Yes / The total energy consumption of many Reverse Osmosis (RO) plants has continuously improved as a result of manufacturing highly impermeable membranes in addition to implementing energy recovery devices. The total energy consumption of the RO process contributes significantly to the total cost of water treatment. Therefore any way of keeping the energy consumption to a minimum is highly desirable but continues to be a real challenge in practice. Potential areas to explore for achieving this include the possibility of optimising the module design parameters and/or the associated operating parameters. This research focuses on this precise aim by evaluating the impact of the design characteristics of membrane length, width, and feed channel height on the total energy consumption for two selected pilot-plant RO process configurations for the removal of chlorophenol from wastewater. The proposed two configurations, with and without an energy recovery device (ERD), consist of four cylindrical pressure vessels connected in series and stuffed with spiral wound membranes. A detailed steady-state model developed earlier by the authors is used here to study such impact via repetitive simulation. The results achieved confirm that the overall energy consumption can be reduced by actually increasing the membrane width with a simultaneous reduction of membrane length at constant membrane area and module volume. Energy savings of more than 60% and 54% have been achieved for the two configurations with and without ERD respectively using process optimization. The energy savings are significantly higher compared to other available similar studies from the literature.

Yes / Reverse Osmosis (RO) process has become one of the most widely utilised technologies for brackish water desalination for its capabilities of producing high-quality water. This paper emphasis on investigating the feasibility of implementing the retentate recycle design on the original design of an industrial medium-sized multistage and multi-pass spiral wound brackish water RO desalination plant (1200 m³/day) of Arab Potash Company (APC) located in Jordan. Specifically, this research explores the impact of recycling the high salinity stream of the 1st pass (at different recycled percentages) to the feed stream on the process performance indicators include, the fresh water salinity, overall recovery rate, and specific energy consumption. The simulation is carried out using an earlier model developed by the same authors for the specified RO plant using gPROMS suits. This confirmed the possibility of increasing the product capacity by around 3% with 100% recycle percentage of the high salinity retentate stream.

Rapid adoption of battery electric vehicles means improving energy consumption is a top priority. Regenerative braking converts kinetic energy to electrical energy stored in the battery pack while the vehicle is decelerating. Coasting is an alternative strategy that minimizes energy consumption by decelerating the vehicle using only road load. This work refines a battery electric vehicle model to assess regen, coasting, and other deceleration strategies. A road load model based on public test data calculates tractive effort based on speed and acceleration. Bidirectional Willans lines are the basis of the powertrain model simulating battery energy consumption. Regen braking tractive and powertrain power are modeled backward from prescribed linear velocity curves, and the coasting trajectory is forward modeled given zero tractive power. Decel modes based on zero battery and motor power are also forward modeled. Multi-Mode decel (using a low power mode with regen) is presented as an intermediate strategy. An example vehicle is modeled in fixed-route simulations using these strategies and is scored based on travel time, energy consumption, and bias towards minimizing one of those metrics. Regen braking has the lowest travel time, and coasting the lowest energy consumption, but such bias increases overall cost. Multi-mode strategies lower overall cost by balancing reductions in travel time and energy consumption. The model is sensitive to grade and accessory load fluctuation, making this work adaptable to different vehicles and environments. This work demonstrates the utility of regen braking alternatives that could enhance connected and automated vehicle systems in battery electric vehicles. / Master of Science / As battery electric vehicle adoption accelerates, reducing energy consumption remains a priority. While regenerative braking saves energy by recharging the battery pack using kinetic energy, coasting (deceleration caused only by road load) has potential as well. This work focuses on refining a battery electric vehicle model and assessing various deceleration strategies. A road load model calculates wheel tractive effort, and Willans lines are used to model powertrain energy consumption. Coasting and other deceleration modes based on zero system power are modeled to produce speed trajectories, and regenerative braking power is modeled using prescribed linear velocity curves. Strategies that use multiple decel modes are also considered. An example battery electric vehicle is assessed using these strategies in fixed-route simulations. Vehicle performance is scored based on battery energy consumption and travel time. Regenerative braking has the lowest travel time, and coasting the lowest energy consumption, but those strategies also have the highest overall cost. Multi-mode strategies lower cost by balancing energy consumption and travel time. The strategies are sensitive to changes in road grade and accessory power, meaning the model can be used with different vehicles and environments. This work demonstrates the utility of alternatives to regenerative braking and how such strategies could enhance battery electric vehicles with autonomous capabilities.

The purpose of this research was to determine if there is empirical evidence to show that an association exists between the energy usage of manufacturing industries and companies and their risk. This possibility was considered important because of the attention that has been focused on energy problems since the OPEC oil embargo and the interest of investors in the risk of securities. Support for the possibility that an association may exist was based on reviewing the energy problem, public opinion polls, writings by economists and business executives, and the stock market's reaction to the embargo. A review of the recent work of accountants in the areas of energy audits and social accounting was also presented to place this research in perspective with other studies. Various hypotheses were tested using Kendall's Tau Rank Correlation test to see if an association exists between the energy usage of U. S. manufacturing industries and companies and their risk and changes in their risk since the OPEC embargo. Each hypothesis was tested using two different definitions of energy usage and two measures of risk. The definitions consisted of one where energy usage was stated as the total consumption of Btu’s or kwh’s by companies and industries. The second definition was based on energy consumption per dollar of sales or receipts. The risk was measured using the variability in the returns to the common stockholders. Both a measure of the total risk and a measure of the systematic risk were used for the statistical tests. The results of the tests of the hypotheses are difficult to interpret since the correlations are not all in the same direction and most of them are not statistically different from zero at a reasonable level of significance. Only the association between the total energy usage of manufacturing industries and their total risk, and the association between the total energy usage of manufacturing companies and the change in their systematic risk since the OPEC embargo were significant at the .10 level. However, both of these correlations were negative as were the correlations for many of the other hypotheses. None of the correlation coefficients with a positive sign were significant at the .10 level. Based on these results, it was concluded that negative association between energy usage and risk was more likely to exist than positive association. A possible explanation for the negative correlation was then presented. This explanation was based on the characteristics of the assets of the firm and the price elasticity of the demand for a company's product. No empirical data were presented, however, to support this explanation. The limitations of the study and areas for future research were also discussed. / Ph. D.

Effective energy management programs rely upon readily accessible energy consumption information. This thesis presents a Lotus Symphony based microcomputer system developed to monitor, analyze, and report on energy usage for a select group of Virginia state facilities. A review of significant program routines and procedures is presented, along with key assumptions and limitations of the system, and example inputs and outputs. A User's Guide is also included to aid first-time users of Symphony and/or the Energy Monitoring and Reporting System (EMRS). Available system outputs, in the form of spreadsheet printouts and graphs, enable detailing energy consumption patterns for each facility by fuel type for any given year. The capability of outlining the progression of a facility's energy management program is provided by comparison of a given year's energy consumption records to the previous and base (FY 1985-86) years. In addition, facilities with similar operational characteristics were segregated into divisions, providing the capability to rank facilities within divisions based upon several energy consumption criteria. Extensive use of the Symphony Command (or Macro) Language permitted creation of customized, interactive menus, which allows those not familiar with Lotus Symphony to fully utilize the capabilities of the EMRS. / Master of Science

With the widespread adoption of smart meters in buildings, an unprecedented amount of high- resolution energy data is released, which provides opportunities to understand building consumption patterns. Accordingly, research efforts have employed data analytics and machine learning methods for the segmentation of consumers based on their load profiles, which help utilities and energy providers for customized/personalized targeting for energy programs. However, building energy segmentation methodologies may present oversimplified representations of load shapes, which do not properly capture the realistic energy consumption patterns, in terms of temporal shapes and magnitude. In this thesis, we introduce a clustering technique that is capable of preserving both temporal patterns and total consumption of load shapes from customers’ energy data. The proposed approach first overpopulates clusters as the initial stage to preserve the accuracy and merges the similar ones to reduce redundancy in the second stage by integrating time-series similarity techniques. For such a purpose, different time-series similarity measures based on Dynamic Time Warping (DTW) are employed. Furthermore, evaluations of different unsupervised clustering methods such as k-means, hierarchical clustering, fuzzy c-means, and self-organizing map were presented on building load shape portfolios, and their performance were quantitatively and qualitatively compared. The evaluation was carried out on real energy data of ~250 households. The comparative assessment (both qualitatively and quantitatively) demonstrated the applicability of the proposed approach compared to benchmark techniques for power time-series clustering of household load shapes. The contribution of this thesis is to: (1) present a comparative assessment of clustering techniques on household electricity load shapes and highlighting the inadequacy of conventional validation indices for choosing the cluster number and (2) propose a two-stage clustering approach to improve the representation of temporal patterns and magnitude of household load shapes. / M.S. / With the unprecedented amount of data collected by smart meters, we have opportunities to systematically analyze the energy consumption patterns of households. Specifically, through using data analytics methods, one could cluster a large number of energy patterns (collected on a daily basis) into a number of representative groups, which could reveal actionable patterns for electric utilities for energy planning. However, commonly used clustering approaches may not properly show the variation of energy patterns or energy volume of customers at a neighborhood scale. Therefore, in this thesis, we introduced a clustering approach to improve the cluster representation by preserving the temporal shapes and energy volume of daily profiles (i.e., the energy data of a household collected during 1 day). In the first part of the study, we evaluated several well-known clustering techniques and validation indices in the literature and showed that they do not necessarily work well for this domain-specific problem. As a result, in the second part, we introduced a two-stage clustering technique to extract the typical energy consumption patterns of households. Different visualization and quantified metrics are shown for the comparison and applicability of the methods. A case-study on several datasets comprising more than 250 households was considered for evaluation. The findings show that datasets with more than thousands of observations can be clustered into 10-50 groups through the introduced two-stage approach, while reasonably maintaining the energy patterns and energy volume of individual profiles.

The purpose of this study was to determine an answer to the question: What are the effects of washer rinse temperature, final washer spin time, or dryer setting on the energy consumption of a household electric clothes dryer? The data were collected in July and August, 1981, using a Maytag washer and dryer. The preconditioned load used was a variation of the Association of Home Appliance Manufacturers' standard eight pound load with fiber content of cotton and polyester. Eight combinations of the independent variables, rinse temperature, spin time, and dryer setting, were replicated five times. Room temperature, relative humidity, and barometric pressure were monitored. Statistical procedures utilized in data analysis included Pearson product-moment correlation coefficients, means, standard deviations, ranges and analysis of variance. The combination requiring the most energy, 3.403 kwh, was cold rinse, short spin time, and extra dry higher setting, and the combination requiring the least energy, 2.588 kwh, was warm rinse, long spin time, and normal dryer setting. However, it was calculated that it required 2.3 kwh to heat the warm water used in rinsing. Therefore, it is recommended that consumers can save the most energy using a combination of cold rinse, long spin time, and normal dryer setting. / Master of Science

The Hybrid Electric Vehicle Team (HEVT) of Virginia Tech is partaking in the 4-Year EcoCar Mobility Challenge organized by Argonne National Labs. The objective of this competition is to modify a stock 2019 traditional internal combustion engine Chevrolet Blazer and to transform the vehicle into a P4 hybrid. Due to the P4 Hybrid architecture, the HEVT vehicle has an internal combustion engine on the front axle and an electric motor on the rear axle. The goal of this competition is to create a vehicle that achieves better fuel economy and increases customer appeal. The general target market of hybrids is smaller vehicles. As a midsize sport utility vehicle (SUV), the Blazer offers a larger vehicle with the perk of better fuel economy. In the competition, the vehicle is assessed on the ability to integrate advanced vehicle technology, improve consumer appeal, and provide comfort for the passenger. The research of this paper is centered around the design of a full range longitudinal Adaptive Cruise Control (ACC) algorithm. Initially, research is conducted on various linear and nonlinear control strategies that provide the necessary functionality. Based on the ability to predict future time instances in an optimal method, the Model Predictive Control (MPC) algorithm is chosen and combined with other standard control strategies to create an ACC system. The main objective of this research is the implementation of Adaptive Cruise Control features that provide comfort and energy savings to the rider while maintaining safety as the priority. Rider comfort is achieved by placing constraints on acceleration and jerk. Lastly, a proper energy analysis is conducted to showcase the potential energy savings with the implementation of the Adaptive Cruise Control system. This implementation includes tuning the algorithm so that the best energy consumption at the wheel is achieved without compromising vehicle safety. The scope of this paper expands on current knowledge of Adaptive Cruise Control by using a simplified nonlinear vehicle system model in MATLAB to simulate different conditions. For each condition, comfort and energy consumption are analyzed. The city 505 simulation of a traditional ACC system show a 14% or 42 Wh/mi reduction in energy at the wheel. The city 505 simulation of the environmentally friendly ACC system show a 29% or 88 Wh/mi reduction in energy at the wheel. Furthermore, these simulations confirm that maximum acceleration and jerk are bounded. Specifically, peak jerk is reduced by 90% or 8 m/s3 during a jerky US06 drive cycle. The main objective of this analysis is to demonstrate that with proper implementation, this ACC system effectively reduces tractive energy consumption while improving rider comfort for any vehicle. / Master of Science / The Hybrid Electric Vehicle Team (HEVT) of Virginia Tech is partaking in the 4-Year EcoCar Mobility Challenge organized by Argonne National Labs. The objective of this competition is to modify a stock 2019 Chevrolet Blazer into a hybrid. This modification is accomplished by creating a vehicle that burns less gasoline and increases customer appeal. The general target market of hybrids is smaller vehicles. As a midsize sport utility vehicle (SUV), the Blazer offers a larger vehicle with the perk of better fuel economy. In the competition, the vehicle is assessed on the ability to integrate advanced technology, improve consumer appeal, and provide comfort for the passenger. The research of this paper is centered around the design of Adaptive Cruise Control (ACC). Initially, research is conducted on various control strategies that provide the necessary functionality. A controller that predicts future events is selected for the Adaptive Cruise Control. The main objective of this research is the implementation of Adaptive Cruise Control features that provide comfort and energy consumption savings to the rider while maintaining safety as the priority. Rider comfort is achieved by creating a smoother ride. Lastly, a proper energy analysis showcases the potential energy savings with the implementation of the Adaptive Cruise Control system. The scope of this paper expands on current knowledge of Adaptive Cruise Control by using a simplified vehicle model to simulate different conditions. The city simulations of a traditional ACC system show a 14% reduction in energy at the wheel. City simulations of the environmentally friendly Adaptive Cruise Controller show a 29% reduction in energy. Both of these simulations allow for comfortable ride. Specifically, maximum car jerk is reduced by 90%. The main objective of this analysis is to demonstrate that with proper implementation, this ACC system effectively reduces energy consumption at the wheel while improving rider comfort.

Directional airflow has been utilized to enable targeted air conditioning in cars and airplanes for many years, where the occupants could adjust the direction of flow. In the building sector however, HVAC systems are usually equipped with stationary diffusors that can only supply the air either in the form of diffusion or with fixed direction to the room in which they have been installed. In the present thesis, the possibility of adopting directional airflow in lieu of the conventional uniform diffusors has been investigated. The potential benefits of such a modification in control capabilities of the HVAC system in terms of improvements in the overall occupant thermal comfort and energy consumption of the HVAC system have been investigated via a simulation study and an experimental study. In the simulation study, an average of 59% per cycle reduction was achieved in the energy consumption. The reduction in the required duration of airflow (proportional to energy consumption) in the experimental study was 64% per cycle. The feasibility of autonomous control of the directional airflow, has been studied in a simulation experiment by utilizing the Reinforcement Learning algorithm which is an artificial intelligence approach that facilitates autonomous control in unknown environments. In order to demonstrate the feasibility of enabling the existing HVAC systems to control the direction of airflow, a device (called active diffusor) was designed and prototyped. The active diffusor successfully replaced the existing uniform diffusor and was able to effectively target the occupant positions by accurately directing the airflow jet to the desired positions. / M.S. / The notion of adjustable direction of airflow has been used in the car industry and airplanes for decades, enabling the users to manually adjust the direction of airflow to their satisfaction. However, in the building the introduction of the incoming airflow to the environment of the room is achieved either by non-adjustable uniform diffusors, aiming to condition the air in the environment in a homogeneous manner. In the present thesis, the possibility of adopting directional airflow in place of the conventional uniform diffusors has been investigated. The potential benefits of such a modification in control capabilities of the HVAC system in terms of improvements in the overall occupant thermal comfort and energy consumption of the HVAC system have been investigated via a simulation study and an experimental study. In the simulation study, an average of 59% per cycle reduction was achieved in the energy consumption. The reduction in the required duration of airflow (proportional to energy consumption) in the experimental study was 64% per cycle on average. The feasibility of autonomous control of the directional airflow, has been studied in a simulation experiment by utilizing the Reinforcement Learning algorithm which is an artificial intelligence approach that facilitates autonomous control in unknown environments. In order to demonstrate the feasibility of enabling the existing HVAC systems to control the direction of airflow, a device (called active diffusor) was designed and prototyped. The active diffusor successfully replaced the existing uniform diffusor and was able to effectively target the occupant positions by accurately directing the airflow jet to the desired positions.

Improving the energy efficiency of manufactured homes is important, as manufactured homes are built to a federal code which may not capture all the cost effective efficiency options available today. Energy efficiency improvements range from simple and inexpensive changes in manufacturing techniques (e.g. sealing duct systems) to more expensive additions (e.g. high performance windows) which still may be cost effective. This study will examine the most cost-effective options for energy conservation in manufactured homes including enhanced envelope and heating and cooling equipment options. Using cost information from various manufacturers and the simulation tool, Energy Gauge USA®, optimum energy conservation packages will be created for one or more climates. These packages will present manufacturers and homeowners with a guide to the costs and savings associated with various levels of energy conservation.