Search results for: splitting wind farms

Authors: Tahani Mashikhi

Abstract:

Countries worldwide rely on electric power as a critical economic growth and progress factor. Renewable energy sources, often referred to as alternative energy sources, such as wind, solar energy, geothermal energy, biomass, and hydropower, have garnered significant interest in response to the rising consumption of fossil fuels. Dye-sensitized solar cells (DSSCs) are a highly promising alternative for energy production as they possess numerous advantages compared to traditional silicon solar cells and thin-film solar cells. These include their low cost, high flexibility, straightforward preparation methodology, ease of production, low toxicity, different colors, semi-transparent quality, and high power conversion efficiency. A solar cell, also known as a photovoltaic cell, is a device that converts the energy of light from the sun into electrical energy through the photovoltaic effect. The Gratzel cell is the initial dye-sensitized solar cell made from colloidal titanium dioxide. The operational mechanism of DSSCs relies on various key elements, such as a layer composed of wide band gap semiconducting oxide materials (e.g. titanium dioxide [TiO₂]), as well as a photosensitizer or dye that absorbs sunlight to inject electrons into the conduction band, the electrolyte utilizes the triiodide/iodide redox pair (I− /I₃−) to regenerate dye molecules and a counter electrode made of carbon or platinum facilitates the movement of electrons across the circuit. Electrospray deposition permits the deposition of fragile, non-volatile molecules in a vacuum environment, including dye sensitizers, complex molecules, nanoparticles, and biomolecules. Surface science techniques, particularly X-ray photoelectron spectroscopy, are employed to examine dye-sensitized solar cells. This study investigates the possible application of electrospray deposition to build high-quality layers in situ in a vacuum. Two distinct categories of dyes can be employed as sensitizers in DSSCs: organometallic semiconductor sensitizers and purely organic dyes. Most organometallic dyes, including Ru533, RuC, and RuP, contain a ruthenium atom, which is a rare element. This ruthenium atom enhances the efficiency of dye-sensitized solar cells (DSSCs). These dyes are characterized by their high cost and typically appear as dark purple powders. On the other hand, organic dyes, such as SQ2, RK1, D5, SC4, and R6, exhibit reduced efficacy due to the lack of a ruthenium atom. These dyes appear in green, red, orange, and blue powder-colored. This study will specifically concentrate on metal-organic dyes. The adsorption of dye molecules onto the rutile TiO₂ (110) surface has been deposited in situ under ultra-high vacuum conditions by combining an electrospray deposition method with X-ray photoelectron spectroscopy. The X-ray photoelectron spectroscopy (XPS) technique examines chemical bonds and interactions between molecules and TiO₂ surfaces. The dyes were deposited at varying times, from 5 minutes to 40 minutes, to achieve distinct layers of coverage categorized as sub-monolayer, monolayer, few layers, or multilayer. Based on the O 1s photoelectron spectra data, it can be observed that the monolayer establishes a strong chemical bond with the Ti atoms of the oxide substrate by deprotonating the carboxylic acid groups through 2M-bidentate bridging anchors. The C 1s and N 1s photoelectron spectra indicate that the molecule remains intact at the surface. This can be due to the existence of all functional groups and a ruthenium atom, where the binding energy of Ru 3d is consistent with Ru2+.

Keywords: deposit, dye, electrospray, TiO₂, XPS

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Authors: Vincent Schlageter, Maroua Mestiri, Florian Denzinger, Hugo Raetzo, Michel Demierre

Abstract:

Precise landslide monitoring with differential global navigation satellite system (GNSS) is well known, but technical or economic reasons limit its application by geotechnical companies. This study demonstrates the reliability and the usefulness of Geomon (Infrasurvey Sàrl, Switzerland), a stand-alone and cost-effective rover network. The system permits deploying up to 15 rovers, plus one reference station for differential GNSS. A dedicated radio communication links all the modules to a base station, where an embedded computer automatically provides all the relative positions (L1 phase, open-source RTKLib software) and populates an Internet server. Each measure also contains information from an internal inclinometer, battery level, and position quality indices. Contrary to standard GNSS survey systems, which suffer from a limited number of beacons that must be placed in areas with good GSM signal, Geomon offers greater flexibility and permits a real overview of the whole landslide with good spatial resolution. Each module is powered with solar panels, ensuring autonomous long-term recordings. In this study, we have tested the system on several sites in the Swiss mountains, setting up to 7 rovers per site, for an 18 month-long survey. The aim was to assess the robustness and the accuracy of the system in different environmental conditions. In one case, we ran forced blind tests (vertical movements of a given amplitude) and compared various session parameters (duration from 10 to 90 minutes). Then the other cases were a survey of real landslides sites using fixed optimized parameters. Sub centimetric-accuracy with few outliers was obtained using the best parameters (session duration of 60 minutes, baseline 1 km or less), with the noise level on the horizontal component half that of the vertical one. The performance (percent of aborting solutions, outliers) was reduced with sessions shorter than 30 minutes. The environment also had a strong influence on the percent of aborting solutions (ambiguity search problem), due to multiple reflections or satellites obstructed by trees and mountains. The length of the baseline (distance reference-rover, single baseline processing) reduced the accuracy above 1 km but had no significant effect below this limit. In critical weather conditions, the system’s robustness was limited: snow, avalanche, and frost-covered some rovers, including the antenna and vertically oriented solar panels, leading to data interruption; and strong wind damaged a reference station. The possibility of changing the sessions’ parameters remotely was very useful. In conclusion, the rover network tested provided the foreseen sub-centimetric-accuracy while providing a dense spatial resolution landslide survey. The ease of implementation and the fully automatic long-term survey were timesaving. Performance strongly depends on surrounding conditions, but short pre-measures should allow moving a rover to a better final placement. The system offers a promising hazard mitigation technique. Improvements could include data post-processing for alerts and automatic modification of the duration and numbers of sessions based on battery level and rover displacement velocity.

Keywords: GNSS, GSM, landslide, long-term, network, solar, spatial resolution, sub-centimeter.

Procedia PDF Downloads 97

Authors: Manavjit Singh Dhindsa, Ursula Das, Kshirasagar Naik, Marzia Zaman, Richard Purcell, Srinivas Sampalli, Abdul Mutakabbir, Chung-Horng Lung, Thambirajah Ravichandran

Abstract:

These days, wildland fires, also known as forest fires, are more prevalent than ever. Wildfires have major repercussions that affect ecosystems, communities, and the environment in several ways. Wildfires lead to habitat destruction and biodiversity loss, affecting ecosystems and causing soil erosion. They also contribute to poor air quality by releasing smoke and pollutants that pose health risks, especially for individuals with respiratory conditions. Wildfires can damage infrastructure, disrupt communities, and cause economic losses. The economic impact of firefighting efforts, combined with their direct effects on forestry and agriculture, causes significant financial difficulties for the areas impacted. This research explores different forest fire spread models and presents a comprehensive review of various techniques and methodologies used in the field. A forest fire spread model is a computational or mathematical representation that is used to simulate and predict the behavior of a forest fire. By applying scientific concepts and data from empirical studies, these models attempt to capture the intricate dynamics of how a fire spreads, taking into consideration a variety of factors like weather patterns, topography, fuel types, and environmental conditions. These models assist authorities in understanding and forecasting the potential trajectory and intensity of a wildfire. Emphasizing the need for a comprehensive understanding of wildfire dynamics, this research explores the approaches, assumptions, and findings derived from various models. By using a comparison approach, a critical analysis is provided by identifying patterns, strengths, and weaknesses among these models. The purpose of the survey is to further wildfire research and management techniques. Decision-makers, researchers, and practitioners can benefit from the useful insights that are provided by synthesizing established information. Fire spread models provide insights into potential fire behavior, facilitating authorities to make informed decisions about evacuation activities, allocating resources for fire-fighting efforts, and planning for preventive actions. Wildfire spread models are also useful in post-wildfire mitigation strategies as they help in assessing the fire's severity, determining high-risk regions for post-fire dangers, and forecasting soil erosion trends. The analysis highlights the importance of customized modeling approaches for various circumstances and promotes our understanding of the way forest fires spread. Some of the known models in this field are Rothermel’s wildland fuel model, FARSITE, WRF-SFIRE, FIRETEC, FlamMap, FSPro, cellular automata model, and others. The key characteristics that these models consider include weather (includes factors such as wind speed and direction), topography (includes factors like landscape elevation), and fuel availability (includes factors like types of vegetation) among other factors. The models discussed are physics-based, data-driven, or hybrid models, also utilizing ML techniques like attention-based neural networks to enhance the performance of the model. In order to lessen the destructive effects of forest fires, this initiative aims to promote the development of more precise prediction tools and effective management techniques. The survey expands its scope to address the practical needs of numerous stakeholders. Access to enhanced early warning systems enables decision-makers to take prompt action. Emergency responders benefit from improved resource allocation strategies, strengthening the efficacy of firefighting efforts.

Keywords: artificial intelligence, deep learning, forest fire management, fire risk assessment, fire simulation, machine learning, remote sensing, wildfire modeling

Procedia PDF Downloads 63