Construction of a DOAS Instrument for Installation at CARB
Understanding the nature of the sulfur and sulfate in combustion and lubricant-derived particles is important to characterize to better understand the relative contribution of fuel vs. oil sulfur, to understand storage vs release of sulfur from catalyzed surfaces, and measure the sulfur fraction partitioning into the PM nucleation particles. The objective of this research is to construct, test and provide to ARB laboratories a differential optical absorption spectrometer (DOAS) that can measure down to 20 ppbV in real time and determine a mass balance between SO2 and sulfate. The DOAS developed under this program was evaluated using a series of laboratory performance tests at the CE-CERT laboratory. The DOAS will be installed and operated in the ARB Heavy Duty Emissions Test Laboratory. The differential optical absorption spectrometer (DOAS) developed for this project was specially designed and built for rapid, extractive detection of sulfur dioxide at low ppb levels for the ARB. The integrated system consists of the extractive sampling train, the sampling cell, and the spectrometer. The sample cell uses a single-path design with beam expanding and focusing optics to minimize cell volume. The sample cell for the UC Riverside system is cylindrical with a length of 1.0 m and a total volume of approximately 0.51 liters. To avoid possible interferences from other exhaust constituents, the spectral range was restricted to the UVB region (200–230 nanometer (nm)). The DOAS software also allows for additional subsections of the spectral range to be selected to minimize interferences from species such as NH3. A deuterium lamp was used as the light source. The cell is designed to operate over a full 8-10 hour testing day at a temperature of 110°C, with a temperature range from 25°C up to 120°C (250oF). Laboratory tests were conducted to evaluate the response of the SO2 monitor to a range of operating conditions, from ambient conditions to those most representative of exhaust gas emanating from vehicles. These laboratory tests showed detection limits were approximately in the range of 12 to 15 ppbV. The instrument showed good linearity over SO2 concentration ranges of 20 to 953 ppbV. A number of interference tests were conducted, focusing on potential interferences between SO2, NO, and NH3. Initial tests showed some interference with either NO or NH3, at levels of 80 ppbV and 680 ppbV. Initial tests at 300 ppmV of NO and 16.6 ppmV of NH3 confirmed that at these high levels they would interfere with the SO2 response over the spectral range specified. With a modification of the spectral range to 206 to 214 nm, the DOAS interference SO2 response was less than 1.5 ppbV with 300 ppmV of NO injected with, and was slight less than 8 ppbV with 16.6 ppmV of NH3 injected. The DOAS was evaluated over vehicle emissions tests in CE-CERT’s light-duty chassis dynamometer laboratory. The results showed no appreciable SO2 interferences for the tests on the diesel truck, which had very low NH3 emissions. Clearly, in a low NH3 environment, as tested here the SO2 detection is unbiased and meets the laboratory tested detection limits of around 20 ppbV. If the DOAS is used in a low NH3 environment, as in this test, it should achieve the expected detection limits. The gasoline vehicles, on the other, had higher levels of NH3, and showed a significant interference, especially when the NH3 values exceeded 11 ppmV. If it is to be used for vehicles with higher levels of NH3, an NH3 scrubber would likely be needed to scrub the level of NH3 in the exhaust to no greater than 10 ppmV, as well as perhaps some additional updates to the software that are currently being developed. If an ammonia scrubber is used, it is recommended that SO2 measurements be made both with and without the scrubber in order to determine whether there is any appreciable effect on the measurements. vi Development and Installation of an SO2 DOAS Acronyms and Abbreviations ARB ...................................................Air Resources Board CARB .................................................California Air Resources Board CE-CERT ...........................................College of Engineering-Center for Environmental Research and Technology (University of California, Riverside) CFO ....................................................critical flow orifice CCD ...................................................charge-coupled device CPU ....................................................central processor unit DECSE ...............................................Diesel Emission Control – Sulfur Effects project DOAS .................................................differential optical absorption spectroscopy DPF ....................................................diesel particulate filter mm .....................................................millimeter NIST ...................................................National Institute of Standards and Technology nm ......................................................nanometer NOx ....................................................nitrogen oxides PM ......................................................Particulate matter SCR ....................................................selective catalytic reduction SO2 .....................................................sulfur dioxide SSR ....................................................solid-state relays UCR ...................................................University of California at Riverside UV ......................................................ultraviolet vii Development and Installation of an SO2 DOAS Executive Summary Since sulfur and sulfate are some of the main components of combustion and lubricant-derived particles, it is important to characterize sulfur species and understand the composition and potential health impacts of particles. Sulfate levels in vehicle exhaust particulates can be readily measured, but it is important to understand the relative contribution between combustion and oil-derived particles and the conversion rates found for SO2 to sulfate. With the low level of sulfur present in the fuel and oil products used in modern vehicles, detection limits in the ppb range are needed to measure the SO2 contribution. One technique that has been shown to provide real-time, low-level measurement of engine-out SO2, is differential optical absorption spectroscopy (DOAS). DOAS technology provides the potential for very low detection limits, along with the capability of measuring SO2 in real-time. Over the past decade, there have been numerous advances in DOAS technology that have allowed for the development of instruments with lower detection limits in conjunction with smaller sample cells to provide improved residence times. The objective of this study was to construct, test, and provide the ARB with a state-of-the-art DOAS that can measure SO2 down to 20 ppbV in real-time. The DOAS developed under this program was evaluated using a series of laboratory performance tests at the CE-CERT laboratory. The DOAS will be installed and operated in the ARB Heavy Duty Emissions Test Laboratory. The major conclusions from the findings are summarized in the following:  The differential optical absorption spectrometer (DOAS) developed for this project was specially designed and built for rapid, extractive detection of sulfur dioxide at low ppb levels for the ARB. The integrated system consists of the extractive sampling train, the sampling cell, and the spectrometer.  The sample cell uses a single-path design with beam expanding and focusing optics to minimize cell volume. This allows the highest amount of throughput for a given amount of initial light energy and is essential with a small diameter sample cell. The small diameter was a design parameter to minimize the cell volume which yields reduced residence time of the exhaust gas in the cell. The sample cell was designed as an open cell with the sample cell residing between the windows, rather than having the transmitting and collecting optics inside the sample cell.  The sample cell for this system is cylindrical with a length of 1.0 m. The total volume of the DOAS sample cell is approximately 0.51 liters.  The cell is designed to operate over a full 8-10 hour testing day at a temperature of 110°C, with a temperature range from 25°C up to 120°C (250oF).  To avoid possible interferences from other exhaust constituents, the spectral range was restricted to the UVB region (200–230 nanometer (nm)). For the 200–230 nanometer (nm) region, a deuterium lamp was used as the light source. Additional adjustments were also made to allow for subsection of the spectral range to be selected for analysis, to minimize interferences from species such as NH3, such as narrowing the spectral range to 206 nm to 214 nm.  The spectrometer/receiver consists of a single working element, a concave 1200 groove/millimeter (mm) holographic diffraction grating optimized for 200–230 nm.  The DOAS software, LasIRView 2009, is a user-friendly Windows-based based package with outputs for data logging and a software manual. The DOAS software was upgraded to provide an adjustable window over the spectral range, such that interferences from species such as NH3 can be minimized. viii Development and Installation of an SO2 DOAS  Laboratory tests were conducted to evaluate the response of the SO2 monitor to a range of operating conditions, from ambient conditions to those most representative of exhaust gas emanating from vehicles.  Detection limits were approximately in the range of 12 to 15 ppbV at three times the standard deviation of zero gas response.  The linearity of the instrument was evaluated over SO2 concentration ranges of 20 to 192 ppbV and 51 to 953 ppbV. The regression analysis showed a slope of 1.011 and 0.995, respectively, and R values of 1.0 and 0.999, respectively, for these ranges.  A number of interference tests were conducted, focus
The oxidation of hydrocarbons and CO over metal oxides: IV. Perovskite-type oxides
Abstract Several metal oxides of the perovskite structure have been prepared and used as catalyst for the oxidation of CO and C 2 H 4 . The oxides include LaCoO 3 , BaCoO 3 , La x Sr 1− x MnO 3 with x = 0 to 1, and La 0.7 Pb 0.3 MnO 3 . In general, CO and C 2 H 4 are stoichiometrically converted to CO 2 and H 2 O. The reaction rates are positive fractional order with respect to both oxygen and the oxidant, and are slightly inhibited by H 2 O. The specific reaction rates for the cobaltites are much lower than those over Co 3 O 4 , but higher than those over the manganites. The reaction over the pure compounds up to 500 °C are severely reduced by the presence of a few ppm of SO 2 in the gaseous phase. For La 0.7 Pb 0.3 MnO 3 prepared in Pt crucibles or doped with 100 ppm of Pt prior to final heating, an increase in activity was observed upon long-time exposure to a reaction mixture containing 10–30 ppm of SO 2 . The reaction kinetics over the SO 2 -treated Pt-contaminated catalysts are similar to the kinetics over the Pt metal. Details of the SO 2 effect are reported.
Towards infield, live plant phenotyping using a reduced-parameter CNN
There is an increase in consumption of agricultural produce as a result of the rapidly growing human population, particularly in developing nations. This has triggered high-quality plant phenotyping research to help with the breeding of high-yielding plants that can adapt to our continuously changing climate. Novel, low-cost, fully automated plant phenotyping systems, capable of infield deployment, are required to help identify quantitative plant phenotypes. The identification of quantitative plant phenotypes is a key challenge which relies heavily on the precise segmentation of plant images. Recently, the plant phenotyping community has started to use very deep convolutional neural networks (CNNs) to help tackle this fundamental problem. However, these very deep CNNs rely on some millions of model parameters and generate very large weight matrices, thus making them difficult to deploy infield on low-cost, resource-limited devices. We explore how to compress existing very deep CNNs for plant image segmentation, thus making them easily deployable infield and on mobile devices. In particular, we focus on applying these models to the pixel-wise segmentation of plants into multiple classes including background, a challenging problem in the plant phenotyping community. We combined two approaches (separable convolutions and SVD) to reduce model parameter numbers and weight matrices of these very deep CNN-based models. Using our combined method (separable convolution and SVD) reduced the weight matrix by up to 95% without affecting pixel-wise accuracy. These methods have been evaluated on two public plant datasets and one non-plant dataset to illustrate generality. We have successfully tested our models on a mobile device.