Search results for: variable refrigerant flow heat pump

Authors: Simone Rondelli, Damiano Rondelli, Bishesh Poudyal, Juan Jose Cabrera-Lazarini

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One of the main obstacles for Social Entrepreneurship is to find a business model that is financially sustainable. In other words, the captured value generates enough cash flow to ensure business continuity and reinvestment for growth. Providing Health Services in poor countries for the uninsured population affected by a high-cost chronical disease is not the exception for this challenge. As a prime example, cancer has become a high impact on a global disease not only because of the high morbidity but also of the financial impact on both the patient family and health services in underdeveloped countries. Therefore, it is relevant to find a Social Entrepreneurship Model that provides affordable treatment for this disease while maintaining healthy finances not only for the patient but also for the organization providing the treatment. Using the methodology of Constructive Research, this paper applied a Positive Theory and four business models of Social Entrepreneurship to a case of a Private Foundation model whose mission is to address the challenge previously described. It was found that the Foundation analyzed, in this case, is organized as an Embedded Business Model and complies with the four propositions of the Positive Theory considered. It is recommended for this Private Foundation to explore implementing the Integrated Business Model to ensure more robust sustainability in the long term. It evolves as a scalable model that can attract investors interested in contributing to expanding this initiative globally.

Keywords: affordable treatment, global healthcare, social entrepreneurship theory, sustainable business model

Procedia PDF Downloads 148

Authors: Shalini TV, Gangadharan GG, Sriranjini S Jaideep, ASN Seshasayee, Awadhesh Pandit

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Introduction: Prakriti (body-mind constitution of an individual) is a conventional, customized and unique understanding of which is essential for the personalized medicine described in Ayurveda, Indian System of Medicine. Based on the Doshas( functional, bio humoral unit in the body), individuals are categorized into three major Prakriti- Vata, Pitta, and Kapha. The human gut microbiome hosts plenty of highly diverse and metabolically active microorganisms, mainly dominated by the bacteria, which are known to influence the physiology of an individual. Few researches have shown the correlation between the Prakriti and the biochemical parameters. In this study, an attempt was made to explore any correlation between the Prakriti (phenotype of an individual) with the Genetic makeup of the gut microbiome in healthy individuals. Materials and methods: 270 multi-ethnic, healthy volunteers of both sex with the age group between 18 to 40 years, with no history of antibiotics in the last 6 months were recruited into three groups of Vata, Pitta, and Kapha. The Prakriti of the individual was determined using Ayusoft, a software designed by CDAC, Pune, India. The volunteers were subjected to initial screening for the assessment of their height, weight, Body Mass Index, Vital signs and Blood investigations to ensure they are healthy. The stool and saliva samples of the recruited volunteers were collected as per the standard operating procedure developed, and the bacterial DNA was isolated using Qiagen kits. The extracted DNA was subjected to 16s rRNA sequencing using the Illumina kits. The sequencing libraries are targeting the variable V3 and V4 regions of the 16s rRNA gene. Paired sequencing was done on the MiSeq system and data were analyzed using the CLC Genomics workbench 11. Results: The 16s rRNA sequencing of the V3 and V4 regions showed a diverse pattern in both the oral and stool microbial DNA. The study did not reveal any specific pattern of bacterial flora amongst the Prakriti. All the p-values were more than the effective alpha values for all OTUs in both the buccal cavity and stool samples. Therefore, there was no observed significant enrichment of an OTU in the patient samples from either the buccal cavity or stool samples. Conclusion: In healthy volunteers of multi-ethnicity, due to the influence of the various factors, the correlation between the Prakriti and the gut microbiome was not seen.

Keywords: gut microbiome, ayurveda Prakriti, sequencing, multi-ethnic urban population

Procedia PDF Downloads 138

Authors: Xiaoxue Jin, Qiong Zhang

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The rapid influx and ongoing flow of young migrants in large cities, alongside the emergence and evolution of new social media, have led to increased interpersonal alienation and weakened place attachment. In the interplay between individuals and space, urban public spaces play a pivotal role in meeting the multifaceted needs of individuals and fostering a sense of attachment. This article aims to investigate the relationship between the place characteristics of public spaces and individuals' needs and perceptions, with an aim to identify the factors influencing place attachment among the youth. This study is conducted in the Shekou community of Shenzhen, focusing on the youth residents to evaluate their place attachment levels and to analyze their perceptions of the place characteristics of selected public spaces. The influencing factors of public spaces on place attachment were sorted out through detailed data analysis. Research has found that rapid urbanization has led to spatial homogenization and spatial segregation caused by uneven resource distribution, which in turn diminishes the utilization of public spaces. The social characteristics of public spaces, such as the quality of social activities and spatial openness, are critical in forming place attachment. In this research, place characteristics impacting place attachment are categorized, aiming to reconstruct the characteristics of public space places and use them as a medium to explore the place attachment of young people, promote their independent creation and participation in public life, and enhance the dynamism between individuals and spaces.

Keywords: place attachment, place characteristics, public spaces, spatial perception

Procedia PDF Downloads 54

Authors: Soheila Sadeghi

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Construction projects are complex and often subject to significant cost overruns due to the multifaceted nature of the activities involved. Accurate cost estimation is crucial for effective budget planning and resource allocation. Traditional methods for predicting overruns often rely on expert judgment or analysis of historical data, which can be time-consuming, subjective, and may fail to consider important factors. However, with the increasing availability of data from construction projects, machine learning techniques can be leveraged to improve the accuracy of overrun predictions. This study applied machine learning algorithms to enhance the prediction of cost overruns in a case study of a construction project. The methodology involved the development and evaluation of two machine learning models: Random Forest and Neural Networks. Random Forest can handle high-dimensional data, capture complex relationships, and provide feature importance estimates. Neural Networks, particularly Deep Neural Networks (DNNs), are capable of automatically learning and modeling complex, non-linear relationships between input features and the target variable. These models can adapt to new data, reduce human bias, and uncover hidden patterns in the dataset. The findings of this study demonstrate that both Random Forest and Neural Networks can significantly improve the accuracy of cost overrun predictions compared to traditional methods. The Random Forest model also identified key cost drivers and risk factors, such as changes in the scope of work and delays in material delivery, which can inform better project risk management. However, the study acknowledges several limitations. First, the findings are based on a single construction project, which may limit the generalizability of the results to other projects or contexts. Second, the dataset, although comprehensive, may not capture all relevant factors influencing cost overruns, such as external economic conditions or political factors. Third, the study focuses primarily on cost overruns, while schedule overruns are not explicitly addressed. Future research should explore the application of machine learning techniques to a broader range of projects, incorporate additional data sources, and investigate the prediction of both cost and schedule overruns simultaneously.

Keywords: cost prediction, machine learning, project management, random forest, neural networks

Procedia PDF Downloads 65

Authors: Nattaporn Khanoonkon, Rangrong Yoksan, Amod A. Ogale

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Polyethylene (PE) is one of the most petroleum-based thermoplastic materials used in many applications including packaging due to its cheap, light-weight, chemically inert and capable to be converted into various shapes and sizes of products. Although PE is a commercially potential material, its non-biodegradability caused environmental problems. At present, bio-based polymers become more interesting owing to its bio-degradability, non-toxicity, and renewability as well as being eco-friendly. Thermoplastic starch (TPS) is a bio-based and biodegradable plastic produced from the plasticization of starch under applying heat and shear force. In many researches, TPS was blended with petroleum-based polymers including PE in order to reduce the cost and the use of those polymers. However, the phase separation between hydrophobic PE and hydrophilic TPS limited the amount of TPS incorporated. The immiscibility of two different polarity polymers can be diminished by adding compatibilizer. PE-based compatibilizers, e.g. polyethylene-grafted-maleic anhydride, polyethylene-co-vinyl alcohol, etc. have been applied for the PE/TPS blend system in order to improve their miscibility. Until now, there is no report about the utilization of starch-based compatibilizer for PE/TPS blend system. The aims of the present research were therefore to synthesize a new starch-based compatibilizer, i.e. stearic acid-grafted starch (SA-g-starch) and to study the effect of SA-g-starch on chemical interaction, morphological properties, tensile properties and water vapor as well as oxygen barrier properties of the PE/TPS blend films. PE/TPS blends without and with incorporating SA-g-starch with a content of 1, 3 and 5 part(s) per hundred parts of starch (phr) were prepared using a twin screw extruder and then blown into films using a film blowing machine. Incorporating 1 phr and 3 phr of SA-g-starch could improve miscibility of the two polymers as confirmed from the reduction of TPS phase size and the good dispersion of TPS phase in PE matrix. In addition, the blend containing SA-g-starch with contents of 1 phr and 3 phr exhibited higher tensile strength and extensibility, as well as lower water vapor and oxygen permeabilities than the naked blend. The above results suggested that SA-g-starch could be potentially applied as a compatibilizer for the PE/TPS blend system.

Keywords: blend, compatibilizer, polyethylene, thermoplastic starch

Procedia PDF Downloads 441

Authors: Yi-Chun Huang, Kuan-Yun Chen, Chuang-Hung Lin

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Urban Green Belt and Blue is a part of the city landscape, it is an important constituent element of the urban environment and appearance. The Hsinchu East Gate Moat is situated in the center of the city, which not only has a wealth of historical and cultural resources, but also combines the Green Belt and the Blue Belt qualities at the same time. The Moat runs more than a thousand meters through the vital Green Belt and the Blue Belt in downtown, and each section is presented in different qualities of moat from south to north. The water area and the green belt of surroundings are presented linear and banded spread. The water body and the rich diverse river banks form an urban green belt of rich layers. The watercourse with green belt design lets users have connections with blue belts in different ways; therefore, the integration of Hsinchu East Gate and moat have become one of the unique urban landscapes in Taiwan. The study is based on the fact-finding case of Hsinchu East Gate Moat where situated in northern Taiwan, to research the impact between the SVF variation of the city and spatial sequence of Urban Green Belt and Blue landscape and visual analysis by constituent cross-section, and then comparing the influence of different leaf area index – the variable ecological factors to the degree of enclosure. We proceed to survey the landscape design of open space, to measure existing structural features of the plant canopy which contain the height of plants and branches, the crown diameter, breast-height diameter through access to diagram of Geographic Information Systems (GIS) and on-the-spot actual measurement. The north and south districts of blue green belt areas are divided 20 meters into a unit from East Gate Roundabout as the epicenter, and to set up a survey points to measure the SVF above the survey points; then we proceed to quantitative analysis from the data to calculate open landscape degree of enclosure. The results can be reference for the composition of future river landscape and the practical operation for dynamic space planning of blue and green belt landscape.

Keywords: sky view factor, degree of enclosure, spatial sequence, leaf area indices

Procedia PDF Downloads 557

Authors: Hanifa Taher, Sulaiman Al-Zuhair, Ali H. Al-Marzouqi, Yousef Haik, Mohammed Farid

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Biodiesel, as an alternative renewable fuel, has been receiving increasing attention due to the limited supply of fossil fuels and the increasing need for energy. Microalgae is a promising source for lipids, which can be converted to biodiesel. The biodiesel production from microalgae lipids using lipase catalyzed reaction in supercritical CO2 medium has several advantages over conventional production processes. However, identifying the optimum microalgae lipid extraction and transesterification conditions is still a challenge. In this study, the lipids extracted from Scenedesmus sp. and their enzymatic transesterification using supercritical carbon dioxide have been investigated. The effect of extraction variables (temperature, pressure and solvent flow rate) and reaction variables (enzyme loading, incubation time, methanol to lipids molar ratio and temperature) were considered. Process parameters and their effects were studied using a full factorial analysis of both. Response Surface Methodology (RSM) and was used to determine the optimum conditions for the extraction and reaction steps. For extraction, the optimum conditions were 53 °C and 500 bar, whereas for the reaction the optimum conditions were 35% enzyme loading, 4 h reaction, 9:1 molar ratio and 50 oC. At these optimum conditions, the highest biodiesel production yield was found to be 82 %. The fuel properties of the produced biodiesel, at optimum reaction condition, were determined and compared to ASTM standards. The properties were found to comply with the limits, and showed a low glycerol content, without any separation step.

Keywords: biodiesel, lipase, supercritical CO2, standards

Procedia PDF Downloads 495

Authors: Corina E. Niculaescu, Ivan Sangiorgi, Adrian R. Bell

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The COVID-19 pandemic has been life-changing in many aspects of people’s daily and social lives, but has it also changed attitudes towards investments? This paper explores the effect of the COVID-19 pandemic on retail investors’ levels of investments in the U.S. during the first COVID-19 wave in summer 2020. This is an unprecedented health crisis, which could lead to changes in investment behavior, including irrational behavior in retail investors. As such, this study aims to inform policymakers of what happened to investment decisions during the COVID-19 pandemic so that they can protect retail investors during extreme events like a global health crisis. The study aims to answer two research questions. First, was the level of investments affected by the COVID-19 pandemic, and if so, why? Second, how were investments affected by retail investors’ personal experience with COVID-19? The research analysis is based on primary survey data collected on the Amazon Mechanical Turk platform from a representative sample of U.S. respondents. Responses were collected between the 15th of July and 28th of August 2020 from 1,148 U.S. retail investors who hold mutual fund investments and a savings account. The research explores whether being affected by COVID-19, change in the level of savings, and risk capacity can explain the change in the level of investments by using regression analysis. The dependent variable is changed in investments measured as decrease, no change, and increase. For this reason, the methodology used is ordered probit regression models. The results show that retail investors in the U.S. increased their investments during the first wave of COVID-19, which is unexpected as investors are usually more cautious in crisis times. Moreover, the study finds that those who were affected personally by COVID-19 (e.g., tested positive) were more likely to increase their investments, which is irrational behavior and contradicts expectations. An increase in the level of savings and risk capacity was also associated with increased investments. Overall, the findings show that having personal experience with a health crisis can have an impact on one’s investment decisions as well. Those findings are important for both retail investors and policymakers, especially now that online trading platforms have made trading easily accessible to everyone. There are risks and potential irrational behaviors associated with investment decisions during times of crisis, and it is important that retail investors are aware of them before making financial decisions.

Keywords: COVID-19, financial decision-making, health crisis retail investors, survey

Procedia PDF Downloads 195

Authors: M. Azharul Islam, M. Russel Sarker, M. Shahadat Hossen

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Air pollution is one of the major environmental problems that have gained importance in all over the world. Air pollution is a problem for all of us. The present study was conducted to explore the people’s perception level and awareness of air pollution in selected areas of Mymensingh in Bangladesh. Health impacts of air pollution also studied through personal interview and structured questionnaire. The relationship of independent variables (age, educational qualification, family size, residence and communication exposure) with the respondent’s perception level and awareness of air pollution (dependent variable) was studied to achieve the objectives of the study. About 600 respondents were selected randomly from six sites for collecting data during the period of July 2016 to June 2017. Pearson’s product-moment correlation coefficients were computed to examine the relationship between the concerned variables. The results revealed that about half (46.67%) of the respondents had a medium level of perception and awareness about air pollution in their areas where 31.67 percent had low, and 21.67 percent had a high level. In rural areas of the study sites, 43.33 percent respondents had low, 50 percent had medium, and only 6.67 percent had high perception and awareness on air pollution. In case of urban areas, 20 percent respondents had low, 43.33 percent had medium, and 36.67 percent had a high level of awareness and perception on air pollution. The majority of the respondents (93.33 percent) were lacking of proper awareness about air pollution in rural areas while 63.33 percent in urban areas. Out of five independent variables, three variables such as- educational qualification, residence status and communication exposure had positive and significant relationship. Age of respondents had negative and significant relationship with their awareness of air pollution where family size of the respondents had no significant relationship with their perception and awareness of air pollution. Thousands of people live in urban areas where urban smog, particle pollution, and toxic pollutants pose serious health concerns. But most of the respondents of the urban sites are not familiarize about the real causes of air pollution. Respondents exposed higher level of experience for air pollutants, such as- irritation of the eyes, coughing, tightness of chest and many health difficulties. But respondents of both rural and urban area hugely suffered such health problems and the tendency of certain difficulties increased day by day. In this study, most of the respondents had lack of knowledge on the causes of such health difficulties due to their lower perception level. Proper attempts should be taken to raise literacy level, communication exposure to increase the perception and awareness of air pollution among the respondents of the study areas. Extra care with above concerned fields should be taken to increase perception and awareness of air pollution in rural areas.

Keywords: air pollution, awareness, health impacts, perception of people

Procedia PDF Downloads 236

Authors: Suraj D. Khadka, Priyantha W. Jayawickrama

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In search of a sustainable construction material, geopolymer has been investigated for past decades to evaluate its advantage over conventional products. Synthesis of geopolymer requires a source of aluminosilicate mixed with sodium hydroxide and sodium silicate at different proportions to maintain a Si/Al molar ratio of 1-3 and Na/Al molar ratio of unity. A comprehensive geopolymer study was performed with Metakaolin and Class C Fly ash as primary aluminosilicate sources. Synthesized geopolymer was analyzed for time-dependent viscosity, setting period and strength at varying initial moisture content, curing temperature and humidity. Different concentration of Ca(OH)₂ and CaSO₄.2H₂O were added to vary the amount of calcium contained in synthesized geopolymer. Influence of calcium content in unconfined compressive strength behavior of geopolymer were analyzed. Finally, Scanning Electron Microscopy-Energy Dispersive Spectroscopy (SEM-EDS) was performed to investigate the hardened product. It was observed that fly ash based geopolymer had shortened setting time and faster increase in viscosity as compared to geopolymer synthesized from metakaolin. This was primarily attributed to higher calcium content resulting in formation of calcium silicate hydrates (CSH). SEM-EDS was performed to verify the presence of CSH phases. Spectral analysis of geopolymer prepared by addition of Ca(OH)₂ and CaSO₄.2H₂O indicated higher CSH phases at higher concentration. It was observed that lower concentration of added calcium favored strength gain in geopolymer. However, at higher calcium concentration, decrease in strength was observed. Strength variation was also observed with humidity at initial curing condition. At 100% humidity, geopolymer with added calcium presented higher strength compared to samples cured at ambient humidity condition (40%). Reduction in strength in these samples at lower humidity was primarily attributed to reduction in moisture content in specimen due to the formation of CSH phases and loss of moisture through evaporation. For low calcium content geopolymers, with increase in temperature, gain in strength was observed with maximum strength observed at 200 ˚C. However, samples with higher calcium content demonstrated severe cracking resulting in low strength at elevated temperatures.

Keywords: calcium silicate hydrates, geopolymer, humidity, Scanning Electron Microscopy-Energy Dispersive Spectroscopy, unconfined compressive strength

Procedia PDF Downloads 132

Authors: Soheila Sadeghi

Abstract:

Construction projects are complex and often subject to significant cost overruns due to the multifaceted nature of the activities involved. Accurate cost estimation is crucial for effective budget planning and resource allocation. Traditional methods for predicting overruns often rely on expert judgment or analysis of historical data, which can be time-consuming, subjective, and may fail to consider important factors. However, with the increasing availability of data from construction projects, machine learning techniques can be leveraged to improve the accuracy of overrun predictions. This study applied machine learning algorithms to enhance the prediction of cost overruns in a case study of a construction project. The methodology involved the development and evaluation of two machine learning models: Random Forest and Neural Networks. Random Forest can handle high-dimensional data, capture complex relationships, and provide feature importance estimates. Neural Networks, particularly Deep Neural Networks (DNNs), are capable of automatically learning and modeling complex, non-linear relationships between input features and the target variable. These models can adapt to new data, reduce human bias, and uncover hidden patterns in the dataset. The findings of this study demonstrate that both Random Forest and Neural Networks can significantly improve the accuracy of cost overrun predictions compared to traditional methods. The Random Forest model also identified key cost drivers and risk factors, such as changes in the scope of work and delays in material delivery, which can inform better project risk management. However, the study acknowledges several limitations. First, the findings are based on a single construction project, which may limit the generalizability of the results to other projects or contexts. Second, the dataset, although comprehensive, may not capture all relevant factors influencing cost overruns, such as external economic conditions or political factors. Third, the study focuses primarily on cost overruns, while schedule overruns are not explicitly addressed. Future research should explore the application of machine learning techniques to a broader range of projects, incorporate additional data sources, and investigate the prediction of both cost and schedule overruns simultaneously.

Keywords: resource allocation, machine learning, optimization, data-driven decision-making, project management

Procedia PDF Downloads 45

Authors: Mohammad Javad Afroughi, Farjad Falahati, Larry W. Kostiuk, Jason S. Olfert

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This study investigates the physical characteristics of nanoparticles generated during pyrolysis of methane in hot products of a premixed propane-air flame. An inverted burner is designed to provide a laminar premixed propane-air flame (35 SLPM) then introduce methane co-flow to be pyrolyzed within a closed cylindrical chamber (20 cm in diameter and 68 cm in length). The formed products are discharged through an exhaust with a sampling branch to measure emission characteristics. Carbon particles are sampled with a preheated nitrogen dilution system, and the size distribution of particles formed by pyrolysis is measured by a scanning mobility particle sizer (SMPS). Dilution ratio is calculated using simultaneously measured CO2 concentrations in the exhaust products and diluted samples. Results show that particle size distribution (PSD) is strongly affected by dilution ratio and preheating temperature. PSD becomes unstable at high dilution ratios (typically above 700 times) and/or low preheating temperatures (below 40° C). At a suitable dilution ratio of 55 and preheating temperature up to 70° C, the median diameter of PSD increases from 20 to 220 nm following the introduction of 0.5 SLPM of methane to the propane-air premixed flame. Furthermore, with pyrolysis of methane, total particle number concentration and estimated total mass concentration of particles in the size range of 14 to 700 nm, increase from 1.12 to 3.90 *107 cm-3 and from 0.11 to 154 µg L-1, respectively.

Keywords: laminar premixed flame, methane pyrolysis, nanoparticle physical characteristics, particle mass concentration, particle number concentration, particle size distribution (PSD)

Procedia PDF Downloads 243

Authors: Wei Sun, Yan Dong

Abstract:

There is an increasing interest in introducing computational thinking at an early age. Computational thinking, like mathematical thinking, engineering thinking, and scientific thinking, is a kind of analytical thinking. Learning computational thinking skills is not only to improve technological literacy, but also allows learners to equip with practicable skills such as problem-solving skills. As people realize the importance of computational thinking, the field of educational technology faces a problem: how to choose appropriate tools and activities to help students develop computational thinking skills. Robots are gradually becoming a popular teaching tool, as robots provide a tangible way for young children to access to technology, and controlling a robot through programming offers them opportunities to engage in developing computational thinking. This study explores whether the introduction of flowcharts into the robotics programming courses can help children convert natural language into a programming language more easily, and then to better cultivate their computational thinking skills. An experimental study was adopted with a sample of children ages six to seven (N = 16) participated, and a one-meter-tall humanoid robot was used as the teaching tool. Results show that children can master basic programming concepts through robotic courses. Children's computational thinking has been significantly improved. Besides, results suggest that flowcharts do have an impact on young children’s computational thinking skills development, but it only has a significant effect on the "sequencing" and "correspondence" skills. Overall, the study demonstrates that the humanoid robot and flowcharts have qualities that foster young children to learn programming and develop computational thinking skills.

Keywords: robotics, computational thinking, programming, young children, flow chart

Procedia PDF Downloads 150

Authors: Peter C. St. John, Michael F. Crowley, Yannick J. Bomble

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Production of chemicals from engineered organisms in a batch culture typically involves a trade-off between productivity, yield, and titer. However, strategies for strain design typically involve designing mutations to achieve the highest yield possible while maintaining growth viability. Such approaches tend to follow the principle of designing static networks with minimum metabolic functionality to achieve desired yields. While these methods are computationally tractable, optimum productivity is likely achieved by a dynamic strategy, in which intracellular fluxes change their distribution over time. One can use multi-stage fermentations to increase either productivity or yield. Such strategies would range from simple manipulations (aerobic growth phase, anaerobic production phase), to more complex genetic toggle switches. Additionally, some computational methods can also be developed to aid in optimizing two-stage fermentation systems. One can assume an initial control strategy (i.e., a single reaction target) in maximizing productivity - but it is unclear how close this productivity would come to a global optimum. The calculation of maximum theoretical yield in metabolic engineering can help guide strain and pathway selection for static strain design efforts. Here, we present a method for the calculation of a maximum theoretical productivity of a batch culture system. This method follows the traditional assumptions of dynamic flux balance analysis: that internal metabolite fluxes are governed by a pseudo-steady state and external metabolite fluxes are represented by dynamic system including Michealis-Menten or hill-type regulation. The productivity optimization is achieved via dynamic programming, and accounts explicitly for an arbitrary number of fermentation stages and flux variable changes. We have applied our method to succinate production in two common microbial hosts: E. coli and A. succinogenes. The method can be further extended to calculate the complete productivity versus yield Pareto surface. Our results demonstrate that nearly optimal yields and productivities can indeed be achieved with only two discrete flux stages.

Keywords: A. succinogenes, E. coli, metabolic engineering, metabolite fluxes, multi-stage fermentations, succinate

Procedia PDF Downloads 218

Authors: Nicolaas Unland, John Webb

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The acidification and mobilisation of metals during the oxidation of acid sulfate soils exposed during lake bed drying is an increasingly common phenomenon under climate scenarios with reduced rainfall. In order to assess the risk of generating high concentrations of acidity and dissolved metals, chromium suite analysis are fundamental, but sometimes limited in characterising the potential risks they pose. This study combines such fundamental test work, along with incubation tests and 1D modelling to investigate the risks associated with the drying of Third Reedy Lake in South East Australia. Core samples were collected from a variable depth of 0.5 m below the lake bed, at 19 locations across the lake’s footprint, using a boat platform. Samples were subjected to a chromium suite of analysis, including titratable actual acidity, chromium reducible sulfur and acid neutralising capacity. Concentrations of reduced sulfur up to 0.08 %S and net acidities up to 0.15 %S indicate that acid sulfate soils have formed on the lake bed during permanent inundation over the last century. A further sub-set of samples were prepared in 7 columns and subject to accelerated heating, drying and wetting over a period of 64 days in laboratory. Results from the incubation trial indicate that while pyrite oxidation proceeded, minimal change to soil pH or the acidity of leachate occurred, suggesting that the internal buffering capacity of lake bed sediments was sufficient to neutralise a large proportion of the acidity produced. A 1D mass balance model was developed to assess potential changes in lake water quality during drying based on the results of chromium suite and incubation tests. Results from the above test work and modelling suggest that acid sulfate soils pose a moderate to low risk to the Third Reedy Lake system. Further, the risks can be effectively managed during the initial stages of lake drying via flushing with available mildly alkaline water. The study finds that while test work such as chromium suite analysis are fundamental in characterizing acid sulfate soil environments, they can the overestimate risks associated with the soils. Subsequent incubation test work may more accurately characterise such soils and lead to better-informed management strategies.

Keywords: acid sulfate soil, incubation, management, model, risk

Procedia PDF Downloads 361

Authors: Shalini Arora, Sri Sivakumar

Abstract:

The deep removal of high molecular weight sulphur compounds (e.g., 4,6, dimethyl dibenzothiophene) is challenging due to their steric hindrance. Hydrogenation desulfurization (HYD) pathway is the main pathway to remove these sulfur compounds, and it is mainly governed by the number of type 2 sites. The formation of type 2 sites can be enhanced by modulating the pore structure and the interaction between the active metal and support. To this end, we report the enhanced HDS catalytic activity of ultrasmall NiMo supported on amorphous alumina (A-Al₂O₃) catalysts by one pot colloidal synthesis method followed by calcination and sulfidation. The amorphous alumina (A-Al₂O₃) was chosen as the support due to its lower surface energy, better physicochemical properties, and enhanced acidic sites (due to the dominance of tetra and penta coordinated [Al] sites) than crystalline alumina phase. At 20% metal oxide composition, NiMo supported on A-Al₂O₃ catalyst showed 1.4 and 1.2 times more reaction rate constant and turn over frequency (TOF) respectively than the conventional catalyst (wet impregnated NiMo catalysts) for HDS reaction of dibenzothiophene reactant molecule. A-Al₂O₃ supported catalysts represented enhanced type 2 sites formation (because this catalystpossesses higher sulfidation degree (80%) and NiMoS sites (19.3 x 10¹⁷ sites/mg) with desired optimum stacking degree (2.5) than wet impregnated catalyst at same metal oxide composition 20%) along with higher active metal dispersion, Mo edge site fraction. The experimental observations were also supported by DFT simulations. Lower heat of adsorption (< 4.2 ev for MoS2 interaction and < 3.15 ev for Ni doped MoS2 interaction) values for A-Al₂O₃ confirmed the presence of weaker metal-support interaction in A-Al₂O₃ in contrast to crystalline ℽ-Al₂O3. The weak metal-support interaction for prepared catalysts clearly suggests the higher formation of type 2 sites which leads to higher catalytic activity for HDS reaction.

Keywords: amorphous alumina, colloidal, desulfurization, metal-support interaction

Procedia PDF Downloads 272

Authors: Aliaa M. El-Borai, Ehab A. Beltagy, Eman E. Gadallah, Samy A. ElAssar

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Bioremediation technology is now used for treatment instead of traditional metal removal methods. A strain was isolated from Marsa Alam, Red sea, Egypt showed high resistance to high lead concentration and was identified by the 16S rRNA gene sequencing technique as Halomonas sp. ES015. Medium optimization was carried out using Plackett-Burman design, and the most significant factors were yeast extract, casamino acid and inoculums size. The optimized media obtained by the statistical design raised the removal efficiency from 84% to 99% from initial concentration 250 ppm of lead. Moreover, Box-Behnken experimental design was applied to study the relationship between yeast extract concentration, casamino acid concentration and inoculums size. The optimized medium increased removal efficiency to 97% from initial concentration 500 ppm of lead. Immobilized Halomonas sp. ES015 cells on sponge cubes, using optimized medium in loop bioremediation column, showed relatively constant lead removal efficiency when reused six successive cycles over the range of time interval. Also metal removal efficiency was not affected by flow rate changes. Finally, the results of this research refer to the possibility of lead bioremediation by free or immobilized cells of Halomonas sp. ES015. Also, bioremediation can be done in batch cultures and semicontinuous cultures using column technology.

Keywords: bioremediation, lead, Box–Behnken, Halomonas sp. ES015, loop bioremediation, Plackett-Burman

Procedia PDF Downloads 200

Authors: Namrata Khatri

Abstract:

The rapid growth of cities is transforming the urban environment and posing significant challenges for environmental quality. This study examines the urban environment of Belagavi in Karnataka, India, using geostatistical methods to assess the spatial pattern and land use distribution of the city and to evaluate the quality of the urban environment. The study is driven by the necessity to assess the environmental impact of urbanisation. Satellite data was utilised to derive information on land use and land cover. The investigation revealed that land use had changed significantly over time, with a drop in plant cover and an increase in built-up areas. High-resolution satellite data was also utilised to map the city's open areas and gardens. GIS-based research was used to assess public green space accessibility and to identify regions with inadequate waste management practises. The findings revealed that garbage collection and disposal techniques in specific areas of the city needed to be improved. Moreover, the study evaluated the city's thermal environment using Landsat 8 land surface temperature (LST) data. The investigation found that built-up regions had higher LST values than green areas, pointing to the city's urban heat island (UHI) impact. The study's conclusions have far-reaching ramifications for urban planners and politicians in Belgaum and other similar cities. The findings may be utilised to create sustainable urban planning strategies that address the environmental effect of urbanisation while also improving the quality of life for city dwellers. Satellite data and high-resolution satellite pictures were gathered for the study, and remote sensing and GIS tools were utilised to process and analyse the data. Ground truthing surveys were also carried out to confirm the accuracy of the remote sensing and GIS-based data. Overall, this study provides a complete assessment of Belgaum's environmental quality and emphasizes the potential of remote sensing and geographic information systems (GIS) approaches in environmental assessment and management.

Keywords: environmental quality, UEQ, remote sensing, GIS

Procedia PDF Downloads 82

Authors: Shan Huang, Guanjun Huang, Yongjun Zeng, Haiyuan Wang

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The impacts of whole-growth warming on grain yield and quality in double rice cropping systems still remain largely unknown. In this study, a two-year field whole-growth warming experiment was conducted with two inbred indica rice cultivars (Zhongjiazao 17 and Xiangzaoxian 45) for early season and two hybrid indica rice cultivars (Wanxiangyouhuazhan and Tianyouhuazhan) for late season. The results showed that whole-growth warming did not affect early rice yield but significantly decreased late rice yield, which was caused by the decreased grain weight that may be related to the increased plant respiration and reduced translocation of dry matter accumulated during the pre-heading phase under warming. Whole-growth warming improved the milling quality of late rice but decreased that of early rice; however, the chalky rice rate and chalkiness degree were increased by 20.7% and 33.9% for early rice and 37.6 % and 51.6% for late rice under warming, respectively. We found that the crude protein content of milled rice was significantly increased by warming in both early and late rice, which would result in deterioration of eating quality. Besides, compared with the control treatment, the setback of late rice was significantly reduced by 17.8 % under warming, while that of early rice was not significantly affected by warming. These results suggest that the negative impacts of whole-growth warming on grain quality may be more severe in early rice than in late rice. Therefore, adaptation in both rice breeding and agronomic practices is needed to alleviate climate warming on the production of a double rice cropping system. Climate-smart agricultural practices ought to be implemented to mitigate the detrimental effects of warming on rice grain quality. For instance, fine-tuning the application rate and timing of inorganic nitrogen fertilizers, along with the introduction of organic amendments and the cultivation of heat-tolerant rice varieties, can help reduce the negative impact of rising temperatures on rice quality. Furthermore, to comprehensively understand the influence of climate warming on rice grain quality, future research should encompass a wider range of rice cultivars and experimental sites.

Keywords: climate warming, double rice cropping, dry matter, grain quality, grain yield

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Authors: Manal Kamel, Sara Maher, Hanan El Baz, Faten Salah, Omar Sayyouh, Zeinab Demerdash

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In the recent COVID 19 pandemic, experts of public health have emphasized testing, tracking infected people, and tracing their contacts as an effective strategy to reduce the spread of the virus. Development of rapid and sensitive diagnostic assays to replace reverse transcription polymerase chain reaction (RT-PCR) is mandatory..Our innovative test strip relying on the application of nanoparticles conjugated to recombinant nanobodies for SARS-COV-2 spike protein (S1) & angiotensin-converting enzyme 2 (that is responsible for the virus entry into host cells) for rapid detection of SARS-COV-2 spike protein (S1) in saliva or sputum specimens. Comparative tests with RT-PCR will be held to estimate the significant effect of using COVID 19 nanobodies for the first time in the development of lateral flow test strip. The SARS-CoV-2 S1 (3 ng of recombinant proteins) was detected by our developed LFIA in saliva specimen of COVID-19 Patients No cross-reaction was detected with Middle East respiratory syndrome coronavirus (MERS-CoV) or SARS- CoV antigens..Our developed system revealed 96 % sensitivity and 100% specificity for saliva samples compared to 89 % and 100% sensitivity and specificity for nasopharyngeal swabs. providing a reliable alternative for the painful and uncomfortable nasopharyngeal swab process and the complexes, time consuming PCR test. An increase in testing compliances to be expected.

Keywords: COVID 19, diagnosis, LFIA, nanobodies, ACE2

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Authors: Dhruv Mehta, Alexander van Zuijlen, Hester Bijl

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

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

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Authors: Khaled A. shady, Fagr B. Bazeed, Nashwa K. Abousamra, Ihab H. Elberawe, Ashraf E. shaalan, Mohamed A. Sobh

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Physical activity activates a variety of adult stem cells which might be released into the circulation or might be activated in their organ-resident state. A variety of stimuli such as metabolic, mechanical, and hormonal stimuli might by responsible for the mobilization. This study was done to know the changes in hematopoietic stem cells and endothelial progenitor in athletes in the 24 hours following 30 min of aerobic exercise. Methods: Ten healthy male's athlete's (age 20.7± 0.61 y) performed moderate running with 30 min at 80% of velocity of The IAT. Blood samples taken pre-, and immediately, 30 min, 2h, 6h and 24h post-exercise were analyzed for hematopoietic stem cells (HSCs ), endothelial progenitor cells (EPCs(, vascular endothelial growth factor (VEGF), nitric oxide (NO), lactic acid (LA), and white blood cells . HSCs and EPCs were quantified by flow cytometry. Results: After 30min of aerobic exercise significant increases in HSCs, EPC, VEGF, NO, LA and WBCs (p ˂ 0.05). This increase will be at different rates according to the timing of taking blood sample and was in the maximum rate of increase after 30 min of aerobic exercise. HSCs, EPC, NO and WBCs were in the maximum rate of increase 2h post exercise. In addition, VEGF was in the maximum rate of increase immediately post exercise and LA concentration not affected after exercise. Conclusion: These data suggest that HSCs and EPCs increased after aerobic exercise due to increase of VEGF which play an important role in mobilization of stem cells and promotes NO increase which contributes to increase EPCs.

Keywords: physical activity, hematopoietic stem cells, mobilization, athletes

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Authors: Bilel Chalghaf, Mathieu Varin

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Forest characterization in Quebec, Canada, is usually assessed based on photo-interpretation at the stand level. For species identification, this often results in a lack of precision. Very high spatial resolution imagery, such as DigitalGlobe, and Light Detection and Ranging (LiDAR), have the potential to overcome the limitations of aerial imagery. To date, few studies have used that data to map a large number of species at the tree level using machine learning techniques. The main objective of this study is to map 11 individual high tree species ( > 17m) at the tree level using an object-based approach in the broadleaf forest of Kenauk Nature, Quebec. For the individual tree crown segmentation, three canopy-height models (CHMs) from LiDAR data were assessed: 1) the original, 2) a filtered, and 3) a corrected model. The corrected CHM gave the best accuracy and was then coupled with imagery to refine tree species crown identification. When compared with photo-interpretation, 90% of the objects represented a single species. For modeling, 313 variables were derived from 16-band WorldView-3 imagery and LiDAR data, using radiance, reflectance, pixel, and object-based calculation techniques. Variable selection procedures were employed to reduce their number from 313 to 16, using only 11 bands to aid reproducibility. For classification, a global approach using all 11 species was compared to a semi-hierarchical hybrid classification approach at two levels: (1) tree type (broadleaf/conifer) and (2) individual broadleaf (five) and conifer (six) species. Five different model techniques were used: (1) support vector machine (SVM), (2) classification and regression tree (CART), (3) random forest (RF), (4) k-nearest neighbors (k-NN), and (5) linear discriminant analysis (LDA). Each model was tuned separately for all approaches and levels. For the global approach, the best model was the SVM using eight variables (overall accuracy (OA): 80%, Kappa: 0.77). With the semi-hierarchical hybrid approach, at the tree type level, the best model was the k-NN using six variables (OA: 100% and Kappa: 1.00). At the level of identifying broadleaf and conifer species, the best model was the SVM, with OA of 80% and 97% and Kappa values of 0.74 and 0.97, respectively, using seven variables for both models. This paper demonstrates that a hybrid classification approach gives better results and that using 16-band WorldView-3 with LiDAR data leads to more precise predictions for tree segmentation and classification, especially when the number of tree species is large.

Keywords: tree species, object-based, classification, multispectral, machine learning, WorldView-3, LiDAR

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Authors: Reza Hadjiaghaie Vafaie, Sevda Givtaj

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Automation and control of biological samples and solutions at the microscale is a major advantage for biochemistry analysis and biological diagnostics. Despite the known potential of miniaturization in biochemistry and biomedical applications, comparatively little is known about fluid automation and control at the microscale. Here, we study the electric field effect inside a fluidic channel and proper electrode structures with different patterns proposed to form forward, reversal, and rotational flows inside the channel. The simulation results confirmed that the ac electro-thermal flow is efficient for the control and automation of high-conductive solutions. In this research, the fluid pumping and mixing effects were numerically studied by solving physic-coupled electric, temperature, hydrodynamic, and concentration fields inside a microchannel. From an experimental point of view, the electrode structures are deposited on a silicon substrate and bonded to a PDMS microchannel to form a microfluidic chip. The motions of fluorescent particles in pumping and mixing modes were captured by using a CCD camera. By measuring the frequency response of the fluid and exciting the electrodes with the proper voltage, the fluid motions (including pumping and mixing effects) are observed inside the channel through the CCD camera. Based on the results, there is good agreement between the experimental and simulation studies.

Keywords: microfluidic, nano/micro actuator, AC electrothermal, Reynolds number, micropump, micromixer, microfabrication, mass transfer, biomedical applications

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Authors: Eitan Yefenof

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Glucocorticoid (GC) hormones, e.g. Dexamethasone and Prednisone, are widely used in the therapy of leukemia and lymphoma owing to their apoptogenic effect on lymphoid cells. However, the emergence of GC resistant cells during therapy is a major cause for treatment failure, urging the need for novel strategies that maintain leukemia sensitivity to the pro-apoptotic activity of GCs. GCs act by binding to the GC receptor (GR), which, in its inactive state, is sequestered in the cytosol by a multi-subunit complex of heat shock proteins. Upon ligand binding, the complex dissociates, allowing GR activation and translocation to the nucleus, where it regulates transcription of multiple genes. We demonstrated that in addition to gene expression, GR also regulates microRNA (miR) expression. Deep-sequencing analysis revealed 14 miRs that are regulated in GC-sensitive but resistant leukemias upon treatment with GC. GC up-regulates miR-103, miR-15~16 and miR-30e/d, while down-regulates miR-17, mir-18a, miR-19a, miR-19b, miR-20a and miR-92a (members of the miR-17∼92a multi-cistron). Upon transfection, miR-103 confers GC apoptotic sensitivity to otherwise GC-resistant cell. Furthermore, knocking down miR-103 expression reduces the GC apoptotic response of sensitive cells. miR-103 abrogates c-Myc expression, an oncogenic transcription factor which is deregulated in many cancers. In addition, miR-103 up-regulates Bim, a pro-apoptotic protein crucial for GC-induced death. Activated glycogen synthase kinase 3 (GSK3) is also crucial for GC-induced apoptosis. GSK3 is active in GC-sensitive but not in GC-resistant cells. We found that GSK3 associates with the GR multi-subunit complex. Upon GC exposure, it dissociates from the GR and interacts with Bim to enable activation of the mitochondrial apoptosis pathway. miR-103 mediated c-Myc ablation is followed by down-regulation of the multi-cistron miR-17~92a, in particular miR-18a and miR-20a. miR-18a targets GR for degradation whereas miR-20a targets Bim degradation. Hence, miR-103 acts, in concert with Bim and GR, as a "tumor suppressor" that leads to reduced proliferation, cell-cycle arrest and cell death. We suggest that miR-103 can provide a diagnostic tool that predicts the sensitivity of leukemia to GC based therapy. Furthermore, exosomal delivery of miR-103 or up-regulation of the endogenous miR-103 could confer apoptotic sensitivity to resistant cells at the outset, thus becoming a useful therapeutic tool combined with GCs.

Keywords: apoptosis, leukemia, micro-RNA, steroids

Procedia PDF Downloads 246

Authors: Glenn Auld Knierim

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Automated robotic winding of high-temperature superconductors (HTS) addresses precision, efficiency, and reliability critical to the commercialization of products. Today’s HTS materials are mature and commercially promising but require manufacturing attention. In particular to the exaggerated rectangular cross-section (very thin by very wide), winding precision is critical to address the stress that can crack the fragile ceramic superconductor (SC) layer and destroy the SC properties. Damage potential is highest during peak operations, where winding stress magnifies operational stress. Another challenge is operational parameters such as magnetic field alignment affecting design performance. Winding process performance, including precision, capability for geometric complexity, and efficient repeatability, are required for commercial production of current HTS. Due to winding limitations, current HTS magnets focus on simple pancake configurations. HTS motors, generators, MRI/NMR, fusion, and other projects are awaiting robotic wound solenoid, planar, and spherical magnet configurations. As with conventional power cables, full transposition winding is required for long length alternating current (AC) and pulsed power cables. Robotic production is required for transposition, periodic swapping of cable conductors, and placing into precise positions, which allows power utility required minimized reactance. A full transposition SC cable, in theory, has no transmission length limits for AC and variable transient operation due to no resistance (a problem with conventional cables), negligible reactance (a problem for helical wound HTS cables), and no long length manufacturing issues (a problem with both stamped and twisted stacked HTS cables). The Infinity Physics team is solving manufacturing problems by developing automated manufacturing to produce the first-ever reliable and utility-grade commercial SC cables and magnets. Robotic winding machines combine mechanical and process design, specialized sense and observer, and state-of-the-art optimization and control sequencing to carefully manipulate individual fragile SCs, especially HTS, to shape previously unattainable, complex geometries with electrical geometry equivalent to commercially available conventional conductor devices.

Keywords: automated winding manufacturing, high temperature superconductor, magnet, power cable

Procedia PDF Downloads 146

Authors: Chanchal Biswas, Anrin Bhattacharyya, Gopes Chandra Das, Mahua Ghosh Chaudhuri, Rajib Dey

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Boiler coals with low fixed carbon and higher ash content have always challenged the metallurgists to develop a suitable method for their utilization. In the present study, an attempt is made to establish an energy effective method for the reduction of iron ore fines in the form of nuggets by using ‘Syngas’. By devolatisation (expulsion of volatile matter by applying heat) of boiler coal, gaseous product (enriched with reducing agents like CO, CO2, H2, and CH4 gases) is generated. Iron ore nuggets are reduced by this syngas. For that reason, there is no direct contact between iron ore nuggets and coal ash. It helps to control the minimization of the sulphur intake of the reduced nuggets. A laboratory scale devolatisation furnace designed with reduction facility is evaluated after in-depth studies and exhaustive experimentations including thermo-gravimetric (TG-DTA) analysis to find out the volatile fraction present in boiler grade coal, gas chromatography (GC) to find out syngas composition in different temperature and furnace temperature gradient measurements to minimize the furnace cost by applying one heating coil. The nuggets are reduced in the devolatisation furnace at three different temperatures and three different times. The pre-reduced nuggets are subjected to analytical weight loss calculations to evaluate the extent of reduction. The phase and surface morphology analysis of pre-reduced samples are characterized using X-ray diffractometry (XRD), energy dispersive x-ray spectrometry (EDX), scanning electron microscopy (SEM), carbon sulphur analyzer and chemical analysis method. Degree of metallization of the reduced nuggets is 78.9% by using boiler grade coal. The pre-reduced nuggets with lesser sulphur content could be used in the blast furnace as raw materials or coolant which would reduce the high quality of coke rate of the furnace due to its pre-reduced character. These can be used in Basic Oxygen Furnace (BOF) as coolant also.

Keywords: alternative ironmaking, coal gasification, extent of reduction, nugget making, syngas based DRI, solid state reduction

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Authors: Fatemeh Elmi, Zahra Etemadifar

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Improvement of soil mechanical properties is crucial before its use in construction, as the low mechanical strength and unstable structure of soil in many parts of the world can lead to the destruction of engineering infrastructure, resulting in financial and human losses. Although, conventional methods, such as chemical injection, are often utilized to enhance soil strength and stiffness, they are generally expensive, require heavy machinery, and cause significant environmental effects due to chemical usage, and also disrupt urban infrastructure. Moreover, they are not suitable for treating large volume of soil. Recently, an alternative method to improve various soil properties, including strength, hardness, and permeability, has received much attention: the application of biological methods. One of the most widely used is biocementation, which is based on the microbial precipitation of calcium carbonte crystalls using ureolytic bacteria However, there are still limitations to its large-scale use that need to be resolved before it can be commercialized. These issues have not received enough attention in prior research. One limitation of MICP (microbially induced calcium carbonate precipitation) is that microorganisms cannot operate effectively in harsh and variable environments, unlike the controlled conditions of a laboratory. Another limitation of applying this technique on a large scale is the high cost of producing a substantial amount of bacterial culture and reagents required for soil treatment. Therefore, the purpose of the present study was to investigate soil improvement using the biocementation activity of poly-extremophile, calcium carbonate crystal- producing bacterial strain, Bhargavaea cecembensis N1, in whey as an inexpensive medium. This strain was isolated and molecularly identified from sandy soils in our previous research, and its 16S rRNA gene sequences was deposited in the NCBI Gene Bank with an accession number MK420385. This strain exhibited a high level of urease activity (8.16 U/ml) and produced a large amount of calcium carbonate (4.1 mg/ ml). It was able to improve the soil by increasing the compressive strength up to 205 kPa and reducing permeability by 36%, with 20% of the improvement attributable of calcium carbonate production. This was achieved using this strain in a whey culture medium. This strain can be an eco-friendly and economical alternative to conventional methods in soil stabilization, and other MICP related applications.

Keywords: biocementation, Bhargavaea cecembensis, soil improvement, whey culture medium

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Authors: Pallavi Gajjar, Vinayak Malhotra

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Rockets have been known to have played a predominant role in spacecraft propulsion. The quintessential aspect of combustion-related requirements of a rocket engine is the minimization of the surrounding risks/hazards. Over time, it has become imperative to understand the combustion rate variation in presence of external energy source(s). Rocket propulsion represents a special domain of chemical propulsion assisted by high speed flows in presence of acoustics and thermal source(s). Jet noise leads to a significant loss of resources and every year a huge amount of financial aid is spent to prevent it. External heat source(s) induce high possibility of fire risk/hazards which can sufficiently endanger the operation of a space vehicle. Appreciable work had been done with justifiable simplification and emphasis on the linear variation of external energy source(s), which yields good physical insight but does not cater to accurate predictions. Present work experimentally attempts to understand the correlation between inter-energy conversions with the non-linear placement of external energy source(s). The work is motivated by the need to have better fire safety and enhanced combustion. The specific objectives of the work are a) To interpret the related energy transfer for combustion in presence of alternate external energy source(s) viz., thermal and acoustic, b) To fundamentally understand the role of key controlling parameters viz., separation distance, the number of the source(s), selected configurations and their non-linear variation to resemble real-life cases. An experimental setup was prepared using incense sticks as potential fuel and paraffin wax candles as the external energy source(s). The acoustics was generated using frequency generator, and source(s) were placed at selected locations. Non-equidistant parametric experimentation was carried out, and the effects were noted on regression rate changes. The results are expected to be very helpful in offering a new perspective into futuristic rocket designs and safety.

Keywords: combustion, acoustic energy, external energy sources, regression rate

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Authors: Lutfi Abosrra, Alseddeq Alabaoub, Nuri Elhaloudi

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Corrosion processes in the Oil & Gas industry can lead to failures that are usually costly to repair, costly in terms of loss of contaminated product, in terms of environmental damage and possibly costly in terms of human safety. This article describes the results of the corrosion review and criticality assessment done at Mellitah Gas (SRU unit) for pressure equipment and piping system. The information gathered through the review was intended for developing a qualitative RBI study. The corrosion criticality assessment has been carried out by applying company procedures and industrial recommended practices such as API 571, API 580/581, ASME PCC 3, which provides a guideline for establishing corrosion integrity assessment. The corrosion review is intimately related to the probability of failure (POF). During the corrosion study, the process units are reviewed by following the applicable process flow diagrams (PFDs) in the presence of Mellitah’s personnel from process engineering, inspection, and corrosion/materials and reliability engineers. The expected corrosion damage mechanism (internal and external) was identified, and the corrosion rate was estimated for every piece of equipment and corrosion loop in the process units. A combination of both Consequence and Likelihood of failure was used for determining the corrosion risk. A qualitative consequence of failure (COF) for each individual item was assigned based on the characteristics of the fluid as per its flammability, toxicity, and pollution into three levels (High, Medium, and Low). A qualitative probability of failure (POF)was applied to evaluate the internal and external degradation mechanism, a high-level point-based (0 to 10) for the purpose of risk prioritizing in the range of Low, Medium, and High.

Keywords: corrosion, criticality assessment, RBI, POF, COF

Procedia PDF Downloads 86