Development of a Village-Scale, Solar-Powered Reverse Osmosis System

This paper details the development of a photovoltaic reverse osmosis water desalination system for a groundwater well in Bercy, Haiti. The well was constructed to provide potable drinking and agricultural water for the 300-person community. However, its water has a salinity level of 5,290 ppm, rendering it harmful for both human consumption and soil fertility. This reverse osmosis system is designed to be low-cost and operational off-grid while providing 900 gallons per day of desalinated water for the community. The system is composed of a photovoltaic power system, a submersible solar pump, and three reverse osmosis membranes. The system is designed to have a material cost significantly below that of any commercially-available system of similar scale. Furthermore, it has an average water production cost of $1.21/m3 and an average specific energy of 1.2 kWh/m3. Its performance was tested in the laboratory by connecting the desalination module to a DC power supply, demonstrating good agreement with its modeled performance. The installation of the full system with the PV module will take place on-site in the summer of 2016. Following implementation, the system will be monitored and compared against predicted performance. The first attempt is meant to serve as a verification and validation of the system as a whole. However, successful operation within the given cost target could pave the way for wider use of off-grid reverse osmosis systems at many remote locations with limited freshwater access around the world.Massachusetts Institute of Technology. Tata Center for Technology and Desig

Deriving archetype templates for UBEMs based on measured monthly energy use

Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2015.Cataloged from PDF version of thesis.Includes bibliographical references (pages 74-78).Interest in urban energy modeling has grown among planners and policy-makers as more and more municipalities set targets for reduction of greenhouse gas emissions. Urban-scale building energy models can help evaluate the efficiency of proposed district designs, consequences of building retrofit interventions, or energy supply options. Bottom-up models based on physical descriptions and engineering calculations are the most versatile for modeling scenarios and evaluating results at high spatial and temporal resolutions. Such urban building energy models (UBEMs) are typically created by grouping buildings with similar properties into archetypes, which standardize many properties that are not uniform in reality, such as occupancy-driven parameters. Since most UBEMs are validated using aggregated, annual measured data, this standardization is usually adequate; however, for a more accurate model that considers end-use differentiation or seasonal variation, neither this standardization nor this validation method are sufficient. This work proposes a new methodology for archetype definition and customization using metered monthly energy data. Customization is done by inferring certain parameters from the energy data and estimating others probabilistically from parametric analysis. The methodology is developed and tested on a case study of 453 low-rise residential buildings in Cambridge, Massachusetts. Four model iterations are compared: single template, eight archetype templates, eight archetypes with individual building customization, and the latter with the addition of parametric analysis and generation of frequency distributions for unknown parameters. The results show an improvement in mean goodness of fit from 46% with one template and 37% with eight templates to 18% for the final iteration. The distribution of energy use intensities, as well as monthly electricity and gas profiles, approach observed values closer with each iteration. The results also demonstrate that error metrics based on aggregated annual consumption, commonly used for urban model validation, are not necessarily representative of the model's fit on a monthly basis.by Julia A. Sokol.S.M

Parametric design and performance validation of low-cost, low-pressure drip emitters and irrigation systems

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mechanical Engineering, September, 2020Cataloged from the official PDF of thesis.Includes bibliographical references.This thesis proposes and validates methods to reduce the cost and energy use of drip irrigation systems, with the aim of increasing their adoption among smallholder farmers. By 2050, the growing world population will require a 55% increase in food production above 2010 levels. Yet, agriculture already places a large strain on the earth's resources, occupying 47% of habitable land area and comprising 70% of freshwater withdrawals. Thus, agricultural intensification needs to occur through increased efficiency, rather than increased resource consumption. While irrigation is an effective means to increase food production over rainfed land, traditional surface and overhead irrigation systems--such as flood, furrow, and sprinkler--have low water use efficiencies. Drip irrigation, which distributes water through a pressurized pipe network and slowly releases it through emitters in the immediate root zone of each crop, has been shown to increase water efficiency by 25-65% over flood or furrow irrigation. However, adoption of drip irrigation is limited by several factors, including high initial cost compared to conventional practices. To address the cost barrier to drip irrigation adoption, this work focuses on modeling, designing, and validating drip components and systems that operate at low pressures, reducing energy consumption and the costs of pumps and power systems. These savings are enabled by pressure-compensating (PC) emitters--which maintain a constant flow rate with variations in pressure--specifically designed for low-pressure operation. The first part of this thesis experimentally validates the ability of low-pressure PC online emitters (used for tree crops) designed by the MIT Global Engineering and Research Lab to reduce pumping power and energy in a series of field trials in the Middle East and North Africa. With a minimum operating pressure of 0.15 bar, these online emitters are shown to reduce pumping energy by at least 43% compared to commercial emitters with higher operating pressures, without compromising water distribution uniformity. The next section focuses on the design of low-pressure PC inline emitters (used for vegetable crops), which are bonded to the interior of irrigation tubing. While inline emitters are manufactured widely, their design in industry occurs largely by trial-and-error, which may limit product performance. To address this gap, this section presents a new, fully-analytical, parametric model for predicting the activation pressure and flow rate of typical inline PC emitters from their geometry and material properties of the membrane. The model's utility is demonstrated by systematically redesigning a commercial emitter to reduce its minimum compensating pressure from 0.4 bar to 0.15-0.25 bar, depending on the membrane used, while maintaining a similar flow rate. The last section of this thesis places low-pressure emitter designs in a system-level context to evaluate their impact and suggest further research directions. Concurrently, it presents a flexible, parametric model for designing cost-optimal drip irrigation systems with grid and off-grid power sources for any farm location, size, and crop. When applied to case studies representative of typical farms in Morocco, the model shows potential reductions of up to 20% in initial cost and up to 9% in lifetime system cost with optimized low-pressure drip systems, compared to conventional system designs. The results are used to identify and recommend opportunities for further system cost reduction.by Julia A. Sokol.Ph. D.Ph.D. Massachusetts Institute of Technology, Department of Mechanical Engineerin

Three Methods for Characterizing Building Archetypes in Urban Energy Simulation. A Case Study in Kuwait City

Significant research effort has gone into developing urban building energy modeling (UBEM) tools, which allow evaluating district-wide energy demand and supply strategies. In order to characterize simulation inputs for UBEM, buildings are typically grouped into representative “archetypes”. This simplification reduces the real diversity of usage patterns, potentially leading to results that misrepresent energy demands. Unfortunately, very little research has focused on identifying the impact of such process in the effectiveness of an UBEM to reliably predict savings from retrofit measures. This paper analyzes two deterministic common approaches for the definition of building archetypes in UBEM, and proposes a probabilistic third method based on the characterization of uncertain parameters related to building occupancy using measured energy data. Frequency distributions for number of occupants, lighting power and cooling set points are generated through parametric simulation of an urban sample, later used for Monte Carlo (MC) simulation of retrofit scenarios. Measured data for the yearly energy use of one hundred and forty residential buildings in Kuwait city is used as a case study for the evaluation of the three methods. Results for the proposed probabilistic method suggest a significant improvement in the fit of the model to the measured energy use distribution