Dr. David Cistola is the Founding Director for the Center of Emphasis in Diabetes & Metabolism, a new diabetes research center at TTUHSC El Paso. He leads a medical research laboratory in clinical/translational science. Current research areas include the development of new diagnostic and therapeutic approaches for diabetes, prediabetes, fatty liver disease, cardiovascular disease and Alzheimer’s disease. From 2012-2016, Dr. Cistola served as Vice President for Research & Innovation at the University of North Texas Health Science Center, Fort Worth. During that time, he also maintained an active research laboratory translating biophysical approaches into new diagnostics. From 2007-2012, Dr. Cistola was Associate Dean for Research and Professor in the College of Allied Health Sciences and Professor in the Brody School of Medicine at East Carolina University. Dr. Cistola established the DoD-funded Operation Re-entry, a university-wide research initiative focused on the concerns of military personnel returning from deployment. From 1988-2007, he served on the faculty at Washington University School of Medicine in St. Louis. Dr. Cistola graduated from the M.D.-Ph.D. program at Boston University, and trained as an NIH Postdoctoral Fellow in the Cardiovascular Institute and held the Andrew Costello Fellowship of the Juvenile Diabetes Foundation International.
In this paper we present an energy management approach for an isolated PV-battery hybrid system. The studied system is composed of a lifepo4 battery pack coupled to a BMS, a PV module and an inverter controlled with a load management device. We aim to provide sufficient energy to different loads and decrease the energy deficit and the loss of power supply probability (LPSP) factor. The simulation results were performed with Matlab/Simulink to evaluate the performance of the proposed management algorithm on the PV-Battery hybrid system. A comparative study between the system without and with the management system shows a 40% reduction of the system energy deficit. Furthermore, the LPSP factor was minimized to 4%.
Forward osmosis (FO), is membrane separation technology in its infancy with various applications such as desalination, power generation, in food industry and others. The technology has shown growing interest due to its numerous advantages and its potential for energy-saving. FO process is, however, still facing important limitations related to efficient membrane and draw agent to achieve higher performances. Water diffusion in FO is driven by the osmotic pressure gradient from a highly concentrated stream (draw solution) to a lower concentration solution (the feed stream) across a semi-permeable membrane. Concentration polarization (CP) phenomena have been reported to be the most important factor causing water flux decline in FO process. Reverse solute flux (RSF) is also considered as a major issue in FO. In this work, an experimental study has been carried out for the evaluation of CP phenomena and RSF in FO process. Ammonium bicarbonate and sodium chloride have been used respectively as draw and feed solutions. Results have shown that dilutive internal concentration polarization phenomena (ICP) induced a draw solution osmotic pressure reduction of 42%. Concentrative external concentration polarization (ECP) effects have shown to affect driving force to a lesser extent, the combined effect of ICP and ECP caused a water decline of 51.5%. A concentration profile based on the obtained results across the FO membrane has been defined.
In this study, the topic is addressed by investigating the effects of the local microclimate on the energy performance of courtyard buildings located in street canyon. To achieve this, an integrated approach in the TRNSYS software validated in previous studies was used. The obtained results highlight firstly, how the microclimate near Raid has an impact on the energy needs, which can be negative in summer when the cooling needs can increase by about 42% and positive in winter when the heating needs can decrease by about 32%. In a second phase, findings highlighted that the impact of solar radiation interreflections generated by the courtyard on building cooling and heating energy needs is relatively low. In contrast, the interreflections generated by a street canyon have a negative impact on the cooling energy needs by increasing them up to about 34%. While the heating needs can be reduced by up to about 42%.
Hourly solar photovoltaic (PV) power measurements in a specific period and site are very important for designing and supervising PV systems. However, in most cases these measures are absent. The focus of this research study is to offer for PV systems engineers an efficient model based on Artificial Neural Network (ANN). To predict the output power of a PV system installed in Agadir city (320°25’39"N 9°35’53"W), Morocco. The architecture has a great impact on the convergence of the Neural Network. This architecture is optimized using Genetic Algorithm (GA). The aim of this approach is to find the optimal number of hidden layers, the activation function and the initial vector of weights. The attained results confirm the accuracy of the proposed model to predict the hourly PV power output in case of missing real values. Consequently, electrical power engineers can adopt this prediction model to design and supervise the PV systems and integrated it into the power grid.
Many countries have committed to reducing their emissions of gases responsible for the increase in the greenhouse effect. This decision could decrease the consumption of fossil fuels in favour of renewable energies, which have the advantage of being economically profitable and ecologically clean since their use does not generate harmful waste or polluting gas emissions. As a developing country, Morocco has adopted an energy policy focused on developing renewable energy through the launch of the Moroccan Solar Project, which aims to reduce greenhouse gas emissions and dependence on fossil fuels.The purpose of this article is therefore to study the economic and environmental impact of a photovoltaic installation in a shopping centre in Sale city, Morocco, while analyzing its investment costs and studying its profitability in time. This study shows that the project can generate sufficient profits to recover the investment capital with a payback period of 5 years, a meaningful reduction of CO2, and a huge annual saving from the production of solar electricity.
The paper proposes to design and construct a bicycle generation station for battery’s charging. The station the team is constructing would be built on a mobile platform for easy transportation for demonstration. The project is designed and inspired to be used for students to charge their phones while exercising on campus. The concept of these stations can be implemented into the recreation activity center (RAC) on campus and outside other buildings on georgia southern university’s campus for use. The station will be constructed from a bicycle and a premanufactured stand with a DC generator that is implemented with a battery for storage. After electrical energy is stored, the power will be converted from 12VDC to 5VDC for the DC circuit, and 12VDC to 120VAC for the AC circuit. The AC power generated by the stand will be used to power small AC devices, such as a clock, or to be supplied back into georgia southern power grid to cut down on power consumption for the university. The project also incorporates other electrical components used for safety and monitoring the bicycle generation station system.
The evaluation of the pyrolysis behaviour of solid fuels is commonly performed by use of thermogravimetric analysis (TGA) instruments. However, most TGA instruments simulate packed bed slow pyrolysis. The results generated by use of such instruments might need further mathematical manipulation in order to be applied in the design and operation of continuous biomass pyrolysis reactors that are being developed nowadays. The purpose of this study is to evaluate the pyrolysis behaviour of algal biomass by use of a laboratory scale rotary kiln pyrolyser. This study managed to show that it is possible to study the pyrolysis behaviour of algal biomass in a rotary kiln reactor. The main decomposition temperature of algal biomass was found to be in the temperature range 200-500 °C.
A major issue for sustainable development is the efficient energy management and the wellbeing of the urban system. One possible way to achieve energy efficiency within this system is to incorporate Integrated Rooftop Greenhouses (IRTGs) into uninhabited rooftops as a form of urban agriculture. This work provides real environmental data and assigned energy simulations to analyze the associated aspect of energy linkage between three IRTGs and a building located in a hot semi-arid climate, Benguerir (Morocco). The simulation results show that, for each year, the implementation of IRTGs generates an estimated energy gain of 189.28 kWh, as it indicates a reduction of CO2 emissions estimated to 0.47 tons CO2, and as a yield, in good years, it can produce up to 50 liters of essential oil using the flowers of Lavandula angustifolia which is the cultivation selected for the greenhouses. Over a period of 50 years the life cycle stages which contribute to global warming are the Construction Materials stage with percentages of 41.38% and 57.84% at the Energy use stage. During this period (50 years), the electricity used in this building generates 56.48% of CO2 emissions compared to other consuming systems in the building. While for floor slabs, ceilings and roofs, they are classified second with 40.28% of CO2 emissions. This type of projects guarantees the main profits of the IRTGs which are the close human-plant symbiosis ensured by a two-flow ventilation system, conveying the O2-enriched air from the IRTGs to the building and the CO2-enriched air from the building to the IRTGs, also enhancing a quality of life through building integrated gardening.
An effective control scheme of a standalone water pumping system powered by a photovoltaic (PV) source is presented in this contribution. The proposed solution aims to achieve robust control of the two stages of the pumping system. Therefore, a cascaded controller is designed for maximum power point tracking (MPPT), in which a perturb and observe (P&O) algorithm provides the optimal PV voltage. Then, a sliding mode controller (SMC) based on PWM is designed to regulate the input PV voltage. Subsequently, a fuzzy logic controller is designed to generate the reference speed which regulates the DC-bus voltage. Finally, predictive torque control (PTC) is applied to drive the induction motor (IM). In order to confirm the effectiveness of the proposed control methods, a simulation has been done within a dynamic change of irradiance.
The Lattice Boltzmann Method is a very strong numerical modeling method that has proved its effectiveness in many areas of study, especially in the field of fluid flow and thermo-hydrodynamic problems. In this paper, we present a new numerical study involving a tube and shell heat exchanger to evaluate the heat storage phenomena, and especially the effect of convection on storage enhancement. A case study and numerical results are provided using.an axisymmetric Lattice Boltzmann model. The results obtained show that convection accelerates latent heat storage.
Wind Energy Conversion Systems (WECs) prefer Doubly-Fed Induction Generators (DFIGs) as these are mostly affected and also economical. The DFIG system with continuous power feeding to the grid follows the grid codes and undergoes Low Voltage Ride Through (LVRT) technique. This paper proposes the Proportion-Resonant (PR) controller for the DFIG system during fault. This PR improves the dynamic response and has compared with PI controller. The LVRT improvement can be seen based on the simulation results. The severity of the 3-phase fault has been reduced.
Smart grid is an advanced concept of power systems which harmonizes electricity and communication in systems networks. It provides information for the producers, operators, and the consumers in real time. There is an extreme demand to efficiently conduct the power supplied to the consumption domains such as households, organizations, industries, and smart cities. In this respect, a smart grid with a stable system is being required to supply the dynamic power requirements. Predicting smart grid stability is still challenging due to the many factors which affect the stability of grid, one of these factors is customer and producer participation because identifying the participation can lead to the stability of smart grid. In this work, we propose a deep learning model based on Densely Connected Convolutional Network and Residual network structure to detect the stability of smart grids. The results of the proposed model are compared to other popular classifier models used in different studies. Those models are Support Vector Machine, Logistic Regression, Decision Tree, Random Forest, Gradient Boosted Trees, Multilayer neural network, Gated Recurrent Units, Recurrent Neural Networks and Long Short-Term Memory and the proposed model outperforms the other models.
With the rise of Electric Vehicles (EVs), significant research has been done to make this technology available for all consumers. The major downside with EVs is the requirement for the automobile to be idle during charging times. This problem can be fixed by implementing dynamic wireless power transfer (WPT). WPT for EVs was simulated and modeled using ANSYS software and a small-scale model respectively. The created system had the capability to charge 12V batteries at a certain distance. This distance will not provide a large clearance for vehicle chassis, so transmission efficiency and distance must be improved.
Seeing the current world energy situation, countries are opting for alternative energy resources, cleaner and more sustainable than fossil fuels, such as biomass. The physicochemical analyses of a biomass before valorization are an essential step to understand its behavior during the energy conversion process. In this context, a series of physicochemical and thermal analyses were performed on olive pomace samples, in order to evaluate the properties of this biomass and to valorize it as a fuel. The ultimate analysis prove that olive pomace constitutes of an important amount of Carbon, Oxygen and Hydrogen (48.2%, 44.3%, and 6.1% respectively) while Nitrogen and Sulphur with small quantities (1.4% and 0% successively). Furthermore, OP samples present a high heating value of 22.5 MJ/kg. Based on thermal analysis, we determine the characteristic combustion parameters. The ignition temperature of samples is about 243 °C while the burnout temperature is 480 °C. in addition, the ignition index D of the studied materiel is 12.8. 10-3 whereas the combustion index S is about 5.95. 10-4. The obtained results prove that olive pomace is a good candidate for exploitation as fuel and can replace fossil fuels.
This paper addresses a robust optimal damping control for the grid-forming converters. The proposed control strategy calculates the optimal damping and inertia parameters in form of constant gains via an H ∞ -based static output feedback control synthesis. The capability and effectiveness of the proposed control methodology are proved by theoretical analysis, simulation, and experimental results on a typical grid-connected converter. It is shown that the designed controller can guarantee the robust performance and improves system stability. The simplicity of control structure, and providing desirable inertia and damping characteristics in a software program can be considered as the main advantages of the proposed methodology.
Wind energy is one of the most available energies in Morocco that could contribute appreciably to the improvement of national energy mix. Identifying optimal locations for wind farm energy is a key issue in the wind energy development process. However, site selection is a complex study that involves not only technical considerations, but also economic, social and environmental requirements. Our research aims to develop a comprehensive and multi-criteria approach based on GIS and MCDM for the assessment of suitable locations for wind farm energy in Morocco.. This paper presents the main results of our approach based on the Analytic Hierarchy Process (AHP) and Weighted Linear Combination (WLC) methods to determine weights of sitting criteria (factors and constraints), and to develop a composite suitability map from single-factor maps representing these criteria. The research leads to a national suitability map for wind farms locations to support Masen’s sustainable spatial policy related to wind energy.
Environmental life cycle impact assessment (E-LCA) has been widely used to quantify the accompanying environmental impact potentials of municipal solid waste (MSW) management and treatment processes. This study reviewed findings of from various E-LCA of anaerobic digestion of biodegradable MSW fractions. Study findings have shown potential of the AD of biomass in contributing to the universal assess of electricity and limiting the emission of GHG responsible for global warming or climate change. However, majority of E-LCA studies reviewed are from Europe whose biogas production is on the rise. The AD of biodegradable MSW fractions can reduce the environmental impacts across several impact categories with net negative global warming impacts likely due to its renewable energy production capabilities. Governments in Sub Saharan Africa should put in place mechanisms that will address issues regarding to low AD uptake which could potentially contribute to universal access to electricity considering the over 600 million people reported not to have electricity access in SSA. There is also the need to increase the penetration of LCA in Africa to aid the development of AD systems. Issues to do with technical expertise, technical capacity, cost, and incentives for the uptake of AD should be of great consideration amongst policy makers in SSA.
The improper disposal of used cooking oils (UCO) in the environment represents significant health and environmental hazard. This paper presents a viable solution to the improper management of UCOs in the city of Ifrane, Morocco (14000 inhabitants). The solution consists of the collection of the city’s UCOs and their transformation into useful biodiesel. The project has been developed over the past four years and included multiple levels of intervention: research, engineering, social engagement, logistics and economics. In this paper, we focus solely on the technical and engineering design aspects of the project. An average of 300 liters of UCOs was collected each week from the various collection points in the city. A pilot unit with a production capacity of 250 liters of biodiesel per batch was developed: schematics, automation, and process flow are discussed. The pilot unit presented several opportunities for innovation beyond what is found in most literature on the subject. Solar energy and biomass are used to fulfill the heat requirement of the chemical reactions. The recycling of methanol and glycerol are performed. The entire transformation process is automated using open-source Arduino microcontrollers and switch relays. The biodiesel yield is estimated to be between 85% and 93% of the transformed UCO in about two hours of reaction. This work can be used by individuals, NGOs, actors in academic institutions, and local authorities as inspiration when tackling the issue of improper disposal of UCOs in their area from a technical design point of view.
During a decade of electric vehicle market rapid growth, Lithium-ion batteries have played a leading role thanks to their high power and energy density. Nonetheless, they still face many challenges, such as the influence of the State of Charge(SOC), ambient temperature and current rates on their life cycle. Which makes battery states prediction and thermal management essential to improve the Li-ion battery performance. Combining a three-state equivalent circuit model and a two-state thermal model, this paper proposes a coupled electro-thermal battery model capturing the battery state of charge, surface and core temperatures. Knowing these three key factors ensures better control of the battery operation conditions to prevent excessive heat generation. The model is expressed in a state-space form to allow battery state estimation, SOC and thermal prediction. A simple state observer and a robust nonlinear state estimator for SOC prediction are presented and compared. Then a nonlinear observer for core and surface temperature is modeled, and the obtained results are discussed.
