site loads during solar outages. Current storage methods of such systems typically use battery storage to store surplus electricity generated by solar panels or coolth thermal energy storage (CTES) to store excess cooling capacity produced by an electric-driven chiller. This study compares three cooling system configurations – no energy storage, with a battery storage, and with a phase change CTES, for a residential building under the climate of Shanghai, Madrid and Brisbane. System simulation of each configuration was conducted using TRNSYS. A CTES component was programmed externally using effectiveness-NTU method. Both energy storage methods were compared with regard to energy change during a summer day, power consumption and primary energy saving ratio (PESR) during the cooling season. In addition, performance of a single battery and a single CTES were evaluated under various operational conditions. The results showed good energy performance of both storage cases. The PESR of battery case and coolth storage case were 2.8 times and 1.9 times higher than that of a reference case with no energy storage

Perspective-Equivariant Imaging: an Unsupervised Framework for Multispectral Pansharpening

Ill-posed image reconstruction problems appear in many scenarios such as remote sensing, where obtaining high quality images is crucial for environmental monitoring, disaster management and urban planning. Deep learning has seen great success in overcoming the limitations of traditional methods. However, these inverse problems rarely come with ground truth data, highlighting the importance of unsupervised learning from partial and noisy measurements alone. We propose perspective-equivariant imaging (EI), a framework that leverages perspective variability in optical camera-based imaging systems, such as satellites or handheld cameras, to recover information lost in ill-posed optical camera imaging problems. This extends previous EI work to include a much richer non-linear class of group transforms and is shown to be an excellent prior for satellite and urban image data, where perspective-EI achieves state-of-the-art results in multispectral pansharpening, outperforming other unsupervised methods in the literature. Code at https://andrewwango.github.io/perspective-equivariant-imagingComment: Pre-prin

Transient Three-Dimensional Side Load Analysis of a Film Cooled Nozzle

Transient three-dimensional numerical investigations on the side load physics for an engine encompassing a film cooled nozzle extension and a regeneratively cooled thrust chamber, were performed. The objectives of this study are to identify the three-dimensional side load physics and to compute the associated aerodynamic side load using an anchored computational methodology. The computational methodology is based on an unstructured-grid, pressure-based computational fluid dynamics formulation, and a transient inlet history based on an engine system simulation. Ultimately, the computational results will be provided to the nozzle designers for estimating of effect of the peak side load on the nozzle structure. Computations simulating engine startup at ambient pressures corresponding to sea level and three high altitudes were performed. In addition, computations for both engine startup and shutdown transients were also performed for a stub nozzle, operating at sea level. For engine with the full nozzle extension, computational result shows starting up at sea level, the peak side load occurs when the lambda shock steps into the turbine exhaust flow, while the side load caused by the transition from free-shock separation to restricted-shock separation comes at second; and the side loads decreasing rapidly and progressively as the ambient pressure decreases. For the stub nozzle operating at sea level, the computed side loads during both startup and shutdown becomes very small due to the much reduced flow area