<p>The potential use of enzymes in industrial synthesis of epoxidized soybean oil has been limited through the high cost of the enzyme catalyst, in this work we evaluate the effectiveness of chemo enzymatic epoxidation of high oleic soybean oil (HOSBO) using lipase B from <i>Candida antarctica </i>(CALB) on immobilization support Immobead 150 and H₂O₂in a solvent-free system. Additionally, we evaluated the production decay rates for hydrolytic activity and epoxide product formation over consecutive batches to determine half-life of the enzyme catalyst. </p> <p> Batch epoxidation of HOSBO using CALB on 4wt% loading shows yields higher than 90% after 12 hrs. of reaction, and with a correlation to the consumption of double bonds suggesting that the reaction is selective and limiting side product reactions. Non-selective hydrolysis of oil was not found beyond the initial hydrolysis degree of raw HOSBO. Evaluations of decay given by epoxide product formation and released free fatty acids shows a half-life of the enzyme catalyst on these activities is of 22 ad 25 hrs. respectively. Finally, we evaluated the physical parameters influencing this decay, and found that H₂O₂ presence is the most important parameter of enzyme inactivation with no significant effect from its slowed addition. We propose a new reactor configuration for the analysis of the specific steps on epoxide formation through peracid intermediates. </p>

The transportation sector consumes around 70% of all petroleum in the US. In recent years, there have been improvements in the efficiency of the vehicles, and hybrid techniques that have been used to improve efficiency for conventional combustion vehicles. Hydraulic systems have been used as an alternative to conventional electric regenerative systems with good results. It has been proven that hydraulic systems can improve energy consumption in conventional combustion vehicles and in refuse collection vehicles. The control strategy has a large impact on the performance of the system and studies have shown the control strategy selection should be optimized and selected based on application. The performance of a hydraulic accumulator was compared with the performance of a set of ultracapacitors with the same energy storage capacity. The energy efficiency for the ultracapacitor was around 79% and the energy efficiency of the hydraulic accumulator was 87.7%. The power/mass ratio in the set of ultracapacitors was 2.21 kW/kg and 2.69 kW/kg in the hydraulic accumulator. The cost/power ratio is 217 US$/kW in the ultracapacitors and 75 US$/kW in the hydraulic accumulator. Based on these results, the hydraulic accumulator was selected as the energy storage device for the system. A testbench was designed, modeled, implemented to test the energy storage system in different conditions of operation. The experimental results of the testbench show how system can be actively controlled for different operating conditions. The operating conditions in the system can be adjusted by changing the number of rheostats connected to the electric generator. Different variables in the system were measured such as the angular shaft speed in the hydraulic pump, the torque and speed in the hydraulic motor, the pressure in the system, the flow rate, and the current and voltage in the electric generator. The control algorithm was successfully implemented, the results for the pressure in the system and the angular speed in the electric generator show how the control system can follow a desired reference value. Two different controllers were implemented: one controller for the pressure in the system, and one controller for the speed.

<p>In the US, seven regional transmission organizations (RTOs) operate wholesale electricity markets within three largely independent transmission systems, the largest of which includes five RTO regions and many vertically integrated utilities.</p> <p>RTOs operate a day-ahead and a real-time market. In the day-ahead market, generation and demand-side resources are optimally scheduled based on bids and offers for the next day. Those schedules are adjusted according to actual operating conditions in the real-time market. Both markets involve a unit commitment calculation, a mixed integer program that determines which generators will be online, and an economic dispatch calculation, an optimization determines the output of each online generator for every interval and calculates locational marginal prices (LMPs).</p> <p>The use of LMPs for the management of congestion in RTO transmission systems has brought efficiency and transparency to the operation of electric power systems and provides price signals that highlight the need for investment in transmission and generation. Through this work, we aim to extend these efficiency and transparency gains to the coordination across RTOs. Existing market-based inter-regional coordination schemes are limited to incremental changes in real-time markets. </p> <p>We propose a multi-regional unit-commitment that enables coordination in the day-ahead timeframe by applying a distributed approach to approximate a system-wide optimal commitment and dispatch while allowing each region to largely maintain their own rules, model only internal transmission up to the boundary, and keep sensitive financial information confidential. A heuristic algorithm based on an extension of the alternating directions method of multipliers (ADMM) for the mixed integer program is applied to the unit commitment. </p> The proposed coordinated solution was simulated and compared to the ideal single-market scenario and to a representation of the current uncoordinated solution, achieving at least 58% of the maximum potential savings, which, in terms of the annual cost of electric generation in the US, could add up to nearly $7 billion per year. In addition to the coordinated day-ahead solution, we develop a distributed solution for financial transmission rights (FTR) auctions with minimal information sharing across RTOs that constitutes the first known work to provide a viable option for market participants to seamlessly hedge price variability exposure on cross-border transactions.

<p>The purpose of this research was to determine what components of a complex centrifugal  compression system inlet needed to be modelled to accurately predict the swirl and total pressure  distortions at the compressor face. Two computational models were developed. A full-fidelity case  where all the inlet geometry was modelled and a reduced model where a small portion of the inlet  was considered. Both the numerical cases were compared with experimental data from a research  compressor rig developed by Honeywell Aerospace. The test apparatus was designed with a  modular inlet system to develop swirl distortion patterns. The modular inlet system utilized  transposable baffles within the radial-to-axial section of the inlet and blockage plates of varying  sizes and geometries at the inlet to this section.  Discerning the dominant inlet component that dictates distortion behavior at the compressor  face would allow the reduced modelling of inlet components for compression systems and would  allow coupling with more tortuous systems. Furthermore, it would reduce the design iteration and  simulation time of the inlet systems. Several investigations utilizing a reduced model only  considering a radial-to-axial inlet are available in literature, but no comprehensive justification has  been presented as to the impact this has on the distortion behavior.   Experimental surveys of flow conditions just upstream of the inducer of the centrifugal  compressor were conducted at several operating conditions. The highest and lowest mass flow  rates of these operating points were simulated using ANSYS CFX 2020R1 for both the  computational models. Multiple inlet configurations were simulated to test the robustness of the  reduced model in comparison to the full fidelity. The numerical simulations highlighted  shortcomings of the instrumentation used to characterize the experimental flow field at the inducer,  particularly with respect to total pressure distortion. Furthermore, transient pressure data were  measured in experiment and indicated unsteady fluctuations in the inlet that would not be captured  by steady computational fluid dynamic simulations. These data matched locations of disagreement  with swirl distortion behavior at high mass flow rates. This suggested that transient vortex  movement occured at the aerodynamic interface plane in certain configurations.   The total pressure distortion metrics between the two models were remarkably comparable.  Furthermore, the simplified model accurately predicted the mixing losses associated with the  blockage plates at the inlet to the radial-to-axial inlet using a simple inlet extension. Swirl  18  distortion was dictated by the radial-to-axial inlet. The reduced model data trends were comparable  with experiment for both the baffle and blocker plate configurations. The swirl intensities for all  configurations were comparable between the two models. The reduced model swirl directivity  trends matched those of experiment. The most notable deviations between the full-fidelity model  and the reduced model were observed with swirl directivity numerics. </p>

<div> Porous lithium ion battery (LIB) electrodes are comprised of electrochemically active material particles that store lithium and a surrounding conductive binder, liquid electrolyte, carbon black mixture that facilitates ionic and electronic transport. Typically, lithium diffusivity is several orders of magnitude smaller in the active material as compared to the surrounding electrolyte, making the electrode microstructure a governing factor in determining the balance between its lithium storage capacity and transport rate. Here, the effects of microstructure on the performance of LIBs are systematically analyzed at three length scales - the single particle length scale, the spatially resolved multiple particle length scale, and the porous electrode layer (homogenized) length scale. At the single particle length scale, a thermodynamically consistent variational framework is presented to examine the effects of crystallographic anisotropy, crystallographic texture, grain size, and grain morphology on the LiNi_1/3Mn_1/3Co_1/3O₂ (NMC111) chemistry. The theory was extended to the spatially resolved multiple particle length scale and the porous electrode layer length scale to explain the microstructural origin of experimentally observed instances of apparent phase separation in NMC111. At the electrode length scale, a data driven framework is presented to evaluate the electrochemical performance of a wide range of particle morphologies and battery architectures. Specifically, microstructural characteristics of 53 356 microstructures are assessed, and strategies to optimize electrode design parameters such as active particle morphology, spatial orientation, electrode porosity, and cell thickness are presented.</div><p></p>

<div>The manufacturing cost of solar cells along with their efficiency and reliability define the levelized cost of electricity (LCOE). One needs to reduce LCOE to make solar cells cost competitive compared to other sources of electricity. After a sustained decrease since 2001 the manufacturing cost of the dominant photovoltaic technology based on c-Si solar cells has recently reached a plateau. Further reduction in LCOE is only possible by increasing the efficiency and/or reliability of c-Si cells. Among alternate technologies, organic photovoltaics (OPV) has reduced manufacturing cost, but they do not offer any LCOE gain because their lifetime and efficiency are significantly lower than c-Si. Recently, perovskite solar cells have showed promising results in terms of both cost and efficiency, but their reliability/stability is still a concern and the physical origin of the efficiency gain is not fully understood.</div><div><br></div>In this work, we have collaborated with scientists industry and academia to explain the origin of the increased cell efficiency of bulk solution-processed perovskite cells. We also explored the possibility of enhancing the efficiency of the c-Si and perovskite cells by using them in a tandem configuration. To improve the intrinsic reliability, we have investigated 2D-perovskite cells with slightly lower efficiency but longer lifetime. We interpreted the behavior of the 2D-perovskite cells using randomly stacked quantum wells in the absorber region. We studied the reliability issues of c-Si modules and correlated series resistance of the modules directly to the solder bond failure. We also found out that finger thinning of the contacts at cell level manifests as a fake shunt resistance but is distinguishable from real shunt resistance by exploring the reverse bias or efficiency vs. irradiance. Then we proposed a physics-based model to predict the energy yield and lifetime of a module that suffers from solder bond failure using real field data by considering the statistical nature of the failure at module level. This model is part of a more comprehensive model that can predict the lifetime of a module that suffers from more degradation mechanisms such as yellowing, potential induced degradation, corrosion, soiling, delamination, etc. simultaneously. This method is called forward modeling since we start from environmental data and initial information of the module, and then predict the lifetime and time-dependent energy yield of a solar cell technology. As the future work, we will use our experience in forward modeling to deconvolve the reliability issues of a module that is fielded since each mechanism has a different electrical signature. Then by calibrating the forward model, we can predict the remaining lifetime of the fielded module. This work opens new pathways to achieve 2030 Sunshot goals of LCOE below 3c/kWh by predicting the lifetime that the product can be guaranteed, helping financial institutions regarding the risk of their investment, or national laboratories to redefine the qualification and reliability protocols.<br>

<div>Energy-efficient communication has remained the primary bottleneck in achieving fully energy-autonomous IoT nodes. Several scenarios including In-Sensor-Analytics (ISA), Collaborative Intelligence (CI) and Context-Aware-Switching (CAS) of the cluster-head during CI have been explored to trade-off the energies required for communication and computation in a wireless sensor network deployed in a mesh for multi-sensor measurement. A real-time co-optimization algorithm was developed for minimizing the energy consumption in the network for maximizing the overall battery lifetime of individual nodes.</div><div><br></div><div>The difficulty of achieving the design goals of lifetime, information accuracy, transmission distance, and cost, using traditional battery powered devices has driven significant research in energy-harvested wireless sensor nodes. This challenge is further amplified by the inherent power intensive nature of long-range communication when sensor networks are required to span vast areas such as agricultural fields and remote terrain. Solar power is a common energy source is wireless sensor nodes, however, it is not reliable due to fluctuations in power stemming from the changing seasons and weather conditions. This paper tackles these issues by presenting a perpetually-powered, energy-harvesting sensor node which utilizes a minimally sized solar cell and is capable of long range communication by dynamically co-optimizing energy consumption and information transfer, termed as Energy-Information Dynamic Co-Optimization (EICO). This energy-information intelligence is achieved by adaptive duty cycling of information transfer based on the total amount of energy available from the harvester and charge storage element to optimize the energy consumption of the sensor node, while employing event driven communication to minimize loss of information. We show results of continuous monitoring across 1Km without replacing the battery and maintaining an information accuracy of at least 95%.</div><div><br></div><div>Decades of continuous scaling in semiconductor technology has resulted in a drastic reduction in the cost and size of unit computing. This has enabled the design and development of small form factor wearable devices which communicate with each other to form a network around the body, commonly known as the Wireless Body Area Network (WBAN). These devices have found significant application for medical purposes such as reading surface bio-potential signals for monitoring, diagnosis, and therapy. One such device for the management of oropharyngeal swallowing disorders is described in this thesis. Radio wave transmission over air is the commonly used method of communication among these devices, but in recent years Human Body Communication has shown great promise to replace wireless communication for information exchange in a WBAN. However, there are very few studies in literature, that systematically study the channel loss of capacitive HBC for <i>wearable devices</i> over a wide frequency range with different terminations at the receiver, partly due to the need for <i>miniaturized wearable devices</i> for an accurate study. This thesis also measures and explores the channel loss of capacitive HBC from 100KHz to 1GHz for both high-impedance and 50Ohm terminations using wearable, battery powered devices; which is mandatory for accurate measurement of the HBC channel-loss, due to ground coupling effects. The measured results provide a consistent wearable, wide-frequency HBC channel loss data and could serve as a backbone for the emerging field of HBC by aiding in the selection of an appropriate operation frequency and termination.</div><div><br></div><div>Lastly, the power and security benefits of human body communication is demonstrated by extending it to animals (animal body communication). A sub-inch^3, custom-designed sensor node is built using off the shelf components which is capable of sensing and transmitting biopotential signals, through the body of the rat at significantly lower powers compared to traditional wireless transmissions. In-vivo experimental analysis proves that ABC successfully transmits acquired electrocardiogram (EKG) signals through the body with correlation accuracy >99% when compared to traditional wireless communication modalities, with a 50x reduction in power consumption.</div>

<div>The compressed air system is widely used throughout the industry. Air compressors are one of the most costly systems to operate in industrial plants in therms of energy consumption. Therefore, it becomes one of the primary target when it comes to electrical energy and load management practices. Load forecasting is the first step in developing energy management systems both on the supply and user side. A comprehensive literature review has been conducted, and there was a need to study if predicting compressed air system’s load is a possibility. </div><div><br></div><div>System’s load profile will be valuable to the industry practitioners as well as related software providers in developing better practice and tools for load management and look-ahead scheduling programs. Feed forward neural networks (FFNN) and long short-term memory (LSTM) techniques have been used to perform 15 minutes ahead prediction. Three cases of different sizes and control methods have been studied. The results proved the possibility of the forecast. In this study two control methods have been developed by using the prediction. The first control method is designed for variable speed driven air compressors. The goal was to decrease the maximum electrical load for the air compressor by using the system's full operational capabilities and the air receiver tank. This goal has been achieved by optimizing the system operation and developing a practical control method. The results can be used to decrease the maximum electrical load consumed by the system as well as assuring the sufficient air for the users during the peak compressed air demand by users. This method can also prevent backup or secondary systems from running during the peak compressed air demand which can result in more energy and demand savings. Load management plays a pivotal role and developing maximum load reduction methods by users can result in more sustainability as well as the cost reduction for developing sustainable energy production sources. The last part of this research is concentrated on reducing the energy consumed by load/unload controlled air compressors. Two novel control methods have been introduced. One method uses the prediction as input, and the other one doesn't require prediction. Both of them resulted in energy consumption reduction by increasing the off period with the same compressed air output or in other words without sacrificing the required compressed air needed for production.</div><div><br></div>