Performance Analysis With Future Predictions of Different Solar Cooling Systems in Guayaquil, Ecuador

This paper presents a comparative analysis of three different solar cooling system configurations developed for a case study building in Guayaquil, Ecuador. Guayaquil is a city located at the Ecuadorian coast with an average annual temperature of 25°C. The city’s need for air conditioning throughout the year and the relatively intense solar radiation provide a great opportunity for implementation of solar cooling systems. The first cooling system includes a 175 kWc single-effect absorption chiller powered by evacuated tubes solar thermal collectors. This system was compared with two 140 kWc compression chiller systems (air-cooled (AC) and water-cooled (WC)) powered by grid-connected photovoltaics. Both constant flow rate (CFR) and variable flow rate (VFR) of chilled water were analyzed. The three systems have to satisfy a cooling demand of the top floor in one governmental building (app. 1296 m2) which was selected as case study. Additionally, two 140 kWc conventional compression chiller systems (AC and WC) were included in the comparison as reference systems. Cooling demand of the building was simulated in EnergyPlus and coupled with the appropriate system configurations developed in TRNSYS. The weather file (TMY) was developed based on real meteorological data collected in the last decade. The present analysis was extended with the prediction scenarios for the years 2020, 2050 and 2080 using climate change adapted weather files.

Comparative Performance of Two Prototypes of a Passive Solar Heat Pipe System

Thermal performance of an improved passive solar heat pipe system was directly compared to that of a previous prototype. Simulated and experimental results for the first prototype established baseline performance. Subsequently, potential improvements were simulated, and a second prototype was built and tested along side the first. The system uses heat pipes for high rates of heat transfer into the building, and limited losses in the reverse direction. The evaporator section of each heat pipe is attached to a glass-covered absorber on the outside of a south wall, and the slightly elevated condenser section is either immersed in water in a thermal storage tank or exposed to air in the room. Two-phase flow occurs in the heat pipe only when the evaporator is warmer than the condenser, creating a thermal diode effect. Computer simulations showed that system performance could be improved by using thicker insulation between the absorber and the storage tanks, and by switching from a copper to a rubber adiabatic section, which both reduced heat losses to ambient from the storage tanks. Early morning heating was improved by exposing one of five condensers directly to room air, which also improved overall system efficiency. A copper solar absorber soldered to the copper evaporator section improved heat conduction compared to the previous aluminum absorber bonded to the copper evaporator. Together these modifications improved simulated annual solar fraction by 20.8%. The new prototype incorporating these changes was tested along side the previous prototype in a two-room passive solar test facility during January through February of 2013. Temperatures were monitored with thermocouples at multiple locations throughout the systems, in each room and outdoors. Insolation was measured by four pyranometers attached to the building. Results showed that the design modifications implemented for the new model increased thermal gains to storage and to the room, and decreased thermal losses to ambient. The load-to-collector ratio for the experiments was 2.7 times lower than for the simulations, which decreased the potential for experimental improvements compared to the simulated improvements. However, average daily peak efficiency for the new system was 85.0%, compared to 80.7% for the previous system. Furthermore, the average storage temperature over the entire testing period for the new model was 13.4% higher than that of the previous model, while the average room temperature over the same period was 24.6% greater for the new system.

Measurement of Laminar Burning Speeds and Investigation of Flame Stability of Acetylene (C2H2)/Air Mixtures

Laminar burning speeds and flame structures of spherically expanding flames of mixtures of acetylene (C2H2) with air have been investigated over a wide range of equivalence ratios, temperatures, and pressures. Experiments have been conducted in a constant volume cylindrical vessel with two large end windows. The vessel was installed in a shadowgraph system equipped with a high speed CMOS camera, capable of taking pictures up to 40,000 frames per second. Shadowgraphy was used to study flame structures and transition from smooth to cellular flames during flame propagation. Pressure measurements have been done using a pressure transducer during the combustion process. Laminar burning speeds were measured using a thermodynamic model employing the dynamic pressure rise during the flame propagation. Burning speeds were measured for temperature range of 300 to 590 K and pressure range of 0.5 to 3.3 atmospheres, and the range of equivalence ratios covered from 0.6 to 2. The measured values of burning speeds compared well with existing data and extended for a wider range of temperatures. Burning speed measurements have only been reported for smooth and laminar flames.

Towards a Data Calibrated, Simulation-Based Campus Energy Analysis Environment for Situational Awareness and Future Energy System Planning

Researchers at Georgia Tech (GT) have recently begun the GT Smart Energy Campus initiative, which combines campus energy metering data with physics-based modeling and simulation to create an integrated analysis environment for campus energy. The environment consists of a digital representation of campus, which supports situational awareness, as well as a virtual test bed for analyzing emerging energy technologies and future scenarios. The first year of the initiative has focused on evaluating campus energy metering data using visual analytics and statistical analysis techniques. Data analysis is presented as having value for two main uses: (1) as attention-directing information to help system operators diagnose anomalies and (2) as a precursor to modeling and simulation (M&S) in future phases of the Smart Energy Campus initiative. The environment is explained using the initial study scoping of the campus thermal energy generation and distribution systems. Furthermore, a modeling and simulation approach leveraging the Modelica M&S language is described, and preliminary results in using it to represent the campus chilled water system are presented.

Developing a Model to Predict the Torrefaction of Biomass

With increasing fuel costs and more emphasis being placed on sustainable sources of energy, biomass from agricultural residues and energy crops are becoming an increasingly viable value-added resource for the rural economies in United States and throughout the world. Torrefaction, a thermochemical reaction process, is a form of mild-pyrolysis that improves the qualities of biomass feedstocks for use as a fuel similar to charcoal. This research presents a user-centered computational framework to predict the effects of torrefaction of biomass. The reaction model is based on recently developed models for the torrefaction of willow. The basis for this model is a two stage, solid mass loss kinetics reaction where Arrhenius kinetic parameters are estimated based on experimentally obtained TGA data. Utilizing these parameters along with solid product formation equations it is possible to determine the solid mass yield, as well as the yields of the two stages of pseudo-volatiles released during reaction. Chemical species composition of the volatiles is determined from a system of constrained linear equations based on calculated volatile yield data and experimental results. The reaction model is implemented into MATLAB R2012b as a standalone program with a graphical user interface to obtain inputs, and display numeric and graphic results. The overall goal of this model is to provide a guide for improving conversion efficiency of biomass to bio-char.

Exergy-Based Optimization of Sub- and Supercritical Thermal Energy Storage Systems

In this work, energy and exergy analyses are applied to a thermal energy storage system employing a storage medium in the two-phase or supercritical regime. First, a numerical model is developed to investigate the transient thermodynamic and heat transfer characteristics of the storage system by coupling conservation of energy with an equation of state to model the spatial and temporal variations in fluid properties during the entire working cycle of the TES tank. Second, parametric studies are conducted to determine the impact of key variables (such as heat transfer fluid mass flow rate and maximum storage temperature) on both energy and exergy efficiencies. The optimum heat transfer fluid mass flow rate during charging must balance exergy destroyed due to heat transfer and exergy destroyed due to pressure losses, which have competing effects. Similarly, the optimum maximum storage fluid temperature is evaluated to optimize exergetic efficiency. By incorporating exergy-based optimization alongside traditional energy analyses, the results of this study evaluate the optimal values for key parameters in the design and operation of TES systems, as well as highlight opportunities to minimize thermodynamic losses.

Transport Energy Demand Modeling of the United States Using Artificial Neural Networks and Multiple Linear Regressions

In 2009, the transportation sector was the second largest consumer of primary energy in the United States, following the electric power sector and followed by the industrial, residential, and commercial sectors. The pattern of energy use varies by sector. For example, petroleum provides 96% of the energy used for transportation but its share is much less in other sectors. While the United States consumes vast quantities of energy, it has also pledged to cut its greenhouse gas emissions by 2050. In order to assist in planning for future energy needs, the purpose of this study is to develop a model for transport energy demand that incorporates past trends. This paper describes the development of two types of transportation energy models which are able to predict the United States’ future transportation energy-demand. One model uses an artificial neural network technique (a feed-forward multilayer perceptron neural network coupled with back-propagation technique), and the other model uses a multiple linear regression technique. Various independent variables (including GDP, population, oil price, and number of vehicles) are tested. The future transport energy demand can then be forecast based on the application of the growth rate of effective parameters on the models. The future trends of independent variables have been predicted based on the historical data from 1980 using a regression method. Using the forecast of independent variables, the energy demand has been forecasted for period of 2010 to 2030. In terms of the forecasts generated, the models show two different trends despite their performances being at the same level during the model-test period. Although, the results from the regression models show a uniform increase with different slopes corresponding to different models for energy demand in the near future, the results from ANN express no significant change in demand in same time frame. Increased sensitivity of the ANN models to the recent fluctuations caused by the economic recession may be the reason for the differences with the regression models which predict based on the total long-term trends. Although a small increase in the energy demand in the transportation sector of the United States has been predicted by the models, additional factors need to be considered regarding future energy policy. For example, the United States may choose to reduce energy consumption in order to reduce CO2 emissions and meet its national and international commitments, or large increases in fuel efficiency may reduce petroleum demand.

Continuous Monitoring, Modeling, and Evaluation of Actual Building Energy Systems

Residential and commercial buildings account for more than 40% of U.S. energy consumption, most of which is related to heating, ventilation and air conditioning (HVAC). Consequently, energy conservation is important to building owners and to the economy generally. In this paper we describe a process under development to continuously evaluate a building’s heating and cooling energy performance in near real-time with a procedure we call Continuous Monitoring, Modeling, and Evaluation (CMME). The concept of CMME is to model the expected operation of a building energy system with actual weather and internal load data and then compare modeled energy consumption with actual energy consumption. For this paper we modeled two buildings on the Georgia Institute of Technology campus. After creating our building models, internal lighting loads and equipment plug-loads were collected through electrical sub-metering, while the building occupancy load was recorded using doorway mounted people counters. We also collected on site weather and solar radiation data. All internal loads were input into the models and simulated with the actual weather data. We evaluated the building’s overall performance by comparing the modeled heating and cooling energy consumption with the building’s actual heating and cooling energy consumption. Our results demonstrated generally acceptable energy performance for both buildings; nevertheless, certain specific energy inefficiencies were discovered and corrective actions are being taken. This experience shows that CMME is a practical procedure for improving the performance of actual well performing buildings. With improved techniques, we believe the CMME procedure could be fully automated and notify building owners in real-time of sub-optimal building performance.

Roof Cover Impact on Cooling Energy Use of Office Buildings

This paper analyzes the impact of roof covers on office building energy use for representative US climate zones. In particular, the study presented in the paper investigates the potential annual cooling energy use savings that roof covers could provide using whole-building simulation analysis to evaluate the performance of a 2-story office building in five US locations. Three parameters of the roof covers including their size, height, and transmittance, are considered in the analysis. The simulation results indicate that while roof covers had similar affects on buildings in all climate zones, their impact in reducing cooling energy usage is different and is more pronounced in cooler climates. Specifically, roof covers could potentially achieve cooling energy savings of up to: 25% in Houston, 33% in Atlanta, 31% in Nashville, 38% in Chicago, and 41% in Madison. Based on the detailed simulation analysis results, a simplified calculation model is developed to help the estimation of cooling energy savings as a function of the roof cover size, height, and transmittance.

A General Methodology for Optimized Takagi-Sugeno Fuzzy Modeling of Nonlinear Continuous Fermenter for Biofuels (Ethanol) Production Using Golden Section Search Approach

The overcoming inclusion of biotechnology in biofuels industry involves several challenges among which are found the variety of operational cycles, the highly nonlinear behavior of the processes and the need for measurement of intermediate variables. In order to reproduce biological conversion of biodiesel production discharge products into other biofuels, experimental data from ethanol production from glycerol/glucose mixture was analyzed implementing fuzzy techniques to investigate and model the nonlinear behavior of the process. This paper presents a general methodology for TS fuzzy modeling based on a novel approach on data structured regression which consists on combination of fuzzy c-regression model and clustering using a golden search algorithm approach to adjust the proper number of membership functions to fit the model and minimize the statistic difference among the experimental data, simulated data and the Fuzzy Inference System results.