Search results for: energy models

Authors: Virender Singh, Mathew Rees, Simon Hampton, Sivaram Annadurai

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Plant identification is a challenging task that aims to identify the family, genus, and species according to plant morphological features. Automated deep learning-based computer vision algorithms are widely used for identifying plants and can help users narrow down the possibilities. However, numerous morphological similarities between and within species render correct classification difficult. In this paper, we tested custom convolution neural network (CNN) and vision transformer (ViT) based models using the PyTorch framework to classify plants. We used a large dataset of 88,000 provided by the Royal Horticultural Society (RHS) and a smaller dataset of 16,000 images from the PlantClef 2015 dataset for classifying plants at genus and species levels, respectively. Our results show that for classifying plants at the genus level, ViT models perform better compared to CNN-based models ResNet50 and ResNet-RS-420 and other state-of-the-art CNN-based models suggested in previous studies on a similar dataset. ViT model achieved top accuracy of 83.3% for classifying plants at the genus level. For classifying plants at the species level, ViT models perform better compared to CNN-based models ResNet50 and ResNet-RS-420, with a top accuracy of 92.5%. We show that the correct set of augmentation techniques plays an important role in classification success. In conclusion, these results could help end users, professionals and the general public alike in identifying plants quicker and with improved accuracy.

Keywords: plant identification, CNN, image processing, vision transformer, classification

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Authors: Ilya Kavalchuk, Hayrettin Arisoy, Alex Stojcevski, Aman Maun Than Oo

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Electric vehicles are one of the most complicated electric devices to simulate due to the significant number of different processes involved in electrical structure of it. There are concurrent processes of energy consumption and generation with different onboard systems, which make simulation tasks more complicated to perform. More accurate simulation on energy consumption can provide a better understanding of all energy management for electric transport. As a result of all those processes, electric transport can allow for a more sustainable future and become more convenient in relation to the distance range and recharging time. This paper discusses the problems of energy consumption simulations for electric vehicles using different software packages to provide ideas on how to make this process more precise, which can help engineers create better energy management strategies for electric vehicles.

Keywords: electric vehicles, EV, power consumption, power management, simulation

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Authors: Dhruv Mehta, Alexander van Zuijlen, Hester Bijl

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Large Eddy Simulation (LES) numerically resolves the large energy-containing eddies of a turbulent flow, while modelling the small dissipative eddies. On a wind farm, these large scales carry the energy wind turbines extracts and are also responsible for transporting the turbines’ wakes, which may interact with downstream turbines and certainly with the atmospheric boundary layer (ABL). In this situation, it is important to conserve the energy that these wake’s carry and which could be altered artificially through numerical dissipation brought about by the schemes used for the spatial discretisation and temporal integration. Numerical dissipation has been reported to cause the premature recovery of turbine wakes, leading to an over prediction in the power produced by wind farms.An energy-conserving scheme is free from numerical dissipation and ensures that the energy of the wakes is increased or decreased only by the action of molecular viscosity or the action of wind turbines (body forces). The aim is to create an LES package with energy-conserving schemes to simulate wind turbine wakes correctly to gain insight into power-production, wake meandering etc. Such knowledge will be useful in designing more efficient wind farms with minimal wake interaction, which if unchecked could lead to major losses in energy production per unit area of the wind farm. For their research, the authors intend to use the Energy-Conserving Navier-Stokes code developed by the Energy Research Centre of the Netherlands.

Keywords: energy-conserving schemes, modelling turbulence, Large Eddy Simulation, atmospheric boundary layer

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Authors: Nadjiba Mahfoudi, Abdelhafid Moummi , Mohammed El Ganaoui

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Thermal applications are drawing increasing attention in the solar energy research field, due to their high performance in energy storage density and energy conversion efficiency. In these applications, solar collectors and thermal energy storage systems are the two core components. This paper presents a thermal analysis of the transient behavior and storage capability of a sensible heat storage device in which sand is used as a storage media. The TES unit with embedded charging tubes is connected to a solar air collector. To investigate it storage characteristics a 3D-model using no linear coupled partial differential equations for both temperature of storage medium and heat transfer fluid (HTF), has been developed. Performances of thermal storage bed of capacity of 17 MJ (including bed temperature, charging time, energy storage rate, charging energy efficiency) have been evaluated. The effect of the number of charging tubes (3 configurations) is presented.

Keywords: design, thermal modeling, heat transfer enhancement, sand, sensible heat storage

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Authors: Balram Panjwani, Bernd Wittgens, Jan Erik Olsen, Stein Tore Johansen

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The Norwegian ferroalloy industry is a world leader in sustainable production of ferrosilicon, silicon and manganese alloys with the lowest global specific energy consumption. One of the byproducts during the metal reduction process is energy rich off-gas and usually this energy is not harnessed. A novel concept for sustainable energy recovery from ferroalloy off-gas is discussed. The concept is founded on the idea of introducing a combustion chamber in the off-gas section in which energy rich off-gas mainly consisting of CO will be combusted. This will provide an additional degree of freedom for optimizing energy recovery. A well-controlled and high off-gas temperature will assure a significant increase in energy recovery and reduction of emissions to the atmosphere. Design and operation of the combustion chamber depend on many parameters, including the total power capacity of the combustion chamber, sufficient residence time for combusting the complex Poly Aromatic Hydrocarbon (PAH), NOx, as well as converting other potential pollutants. The design criteria for the combustion chamber have been identified and discussed and sizing of the combustion chamber has been carried out considering these design criteria. Computational Fluid Dynamics (CFD) has been utilized extensively for sizing the combustion chamber. The results from our CFD simulations of the flow in the combustion chamber and exploring different off-gas fuel composition are presented. In brief, the paper covers all aspect which impacts the sizing of the combustion chamber, including insulation thickness, choice of insulating material, heat transfer through extended surfaces, multi-staging and secondary air injection.

Keywords: CFD, combustion chamber, arc furnace, energy recovery

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Authors: Ali Cheshmehzangi, Ayotunde Dawodu

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In urban planning, Floor Area Ratio (FAR) of the site plays a major role in the multiplicity of performances, from humane living environments to energy performance. When one considers the astounding volume of new housing that is going to be constructed across the globe during the next few decades due to growing urbanisation (e.g. particularly in developing world), it is imperative that we have an empirically grounded grasp of which building configurations are more energy efficient. As a common planning metric, it would be helpful to know exactly how managing FAR connects with energy efficiency. Hence, this study puts together a set of modelling of various FARs for a typical residential compound and address the considerations of energy planning integration in the practice of building configuration and urban planning. Such decision makings at the planning and design stage enable us to provide pathways of optimising urban climate at mesoscale of the built environment, i.e. the neighbourhood or community level. In this study, a comparative study is conducted using Eco-Tect Software, using a case study in the City of Ningbo, China. Findings of the study contribute to identifying scenarios of various FAR use and energy planning at mesoscale. The final results contribute to studies in urban climate, from the perspectives of urban planning, energy planning, and urban modelling.

Keywords: China, energy planning, FAR, floor-area-ratio, mesoscale, urban climate, urban modelling

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Authors: Mina Bakhshi

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Nowadays, issues such as various types of pollution, problems caused by energy consumption, population density, social activities, difficulties related to urban access and communication, transportation, etc., have challenged different communities and become the subject of their discussions. In response to this issue, theories and movements have emerged to achieve sustainable urban development, including the smart growth movement. This theory emphasizes that the physical growth and expansion of cities should serve the community and the environment, aiming to improve the quality of life and promote the use of renewable energy resources for sustainability. The smart city network system not only improves the economic situation of the society and benefits the environment but also enables the achievement of important issues such as sustainable development, continuity, and diversity of energy resources. In this article, we investigate the impact of using renewable energy sources on optimizing energy consumption and reducing pollution caused by fossil fuels with the help of smart city development. The aim of this article is to introduce renewable energy sources and their utilization as a solution to address the energy crisis and reduce environmental pollution. This research has attempted to introduce the smart city and the use of renewable energy sources as a method for solving many urban problems and achieving efficient urban control and management.

Keywords: smart city, renewable energy sources, sustainable development, sustainable city

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Authors: A. B. M. Rezaul Islam, Ernur Karadogan

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Shape memory alloys (SMAs) are known for their shape memory effect and pseudoelasticity behavior. Their thermomechanical behaviors are modeled by numerous researchers using microscopic thermodynamic and macroscopic phenomenological point of view. Tanaka, Liang-Rogers and Ivshin-Pence models are some of the most popular SMA macroscopic phenomenological constitutive models. They describe SMA behavior in terms of stress, strain and temperature. These models involve material parameters and they have associated uncertainty present in them. At different operating temperatures, the uncertainty propagates to the output when the material is subjected to loading followed by unloading. The propagation of uncertainty while utilizing these models in real-life application can result in performance discrepancies or failure at extreme conditions. To resolve this, we used probabilistic approach to perform the sensitivity and uncertainty analysis of Tanaka, Liang-Rogers, and Ivshin-Pence models. Sobol and extended Fourier Amplitude Sensitivity Testing (eFAST) methods have been used to perform the sensitivity analysis for simulated isothermal loading/unloading at various operating temperatures. As per the results, it is evident that the models vary due to the change in operating temperature and loading condition. The average and stress-dependent sensitivity indices present the most significant parameters at several temperatures. This work highlights the sensitivity and uncertainty analysis results and shows comparison of them at different temperatures and loading conditions for all these models. The analysis presented will aid in designing engineering applications by eliminating the probability of model failure due to the uncertainty in the input parameters. Thus, it is recommended to have a proper understanding of sensitive parameters and the uncertainty propagation at several operating temperatures and loading conditions as per Tanaka, Liang-Rogers, and Ivshin-Pence model.

Keywords: constitutive models, FAST sensitivity analysis, sensitivity analysis, sobol, shape memory alloy, uncertainty analysis

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Authors: Abdelazziz Aouiche, El Moundher Aouiche, Mouhamed Salah Soudani

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Renewable energy sources now significantly contribute to the replacement of traditional fossil fuel energy sources. One of the most potent types of renewable energy that has developed quickly in recent years is photovoltaic energy. We all know that solar energy, which is sustainable and non-depleting, is the best knowledge form of energy that we have at our disposal. Due to changing weather conditions, the primary drawback of conventional solar PV cells is their inability to track their maximum power point. In this study, we apply artificial neural networks (ANN) to automatically track and measure the maximum power point (MPP) of solar panels. In MATLAB, the complete system is simulated, and the results are adjusted for the external environment. The results are better performance than traditional MPPT methods and the results demonstrate the advantages of using neural networks in solar PV systems.

Keywords: modeling, photovoltaic panel, artificial neural networks, maximum power point tracking

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Authors: Iman Farasat, Howard M. Salis

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Engineered genetic circuits reprogram cellular behavior to act as living computers with applications in detecting cancer, creating self-controlling artificial tissues, and dynamically regulating metabolic pathways. Phenemenological models are often used to simulate and design genetic circuit behavior towards a desired behavior. While such models assume that each circuit component’s function is modular and independent, even small changes in a circuit (e.g. a new promoter, a change in transcription factor expression level, or even a new media) can have significant effects on the circuit’s function. Here, we use statistical thermodynamics to account for the several factors that control transcriptional regulation in bacteria, and experimentally demonstrate the model’s accuracy across 825 measurements in several genetic contexts and hosts. We then employ our first principles model to design, experimentally construct, and characterize a family of signal amplifying genetic circuits (genetic OpAmps) that expand the dynamic range of cell sensors. To develop these models, we needed a new approach to measuring the in vivo binding free energies of transcription factors (TFs), a key ingredient of statistical thermodynamic models of gene regulation. We developed a new high-throughput assay to measure RNA polymerase and TF binding free energies, requiring the construction and characterization of only a few constructs and data analysis (Figure 1A). We experimentally verified the assay on 6 TetR-homolog repressors and a CRISPR/dCas9 guide RNA. We found that our binding free energy measurements quantitatively explains why changing TF expression levels alters circuit function. Altogether, by combining these measurements with our biophysical model of translation (the RBS Calculator) as well as other measurements (Figure 1B), our model can account for changes in TF binding sites, TF expression levels, circuit copy number, host genome size, and host growth rate (Figure 1C). Model predictions correctly accounted for how these 8 factors control a promoter’s transcription rate (Figure 1D). Using the model, we developed a design framework for engineering multi-promoter genetic circuits that greatly reduces the number of degrees of freedom (8 factors per promoter) to a single dimensionless unit. We propose the Ptashne (Pt) number to encapsulate the 8 co-dependent factors that control transcriptional regulation into a single number. Therefore, a single number controls a promoter’s output rather than these 8 co-dependent factors, and designing a genetic circuit with N promoters requires specification of only N Pt numbers. We demonstrate how to design genetic circuits in Pt number space by constructing and characterizing 15 2-repressor OpAmp circuits that act as signal amplifiers when within an optimal Pt region. We experimentally show that OpAmp circuits using different TFs and TF expression levels will only amplify the dynamic range of input signals when their corresponding Pt numbers are within the optimal region. Thus, the use of the Pt number greatly simplifies the genetic circuit design, particularly important as circuits employ more TFs to perform increasingly complex functions.

Keywords: transcription factor, synthetic biology, genetic circuit, biophysical model, binding energy measurement

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Authors: M. Benbouzid, Q. Bresson, A. Duclos, K. Longo, Q. Morel

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Currently, electricity suppliers must predict the consumption of their customers in order to deduce the power they need to produce. It is, then, important in a first step to optimize household consumption to obtain more constant curves by limiting peaks in energy consumption. Here centralized real time scheduling is proposed to manage the equipment's starting in parallel. The aim is not to exceed a certain limit while optimizing the power consumption across a habitat. The Raspberry Pi is used as a box; this scheduler interacts with the various sensors in 6LoWPAN. At the scale of a single dwelling, household consumption decreases, particularly at times corresponding to the peaks. However, it would be wiser to consider the use of a residential complex so that the result would be more significant. So, the ceiling would no longer be fixed. The scheduling would be done on two scales, firstly, per dwelling, and secondly, at the level of a residential complex.

Keywords: smart grid, energy box, scheduling, Gang Model, energy consumption, energy management system, wireless sensor network

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Authors: Nayden Takev, Remy van Leeuwen, Shiva Chotoe, Hani Alers, Xiao Peng

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Citizens, businesses, and public authorities all around the world are becoming aware of the impact that they have on the environment. Currently, climate change is an apparent cause to urge everyone to act and move to sustainable energy solutions. After the Paris Climate Agreement, every country has thought of a way to cut down carbon emissions. The Netherlands formulated the National Climate Agreement. “The government’s central goal with the National Climate Agreement is to reduce greenhouse gas emissions in the Netherlands by 49% compared to 1990 levels. At a European level, the government is advocating a 55% reduction of greenhouse gas emissions by 2030.” [5]. From a survey of the CBS, it is apparent that citizens are not putting in as much effort into the transition to sustainable energy as the government would like them to. After analysing the data, it became clear that the citizens miss the motivation to switch to sustainable energy because they do not believe it is urgent at this point and it is too expensive for them [2]. This needs to be changed. The citizens need to be aware of their impact on the climate and the advantages that this process will bring them. For example, the implementation of smart home displays 4 for real time energy measuring will give the citizens an overview of their energy usage so they are aware of the impact they have. Researchers have also found that the citizens must be included in the decision-making aimed at changing their behaviour [4, 3, 1]. In the future, the government will need to include the citizens when they create campaigns, strategies or introduce new policies [7, 6]. By including and informing the citizens about the policies it will be more attractive for them to choose sustainable energy. However, is all of this enough to motivate the citizens towards energy transition? Or are there other and better ways to do it?

Keywords: Awereness, Energy Transition, Netherlands, citizens

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Authors: Aldona Kluczek

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Energy efficient process have become a pressing research field in manufacturing. The arguments for having an effective industrial energy efficiency processes are interacted with factors: economic and environmental impact, and energy security. Improvements in energy efficiency are most often achieved by implementation of more efficient technology or manufacturing process. Current processes of electricity production represents the biggest consumption of energy and the greatest amount of emissions to the environment. The goal of this study is to improve the potential energy-savings and reduce greenhouse emissions related to improvement scenarios for the treatment of hardwood lumber produced by an industrial plant operating in the U.S. through the application of green balancing procedure, in order to find the preferable efficient technology. The green procedure for energy is based on analysis of energy efficiency data. Three alternative scenarios of the cogeneration systems plant (CHP) construction are considered: generation of fresh steam, the purchase of a new boiler with the operating pressure 300 pounds per square inch gauge (PSIG), an installation of a new boiler with a 600 PSIG pressure. In this paper, the application of a bottom-down modelling for energy flow to devise a streamlined Energy and Emission Flow Analyze method for the technology of producing electricity is illustrated. It will identify efficiency or technology of a given process to be reached, through the effective use of energy, or energy management. Results have shown that the third scenario seem to be the efficient alternative scenario considered from the environmental and economic concerns for treating hardwood lumber. The energy conservation evaluation options could save an estimated 6,215.78 MMBtu/yr in each year, which represents 9.5% of the total annual energy usage. The total annual potential cost savings from all recommendations is $143,523/yr, which represents 30.1% of the total annual energy costs. Estimation have presented that energy cost savings are possible up to 43% (US$ 143,337.85), representing 18.6% of the total annual energy costs.

Keywords: alternative scenario improvements, cogeneration system, energy and emission flow analyze, energy balancing, green procedure, hardwood lumber manufacturing process

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Authors: Ali Jamshidi, Kiyofumi Kurumisawa, Toyoharu Nawa

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Transportation infrastructure assets can be considered as backbone of transportation system. They are routinely developed and or maintained which can be used effectively for movement of passengers, commodities and providing vital services. However, the infrastructure assets construction, maintenance and rehabilitation significantly depend on non-renewable natural resources, such as carbon-based energy carriers and aggregate materials. In this study, effects of thermal properties of aggregate materials were characterized for production of hot-mix asphalt in Japan, as a case study. The results indicated that incorporation of the aggregate with lower required heat energy significantly reduces fuel consumption greenhouse gas emission, irrespective of physical property of aggregate. The results also clearly showed that as 75% high-energy limestone is replaced with low-energy limestone in producing an asphalt mixture at 180 °C, 97,879 Japanese households would be energized per annum using the saved energy without any modification in the current asphalt mixing plants.

Keywords: zero energy infrastructure, sustainable development, greenhouse gas emission, asphalt pavement

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Authors: Chia-Chi Chang, Chuan-Bi Lin, Chia-Min Chan

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Most ZigBee sensor networks to date make use of nodes with limited processing, communication, and energy capabilities. Energy consumption is of great importance in wireless sensor applications as their nodes are commonly battery-driven. Once ZigBee nodes are deployed outdoors, limited power may make a sensor network useless before its purpose is complete. At present, there are two strategies for long node and network lifetime. The first strategy is saving energy as much as possible. The energy consumption will be minimized through switching the node from active mode to sleep mode and routing protocol with ultra-low energy consumption. The second strategy is to evaluate the energy consumption of sensor applications as accurately as possible. Erroneous energy model may render a ZigBee sensor network useless before changing batteries. In this paper, we present a ZigBee wireless sensor node with four key modules: a processing and radio unit, an energy harvesting unit, an energy storage unit, and a sensor unit. The processing unit uses CC2530 for controlling the sensor, carrying out routing protocol, and performing wireless communication with other nodes. The harvesting unit uses a 2W solar panel to provide lasting energy for the node. The storage unit consists of a rechargeable 1200 mAh Li-ion battery and a battery charger using a constant-current/constant-voltage algorithm. Our solution to extend node lifetime is implemented. Finally, a long-term sensor network test is used to exhibit the functionality of the solar powered system.

Keywords: ZigBee, Li-ion battery, solar panel, CC2530

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Authors: Arash Gharibi

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Mankind has always used models to solve problems. Essentially, models are simplified versions of reality, whose need stems from having to deal with complexity; many processes or phenomena are too complex to be described completely. Thus a fundamental model requirement is that it contains the characteristic features that are essential in the context of the problem to be solved or described. Models are used in virtually every scientific domain to deal with various problems. During the recent decades, the number of models has increased exponentially. Publication of models as part of original research has traditionally been in in scientific periodicals, series, monographs, agency reports, national journals and laboratory reports. This makes it difficult for interested groups and communities to stay informed about the state-of-the-art. During the modeling process, many important decisions are made which impact the final form of the model. Without a record of these considerations, the final model remains ill-defined and open to varying interpretations. Unfortunately, the details of these considerations are often lost or in case there is any existing information about a model, it is likely to be written intuitively in different layouts and in different degrees of detail. In order to overcome these issues, different domains have attempted to implement their own approaches to preserve their models’ information in forms of model documentation. The most frequently cited model documentation approaches show that they are domain specific, not to applicable to the existing models and evolutionary flexibility and intrinsic corrections and improvements are not possible with the current approaches. These issues are all because of a lack of unified standards for model documentation. As a way forward, this research will propose a new standard for capturing and managing models’ information in a unified way so that interoperability and reusability of models become possible. This standard will also be evolutionary, meaning members of modeling realm could contribute to its ongoing developments and improvements. In this paper, the current 3 of the most common metamodels are reviewed and according to pros and cons of each, a new metamodel is proposed.

Keywords: metamodel, modeling, interoperability, reuse

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Authors: Fatma Ünal, Hasancan Okutan

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Increasing worldwide population and technological requirements lead to an increase in energy demand every year. The demand has been mainly supplied from fossil fuels such as coal and petroleum due to insufficient natural gas resources. In recent years, the amount of coal reserves has reached almost 21 billion tons in Türkiye. These are mostly lignite (%92,7), that contains high levels of moisture and sulfur components. Underground coal gasification technology is one of the most suitable methods in comparison with direct combustion techniques for the evaluation of such coal types. In this study, the applicability of the underground coal gasification process is investigated in the Eskişehir-Alpu lignite reserve as a pilot region, both technologically and economically. It is assumed that the electricity is produced from the obtained synthesis gas in an integrated gasification combined cycle (IGCC). Firstly, an equilibrium model has been developed by using the thermodynamic properties of the gasification reactions. The effect of the type of oxidizing gas, the sulfur content of coal, the rate of water vapor/air, and the pressure of the system have been investigated to find optimum process conditions. Secondly, the parallel and linear controlled recreation and injection point (CRIP) models were implemented as drilling methods, and costs were calculated under the different oxidizing agents (air and high-purity O2). In Parallel CRIP (P-CRIP), drilling cost is found to be lower than the linear CRIP (L-CRIP) since two coal beds simultaneously are gasified. It is seen that CO2 Capture and Storage (CCS) technology was the most effective unit on the total cost in both models. The cost of the synthesis gas produced varies between 0,02 $/Mcal and 0,09 $/Mcal. This is the promising result when considering the selling price of Türkiye natural gas for Q1-2023 (0.103 $ /Mcal).

Keywords: energy, lignite reserve, techno-economic analysis, underground coal gasification.

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Authors: Mimi Hafizah Abdullah, Hanani Farhah Harun, Nik Ruzni Nik Idris

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With the implied volatility as an important factor in financial decision-making, in particular in option pricing valuation, and also the given fact that the pricing biases of Leland option pricing models and the implied volatility structure for the options are related, this study considers examining the implied adjusted volatility smile patterns and term structures in the S&P/ASX 200 index options using the different Leland option pricing models. The examination of the implied adjusted volatility smiles and term structures in the Australian index options market covers the global financial crisis in the mid-2007. The implied adjusted volatility was found to escalate approximately triple the rate prior the crisis.

Keywords: implied adjusted volatility, financial crisis, Leland option pricing models, Australian index options

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Authors: Li Hung-Hui, Chen Chi-Chieh, Yang Zon-Yee

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This study aims to explore the impact energy absorption in the fragmented processes of rock samples during the split-Hopkinson-pressure-bar tests. Three kinds of rock samples including granite, marble and sandstone were tested. The impact energy absorptions were calculated according to the incident, reflected and transmitted strain wave histories measured by a oscilloscope. The degree of fragment rocks after tests was quantified by the box dimension of the fractal theory. The box dimension of rock fragments was obtained from the particle size distribution curve by the sieve analysis. The results can be concluded that: (1) the degree of rock fragments after tests can be well described by the value of box dimension; (2) with the impact energy absorption increasing, the degrees of rock fragments are varied from the very large fragments to very small fragments, and the corresponding box dimension varies from 2.9 to 1.2.

Keywords: SHPB test, energy absorption, rock fragments, impact loading, box dimension

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Authors: D. Deepak, N. Yagnesh Sharma

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Abrasive Water Jet (AWJ) machining is a relatively new nontraditional machine tool used in machining of fiber reinforced composite. The quality of machined surface depends on jet exit kinetic energy which depends on various operating and material parameters. In the present work the effect abrasive parameters such as its size, concentration and type on jet kinetic energy is investigated using computational fluid dynamics (CFD). In addition, the effect of these parameters on wall shear stress developed inside the nozzle is also investigated. It is found that for the same operating parameters, increase in the abrasive volume fraction (concentration) results in significant decrease in the wall shear stress as well as the jet exit kinetic energy. Increase in the abrasive particle size results in marginal decrease in the jet exit kinetic energy. Numerical simulation also indicates that garnet abrasives produce better jet exit kinetic energy than aluminium oxide and silicon carbide.

Keywords: abrasive water jet machining, jet kinetic energy, operating pressure, wall shear stress, Garnet abrasive

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Authors: Ercan Karadogan, Fatih Usta

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Auxetic materials have a negative Poisson’s ratio (NPR), which is not often found in nature. They are metamaterials that have potential applications in many engineering fields. Mechanical metamaterials are synthetically designed structures with unusual mechanical properties. These mechanical properties are dependent on the properties of the matrix structure. They have the following special characteristics, i.e., improved shear modulus, increased energy absorption, and intensive fracture toughness. Non-auxetic materials compress transversely when they are stretched. The system naturally is inclined to keep its density constant. The transversal compression increases the density to balance the loss in the longitudinal direction. This study proposes to improve the crushing performance of hybrid auxetic materials. The re-entrant honeycomb structure has been combined with a star honeycomb, an S-shaped unit cell, a double arrowhead, and a structurally hexagonal re-entrant honeycomb by 9 X 9 cells, i.e., the number of cells is 9 in the lateral direction and 9 in the vertical direction. The Finite Element (FE) and experimental methods have been used to determine the compression behavior of the developed hybrid auxetic structures. The FE models have been developed by using Abaqus software. The specimens made of polymer plastic materials have been 3D printed and subjected to compression loading. The results are compared in terms of specific energy absorption and strength. This paper describes the quasi-static crushing behavior of two types of hybrid lattice structures (auxetic + auxetic and auxetic + non-auxetic). The results show that the developed hybrid structures can be useful to control collapse mechanisms and present larger energy absorption compared to conventional re-entrant auxetic structures.

Keywords: auxetic materials, compressive behavior, metamaterials, negative Poisson’s ratio

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Authors: Wided Abidi, Tahar Ezzedine

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Low-Energy Adaptive Clustering Hierarchy (LEACH) has been considered as one of the effective hierarchical routing algorithms that optimize energy and prolong the lifetime of network. Since the selection of Cluster Head (CH) in LEACH is carried out randomly, in this paper, we propose an approach of electing CH based on LEACH protocol. In other words, we present a formula for calculating the threshold responsible for CH election. In fact, we adopt three principle criteria: the remaining energy of node, the number of neighbors within cluster range and the distance between node and CH. Simulation results show that our proposed approach beats LEACH protocol in regards of prolonging the lifetime of network and saving residual energy.

Keywords: wireless sensors networks, LEACH protocol, cluster head election, energy efficiency

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Authors: Jaroslav Frantík, Jan Najser

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This article deals with increasing the energy efficiency of a plant in terms of optimizing the process. The European Union is striving to achieve the climate-energy package in the area increasing of energy efficiency. The goal of energy efficiency is to reduce primary energy consumption by 20% within the EU until 2020. The objective of saving energy consumption in the Czech Republic was set at 47.84 PJ (13.29 TWh). For reducing electricity consumption, it is possible to choose: a) mandatory increasing of energy efficiency, b) alternative scheme, c) combination of both actions. The Czech Republic has chosen for reducing electricity consumption using-alternative scheme. The presentation is focused on the proposal of a technological unit dealing with the gasification process of processing of biomass with an increase of power in the output. The synthesis gas after gasification of biomass is used as fuel in a cogeneration process of reciprocating internal combustion engine with the classic production of heat and electricity. Subsequently, there is an explanation of the ORC system dealing with the conversion of waste heat to electricity with the using closed cycle of the steam process with organic medium. The arising electricity is distributed to the power grid as a further energy source, or it is used for needs of the partial coverage of the technological unit. Furthermore, there is a presented schematic description of the technology with the identification of energy flows starting from the biomass treatment by drying, through its conversion to gaseous fuel, producing of electricity and utilize of thermal energy with minimizing losses. It has been found that using of ORC system increased the efficiency of the produced electricity by 7.5%.

Keywords: biomass, efficiency, gasification, ORC system

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Authors: M. Yosofi, S. Ezeddini, A. Ollivier, V. Lavaste, C. Mayousse

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Additive manufacturing processes have changed significantly in a wide range of industries and their application progressed from rapid prototyping to production of end-use products. However, their environmental impact is still a rather open question. In order to support the growth of this technology in the industrial sector, environmental aspects should be considered and predictive models may help monitor and reduce the environmental footprint of the processes. This work presents predictive models based on a previously developed methodology for the environmental impact evaluation combined with a technical and economical assessment. Here we applied the methodology to the Fused Deposition Modeling process. First, we present the predictive models relative to different types of machines. Then, we present a decision-making tool designed to identify the optimum manufacturing strategy regarding technical, economic, and environmental criteria.

Keywords: additive manufacturing, decision-makings, environmental impact, predictive models

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Authors: Enas R. Shouman

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Economic development with population growth is leading to a continuous increase in energy demand. At the same time, growing global concern for the environment is driving to decrease the use of conventional energy sources and to increase the use of renewable energy sources. The objective of this study is to present the market trends of solar energy photovoltaic technology over the world and to represent economics methods for PV financial analyzes on the basis of expectations for the expansion of PV in many applications. In the course of this study, detailed information about the current PV market was gathered and analyzed to find factors influencing the penetration of PV energy. The paper methodology depended on five relevant economic financial analysis methods that are often used for investment decisions maker. These methods are payback analysis, net benefit analysis, saving-to-investment ratio, adjusted internal rate of return, and life-cycle cost. The results of this study may be considered as a marketing guide that helps diffusion of using PV Energy. The study showed that PV cost is economically reliable. The consumers will pay higher purchase prices for PV system installation but will get lower electricity bill.

Keywords: photovoltaic, financial methods, solar energy, economics, PV panel

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Authors: Safieh Javadinejad

Abstract:

In order to make well-informed decisions respecting long-term system planning, resource managers and policy creators necessitate to comprehend the interconnections among energy and water utilization and manufacture—and also the energy-water nexus. Planning and assessment issues contain the enhancement of strategies for declining the water and energy system’s vulnerabilities to climate alteration with also emissions of decreasing greenhouse gas. In order to deliver beneficial decision support for climate adjustment policy and planning, understanding the regionally-specific features of the energy-water nexus, and the history-future of the water and energy source systems serving is essential. It will be helpful for decision makers understand the nature of current water-energy system conditions and capacity for adaptation plans for future. This research shows an integrated hydrology/energy modeling platform which is able to extend water-energy examines based on a detailed illustration of local circumstances. The modeling links the Water Evaluation and Planning (WEAP) and the Long Range Energy Alternatives Planning (LEAP) system to create full picture of water-energy processes. This will allow water managers and policy-decision makers to simply understand links between energy system improvements and hydrological processing and realize how future climate change will effect on water-energy systems. The Zayandeh Rud river basin in Iran is selected as a case study to show the results and application of the analysis. This region is known as an area with large integration of both the electric power and water sectors. The linkages between water, energy and climate change and possible adaptation strategies are described along with early insights from applications of the integration modeling system.

Keywords: climate impacts, hydrology, water systems, adaptation planning, electricity, integrated modeling

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Authors: Zihua Wu, Yueming He, Xiaoxiao Yu, Yuanyuan Wang, Huaqing Xie

Abstract:

The match of Solar thermoelectric generator (STEG) and phase change materials (PCM) can enhance the solar energy storage and reduce environmental impact from the day-and-night transformation and weather changes. This work utilizes D-mannitol (DM) matrix as the suitable PCM for coupling with thermoelectric generator to achieve the middle-temperature solar energy storage performance at 165℃-167℃. DM/MWCNT composite phase change materials prepared by ball milling not only can keep a high phase change enthalpy of DM material but also have great photo-thermal conversion efficiency of 82%. Based on the self-made storage device container, the effect of PCM thickness on the solar energy storage performance is further discussed and analyzed. The experimental results prove that PCM-STEG coupling system can output more electric energy than pure STEG system because PCM can decline the heat transfer and storage thermal energy to further generate the electric energy through thermal-to-electric conversion when the light is removed. The increase of PCM thickness can reduce the heat transfer and enhance thermal storage, and then the power generation performance of PCM-STEG coupling system can be improved. As the increase of light intensity, the output electric energy of the coupling system rises accordingly, and the maximum amount of electrical energy can reach by 113.85 J at 1.6 W/cm2. The study of the PCM-STEG coupling system has certain reference for the development of solar energy storage and application.

Keywords: solar energy, solar thermoelectric generator, phase change materials, solar-to-electric energy, DM/MWCNT

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Authors: Touila Ahmed, Elie Louis, Hamza Gharbi

Abstract:

State of the art question answering models have recently shown impressive performance especially in a zero-shot setting. This approach is particularly useful when confronted with a highly diverse domain such as the legal field, in which it is increasingly difficult to have a dataset covering every notion and concept. In this work, we propose a flexible generative question answering approach to contract analysis as well as a weakly supervised procedure to leverage unannotated data and boost our models’ performance in general, and their zero-shot performance in particular.

Keywords: question answering, contract analysis, zero-shot, natural language processing, generative models, self-supervision

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Authors: Wendy Flory, Kazi Czarnecki, Matthijs Mercy, Mark Joswiak, Mary Beth Seasholtz

Abstract:

Polyurethane Materials are present in a wide range of industrial segments such as Furniture, Building and Construction, Composites, Automotive, Electronics, and more. Dow is one of the leaders for the manufacture of the two main raw materials, Isocyanates and Polyols used to produce polyurethane products. Dow is also a key player for the manufacture of Polyurethane Systems/Formulations designed for targeted applications. In 1990, the first analytical chemometric models were developed and deployed for use in the Dow QC labs of the polyols business for the quantification of OH, water, cloud point, and viscosity. Over the years many models have been added; there are now over 140 models for quantification and hundreds for product identification, too many to be reasonable for support. There are 29 global models alone for the quantification of OH across > 70 products at many sites. An attempt was made to consolidate these into a single model. While the consolidated model proved good statistics across the entire range of OH, several products had a bias by ASTM E1655 with individual product validation. This project summary will show the strategy for global model updates for OH, to reduce the number of models for quantification from over 140 to 5 or less using chemometric methods. In order to gain an understanding of the best product groupings, we identify clusters by reducing spectra to a few dimensions via Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP). Results from these cluster analyses and a separate validation set allowed dow to reduce the number of models for predicting OH from 29 to 3 without loss of accuracy.

Keywords: hydroxyl, global model, model maintenance, near infrared, polyol

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Authors: Rachel Y. Zhang, Christopher K. Anderson

Abstract:

A critical aspect of revenue management is a firm’s ability to predict demand as a function of price. Historically hotels have used simple time series models (regression and/or pick-up based models) owing to the complexities of trying to build casual models of demands. Machine learning approaches are slowly attracting attention owing to their flexibility in modeling relationships. This study provides an overview of approaches to forecasting hospitality demand – focusing on the opportunities created by machine learning approaches, including K-Nearest-Neighbors, Support vector machine, Regression Tree, and Artificial Neural Network algorithms. The out-of-sample performances of above approaches to forecasting hotel demand are illustrated by using a proprietary sample of the market level (24 properties) transactional data for Las Vegas NV. Causal predictive models can be built and evaluated owing to the availability of market level (versus firm level) data. This research also compares and contrast model accuracy of firm-level models (i.e. predictive models for hotel A only using hotel A’s data) to models using market level data (prices, review scores, location, chain scale, etc… for all hotels within the market). The prospected models will be valuable for hotel revenue prediction given the basic characters of a hotel property or can be applied in performance evaluation for an existed hotel. The findings will unveil the features that play key roles in a hotel’s revenue performance, which would have considerable potential usefulness in both revenue prediction and evaluation.

Keywords: hotel revenue, k-nearest-neighbors, machine learning, neural network, prediction model, regression tree, support vector machine

Procedia PDF Downloads 120