Search results for: Whale Optimization Algorithm

Authors: Same Noel Ngando, Yakub Abdulfatai Olatunji

Abstract:

Renewable energy micro-grids, such as those powered by solar or wind energy, are often intermittent in nature. This means that the amount of energy generated by these systems can vary depending on weather conditions or other factors, which can make it difficult to ensure a steady supply of power. To address this issue, energy storage systems have been developed to increase the reliability of renewable energy micro-grids. Battery systems have been the dominant energy storage technology for renewable energy micro-grids. Batteries can store large amounts of energy in a relatively small and compact package, making them easy to install and maintain in a micro-grid setting. Additionally, batteries can be quickly charged and discharged, allowing them to respond quickly to changes in energy demand. However, the process involved in recycling batteries is quite costly and difficult. An alternative energy storage system that is gaining popularity is hydrogen storage. Hydrogen is a versatile energy carrier that can be produced from renewable energy sources such as solar or wind. It can be stored in large quantities at low cost, making it suitable for long-distance mass storage. Unlike batteries, hydrogen does not degrade over time, so it can be stored for extended periods without the need for frequent maintenance or replacement, allowing it to be used as a backup power source when the micro-grid is not generating enough energy to meet demand. When hydrogen is needed, it can be converted back into electricity through a fuel cell. Energy consumption data is got from a particular residential area in Daegu, South Korea, and the data is processed and analyzed. From the analysis, the total energy demand is calculated, and different hybrid energy system configurations are designed using HOMER Pro (Hybrid Optimization for Multiple Energy Resources) and MATLAB software. A techno-economic and environmental comparison and life cycle assessment (LCA) of the different configurations using battery and hydrogen as storage systems are carried out. The various scenarios included PV-hydrogen-grid system, PV-hydrogen-grid-wind, PV-hydrogen-grid-biomass, PV-hydrogen-wind, PV-hydrogen-biomass, biomass-hydrogen, wind-hydrogen, PV-battery-grid-wind, PV- battery -grid-biomass, PV- battery -wind, PV- battery -biomass, and biomass- battery. From the analysis, the least cost system for the location was the PV-hydrogen-grid system, with a net present cost of about USD 9,529,161. Even though all scenarios were environmentally friendly, taking into account the recycling cost and pollution involved in battery systems, all systems with hydrogen as a storage system produced better results. In conclusion, hydrogen is becoming a very prominent energy storage solution for renewable energy micro-grids. It is easier to store compared with electric power, so it is suitable for long-distance mass storage. Hydrogen storage systems have several advantages over battery systems, including flexibility, long-term stability, and low environmental impact. The cost of hydrogen storage is still relatively high, but it is expected to decrease as more hydrogen production, and storage infrastructure is built. With the growing focus on renewable energy and the need to reduce greenhouse gas emissions, hydrogen is expected to play an increasingly important role in the energy storage landscape.

Keywords: renewable energy systems, microgrid, hydrogen production, energy storage systems

Procedia PDF Downloads 65

Authors: Cristina Opris, Bogdan Cojocaru, Madalina Tudorache, Simona M. Coman, Vasile I. Parvulescu, Camelia Bala, Bahir Duraki, Jeroen A. Van Bokhoven

Abstract:

The tremendous achievements in the development of the society concretized by more sophisticated materials and systems are merely based on non-renewable resources. Consequently, after more than two centuries of intensive development, among others, we are faced with the decrease of the fossil fuel reserves, an increased impact of the greenhouse gases on the environment, and economic effects caused by the fluctuations in oil and mineral resource prices. The use of biomass may solve part of these problems, and recent analyses demonstrated that from the perspective of the reduction of the emissions of carbon dioxide, its valorization may bring important advantages conditioned by the usage of genetic modified fast growing trees or wastes, as primary sources. In this context, the abundance and complex structure of lignin may offer various possibilities of exploitation. However, its transformation in fuels or chemicals supposes a complex chemistry involving the cleavage of C-O and C-C bonds and altering of the functional groups. Chemistry offered various solutions in this sense. However, despite the intense work, there are still many drawbacks limiting the industrial application. Thus, the proposed technologies considered mainly homogeneous catalysts meaning expensive noble metals based systems that are hard to be recovered at the end of the reaction. Also, the reactions were carried out in organic solvents that are not acceptable today from the environmental point of view. To avoid these problems, the concept of this work was to investigate the synthesis of superparamagnetic core shell catalysts for the fragmentation of lignin directly in the aqueous phase. The magnetic nanoparticles were covered with a nanoshell of an oxide (niobia) with a double role: to protect the magnetic nanoparticles and to generate a proper (acidic) catalytic function and, on this composite, cobalt nanoparticles were deposed in order to catalyze the C-C bond splitting. With this purpose, we developed a protocol to prepare multifunctional and magnetic separable nano-composite Co@Nb2O5@Fe3O4 catalysts. We have also established an analytic protocol for the identification and quantification of the fragments resulted from lignin depolymerization in both liquid and solid phase. The fragmentation of various lignins occurred on the prepared materials in high yields and with very good selectivity in the desired fragments. The optimization of the catalyst composition indicated a cobalt loading of 4wt% as optimal. Working at 180 oC and 10 atm H2 this catalyst allowed a conversion of lignin up to 60% leading to a mixture containing over 96% in C20-C28 and C29-C37 fragments that were then completely fragmented to C12-C16 in a second stage. The investigated catalysts were completely recyclable, and no leaching of the elements included in the composition was determined by inductively coupled plasma optical emission spectrometry (ICP-OES).

Keywords: superparamagnetic core-shell catalysts, environmental production of fuels, renewable lignin, recyclable catalysts

Procedia PDF Downloads 305

Authors: R. P. Naik, A. K. Rakshit

Abstract:

In recent years, with the rapid development in science and technology, people have increasing requirements on uses of clothing for new functions, which contributes to opportunities for further development and incorporation of new technologies along with novel materials. In this context, textiles are of fast decalescence or fast heat radiation media as per as comfort accountability of textile articles are concern. The microstructure and texture of textiles play a vital role in determining the heat-moisture comfort level of the human body because clothing serves as a barrier to the outside environment and a transporter of heat and moisture from the body to the surrounding environment to keep thermal balance between body heat produced and body heat loss. The main bottleneck which is associated with textile materials to be successful as thermal insulation materials can be enumerated as; firstly, high loft or bulkiness of material so as to provide predetermined amount of insulation by ensuring sufficient trapping of air. Secondly, the insulation depends on forced convection; such convective heat loss cannot be prevented by textile material. Third is that the textile alone cannot reach the level of thermal conductivity lower than 0.025 W/ m.k of air. Perhaps, nano-fibers can do so, but still, mass production and cost-effectiveness is a problem. Finally, such high loft materials for thermal insulation becomes heavier and uneasy to manage especially when required to carry over a body. The proposed works aim at developing lightweight effective thermal insulation textiles in combination with nanoporous silica-gel which provides the fundamental basis for the optimization of material properties to achieve good performance of the clothing system. This flexible nonwoven silica-gel composites fabric in intact monolith was successfully developed by reinforcing SiO2-gel in thermal bonded nonwoven fabric via sol-gel processing. Ambient Pressure Drying method is opted for silica gel preparation for cost-effective manufacturing. The formed structure of the nonwoven / SiO₂ -gel composites were analyzed, and the transfer properties were measured. The effects of structure and fibre on the thermal properties of the SiO₂-gel composites were evaluated. Samples are then tested against untreated samples of same GSM in order to study the effect of SiO₂-gel application on various properties of nonwoven fabric. The nonwoven fabric composites reinforced with aerogel showed intact monolith structure were also analyzed for their surface structure, functional group present, microscopic images. Developed product reveals a significant reduction in pores' size and air permeability than the conventional nonwoven fabric. Composite made from polyester fibre with lower GSM shows lowest thermal conductivity. Results obtained were statistically analyzed by using STATISTICA-6 software for their level of significance. Univariate tests of significance for various parameters are practiced which gives the P value for analyzing significance level along with that regression summary for dependent variable are also studied to obtain correlation coefficient.

Keywords: silica-gel, heat insulation, nonwoven fabric, thermal barrier clothing

Procedia PDF Downloads 88

Authors: Zatovska T. V., Nesterova N. V., Baranova G. V., Zagorodnya S. D.

Abstract:

In recent years, biosensor technologies based on the phenomenon of surface plasmon resonance (SPR) are becoming increasingly used in biology and medicine. Their application facilitates exploration in real time progress of binding of biomolecules and identification of agents that specifically interact with biologically active substances immobilized on the biosensor surface (biochips). Special attention is paid to the use of Biosensor analysis in determining the antibody-antigen interaction in the diagnostics of diseases caused by viruses and bacteria. According to WHO, the diseases that are caused by the herpes simplex virus (HSV), take second place (15.8%) after influenza as a cause of death from viral infections. Current diagnostics of HSV infection include PCR and ELISA assays. The latter allows determination the degree of immune response to viral infection and respective stages of its progress. In this regard, the searches for new and available diagnostic methods are very important. This work was aimed to develop Biosensor chip for detection of specific antibodies to HSV-1 in the human blood serum. The proteins of HSV1 (strain US) were used as antigens. The viral particles were accumulated in cell culture MDBK and purified by differential centrifugation in cesium chloride density gradient. Analysis of the HSV1 proteins was performed by polyacrylamide gel electrophoresis and ELISA. The protein concentration was measured using De Novix DS-11 spectrophotometer. The device for detection of antigen-antibody interactions was an optoelectronic two-channel spectrometer ‘Plasmon-6’, using the SPR phenomenon in the Krechman optical configuration. It was developed at the Lashkarev Institute of Semiconductor Physics of NASU. The used carrier was a glass plate covered with 45 nm gold film. Screening of human blood serums was performed using the test system ‘HSV-1 IgG ELISA’ (GenWay, USA). Development of Biosensor chip included optimization of conditions of viral antigen sorption and analysis steps. For immobilization of viral proteins 0.2% solution of Dextran 17, 200 (Sigma, USA) was used. Sorption of antigen took place at 4-8°C within 18-24 hours. After washing of chip, three times with citrate buffer (pH 5,0) 1% solution of BSA was applied to block the sites not occupied by viral antigen. It was found direct dependence between the amount of immobilized HSV1 antigen and SPR response. Using obtained biochips, panels of 25 positive and 10 negative for the content of antibodies to HSV-1 human sera were analyzed. The average value of SPR response was 185 a.s. for negative sera and from 312 to. 1264 a.s. for positive sera. It was shown that SPR data were agreed with ELISA results in 96% of samples proving the great potential of SPR in such researches. It was investigated the possibility of biochip regeneration and it was shown that application of 10 mM NaOH solution leads to rupture of intermolecular bonds. This allows reuse the chip several times. Thus, in this study biosensor chip for detection of specific antibodies to HSV1 was successfully developed expanding a range of diagnostic methods for this pathogen.

Keywords: biochip, herpes virus, SPR

Procedia PDF Downloads 397

Authors: Guillaume Soubeyran, Fabrice Micoud, Benoit Morin, Jean-Philippe Poirot-Crouvezier, Magali Reytier

Abstract:

Proton Exchange Membrane Fuel Cell (PEMFC) technology is considered as a solution for the reduction of CO2 emissions. However, this technology still meets several challenges for high-scale industrialization. In this context, the increase of durability remains a critical aspect for competitiveness of this technology. Fortunately, performance degradations in nominal operating conditions is partially reversible, meaning that if specific conditions are applied, a partial recovery of fuel cell performance can be achieved, while irreversible degradations can only be mitigated. Thus, it is worth studying the optimal conditions to rejuvenate these reversible degradations and assessing the long-term impact of such procedures on the performance of the cell. Reversible degradations consist mainly of anode Pt active sites poisoning by carbon monoxide at the anode, heterogeneities in water management during use, and oxidation/deactivation of Pt active sites at the cathode. The latter is identified as a major source of reversible performance loss caused by the presence oxygen, high temperature and high cathode potential that favor platinum oxidation, especially in high efficiency operating points. Hence, we studied here a recovery procedure aiming at reducing the platinum oxides by decreasing cathode potential during operation. Indeed, the application of short air starvation phase leads to a drop of cathode potential. Cell performances are temporarily increased afterwards. Nevertheless, local temperature and current heterogeneities within the cells are favored and shall be minimized. The consumption of fuel during the recovery phase shall also be considered to evaluate the global efficiency. Consequently, the purpose of this work is to find an optimal compromise between the recovery of reversible degradations by air starvation, the increase of global cell efficiency and the mitigation of irreversible degradations effects. Different operating parameters have first been studied such as cell voltage, temperature and humidity in single cell set-up. Considering the global PEMFC system efficiency, tests showed that reducing duration of recovery phase and reducing cell voltage was the key to ensure an efficient recovery. Recovery phase frequency was a major factor as well. A specific method was established to find the optimal frequency depending on the duration and voltage of the recovery phase. Then, long-term degradations have also been studied by applying FC-DLC cycles based on NEDC cycles on a 4-cell short stack by alternating test sequences with and without recovery phases. Depending on recovery phase timing, cell efficiency during the cycle was increased up to 2% thanks to a mean voltage increase of 10 mV during test sequences with recovery phases. However, cyclic voltammetry tests results suggest that the implementation of recovery phases causes an acceleration of the decrease of platinum active areas that could be due to the high potential variations applied to the cathode electrode during operation.

Keywords: durability, PEMFC, recovery procedure, reversible degradation

Procedia PDF Downloads 106

Authors: Mohammad Alipour, Ekin Esen, Riza Kizilel

Abstract:

Lithium-ion batteries are a commonly used type of rechargeable batteries because of their high specific energy and specific power. With the growing popularity of electric vehicles and hybrid electric vehicles, increasing attentions have been paid to rechargeable Lithium-ion batteries. However, safety problems, high cost and poor performance in low ambient temperatures and high current rates, are big obstacles for commercial utilization of these batteries. By proper thermal management, most of the mentioned limitations could be eliminated. Temperature profile of the Li-ion cells has a significant role in the performance, safety, and cycle life of the battery. That is why little temperature gradient can lead to great loss in the performances of the battery packs. In recent years, numerous researchers are working on new techniques to imply a better thermal management on Li-ion batteries. Keeping the battery cells within an optimum range is the main objective of battery thermal management. Commercial Li-ion cells are composed of several electrochemical layers each consisting negative-current collector, negative electrode, separator, positive electrode, and positive current collector. However, many researchers have adopted a single-layer cell to save in computing time. Their hypothesis is that thermal conductivity of the layer elements is so high and heat transfer rate is so fast. Therefore, instead of several thin layers, they model the cell as one thick layer unit. In previous work, we showed that single-layer model is insufficient to simulate the thermal behavior and temperature nonuniformity of the high-capacity Li-ion cells. We also studied the effects of the number of layers on thermal behavior of the Li-ion batteries. In this work, first thermal and electrochemical behavior of the LiFePO₄ battery is modeled with 3D multilayer cell. The model is validated with the experimental measurements at different current rates and ambient temperatures. Real time heat generation rate is also studied at different discharge rates. Results showed non-uniform temperature distribution along the cell which requires thermal management system. Therefore, aluminum plates with mini-channel system were designed to control the temperature uniformity. Design parameters such as channel number and widths, inlet flow rate, and cooling fluids are optimized. As cooling fluids, water and air are compared. Pressure drop and velocity profiles inside the channels are illustrated. Both surface and internal temperature profiles of single cell and battery packs are investigated with and without cooling systems. Our results show that using optimized Mini-channel cooling plates effectively controls the temperature rise and uniformity of the single cells and battery packs. With increasing the inlet flow rate, cooling efficiency could be reached up to 60%.

Keywords: lithium ion battery, 3D multilayer model, mini-channel cooling plates, thermal management

Procedia PDF Downloads 135

Authors: Smita Mondal, Dinesh Kumar Pandey, Prakash Biswas

Abstract:

During past two decades, considerable attention has been given to the value addition of biodiesel derived glycerol (~10wt.%) to make the biodiesel industry economically viable. Among the various glycerol value-addition methods, hydrogenolysis of glycerol to 1,2-propanediol is one of the attractive and promising routes. In this study, highly active and selective γ-Al₂O₃ supported bimetallic Cu-Ni catalyst was developed for selective hydrogenolysis of glycerol to 1,2-propanediol in the liquid phase. The catalytic performance was evaluated in a high-pressure autoclave reactor. The formation of mixed oxide indicated the strong interaction of Cu, Ni with the alumina support. Experimental results demonstrated that bimetallic copper-nickel catalyst was more active and selective to 1,2-PDO as compared to monometallic catalysts due to bifunctional behavior. To verify the effect of calcination temperature on the formation of Cu-Ni mixed oxide phase, the calcination temperature of 20wt.% Cu:Ni(1:1)/Al₂O₃ catalyst was varied from 300°C-550°C. The physicochemical properties of the catalysts were characterized by various techniques such as specific surface area (BET), X-ray diffraction study (XRD), temperature programmed reduction (TPR), and temperature programmed desorption (TPD). The BET surface area and pore volume of the catalysts were in the range of 71-78 m²g⁻¹, and 0.12-0.15 cm³g⁻¹, respectively. The peaks at the 2θ range of 43.3°-45.5° and 50.4°-52°, was corresponded to the copper-nickel mixed oxidephase [JCPDS: 78-1602]. The formation of mixed oxide indicated the strong interaction of Cu, Ni with the alumina support. The crystallite size decreased with increasing the calcination temperature up to 450°C. Further, the crystallite size was increased due to agglomeration. Smaller crystallite size of 16.5 nm was obtained for the catalyst calcined at 400°C. Total acidic sites of the catalysts were determined by NH₃-TPD, and the maximum total acidic of 0.609 mmol NH₃ gcat⁻¹ was obtained over the catalyst calcined at 400°C. TPR data suggested the maximum of 75% degree of reduction of catalyst calcined at 400°C among all others. Further, 20wt.%Cu:Ni(1:1)/γ-Al₂O₃ catalyst calcined at 400°C exhibited highest catalytic activity ( > 70%) and 1,2-PDO selectivity ( > 85%) at mild reaction condition due to highest acidity, highest degree of reduction, smallest crystallite size. Further, the modified Power law kinetic model was developed to understand the true kinetic behaviour of hydrogenolysis of glycerol over 20wt.%Cu:Ni(1:1)/γ-Al₂O₃ catalyst. Rate equations obtained from the model was solved by ode23 using MATLAB coupled with Genetic Algorithm. Results demonstrated that the model predicted data were very well fitted with the experimental data. The activation energy of the formation of 1,2-PDO was found to be 45 kJ mol⁻¹.

Keywords: glycerol, 1, 2-PDO, calcination, kinetic

Procedia PDF Downloads 123

Authors: Chitresh Prasad, Arvind Ramesh, Aditya Virkar, Karan Dholkaria, Vinayak Malhotra

Abstract:

Solid Propellant Rocket is a rocket that utilises a combination of a solid Oxidizer and a solid Fuel. Success in Solid Rocket Motor design and development depends significantly on knowledge of burning rate behaviour of the selected solid propellant under all motor operating conditions and design limit conditions. Most Solid Motor Rockets consist of the Main Engine, along with multiple Boosters that provide an additional thrust to the space-bound vehicle. Though widely used, they have been eclipsed by Liquid Propellant Rockets, because of their better performance characteristics. The addition of a catalyst such as Iron Oxide, on the other hand, can drastically enhance the performance of a Solid Rocket. This scientific investigation tries to emulate the working of a Solid Rocket using Sparklers and Energized Candles, with a central Energized Candle acting as the Main Engine and surrounding Sparklers acting as the Booster. The Energized Candle is made of Paraffin Wax, with Magnesium filings embedded in it’s wick. The Sparkler is made up of 45% Barium Nitrate, 35% Iron, 9% Aluminium, 10% Dextrin and the remaining composition consists of Boric Acid. The Magnesium in the Energized Candle, and the combination of Iron and Aluminium in the Sparkler, act as catalysts and enhance the burn rates of both materials. This combustion of Metallized Propellants has an influence over the regression rate of the subject candle. The experimental parameters explored here are Separation Distance, Systematically varying Configuration and Layout Symmetry. The major performance parameter under observation is the Regression Rate of the Energized Candle. The rate of regression is significantly affected by the orientation and configuration of the sparklers, which usually act as heat sources for the energized candle. The Overall Efficiency of any engine is factorised by the thermal and propulsive efficiencies. Numerous efforts have been made to improve one or the other. This investigation focuses on the Orientation of Rocket Motor Design to maximize their Overall Efficiency. The primary objective is to analyse the Flame Spread Rate variations of the energized candle, which resembles the solid rocket propellant used in the first stage of rocket operation thereby affecting the Specific Impulse values in a Rocket, which in turn have a deciding impact on their Time of Flight. Another objective of this research venture is to determine the effectiveness of the key controlling parameters explored. This investigation also emulates the exhaust gas interactions of the Solid Rocket through concurrent ignition of the Energized Candle and Sparklers, and their behaviour is analysed. Modern space programmes intend to explore the universe outside our solar system. To accomplish these goals, it is necessary to design a launch vehicle which is capable of providing incessant propulsion along with better efficiency for vast durations. The main motivation of this study is to enhance Rocket performance and their Overall Efficiency through better designing and optimization techniques, which will play a crucial role in this human conquest for knowledge.

Keywords: design modifications, improving overall efficiency, metallized combustion, regression rate variations

Procedia PDF Downloads 152

Authors: Emily Moore, Dora Amos, Tracy Ellimah, Natasha Parrott

Abstract:

Postoperative haematoma is a well-recognised complication of thyroid surgery with an incidence of 1-5%. Haematoma formation causes progressive airway obstruction, necessitating emergency bedside haematoma evacuation in up to ¼ of patients. ENT UK, BAETS and DAS have developed consensus guidelines to improve perioperative care, recommending that all healthcare staff interacting with patients undergoing thyroid surgery should be trained in managing post-thyroidectomy haematoma. The aim was to assess the effectiveness of a hybrid simulation model in improving clinician’s confidence in dealing with this surgical emergency. A hybrid simulation was designed, consisting of a standardised patient wearing a part-task trainer to mimic a post-thyroidectomy haematoma in a real patient. The part-task trainer was an adapted C-spine collar with layers of silicone representing the skin and strap muscles and thickened jelly representing the haematoma. Both the skin and strap muscle layers had to be opened in order to evacuate the haematoma. Boxes have been implemented into the appropriate post operative areas (recovery and surgical wards), which contain a printed algorithm designed to assist in remembering a sequence of steps for haematoma evacuation using the ‘SCOOP’ method (skin exposure, cut sutures, open skin, open muscles, pack wound) along with all the necessary equipment to open the front of the neck. Small-group teaching sessions were delivered by ENT and anaesthetic trainees to members of the multidisciplinary team normally involved in perioperative patient care, which included ENT surgeons, anaesthetists, recovery nurses, HCAs and ODPs. The DESATS acronym of signs and symptoms to recognise (difficulty swallowing, EWS score, swelling, anxiety, tachycardia, stridor) was highlighted. Then participants took part in the hybrid simulation in order to practice this ‘SCOOP’ method of haematoma evacuation. Participants were surveyed using a Likert scale to assess their level of confidence pre- and post teaching session. 30 clinicians took part. Confidence (agreed/strongly agreed) in recognition of post thyroidectomy haematoma improved from 58.6% to 96.5%. Confidence in management improved from 27.5% to 89.7%. All participants successfully decompressed the haematoma. All participants agreed/strongly agreed, that the sessions were useful for their learning. Multidisciplinary team simulation teaching is effective at significantly improving confidence in both the recognition and management of postoperative haematoma. Hybrid simulation sessions are useful and should be incorporated into training for clinicians.

Keywords: thyroid surgery, haematoma, teaching, hybrid simulation

Procedia PDF Downloads 74

Authors: Oibar Martinez, Clara Oliver, Jose Miguel Miranda

Abstract:

In this paper, we discuss the standard improvements which can be made to reduce the earth resistance in difficult terrains for optimum lightning protection, what are the practical limitations, and how the modeling can be refined for accurate diagnostics and ground resistance minimization. Ground resistance minimization can be made via three different approaches: burying vertical electrodes connected in parallel, burying horizontal conductive plates or meshes, or modifying the own terrain, either by changing the entire terrain material in a large volume or by adding earth-enhancing compounds. The use of vertical electrodes connected in parallel pose several practical limitations. In order to prevent loss of effectiveness, it is necessary to keep a minimum distance between each electrode, which is typically around five times larger than the electrode length. Otherwise, the overlapping of the local equipotential lines around each electrode reduces the efficiency of the configuration. The addition of parallel electrodes reduces the resistance and facilitates the measurement, but the basic parallel resistor formula of circuit theory will always underestimate the final resistance. Numerical simulation of equipotential lines around the electrodes overcomes this limitation. The resistance of a single electrode will always be proportional to the soil resistivity. The electrodes are usually installed with a backfilling material of high conductivity, which increases the effective diameter. However, the improvement is marginal, since the electrode diameter counts in the estimation of the ground resistance via a logarithmic function. Substances that are used for efficient chemical treatment must be environmentally friendly and must feature stability, high hygroscopicity, low corrosivity, and high electrical conductivity. A number of earth enhancement materials are commercially available. Many are comprised of carbon-based materials or clays like bentonite. These materials can also be used as backfilling materials to reduce the resistance of an electrode. Chemical treatment of soil has environmental issues. Some products contain copper sulfate or other copper-based compounds, which may not be environmentally friendly. Carbon-based compounds are relatively inexpensive and they do have very low resistivities, but they also feature corrosion issues. Typically, the carbon can corrode and destroy a copper electrode in around five years. These compounds also have potential environmental concerns. Some earthing enhancement materials contain cement, which, after installation acquire properties that are very close to concrete. This prevents the earthing enhancement material from leaching into the soil. After analyzing different configurations, we conclude that a buried conductive ring with vertical electrodes connected periodically should be the optimum baseline solution for the grounding of a large size structure installed on a large resistivity terrain. In order to show this, a practical example is explained here where we simulate the ground resistance of a conductive ring buried in a terrain with a resistivity in the range of 1 kOhm·m.

Keywords: grounding improvements, large scale scientific instrument, lightning risk assessment, lightning standards

Procedia PDF Downloads 110

Authors: Bruno RamaëL, GwenaëL Page, Catherine Knopf-Lenoir, Olivier Baledent, Anne-Virginie Salsac

Abstract:

No routine technique currently exists for clinicians to measure the mechanical properties of vascular walls non-invasively. Most of the data available in the literature come from traction or dilatation tests conducted ex vivo on native blood vessels. The objective of the study is to develop a non-invasive characterization technique based on Magnetic Resonance Imaging (MRI) measurements of the deformation of vascular walls under pulsating blood flow conditions. The goal is to determine the mechanical properties of the vessels by inverse analysis, coupling imaging measurements and numerical simulations of the fluid-structure interactions. The hyperelastic properties are identified using Solidworks and Ansys workbench (ANSYS Inc.) solving an optimization technique. The vessel of interest targeted in the study is the common carotid artery. In vivo MRI measurements of the vessel anatomy and inlet velocity profiles was acquired along the facial vascular network on a cohort of 30 healthy volunteers: - The time-evolution of the blood vessel contours and, thus, of the cross-section surface area was measured by 3D imaging angiography sequences of phase-contrast MRI. - The blood flow velocity was measured using a 2D CINE MRI phase contrast (PC-MRI) method. Reference arterial pressure waveforms were simultaneously measured in the brachial artery using a sphygmomanometer. The three-dimensional (3D) geometry of the arterial network was reconstructed by first creating an STL file from the raw MRI data using the open source imaging software ITK-SNAP. The resulting geometry was then transformed with Solidworks into volumes that are compatible with Ansys softwares. Tetrahedral meshes of the wall and fluid domains were built using the ANSYS Meshing software, with a near-wall mesh refinement method in the case of the fluid domain to improve the accuracy of the fluid flow calculations. Ansys Structural was used for the numerical simulation of the vessel deformation and Ansys CFX for the simulation of the blood flow. The fluid structure interaction simulations showed that the systolic and diastolic blood pressures of the common carotid artery could be taken as reference pressures to identify the mechanical properties of the different arteries of the network. The coefficients of the hyperelastic law were identified using Ansys Design model for the common carotid. Under large deformations, a stiffness of 800 kPa is measured, which is of the same order of magnitude as the Young modulus of collagen fibers. Areas of maximum deformations were highlighted near bifurcations. This study is a first step towards patient-specific characterization of the mechanical properties of the facial vessels. The method is currently applied on patients suffering from facial vascular malformations and on patients scheduled for facial reconstruction. Information on the blood flow velocity as well as on the vessel anatomy and deformability will be key to improve surgical planning in the case of such vascular pathologies.

Keywords: identification, mechanical properties, arterial walls, MRI measurements, numerical simulations

Procedia PDF Downloads 292

Authors: David Edgar

Abstract:

To understand how the brain’s subconscious and conscious functions, we must conquer the physics of Unity, which leads to duality’s algorithm. Where the subconscious (bottom-up) and conscious (top-down) processes function together to produce and consume intelligence, we use terms like ‘time is relative,’ but we really do understand the meaning. In the brain, there are different processes and, therefore, different observers. These different processes experience time at different rates. A sensory system such as the eyes cycles measurement around 33 milliseconds, the conscious process of the frontal lobe cycles at 300 milliseconds, and the subconscious process of the thalamus cycle at 5 milliseconds. Three different observers experience time differently. To bridge observers, the thalamus, which is the fastest of the processes, maintains a synchronous state and entangles the different components of the brain’s physical process. The entanglements form a synchronous cohesion between the brain components allowing them to share the same state and execute in the same measurement cycle. The thalamus uses the shared state to control the firing sequence of the brain’s linear subconscious process. Sharing state also allows the brain to cheat on the amount of sensory data that must be exchanged between components. Only unpredictable motion is transferred through the synchronous state because predictable motion already exists in the shared framework. The brain’s synchronous subconscious process is entirely based on energy conservation, where prediction regulates energy usage. So, the eyes every 33 milliseconds dump their sensory data into the thalamus every day. The thalamus is going to perform a motion measurement to identify the unpredictable motion in the sensory data. Here is the trick. The thalamus conducts its measurement based on the original observation time of the sensory system (33 ms), not its own process time (5 ms). This creates a data payload of synchronous motion that preserves the original sensory observation. Basically, a frozen moment in time (Flat 4D). The single moment in time can then be processed through the single state maintained by the synchronous process. Other processes, such as consciousness (300 ms), can interface with the synchronous state to generate awareness of that moment. Now, synchronous data traveling through a separate faster synchronous process creates a theoretical time tunnel where observation time is tunneled through the synchronous process and is reproduced on the other side in the original time-relativity. The synchronous process eliminates time dilation by simply removing itself from the equation so that its own process time does not alter the experience. To the original observer, the measurement appears to be instantaneous, but in the thalamus, a linear subconscious process generating sensory perception and thought production is being executed. It is all just occurring in the time available because other observation times are slower than thalamic measurement time. For life to exist in the physical universe requires a linear measurement process, it just hides by operating at a faster time relativity. What’s interesting is time dilation is not the problem; it’s the solution. Einstein said there was no universal time.

Keywords: neural synchronization, natural intelligence, 99.95% IoT data transmission savings, artificial subconscious intelligence (ASI)

Procedia PDF Downloads 104

Authors: Riadh Zorgati, Thomas Triboulet

Abstract:

In quite diverse application areas such as astronomy, medical imaging, geophysics or nondestructive evaluation, many problems related to calibration, fitting or estimation of a large number of input parameters of a model from a small amount of output noisy data, can be cast as inverse problems. Due to noisy data corruption, insufficient data and model errors, most inverse problems are ill-posed in a Hadamard sense, i.e. existence, uniqueness and stability of the solution are not guaranteed. A wide class of inverse problems in physics relates to the Fredholm equation of the first kind. The ill-posedness of such inverse problem results, after discretization, in a very ill-conditioned linear system of equations, the condition number of the associated matrix can typically range from 109 to 1018. This condition number plays the role of an amplifier of uncertainties on data during inversion and then, renders the inverse problem difficult to handle numerically. Similar problems appear in other areas such as numerical optimization when using interior points algorithms for solving linear programs leads to face ill-conditioned systems of linear equations. Devising efficient solution approaches for such system of equations is therefore of great practical interest. Efficient iterative algorithms are proposed for solving a system of linear equations. The approach is based on a preconditioning of the initial matrix of the system with an approximation of a generalized inverse leading to a stochastic preconditioned matrix. This approach, valid for non-negative matrices, is first extended to hermitian, semi-definite positive matrices and then generalized to any complex rectangular matrices. The main results obtained are as follows: 1) We are able to build a generalized inverse of any complex rectangular matrix which satisfies the convergence condition requested in iterative algorithms for solving a system of linear equations. This completes the (short) list of generalized inverse having this property, after Kaczmarz and Cimmino matrices. Theoretical results on both the characterization of the type of generalized inverse obtained and the convergence are derived. 2) Thanks to its properties, this matrix can be efficiently used in different solving schemes as Richardson-Tanabe or preconditioned conjugate gradients. 3) By using Lp norms, we propose generalized Kaczmarz’s type matrices. We also show how Cimmino's matrix can be considered as a particular case consisting in choosing the Euclidian norm in an asymmetrical structure. 4) Regarding numerical results obtained on some pathological well-known test-cases (Hilbert, Nakasaka, …), some of the proposed algorithms are empirically shown to be more efficient on ill-conditioned problems and more robust to error propagation than the known classical techniques we have tested (Gauss, Moore-Penrose inverse, minimum residue, conjugate gradients, Kaczmarz, Cimmino). We end on a very early prospective application of our approach based on stochastic matrices aiming at computing some parameters (such as the extreme values, the mean, the variance, …) of the solution of a linear system prior to its resolution. Such an approach, if it were to be efficient, would be a source of information on the solution of a system of linear equations.

Keywords: conditioning, generalized inverse, linear system, norms, stochastic matrix

Procedia PDF Downloads 111

Authors: Hossein Havaeji, Tony Wong, Thien-My Dao

Abstract:

1. Research Problems and Research Objectives: Blockchain Technology-enabled Supply Chain System (BT-enabled SCS) is the system using BT to drive SCS transparency, security, durability, and process integrity as SCS data is not always visible, available, or trusted. The costs of operating BT in the SCS are a common problem in several organizations. The costs must be estimated as they can impact existing cost control strategies. To account for system and deployment costs, it is necessary to overcome the following hurdle. The problem is that the costs of developing and running a BT in SCS are not yet clear in most cases. Many industries aiming to use BT have special attention to the importance of BT installation cost which has a direct impact on the total costs of SCS. Predicting BT installation cost in SCS may help managers decide whether BT is to be an economic advantage. The purpose of the research is to identify some main BT installation cost components in SCS needed for deeper cost analysis. We then identify and categorize the main groups of cost components in more detail to utilize them in the prediction process. The second objective is to determine the suitable Supervised Learning technique in order to predict the costs of developing and running BT in SCS in a particular case study. The last aim is to investigate how the running BT cost can be involved in the total cost of SCS. 2. Work Performed: Applied successfully in various fields, Supervised Learning is a method to set the data frame, treat the data, and train/practice the method sort. It is a learning model directed to make predictions of an outcome measurement based on a set of unforeseen input data. The following steps must be conducted to search for the objectives of our subject. The first step is to make a literature review to identify the different cost components of BT installation in SCS. Based on the literature review, we should choose some Supervised Learning methods which are suitable for BT installation cost prediction in SCS. According to the literature review, some Supervised Learning algorithms which provide us with a powerful tool to classify BT installation components and predict BT installation cost are the Support Vector Regression (SVR) algorithm, Back Propagation (BP) neural network, and Artificial Neural Network (ANN). Choosing a case study to feed data into the models comes into the third step. Finally, we will propose the best predictive performance to find the minimum BT installation costs in SCS. 3. Expected Results and Conclusion: This study tends to propose a cost prediction of BT installation in SCS with the help of Supervised Learning algorithms. At first attempt, we will select a case study in the field of BT-enabled SCS, and then use some Supervised Learning algorithms to predict BT installation cost in SCS. We continue to find the best predictive performance for developing and running BT in SCS. Finally, the paper will be presented at the conference.

Keywords: blockchain technology, blockchain technology-enabled supply chain system, installation cost, supervised learning

Procedia PDF Downloads 99

Authors: Shankaran Sitarama

Abstract:

New Product Development involves team members coming together and working in teams to come up with innovative solutions to problems, resulting in new products. Thus, a core attribute of a successful NPD team is their creativity and innovation. They need to be creative as a group, generating a breadth of ideas and innovative solutions that solve or address the problem they are targeting and meet the user’s needs. They also need to be very efficient in their teamwork as they work through the various stages of the development of these ideas, resulting in a POC (proof-of-concept) implementation or a prototype of the product. There are two distinctive traits that the teams need to have, one is ideational creativity, and the other is effective and efficient teamworking. There are multiple types of tensions that each of these traits cause in the teams, and these tensions reflect in the team dynamics. Ideational conflicts arising out of debates and deliberations increase the collective knowledge and affect the team creativity positively. However, the same trait of challenging each other’s viewpoints might lead the team members to be disruptive, resulting in interpersonal tensions, which in turn lead to less than efficient teamwork. Teams that foster and effectively manage these creative tensions are successful, and teams that are not able to manage these tensions show poor team performance. In this paper, it explore these tensions as they result in the team communication social network and propose a Creative Tension Balance index along the lines of Degree of Balance in social networks that has the potential to highlight the successful (and unsuccessful) NPD teams. Team communication reflects the team dynamics among team members and is the data set for analysis. The emails between the members of the NPD teams are processed through a semantic analysis algorithm (LSA) to analyze the content of communication and a semantic similarity analysis to arrive at a social network graph that depicts the communication amongst team members based on the content of communication. This social network is subjected to traditional social network analysis methods to arrive at some established metrics and structural balance analysis metrics. Traditional structural balance is extended to include team interaction pattern metrics to arrive at a creative tension balance metric that effectively captures the creative tensions and tension balance in teams. This CTB (Creative Tension Balance) metric truly captures the signatures of successful and unsuccessful (dissonant) NPD teams. The dataset for this research study includes 23 NPD teams spread out over multiple semesters and computes this CTB metric and uses it to identify the most successful and unsuccessful teams by classifying these teams into low, high and medium performing teams. The results are correlated to the team reflections (for team dynamics and interaction patterns), the team self-evaluation feedback surveys (for teamwork metrics) and team performance through a comprehensive team grade (for high and low performing team signatures).

Keywords: team dynamics, social network analysis, new product development teamwork, structural balance, NPD teams

Procedia PDF Downloads 51

Authors: Pornpimol Chaiwuttisak, Honora Smith, Yue Wu

Abstract:

Blood is a perishable product which suffers from physical deterioration with specific fixed shelf life. Although its value during the shelf life is constant, fresh blood is preferred for treatment. However, transportation costs are a major factor to be considered by administrators of Regional Blood Centres (RBCs) which act as blood collection and distribution centres. A trade-off must therefore be reached between transportation costs and short-term holding costs. In this paper we propose a number of algorithms for online allocation and routing of blood supplies, for use in conditions of variable and insufficient blood supply. A case study in northern Thailand provides an application of the allocation and routing policies tested. The plan proposed for daily allocation and distribution of blood supplies consists of two components: firstly, fixed routes are determined for the supply of hospitals which are far from an RBC. Over the planning period of one week, each hospital on the fixed routes is visited once. A robust allocation of blood is made to hospitals on the fixed routes that can be guaranteed on a suitably high percentage of days, despite variable supplies. Secondly, a variable daily route is employed for close-by hospitals, for which more than one visit per week may be needed to fulfil targets. The variable routing takes into account the amount of blood available for each day’s deliveries, which is only known on the morning of delivery. For hospitals on the variables routes, the day and amounts of deliveries cannot be guaranteed but are designed to attain targets over the six-day planning horizon. In the conditions of blood shortage encountered in Thailand, and commonly in other developing countries, it is often the case that hospitals request more blood than is needed, in the knowledge that only a proportion of all requests will be met. Our proposal is for blood supplies to be allocated and distributed to each hospital according to equitable targets based on historical demand data, calculated with regard to expected daily blood supplies. We suggest several policies that could be chosen by the decision makes for the daily distribution of blood. The different policies provide different trade-offs between transportation and holding costs. Variations in the costs of transportation, such as the price of petrol, could make different policies the most beneficial at different times. We present an application of the policies applied to a realistic case study in the RBC at Chiang Mai province which is located in Northern region of Thailand. The analysis includes a total of more than 110 hospitals, with 29 hospitals considered in the variable route. The study is expected to be a pilot for other regions of Thailand. Computational experiments are presented. Concluding remarks include the benefits gained by the online methods and future recommendations.

Keywords: online algorithm, blood distribution, developing country, insufficient blood supply

Procedia PDF Downloads 308

Authors: William Ho, Agus Wicaksana

Abstract:

Anecdotal evidence in the last decade shows that the occurrence of disruptive events and uncertainties in the supply chain is increasing. The coupling of these events with the nature of an increasingly complex and interdependent business environment leads to devastating impacts that quickly propagate within and across organizations. For example, the recent COVID-19 pandemic increased the global supply chain disruption frequency by at least 20% in 2020 and is projected to have an accumulative cost of $13.8 trillion by 2024. This crisis raises attention to organizational resilience to weather business uncertainty. However, the concept has been criticized for being vague and lacking a consistent definition, thus reducing the significance of the concept for practice and research. This study is intended to solve that issue by providing a comprehensive review of the conceptualization, measurement, and antecedents of operational resilience that have been discussed in the supply chain risk management literature (SCRM). We performed a Scholarly Network Analysis, combining citation-based and text-based approaches, on 252 articles published from 2000 to 2021 in top-tier journals based on three parameters: AJG ranking and ABS ranking, UT Dallas and FT50 list, and editorial board review. We utilized a hybrid scholarly network analysis by combining citation-based and text-based approaches to understand the conceptualization, measurement, and antecedents of operational resilience in the SCRM literature. Specifically, we employed a Bibliographic Coupling Analysis in the research cluster formation stage and a Co-words Analysis in the research cluster interpretation and analysis stage. Our analysis reveals three major research clusters of resilience research in the SCRM literature, namely (1) supply chain network design and optimization, (2) organizational capabilities, and (3) digital technologies. We portray the research process in the last two decades in terms of the exemplar studies, problems studied, commonly used approaches and theories, and solutions provided in each cluster. We then provide a conceptual framework on the conceptualization and antecedents of resilience based on studies in these clusters and highlight potential areas that need to be studied further. Finally, we leverage the concept of abnormal operating performance to propose a new measurement strategy for resilience. This measurement overcomes the limitation of most current measurements that are event-dependent and focus on the resistance or recovery stage - without capturing the growth stage. In conclusion, this study provides a robust literature review through a scholarly network analysis that increases the completeness and accuracy of research cluster identification and analysis to understand conceptualization, antecedents, and measurement of resilience. It also enables us to perform a comprehensive review of resilience research in SCRM literature by including research articles published during the pandemic and connects this development with a plethora of articles published in the last two decades. From the managerial perspective, this study provides practitioners with clarity on the conceptualization and critical success factors of firm resilience from the SCRM perspective.

Keywords: supply chain risk management, organizational resilience, scholarly network analysis, systematic literature review

Procedia PDF Downloads 50

Authors: Nynne Nørup, Julie Helles Eriksen, Rikke M. Moalem, Else Skjold

Abstract:

Despite a growing focus on the high environmental impact of textiles, textile waste is only recently considered as part of the waste field. Consequently, there is a general lack of knowledge and data within this field. Particularly the lack of a common perception of textiles generates several problems e.g., to recognize the full material potential the fraction contains, which is cruel if the textile must enter the circular economy. This study aims to qualify a method to make the resources in textile waste visible in a way that makes it possible to move them as high up in the waste hierarchy as possible. Textiles are complex and cover many different types of products, fibers and combinations of fibers and production methods. In garments alone, there is a great variety, even when narrowing it to only undergarments. However, textile waste is often reduced to one fraction, assessed solely by quantity, and compared to quantities of other waste fractions. Disregarding the complexity and reducing textiles to a single fraction that covers everything made of textiles increase the risk of neglecting the value of the materials, both with regards to their properties and economical. Instead of trying to fit textile waste into the current primarily linear waste system where volume is a key part of the business models, this study focused on integrating textile waste as a resource in the design and production phase. The study combined interdisciplinary methods for determining replacement rates used in Life Cycle Assessments and Mass Flow Analysis methods with the designer’s toolbox to hereby activate the properties of textile waste in a way that can unleash its potential optimally. It was hypothesized that by activating Denmark's tradition for design and high level of craftsmanship, it is possible to find solutions that can be used today and create circular resource models that reduce the use of virgin fibers. Through waste samples, case studies, and testing of various design approaches, this study explored how to functionalize the method so that the product after the end-use is kept as a material and only then processed at fiber level to obtain the best environmental utilization. The study showed that the designers' ability to decode the properties of the materials and understanding of craftsmanship were decisive for how well the materials could be utilized today. The later in the life cycle the textiles appeared as waste, the more demanding the description of the materials to be sufficient, especially if to achieve the best possible use of the resources and thus a higher replacement rate. In addition, it also required adaptation in relation to the current production because the materials often varied more. The study found good indications that part of the solution is to use geodata i.e., where in the life cycle the materials were discarded. An important conclusion is that a fully developed method can help support better utilization of textile resources. However, it stills requires a better understanding of materials by the designers, as well as structural changes in business and society.

Keywords: circular economy, development of sustainable processes, environmental impacts, environmental management of textiles, environmental sustainability through textile recycling, interdisciplinary method development, resource optimization, recycled textile materials and the evaluation of recycling, sustainability and recycling opportunities in the textile and apparel sector

Procedia PDF Downloads 56

Authors: Visith Siriphuwanun, Yodying Punjasawadwong, Suwinai Saengyo, Kittipan Rerkasem

Abstract:

Objective: To determine incidences and factors associated with perioperative cardiac arrest in trauma patients who received anesthesia for emergency surgery. Design and setting: Retrospective cohort study in trauma patients during anesthesia for emergency surgery at a university hospital in northern Thailand country. Patients and methods: This study was permitted by the medical ethical committee, Faculty of Medicine at Maharaj Nakorn Chiang Mai Hospital, Thailand. We clarified data of 19,683 trauma patients receiving anesthesia within a decade between January 2007 to March 2016. The data analyzed patient characteristics, traumas surgery procedures, anesthesia information such as ASA physical status classification, anesthesia techniques, anesthetic drugs, location of anesthesia performed, and cardiac arrest outcomes. This study excluded the data of trauma patients who had received local anesthesia by surgeons or monitoring anesthesia care (MAC) and the patient which missing more information. The factor associated with perioperative cardiac arrest was identified with univariate analyses. Multiple regressions model for risk ratio (RR) and 95% confidence intervals (CI) were used to conduct factors correlated with perioperative cardiac arrest. The multicollinearity of all variables was examined by bivariate correlation matrix. A stepwise algorithm was chosen at a p-value less than 0.02 was selected to further multivariate analysis. A P-value of less than 0.05 was concluded as statistically significant. Measurements and results: The occurrence of perioperative cardiac arrest in trauma patients receiving anesthesia for emergency surgery was 170.04 per 10,000 cases. Factors associated with perioperative cardiac arrest in trauma patients were age being more than 65 years (RR=1.41, CI=1.02–1.96, p=0.039), ASA physical status 3 or higher (RR=4.19–21.58, p < 0.001), sites of surgery (intracranial, intrathoracic, upper intra-abdominal, and major vascular, each p < 0.001), cardiopulmonary comorbidities (RR=1.55, CI=1.10–2.17, p < 0.012), hemodynamic instability with shock prior to receiving anesthesia (RR=1.60, CI=1.21–2.11, p < 0.001) , special techniques for surgery such as cardiopulmonary bypass (CPB) and hypotensive techniques (RR=5.55, CI=2.01–15.36, p=0.001; RR=6.24, CI=2.21–17.58, p=0.001, respectively), and patients who had a history of being alcoholic (RR=5.27, CI=4.09–6.79, p < 0.001). Conclusion: Incidence of perioperative cardiac arrest in trauma patients receiving anesthesia for emergency surgery was very high and correlated with many factors, especially age of patient and cardiopulmonary comorbidities, patient having a history of alcoholic addiction, increasing ASA physical status, preoperative shock, special techniques for surgery, and sites of surgery including brain, thorax, abdomen, and major vascular region. Anesthesiologists and multidisciplinary teams in pre- and perioperative periods should remain alert for warning signs of pre-cardiac arrest and be quick to manage the high-risk group of surgical trauma patients. Furthermore, a healthcare policy should be promoted for protecting against accidents in high-risk groups of the population as well.

Keywords: perioperative cardiac arrest, trauma patients, emergency surgery, anesthesia, factors risk, incidence

Procedia PDF Downloads 143

Authors: Julio Aguirre, Yohnny Campana, Elmer Guerrero, Daniel De La Torre Ugarte

Abstract:

A well-developed transportation network is a necessary condition for a country to derive full benefits from good trade and macroeconomic policies. Road infrastructure plays a key role in the economic development of rural areas of developing countries; where agriculture is the main economic activity. The ability to move agricultural production from the place of production to the market, and then to the place of consumption, greatly influence the economic value of farming activities, and of the resources involved in the production process, i.e., labor and land. Consequently, investment in transportation networks contributes to enhance or overcome the natural advantages or disadvantages that topography and location have imposed over the agricultural sector. This is of particular importance when dealing with countries, like Peru, with a great topographic diversity. The objective of this research is to estimate the impacts of road infrastructure on the performance of the agricultural sector. Specific variables of interest are changes in travel time, shifts of production for self-consumption to production for the market, changes in farmers income, and impacts on the diversification of the agricultural sector. In the study, a cross-section model with instrumental variables is the central methodological instrument. The data is obtained from agricultural and transport geo-referenced databases, and the instrumental variable specification utilized is based on the Kruskal algorithm. The results show that the expansion of road connectivity reduced farmers' travel time by an average of 3.1 hours and the proportion of output sold in the market increases by up to 40 percentage points. The increase in connectivity has an unexpected increase in the districts index of diversification of agricultural production. The results are robust to the inclusion of year and region fixed-effects, and to control for geography (i.e., slope and altitude), population variables, and mining activity. Other results are also very eloquent. For example, a clear positive impact can be seen in access to local markets, but this does not necessarily correlate with an increase in the production of the sector. This can be explained by the fact that agricultural development not only requires provision of roads but additional complementary infrastructure and investments intended to provide the necessary conditions so that producers can offer quality products (improved management practices, timely maintenance of irrigation infrastructure, transparent management of water rights, among other factors). Therefore, complementary public goods are needed to enhance the effects of roads on the welfare of the population, beyond enabling them to increase their access to markets.

Keywords: agriculture devolepment, market access, road connectivity, regional development

Procedia PDF Downloads 174

Authors: Goutam Kumar Ghorai, Sandip Sadhukhan, Arpita Sarkar, Debprasad Sinha, G. Sarkar, Ashis K. Dhara

Abstract:

Diabetes mellitus is one of the most common chronic diseases in all countries and continues to increase in numbers significantly. Diabetic retinopathy (DR) is damage to the retina that occurs with long-term diabetes. DR is a major cause of blindness in the Indian population. Therefore, its early diagnosis is of utmost importance towards preventing progression towards imminent irreversible loss of vision, particularly in the huge population across rural India. The barriers to eye examination of all diabetic patients are socioeconomic factors, lack of referrals, poor access to the healthcare system, lack of knowledge, insufficient number of ophthalmologists, and lack of networking between physicians, diabetologists and ophthalmologists. A few diabetic patients often visit a healthcare facility for their general checkup, but their eye condition remains largely undetected until the patient is symptomatic. This work aims to focus on the design and development of a fully automated intelligent decision system for screening retinal fundus images towards detection of the pathophysiology caused by microaneurysm in the early stage of the diseases. Automated detection of microaneurysm is a challenging problem due to the variation in color and the variation introduced by the field of view, inhomogeneous illumination, and pathological abnormalities. We have developed aconvolutional neural network for efficient detection of microaneurysm. A loss function is also developed to handle severe class imbalance due to very small size of microaneurysms compared to background. The network is able to locate the salient region containing microaneurysms in case of noisy images captured by non-mydriatic cameras. The ground truth of microaneurysms is created by expert ophthalmologists for MESSIDOR database as well as private database, collected from Indian patients. The network is trained from scratch using the fundus images of MESSIDOR database. The proposed method is evaluated on DIARETDB1 and the private database. The method is successful in detection of microaneurysms for dilated and non-dilated types of fundus images acquired from different medical centres. The proposed algorithm could be used for development of AI based affordable and accessible system, to provide service at grass root-level primary healthcare units spread across the country to cater to the need of the rural people unaware of the severe impact of DR.

Keywords: retinal fundus image, deep convolutional neural network, early detection of microaneurysms, screening of diabetic retinopathy

Procedia PDF Downloads 109

Authors: Ayman Hijazi, Witold Kwapinski, J. J. Leahy

Abstract:

Finding a sustainable alternative energy to fossil fuel is an urgent need as various environmental challenges in the world arise. Therefore, formic acid (FA) decomposition has been an attractive field that lies at the center of biomass platform, comprising a potential pool of hydrogen energy that stands as a new energy vector. Liquid FA features considerable volumetric energy density of 6.4 MJ/L and a specific energy density of 5.3 MJ/Kg that qualifies it in the prime seat as an energy source for transportation infrastructure. Additionally, the increasing research interest in FA decomposition is driven by the need of in-situ H2 production, which plays a key role in the hydrogenation reactions of biomass into higher value components. It is reported elsewhere in literature that catalytic decomposition of FA is usually performed in poorly designed setup using simple glassware under magnetic stirring, thus demanding further energy investment to retain the used catalyst. it work suggests an approach that integrates designing a novel catalyst featuring magnetic property with a robust setup that minimizes experimental & measurement discrepancies. One of the most prominent active species for dehydrogenation/hydrogenation of biomass compounds is palladium. Accordingly, we investigate the potential of engrafting palladium metal onto functionalized magnetic nanoparticles as a heterogeneous catalyst to favor the production of CO-free H2 gas from FA. Using ordinary magnet to collect the spent catalyst renders core-shell magnetic nanoparticles as the backbone of the process. Catalytic experiments were performed in a jacketed batch reactor equipped with an overhead stirrer under inert medium. Through a novel approach, FA is charged into the reactor via high-pressure positive displacement pump at steady state conditions. The produced gas (H2+CO2) was measured by connecting the gas outlet to a measuring system based on the amount of the displaced water. The novelty of this work lies in designing a very responsive catalyst, pumping consistent amount of FA into a sealed reactor running at steady state mild temperatures, and continuous gas measurement, along with collecting the used catalyst without the need for centrifugation. Catalyst characterization using TEM, XRD, SEM, and CHN elemental analyzer provided us with details of catalyst preparation and facilitated new venues to alter the nanostructure of the catalyst framework. Consequently, the introduction of amine groups has led to appreciable improvements in terms of dispersion of the doped metals and eventually attaining nearly complete conversion (100%) of FA after 7 hours. The relative importance of the process parameters such as temperature (35-85°C), stirring speed (150-450rpm), catalyst loading (50-200mgr.), and Pd doping ratio (0.75-1.80wt.%) on gas yield was assessed by a Taguchi design-of-experiment based model. Experimental results showed that operating at lower temperature range (35-50°C) yielded more gas while the catalyst loading and Pd doping wt.% were found to be the most significant factors with a P-values 0.026 & 0.031, respectively.

Keywords: formic acid decomposition, green catalysis, hydrogen, mesoporous silica, process optimization, nanoparticles

Procedia PDF Downloads 19

Authors: Zheng Wang, Zhenhong Li, Jon Mills

Abstract:

Ground-based synthetic aperture radar (GBSAR) represents a powerful remote sensing tool for deformation monitoring towards various geohazards, e.g. landslides, mudflows, avalanches, infrastructure failures, and the subsidence of residential areas. Unlike spaceborne SAR with a fixed revisit period, GBSAR data can be acquired with an adjustable temporal resolution through either continuous or discontinuous operation. However, challenges arise from processing high temporal-resolution continuous GBSAR data, including the extreme cost of computational random-access-memory (RAM), the delay of displacement maps, and the loss of temporal evolution. Moreover, repositioning errors between discontinuous campaigns impede the accurate measurement of surface displacements. Therefore, a versatile package with two complete chains is developed in this study in order to process both continuous and discontinuous GBSAR data and address the aforementioned issues. The first chain is based on a small-baseline subset concept and it processes continuous GBSAR images unit by unit. Images within a window form a basic unit. By taking this strategy, the RAM requirement is reduced to only one unit of images and the chain can theoretically process an infinite number of images. The evolution of surface displacements can be detected as it keeps temporarily-coherent pixels which are present only in some certain units but not in the whole observation period. The chain supports real-time processing of the continuous data and the delay of creating displacement maps can be shortened without waiting for the entire dataset. The other chain aims to measure deformation between discontinuous campaigns. Temporal averaging is carried out on a stack of images in a single campaign in order to improve the signal-to-noise ratio of discontinuous data and minimise the loss of coherence. The temporal-averaged images are then processed by a particular interferometry procedure integrated with advanced interferometric SAR algorithms such as robust coherence estimation, non-local filtering, and selection of partially-coherent pixels. Experiments are conducted using both synthetic and real-world GBSAR data. Displacement time series at the level of a few sub-millimetres are achieved in several applications (e.g. a coastal cliff, a sand dune, a bridge, and a residential area), indicating the feasibility of the developed GBSAR data processing package for deformation monitoring of a wide range of scientific and practical applications.

Keywords: ground-based synthetic aperture radar, interferometry, small baseline subset algorithm, deformation monitoring

Procedia PDF Downloads 134

Authors: Ravija Gunawardana, Banuka Athuraliya

Abstract:

Machine learning has emerged as a powerful tool for disease diagnosis and prediction. The use of machine learning algorithms has the potential to improve the accuracy of disease prediction, thereby enabling medical professionals to provide more effective and personalized treatments. This study focuses on developing a machine-learning model for disease prediction using symptoms and X-ray images. The importance of this study lies in its potential to assist medical professionals in accurately diagnosing diseases, thereby improving patient outcomes. Respiratory diseases are a significant cause of morbidity and mortality worldwide, and chest X-rays are commonly used in the diagnosis of these diseases. However, accurately interpreting X-ray images requires significant expertise and can be time-consuming, making it difficult to diagnose respiratory diseases in a timely manner. By incorporating machine learning algorithms, we can significantly enhance disease prediction accuracy, ultimately leading to better patient care. The study utilized the Mask R-CNN algorithm, which is a state-of-the-art method for object detection and segmentation in images, to process chest X-ray images. The model was trained and tested on a large dataset of patient information, which included both symptom data and X-ray images. The performance of the model was evaluated using a range of metrics, including accuracy, precision, recall, and F1-score. The results showed that the model achieved an accuracy rate of over 90%, indicating that it was able to accurately detect and segment regions of interest in the X-ray images. In addition to X-ray images, the study also incorporated symptoms as input data for disease prediction. The study used three different classifiers, namely Random Forest, K-Nearest Neighbor and Support Vector Machine, to predict diseases based on symptoms. These classifiers were trained and tested using the same dataset of patient information as the X-ray model. The results showed promising accuracy rates for predicting diseases using symptoms, with the ensemble learning techniques significantly improving the accuracy of disease prediction. The study's findings indicate that the use of machine learning algorithms can significantly enhance disease prediction accuracy, ultimately leading to better patient care. The model developed in this study has the potential to assist medical professionals in diagnosing respiratory diseases more accurately and efficiently. However, it is important to note that the accuracy of the model can be affected by several factors, including the quality of the X-ray images, the size of the dataset used for training, and the complexity of the disease being diagnosed. In conclusion, the study demonstrated the potential of machine learning algorithms for disease prediction using symptoms and X-ray images. The use of these algorithms can improve the accuracy of disease diagnosis, ultimately leading to better patient care. Further research is needed to validate the model's accuracy and effectiveness in a clinical setting and to expand its application to other diseases.

Keywords: K-nearest neighbor, mask R-CNN, random forest, support vector machine

Procedia PDF Downloads 109

Authors: Marcela De Oliveira, Marina P. Da Silva, Fernando C. G. Da Rocha, Jorge M. Santos, Jaime S. Cardoso, Paulo N. Lisboa-Filho

Abstract:

Multiple Sclerosis (MS) is an immune-mediated disease of the central nervous system characterized by neurodegeneration, inflammation, demyelination, and axonal loss. Magnetic resonance imaging (MRI), due to the richness in the information details provided, is the gold standard exam for diagnosis and follow-up of neurodegenerative diseases, such as MS. Brain atrophy, the gradual loss of brain volume, is quite extensive in multiple sclerosis, nearly 0.5-1.35% per year, far off the limits of normal aging. Thus, the brain volume quantification becomes an essential task for future analysis of the occurrence atrophy. The analysis of MRI has become a tedious and complex task for clinicians, who have to manually extract important information. This manual analysis is prone to errors and is time consuming due to various intra- and inter-operator variability. Nowadays, computerized methods for MRI segmentation have been extensively used to assist doctors in quantitative analyzes for disease diagnosis and monitoring. Thus, the purpose of this work was to evaluate the brain volume in MRI of MS patients. We used MRI scans with 30 slices of the five patients diagnosed with multiple sclerosis according to the McDonald criteria. The computational methods for the analysis of images were carried out in two steps: segmentation of the brain and brain volume quantification. The first image processing step was to perform brain extraction by skull stripping from the original image. In the skull stripper for MRI images of the brain, the algorithm registers a grayscale atlas image to the grayscale patient image. The associated brain mask is propagated using the registration transformation. Then this mask is eroded and used for a refined brain extraction based on level-sets (edge of the brain-skull border with dedicated expansion, curvature, and advection terms). In the second step, the brain volume quantification was performed by counting the voxels belonging to the segmentation mask and converted in cc. We observed an average brain volume of 1469.5 cc. We concluded that the automatic method applied in this work can be used for the brain extraction process and brain volume quantification in MRI. The development and use of computer programs can contribute to assist health professionals in the diagnosis and monitoring of patients with neurodegenerative diseases. In future works, we expect to implement more automated methods for the assessment of cerebral atrophy and brain lesions quantification, including machine-learning approaches. Acknowledgements: This work was supported by a grant from Brazilian agency Fundação de Amparo à Pesquisa do Estado de São Paulo (number 2019/16362-5).

Keywords: brain volume, magnetic resonance imaging, multiple sclerosis, skull stripper

Procedia PDF Downloads 108

Authors: Carlos E. Rodríguez-Rodríguez, Karla Ruiz-Hidalgo, Kattia Madrigal-Zúñiga, Juan Salvador Chin-Pampillo, Mario Masís-Mora, Elizabeth Carazo-Rojas

Abstract:

One of the main causes of contamination linked to agricultural activities is the spillage and disposal of pesticides, especially during the loading, mixing or cleaning of agricultural spraying equipment. One improvement in the handling of pesticides is the use of biopurification systems (BPS), simple and cheap degradation devices where the pesticides are biologically degraded at accelerated rates. The biologically active core of BPS is the biomixture, which is constituted by soil pre-exposed to the target pesticide, a lignocellulosic substrate to promote the activity of ligninolitic fungi and a humic component (peat or compost), mixed at a volumetric proportion of 50:25:25. Considering the known ability of lignocellulosic fungi to degrade a wide range of organic pollutants, and the high amount of lignocellulosic waste used in biomixture preparation, the bioaugmentation of biomixtures with these fungi represents an interesting approach for improving biomixtures. The present work aimed at evaluating the effect of the bioaugmentation of rice husk based biomixtures with the fungus Trametes versicolor in the removal of the insectice/nematicide carbofuran (CFN) and to optimize the composition of the biomixture to obtain the best performance in terms of CFN removal and mineralization, reduction in formation of transformation products and decrease in residual toxicity of the matrix. The evaluation of several lignocellulosic residues (rice husk, wood chips, coconut fiber, sugarcane bagasse or newspaper print) revealed the best colonization by T. versicolor in rice husk. Pre-colonized rice husk was then used in the bioaugmentation of biomixtures also containing soil pre-exposed to CFN and either peat (GTS biomixture) or compost (GCS biomixture). After spiking with 10 mg/kg CBF, the efficiency of the biomixture was evaluated through a multi-component approach that included: monitoring of CBF removal and production of CBF transformation products, mineralization of radioisotopically labeled carbofuran (14C-CBF) and changes in the toxicity of the matrix after the treatment (Daphnia magna acute immobilization test). Estimated half-lives of CBF in the biomixtures were 3.4 d and 8.1 d in GTS and GCS, respectively. The transformation products 3-hydroxycarbofuran and 3-ketocarbofuran were detected at the moment of CFN application, however their concentration continuously disappeared. Mineralization of 14C-CFN was also faster in GTS than GCS. The toxicological evaluation showed a complete toxicity removal in the biomixtures after 48 d of treatment. The composition of the GCS biomixture was optimized using a central composite design and response surface methodology. The design variables were the volumetric content of fungally pre-colonized rice husk and the volumetric ratio compost/soil. According to the response models, maximization of CFN removal and mineralization rate, and minimization in the accumulation of transformation products were obtained with an optimized biomixture of composition 30:43:27 (pre-colonized rice husk:compost:soil), which differs from the 50:25:25 composition commonly employed in BPS. Results suggest that fungal bioaugmentation may enhance the performance of biomixtures in CFN removal. Optimization reveals the importance of assessing new biomixture formulations in order to maximize their performance.

Keywords: bioaugmentation, biopurification systems, degradation, fungi, pesticides, toxicity

Procedia PDF Downloads 287

Authors: Amir E. Amirzadeh, Richard K. Strand

Abstract:

Moisture-induced problems are a significant concern for building owners, architects, construction managers, and building engineers, as they can have substantial impacts on building enclosures' durability and performance. Computational analyses, such as hygrothermal and thermal analysis, can provide valuable information and demonstrate the expected relative performance of building enclosure systems but are not grounded in absolute certainty. This paper evaluates the hygrothermal performance of common enclosure systems in hospitals in cold climates. The study aims to investigate the impact of exterior wall systems on hospitals, focusing on factors such as durability, construction deficiencies, and energy performance. The study primarily examines the impact of air leakage and vapor retarding layers relative to energy consumption. While these factors have been studied in residential and commercial buildings, there is a lack of information on their impact on hospitals in a holistic context. The study integrates various research studies and professional experience in hospital building design to achieve its objective. The methodology involves surveying and observing exterior wall assemblies, reviewing common exterior wall assemblies and details used in hospital construction, performing simulations and numerical analyses of various variables, validating the model and mechanism using available data from industry and academia, visualizing the outcomes of the analysis, and developing a mechanism to demonstrate the relative performance of exterior wall systems for hospitals under specific conditions. The data sources include case studies from real-world projects and peer-reviewed articles, industry standards, and practices. This research intends to integrate and analyze the in-situ and as-designed performance and durability of building enclosure assemblies with numerical analysis. The study's primary objective is to provide a clear and precise roadmap to better visualize and comprehend the correlation between the durability and performance of common exterior wall systems used in the construction of hospitals and the energy consumption of these buildings under certain static and dynamic conditions. As the construction of new hospitals and renovation of existing ones have grown over the last few years, it is crucial to understand the effect of poor detailing or construction deficiencies on building enclosure systems' performance and durability in healthcare buildings. This study aims to assist stakeholders involved in hospital design, construction, and maintenance in selecting durable and high-performing wall systems. It highlights the importance of early design evaluation, regular quality control during the construction of hospitals, and understanding the potential impacts of improper and inconsistent maintenance and operation practices on occupants, owner, building enclosure systems, and Heating, Ventilation, and Air Conditioning (HVAC) systems, even if they are designed to meet the project requirements.

Keywords: hygrothermal analysis, building enclosure, hospitals, energy efficiency, optimization and visualization, uncertainty and decision making

Procedia PDF Downloads 41

Authors: Kaustav Mukherjee

Abstract:

In this paper we propose a Neural Style Transfer solution whereby we have created a Lightweight Separable Convolution Kernel Based GAN Architecture (SC-GAN) which will very useful for designing filter for Mobile Phone Cameras and also Edge Devices which will convert any image to its 2D ANIMATED COMIC STYLE Movies like HEMAN, SUPERMAN, JUNGLE-BOOK. This will help the 2D animation artist by relieving to create new characters from real life person's images without having to go for endless hours of manual labour drawing each and every pose of a cartoon. It can even be used to create scenes from real life images.This will reduce a huge amount of turn around time to make 2D animated movies and decrease cost in terms of manpower and time. In addition to that being extreme light-weight it can be used as camera filters capable of taking Comic Style Shots using mobile phone camera or edge device cameras like Raspberry Pi 4,NVIDIA Jetson NANO etc. Existing Methods like CartoonGAN with the model size close to 170 MB is too heavy weight for mobile phones and edge devices due to their scarcity in resources. Compared to the current state of the art our proposed method which has a total model size of 31 MB which clearly makes it ideal and ultra-efficient for designing of camera filters on low resource devices like mobile phones, tablets and edge devices running OS or RTOS. .Owing to use of high resolution input and usage of bigger convolution kernel size it produces richer resolution Comic-Style Pictures implementation with 6 times lesser number of parameters and with just 25 extra epoch trained on a dataset of less than 1000 which breaks the myth that all GAN need mammoth amount of data. Our network reduces the density of the Gan architecture by using Depthwise Separable Convolution which does the convolution operation on each of the RGB channels separately then we use a Point-Wise Convolution to bring back the network into required channel number using 1 by 1 kernel.This reduces the number of parameters substantially and makes it extreme light-weight and suitable for mobile phones and edge devices. The architecture mentioned in the present paper make use of Parameterised Batch Normalization Goodfellow etc al. (Deep Learning OPTIMIZATION FOR TRAINING DEEP MODELS page 320) which makes the network to use the advantage of Batch Norm for easier training while maintaining the non-linear feature capture by inducing the learnable parameters

Keywords: comic stylisation from camera image using GAN, creating 2D animated movie style custom stickers from images, depth-wise separable convolutional neural network for light-weight GAN architecture for EDGE devices, GAN architecture for 2D animated cartoonizing neural style, neural style transfer for edge, model distilation, perceptual loss

Procedia PDF Downloads 100

Authors: Ayman Hijazi, Witold Kwapinski, J. J. Leahy

Abstract:

Finding a sustainable alternative energy to fossil fuel is an urgent need as various environmental challenges in the world arise. Therefore, formic acid (FA) decomposition has been an attractive field that lies at the center of the biomass platform, comprising a potential pool of hydrogen energy that stands as a distinct energy vector. Liquid FA features considerable volumetric energy density of 6.4 MJ/L and a specific energy density of 5.3 MJ/Kg that qualifies it in the prime seat as an energy source for transportation infrastructure. Additionally, the increasing research interest in FA decomposition is driven by the need for in-situ H₂ production, which plays a key role in the hydrogenation reactions of biomass into higher-value components. It is reported elsewhere in the literature that catalytic decomposition of FA is usually performed in poorly designed setups using simple glassware under magnetic stirring, thus demanding further energy investment to retain the used catalyst. Our work suggests an approach that integrates designing a distinct catalyst featuring magnetic properties with a robust setup that minimizes experimental & measurement discrepancies. One of the most prominent active species for the dehydrogenation/hydrogenation of biomass compounds is palladium. Accordingly, we investigate the potential of engrafting palladium metal onto functionalized magnetic nanoparticles as a heterogeneous catalyst to favor the production of CO-free H₂ gas from FA. Using an ordinary magnet to collect the spent catalyst renders core-shell magnetic nanoparticles as the backbone of the process. Catalytic experiments were performed in a jacketed batch reactor equipped with an overhead stirrer under an inert medium. Through a distinct approach, FA is charged into the reactor via a high-pressure positive displacement pump at steady-state conditions. The produced gas (H₂+CO₂) was measured by connecting the gas outlet to a measuring system based on the amount of the displaced water. The uniqueness of this work lies in designing a very responsive catalyst, pumping a consistent amount of FA into a sealed reactor running at steady-state mild temperatures, and continuous gas measurement, along with collecting the used catalyst without the need for centrifugation. Catalyst characterization using TEM, XRD, SEM, and CHN elemental analyzer provided us with details of catalyst preparation and facilitated new venues to alter the nanostructure of the catalyst framework. Consequently, the introduction of amine groups has led to appreciable improvements in terms of dispersion of the doped metals and eventually attaining nearly complete conversion (100%) of FA after 7 hours. The relative importance of the process parameters such as temperature (35-85°C), stirring speed (150-450rpm), catalyst loading (50-200mgr.), and Pd doping ratio (0.75-1.80wt.%) on gas yield was assessed by a Taguchi design-of-experiment based model. Experimental results showed that operating at a lower temperature range (35-50°C) yielded more gas, while the catalyst loading and Pd doping wt.% were found to be the most significant factors with P-values 0.026 & 0.031, respectively.

Keywords: formic acid decomposition, green catalysis, hydrogen, mesoporous silica, process optimization, nanoparticles

Procedia PDF Downloads 18

Authors: Jollen Dai, Truman You, Xinyun Du, Katrina Liu

Abstract:

Transportation is one of the leading sources of greenhouse gas emissions (GHG). Thus, to meet the Paris Agreement 2015, all countries must adopt a different and more sustainable transportation system. From bikes to Maglev, the world is slowly shifting to sustainable transportation. To develop a utility public transit system, a sustainable web of buses must be implemented. As of now, only a handful of cities have adopted a detailed plan to implement a full fleet of e-buses by the 2030s, with Shenzhen in the lead. Every change requires a detailed plan and a focused analysis of the impacts of the change. In this report, the economic implications and financial implications have been taken into consideration to develop a well-rounded 10-year plan for New York City. We also apply the same financial model to the other cities, LA and Chicago. We picked NYC, Chicago, and LA to conduct the comparative NPB analysis since they are all big metropolitan cities and have complex transportation systems. All three cities have started an action plan to achieve a full fleet of e-bus in the decades. Plus, their energy carbon footprint and their energy price are very different, which are the key factors to the benefits of electric buses. Using TCO (Total Cost Ownership) financial analysis, we developed a model to calculate NPB (Net Present Benefit) /and compare EBS (electric buses) to DBS (diesel buses). We have considered all essential aspects in our model: initial investment, including the cost of a bus, charger, and installation, government fund (federal, state, local), labor cost, energy (electricity or diesel) cost, maintenance cost, insurance cost, health and environment benefit, and V2G (vehicle to grid) benefit. We see about $1,400,000 in benefits for a 12-year lifetime of an EBS compared to DBS provided the government fund to offset 50% of EBS purchase cost. With the government subsidy, an EBS starts to make positive cash flow in 5th year and can pay back its investment in 5 years. Please remember that in our model, we consider environmental and health benefits, and every year, $50,000 is counted as health benefits per bus. Besides health benefits, the significant benefits come from the energy cost savings and maintenance savings, which are about $600,000 and $200,000 in 12-year life cycle. Using linear regression, given certain budget limitations, we then designed an optimal three-phase process to replace all NYC electric buses in 10 years, i.e., by 2033. The linear regression process is to minimize the total cost over the years and have the lowest environmental cost. The overall benefits to replace all DBS with EBS for NYC is over $2.1 billion by the year of 2033. For LA, and Chicago, the benefits for electrification of the current bus fleet are $1.04 billion and $634 million by 2033. All NPB analyses and the algorithm to optimize the electrification phase process are implemented in Python code and can be shared.

Keywords: financial modeling, total cost ownership, net present benefits, electric bus, diesel bus, NYC, LA, Chicago

Procedia PDF Downloads 16