Increasing reliable produce farming and clean energy generation in the southwestern United States will be important for increasing the food supply for a growing population and reducing reliance on fossil fuels to generate energy. Combining greenhouses with photovoltaic (PV) films can allow both food and electric power to be produced simultaneously. This study tests if the combination of semi-transparent PV films and a transmission control layer can generate energy and spectrally control the transmission of light into a greenhouse. Testing the layer combinations in a variety of real-world conditions, it was shown that light can be spectrally controlled in a greenhouse. The transmission was overall able to be controlled by an average of 11.8% across the spectrum of sunlight, with each semi-transparent PV film able to spectrally select transmission of light in both the visible and near-infrared light wavelength. The combination of layers was also able to generate energy at an average efficiency of 8.71% across all panels and testing conditions. The most efficient PV film was the blue dyed, at 9.12%. This study also suggests additional improvements for this project, including the removal of the red PV film due to inefficiencies in spectral selection and additional tests with new materials to optimize plant growth and energy generation in a variety of light conditions.
This thesis will also go in detail into equations and calculations created for a techno-economic assessment for installation and maintenance of a CHP system at a WWTP. The equations and calculations created here were then utilized with data from a typical WWTP in the Southwestern United States to create an accurate case study. In this case study, a payback of 5.7 years and a net present value of $709,000 can be achieved when the WWTP generates over 2,000,000 m3 of biogas per year and utilizes over 36,000 GJ of natural gas per year.
Wastewater treatment plants (WWTP) are facilities with a large potential for energy savings and improvements, but the factors behind their efficiency remain largely unstudied. In this thesis, a limited study toward developing a benchmarking tool to allow comparison of operation of WWTPs in terms of energy intensity (EI) will be analyzed. While the comparison of WWTPs is very complex, an initial start with comparing EI will be a useful tool. The methodology for this will first involve a literature review into EI at WWTPs to understand current statistics. After this, publicly available data gathered by Department of Energy sponsored Industrial Assessment Centers (IAC) from 2009 to 2021 of WWTP EI will be studied to show the potential for improvement of EI. This comparison can highlight certain states that currently exhibit more efficient plants, change in efficiency over time, as well as compare specific treatment technologies in literature to the general data gathered from the IAC. Lastly, the first step toward development of this benchmarking tool is a study of the 13 WWTP operations analyzed by the Arizona State University (ASU) IAC using a data envelopment analysis (DEA). This DEA can begin to show how a tool could be used with more data to accurately compare and benchmark a WWTP based on performances of similar WWTPs. This tool could allow operators a possibility of seeing how well their performance compares, and work toward an improvement in their EI.
To address these issues, a framework was established in this dissertation to aid mobile food retailers with reaching economic sustainability by addressing two key operational decisions. The first decision was the stocked product mix of the mobile retailer. In this problem, it was assumed that mobile retailers want to balance the health, consumer cost, and retailer profitability of their product mix. The second investigated decision was the scheduling and routing plan of the mobile retailer. In this problem, it was assumed that mobile retailers operate similarly to traditional distribution vehicles with the exception that their customers are willing to travel between service locations so long as they are in close proximity.
For each of these problems, multiple formulations were developed which address many of the nuances for most existing mobile food retailers. For each problem, a combination of exact and heuristic solution procedures were developed with many utilizing software independent methodologies as it was assumed that mobile retailers would not have access to advanced computational software. Extensive computational tests were performed on these algorithm with the findings demonstrating the advantages of the developed procedures over other algorithms and commercial software.
The applicability of these techniques to mobile food retailers was demonstrated through a case study on a local Phoenix, AZ mobile retailer. Both the product mix and routing of the retailer were evaluated using the developed tools under a variety of conditions and assumptions. The results from this study clearly demonstrate that improved decision making can result in improved profits and longitudinal sustainability for the Phoenix mobile food retailer and similar entities.
As part of this dissertation, a yield approximation method is developed and integrated with a mixed-integer program to estimate a region’s potential to produce non-perennial, vegetable items. This integration offers practical approximations that help decision-makers identify technologies needed to protect agricultural production, alter harvesting patterns to better match market behavior, and provide an analytical framework through which external investment entities can assess different production options.
The first contribution proposes an event-crossover (ECO) methodology to analyze performance in learning environments. The methodology is relevant to studies where it is desired to evaluate the relationships between sentinel events in a learning environment and a physiological measurement which is provided in real time.
The second contribution introduces analytical methods to study relationships between multi-dimensional physiological signals and sentinel events in a learning environment. The methodology proposed learns physiological patterns in the form of node activations near time of events using different statistical techniques.
The third contribution addresses the challenge of performance prediction from physiological signals. Features from the sensors which could be computed early in the learning activity were developed for input to a machine learning model. The objective is to predict success or failure of the student in the learning environment early in the activity. EEG was used as the physiological signal to train a pattern recognition algorithm in order to derive meta affective states.
The last contribution introduced a methodology to predict a learner's performance using Bayes Belief Networks (BBNs). Posterior probabilities of latent nodes were used as inputs to a predictive model in real-time as evidence was accumulated in the BBN.
The methodology was applied to data streams from a video game and from a Damage Control Simulator which were used to predict and quantify performance. The proposed methods provide cognitive scientists with new tools to analyze subjects in learning environments.