Evaluation and parametric modeling of 50 kW organic rankine cycle for waste heat recovery from rural Alaska diesel generator power plants

Thesis (Ph.D.) University of Alaska Fairbanks, 2015In rural Alaska, there are about 180 villages that run independent electrical power systems using diesel generator sets. A diesel engine generator loses fuel energy in the form of waste heat through the charge air cooler (after cooler), the jacket water cooler, friction, and exhaust. Diesel engine jacket water and exhaust account for about 20% and 30% of the total fuel energy, respectively. In previous studies it has been demonstrated that about 80% of the heat present in jacket water and 50% of the heat from exhaust gases can be recovered for useful purposes such as heating, power generation, refrigeration, and desalination. In this study, the diesel engine waste heat application selected was power generation using an organic Rankine cycle (ORC) heat engine. The basic principle of an ORC system is similar to that of the traditional steam Rankine cycle; the only difference is the working fluid. The working fluids generally used in an ORC are refrigerants, such as R11, R113, R123, R134a, R245fa, and HFE-7000. The working fluid in the ORC system under study is R245fa. A typical ORC consists of a pump, preheater, evaporator, expansion machine (expander), and condenser. The working fluid is pressurized through the pump and supplied to the preheater and evaporator, where it is heated by the heat source. The working fluid exits the evaporator as vapor or liquid/vapor. It expands in the expander, generating power. The low-pressure working fluid exiting the expansion machine is liquefied in the condenser by a cooling source, returned to the pump, and the cycle repeats. At the University of Alaska Fairbanks (UAF) power plant, a lab experimental setup was designed: a hot water loop (heat source) and cold water loop (heat sink) for testing the 50 kW ORC power unit. Different diesel engine waste heat recovery conditions were simulated to study the unit's reliability and performance. After lab testing, the ORC system was installed permanently on a 2 MW Caterpillar diesel engine for jacket water heat recovery in Tok, Alaska, and tested further. These two tests provide for the goals of the present dissertation which are: (i) testing of a 50 kW ORC system for different heat source and heat sink supply conditions, (ii) develop guidelines on applying the present 50 kW ORC system for individual rural Alaska diesel gen-sets, (iii) develop empirical models for the screw expander, (iv) develop heat transfer correlations for single-phase and two-phase evaporation, and two-phase condensation for refrigerant R245fa in the preheater, evaporator and condenser, respectively, and (v) parametric modeling and validation of the present ORC system using the empirical correlations developed for a screw expander and R245fa in heat exchangers to predict the performance of the ORC system for individual diesel generator sets. The lab experimental data were used to plot performance maps for the power unit. These maps were plotted with respect to hot water supply temperature for different ORC parameters, such as heat input to power unit in evaporator and preheater, heat rejection by power unit in condenser, operating power output, payback period, and emissions. An example of how performance maps can be used is included in this dissertation. As detailed in this dissertation, the resulting lab experimental data were used to develop guidelines for independent diesel power plant personnel installing this ORC power unit. The factors influencing selection of a waste heat recovery application (heating or power) are also discussed. A procedure to find a match between the ORC system and any rural diesel generator set is presented. Based on annual electrical load information published in Power Cost Equalization data for individual villages, a list of villages where this ORC system could potentially be beneficial is included. During lab work at the UAF power plant, experimental data were also collected on the refrigerant side (R245fa) of the ORC system. Inlet and outlet pressures and temperatures of each component (evaporator, pump, and expander) of the ORC were measured. Two empirical models to predict screw expander power output were developed. The first model was based on polytropic work output, and the second was based on isentropic work output. Both models predicted screw expander power output within ±10% error limits. Experimental data pertaining to the preheater, evaporator, and condenser were used to develop R245fa heat transfer correlations for single-phase and two-phase evaporation and two-phase condensation in respective heat exchangers. For this study the preheater, evaporator, and condenser were brazed plate heat exchangers (BPHEs). For single-phase heat transfer in the preheater, a Dittus-Boelter type of correlation was developed for R245fa and hot water. For R245fa evaporation in the evaporator, two heat transfer correlations were proposed based on two-phase equation formats given in the literature. For condensation of R245fa in the condenser, one heat transfer correlation was proposed based on a format given in the literature. All the proposed heat transfer correlations were observed to have good agreement with experimental data. Finally, an ORC parametric model for predicting power unit performance (such as power output, heat input, and heat rejection) was developed using the screw expander model and proposed heat transfer correlations for R245fa in heat exchangers. The inputs for the parametric model are heating fluid supply conditions (flow rate and temperature) and cooling fluid supply conditions, generally the only information available in rural Alaska power plant locations. The developed ORC parametric model was validated using both lab experimental data and field installation data. Validation has shown that the ORC computation model is acceptable for predicting ORC performance for different individual diesel gen-sets

ENTRY MODE DECISION MAKING PROCESS

Decision making is the important process in formulating the entry mode strategies. The main objective of this study is to explore the entry mode decision making process a firm entering in to new market. This study explains the entry mode decision making of the case company. In the theoretical part of this study firstly the strategic decision making is discussed. The next part of the theoretical part explains the various modes in strategic decision making. Furthermore the theoretical part discusses about the decision making in the context of entry mode and as well as in the context of entry mode choice approaches. The later part of the theory explores the stages that are involved in the entry mode decision making process. At final the theoretical part explains the importance of emerging markets. The empirical part of the study is done through the face to face interview with the case company. Moreover, the case company’s annual reports, publications and the internet pages were also used in the empirical part of the study. The empirical results of the study explains three stages in the entry decision making process of the case company which are market and need identification, entry mode data collection and decision making stage. It is revealed from the results that the case company’s decision making process is based on avoidance mode.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

A Tractable Online Learning Algorithm for the Multinomial Logit Contextual Bandit

In this paper, we consider the contextual variant of the MNL-Bandit problem. More specifically, we consider a dynamic set optimization problem, where a decision-maker offers a subset (assortment) of products to a consumer and observes their response in every round. Consumers purchase products to maximize their utility. We assume that a set of attributes describes the products, and the mean utility of a product is linear in the values of these attributes. We model consumer choice behavior using the widely used Multinomial Logit (MNL) model and consider the decision maker problem of dynamically learning the model parameters while optimizing cumulative revenue over the selling horizon $T$. Though this problem has attracted considerable attention in recent times, many existing methods often involve solving an intractable non-convex optimization problem. Their theoretical performance guarantees depend on a problem-dependent parameter which could be prohibitively large. In particular, existing algorithms for this problem have regret bounded by $O(\sqrt{\kappa d T})$, where $\kappa$ is a problem-dependent constant that can have an exponential dependency on the number of attributes. In this paper, we propose an optimistic algorithm and show that the regret is bounded by $O(\sqrt{dT} + \kappa)$, significantly improving the performance over existing methods. Further, we propose a convex relaxation of the optimization step, which allows for tractable decision-making while retaining the favourable regret guarantee.Comment: updated version, under revie

The MNL-Bandit Problem: Theory and Applications

One fundamental problem in revenue management that arises in many settings including retail and display-based advertising is assortment planning. Here, the focus is on understanding how consumers select from a large number of substitutable items and identifying the optimal offer set to maximize revenues. Typically, for tractability, we assume a model that captures consumer preferences and focus on computing the optimal offer set. A significant challenge here is the lack of knowledge on consumer preferences. In this thesis, we consider the multinomial logit choice model, the most popular model for this application domain and develop tractable robust algorithms for assortment planning under uncertainty. We also quantify the fundamental performance limits from both computational and information theoretic perspectives for such problems. The existing methods for the dynamic problem follow ``estimate, then optimize'' paradigm, which require knowledge of certain parameters that are not readily available, thereby limiting their applicability in practice. We address this gap between theory and practice by developing new theoretical tools which will aid in designing algorithms that judiciously combine exploration and exploitation to maximize revenues. We first present an algorithm based on the principle of ``optimism under uncertainty'' that is simultaneously robust and adaptive to instance complexity. We then leverage this theory to develop a Thompson Sampling (TS) based framework with theoretical guarantees for the dynamic problem. This is primarily motivated by the growing popularity of TS approaches in practice due to their attractive empirical properties. We also indicate how to generalize the TS framework to design scalable dynamic learning algorithms for high-dimensional data and discuss empirical gains of such approaches from preliminary implementations on Flipkart, a large e-commerce firm in India

Cooperative Collision Avoidance on Small-sized Quadcopters With Indoor Loco Positioning System

Disaster response and surveillance operations will increasingly incorporate Unmanned Aerial Vehicles (UAV)s due to their cost-effectiveness and maneuverability. This trend has driven the research on small-sized quadcopters for varying indoor applications. Small-sized quadcopters' payload and computational capacity limit the usage of complex vision-based collision avoidance algorithms that are employed by larger quadcopters. The existing collision avoidance algorithms for Crazyflies assume near-accurate positioning from a vision-based motion capture system, which has higher acquisition costs and is impractical for large indoor spaces. This challenge is addressed by proposing an uncertainty-aware collision avoidance algorithm capable of handling the positioning uncertainty induced by the Loco Positioning System (LPS), an cost-effective alternative to motion capture. The algorithm aims to enable collision avoidance with dynamically moving peers, enhancing the multiple quadcopter system's capabilities to achieve their goal with tighter tolerances in congested indoor environments with LPS. This project also presents an improved Crazyflie simulation setup by integrating Crazyflie's controller into a physics simulator. This comprehensive simulation setup enables thorough testing and validation for Crazyflies. Additionally, the distance-based performance metrics are designed with inter-Crazyflie distances between the Crazyflies flying in the shared space. These metrics aim to measure the effectiveness of the algorithms by quantifying the Quadcopters' coordination and contribute to the development of multiple quadcopter system evaluations

Studies on the ketocarotenoid pigments in the sea urchin gonads of Stomopneustes variolaris from the Mandapam Coast of Gulf of Mannar

Sea urchin gonads of Stomopneustes variolaris is a delicacy in many parts of the world. The bright yellow orange colour of the gonad derives from the carotenoids pigments and an important organoleptic criterion for its quality. The major carotenoids naturally found in sea urchins are β-carotene, α-carotene, β-echinenone, zeaxanthin, canthaxanthin, lutein, astaxanthin, diatoxanthin, fucoxanthin and alloxanthin. In the present study sea urchin S.variolaris was collected along the Mandapam coast following the lunar phase during pre-monsoon period and they were subjected to the pigment analysis