Search results for: automated generation
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 4163

Search results for: automated generation

2933 Energy Storage in the Future of Ethiopia Renewable Electricity Grid System

Authors: Dawit Abay Tesfamariam

Abstract:

Ethiopia’s Climate- Resilient Green Economy strategy focuses mainly on generating and utilization of Renewable Energy (RE). The data collected in 2016 by Ethiopian Electric Power (EEP) indicates that the intermittent RE sources on the grid from solar and wind energy were only 8 % of the total energy produced. On the other hand, the EEP electricity generation plan in 2030 indicates that 36 % of the energy generation share will be covered by solar and wind sources. Thus, a case study was initiated to model and compute the balance and consumption of electricity in three different scenarios: 2016, 2025, and 2030 using the Energy PLAN Model (EPM). Initially, the model was validated using the 2016 annual power-generated data to conduct the EPM analysis for two predictive scenarios. The EPM simulation analysis using EPM for 2016 showed that there was no significant excess power generated. Hence, the model’s results are in line with the actual 2016 output. Thus, the EPM was applied to analyze the role of energy storage in RE in Ethiopian grid systems. The results of the EPM simulation analysis showed there will be excess production of 402 /7963 MW average and maximum, respectively, in 2025. The excess power was dominant in all months except in the three rainy months of the year (June, July, and August). Consequently, based on the validated outcomes of EPM indicates, there is a good reason to think about other alternatives for the utilization of excess energy and storage of RE. Thus, from the scenarios and model results obtained, it is realistic to infer that; if the excess power is utilized with a storage mechanism that can stabilize the grid system; as a result, the extra RE generated can be exported to support the economy. Therefore, researchers must continue to upgrade the current and upcoming energy storage system to synchronize with RE potentials that can be generated from RE.

Keywords: renewable energy, storage, wind, energyplan

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2932 Adjusting Electricity Demand Data to Account for the Impact of Loadshedding in Forecasting Models

Authors: Migael van Zyl, Stefanie Visser, Awelani Phaswana

Abstract:

The electricity landscape in South Africa is characterized by frequent occurrences of loadshedding, a measure implemented by Eskom to manage electricity generation shortages by curtailing demand. Loadshedding, classified into stages ranging from 1 to 8 based on severity, involves the systematic rotation of power cuts across municipalities according to predefined schedules. However, this practice introduces distortions in recorded electricity demand, posing challenges to accurate forecasting essential for budgeting, network planning, and generation scheduling. Addressing this challenge requires the development of a methodology to quantify the impact of loadshedding and integrate it back into metered electricity demand data. Fortunately, comprehensive records of loadshedding impacts are maintained in a database, enabling the alignment of Loadshedding effects with hourly demand data. This adjustment ensures that forecasts accurately reflect true demand patterns, independent of loadshedding's influence, thereby enhancing the reliability of electricity supply management in South Africa. This paper presents a methodology for determining the hourly impact of load scheduling and subsequently adjusting historical demand data to account for it. Furthermore, two forecasting models are developed: one utilizing the original dataset and the other using the adjusted data. A comparative analysis is conducted to evaluate forecast accuracy improvements resulting from the adjustment process. By implementing this methodology, stakeholders can make more informed decisions regarding electricity infrastructure investments, resource allocation, and operational planning, contributing to the overall stability and efficiency of South Africa's electricity supply system.

Keywords: electricity demand forecasting, load shedding, demand side management, data science

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2931 Application of Artificial Neural Network in Initiating Cleaning Of Photovoltaic Solar Panels

Authors: Mohamed Mokhtar, Mostafa F. Shaaban

Abstract:

Among the challenges facing solar photovoltaic (PV) systems in the United Arab Emirates (UAE), dust accumulation on solar panels is considered the most severe problem that faces the growth of solar power plants. The accumulation of dust on the solar panels significantly degrades output from these panels. Hence, solar PV panels have to be cleaned manually or using costly automated cleaning methods. This paper focuses on initiating cleaning actions when required to reduce maintenance costs. The cleaning actions are triggered only when the dust level exceeds a threshold value. The amount of dust accumulated on the PV panels is estimated using an artificial neural network (ANN). Experiments are conducted to collect the required data, which are used in the training of the ANN model. Then, this ANN model will be fed by the output power from solar panels, ambient temperature, and solar irradiance, and thus, it will be able to estimate the amount of dust accumulated on solar panels at these conditions. The model was tested on different case studies to confirm the accuracy of the developed model.

Keywords: machine learning, dust, PV panels, renewable energy

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2930 Open Innovation Laboratory for Rapid Realization of Sensing, Smart and Sustainable Products (S3 Products) for Higher Education

Authors: J. Miranda, D. Chavarría-Barrientos, M. Ramírez-Cadena, M. E. Macías, P. Ponce, J. Noguez, R. Pérez-Rodríguez, P. K. Wright, A. Molina

Abstract:

Higher education methods need to evolve because the new generations of students are learning in different ways. One way is by adopting emergent technologies, new learning methods and promoting the maker movement. As a result, Tecnologico de Monterrey is developing Open Innovation Laboratories as an immediate response to educational challenges of the world. This paper presents an Open Innovation Laboratory for Rapid Realization of Sensing, Smart and Sustainable Products (S3 Products). The Open Innovation Laboratory is composed of a set of specific resources where students and teachers use them to provide solutions to current problems of priority sectors through the development of a new generation of products. This new generation of products considers the concepts Sensing, Smart, and Sustainable. The Open Innovation Laboratory has been implemented in different courses in the context of New Product Development (NPD) and Integrated Manufacturing Systems (IMS) at Tecnologico de Monterrey. The implementation consists of adapting this Open Innovation Laboratory within the course’s syllabus in combination with the implementation of specific methodologies for product development, learning methods (Active Learning and Blended Learning using Massive Open Online Courses MOOCs) and rapid product realization platforms. Using the concepts proposed it is possible to demonstrate that students can propose innovative and sustainable products, and demonstrate how the learning process could be improved using technological resources applied in the higher educational sector. Finally, examples of innovative S3 products developed at Tecnologico de Monterrey are presented.

Keywords: active learning, blended learning, maker movement, new product development, open innovation laboratory

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2929 Evaluation of Antibiotic Resistance and Extended-Spectrum β-Lactamases Production Rates of Gram Negative Rods in a University Research and Practice Hospital, 2012-2015

Authors: Recep Kesli, Cengiz Demir, Onur Turkyilmaz, Hayriye Tokay

Abstract:

Objective: Gram-negative rods are a large group of bacteria, and include many families, genera, and species. Most clinical isolates belong to the family Enterobacteriaceae. Resistance due to the production of extended-spectrum β-lactamases (ESBLs) is a difficulty in the handling of Enterobacteriaceae infections, but other mechanisms of resistance are also emerging, leading to multidrug resistance and threatening to create panresistant species. We aimed in this study to evaluate resistance rates of Gram-negative rods bacteria isolated from clinical specimens in Microbiology Laboratory, Afyon Kocatepe University, ANS Research and Practice Hospital, between October 2012 and September 2015. Methods: The Gram-negative rods strains were identified by conventional methods and VITEK 2 automated identification system (bio-Mérieux, Marcy l’etoile, France). Antibiotic resistance tests were performed by both the Kirby-Bauer disk-diffusion and automated Antimicrobial Susceptibility Testing (AST, bio-Mérieux, Marcy l’etoile, France) methods. Disk diffusion results were evaluated according to the standards of Clinical and Laboratory Standards Institute (CLSI). Results: Of the totally isolated 1.701 Enterobacteriaceae strains 1434 (84,3%) were Klebsiella pneumoniae, 171 (10%) were Enterobacter spp., 96 (5.6%) were Proteus spp., and 639 Nonfermenting gram negatives, 477 (74.6%) were identified as Pseudomonas aeruginosa, 135 (21.1%) were Acinetobacter baumannii and 27 (4.3%) were Stenotrophomonas maltophilia. The ESBL positivity rate of the totally studied Enterobacteriaceae group were 30.4%. Antibiotic resistance rates for Klebsiella pneumoniae were as follows: amikacin 30.4%, gentamicin 40.1%, ampicillin-sulbactam 64.5%, cefepime 56.7%, cefoxitin 35.3%, ceftazidime 66.8%, ciprofloxacin 65.2%, ertapenem 22.8%, imipenem 20.5%, meropenem 20.5 %, and trimethoprim-sulfamethoxazole 50.1%, and for 114 Enterobacter spp were detected as; amikacin 26.3%, gentamicin 31.5%, cefepime 26.3%, ceftazidime 61.4%, ciprofloxacin 8.7%, ertapenem 8.7%, imipenem 12.2%, meropenem 12.2%, and trimethoprim-sulfamethoxazole 19.2 %. Resistance rates for Proteus spp. were: 24,3% meropenem, 26.2% imipenem, 20.2% amikacin 10.5% cefepim, 33.3% ciprofloxacin and levofloxacine, 31.6% ceftazidime, 20% ceftriaxone, 15.2% gentamicin, 26.6% amoxicillin-clavulanate, and 26.2% trimethoprim-sulfamethoxale. Resistance rates of P. aeruginosa was found as follows: Amikacin 32%, gentamicin 42 %, imipenem 43%, merpenem 43%, ciprofloxacin 50%, levofloxacin 52%, cefepim 38%, ceftazidim 63%, piperacillin/tacobactam 85%, for Acinetobacter baumannii; Amikacin 53.3%, gentamicin 56.6 %, imipenem 83%, merpenem 86%, ciprofloxacin 100%, ceftazidim 100%, piperacillin/tacobactam 85 %, colisitn 0 %, and for S. malthophilia; levofloxacin 66.6 % and trimethoprim/sulfamethoxozole 0 %. Conclusions: This study showed that resistance in Gram-negative rods was a serious clinical problem in our hospital and suggested the need to perform typification of the isolated bacteria with susceptibility testing regularly in the routine laboratory procedures. This application guided to empirical antibiotic treatment choices truly, as a consequence of the reality that each hospital shows different resistance profiles.

Keywords: antibiotic resistance, gram negative rods, ESBL, VITEK 2

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2928 Economic Evaluation of Varying Scenarios to Fulfill the Regional Electricity Demand in Pakistan

Authors: Muhammad Shahid, Kafait Ullah, Kashif Imran, Arshad Mahmood, Maarten Arentsen

Abstract:

Poor planning and governance in the power sector of Pakistan have generated several issues ranging from gradual reliance on thermal-based expensive energy mix, supply shortages, unrestricted demand, subsidization, inefficiencies at different levels of the value chain and resultantly, the circular debt. This situation in the power sector has also hampered the growth of allied economic sectors. This study uses the Long-range Energy Alternative Planning (LEAP) system for electricity modelling of Pakistan from the period of 2016 to 2040. The study has first time in Pakistan forecasted the electricity demand at the provincial level. At the supply side, five scenarios Business as Usual Scenario (BAUS), Coal Scenario (CS), Gas Scenario (GS), Nuclear Scenario (NS) and Renewable Scenario (RS) have been analyzed based on the techno-economic and environmental parameters. The study has also included environmental externality costs for evaluating the actual costs and benefits of different scenarios. Contrary to the expectations, RS has a lower output than even BAUS. The study has concluded that the generation from RS has five times lesser costs than BAUS, CS, and GS. NS can also be an alternative for the sustainable future of Pakistan. Generation from imported coal is not a good option, however, indigenous coal with clean coal technologies should be promoted. This paper proposes energy planners of the country to devise incentives for the utilization of indigenous energy resources including renewables on priority and then clean coal to reduce the energy crises of Pakistan.

Keywords: economic evaluation, externality cost, penetration of renewable energy, regional electricity supply-demand planning

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2927 An Indoor Guidance System Combining Near Field Communication and Bluetooth Low Energy Beacon Technologies

Authors: Rung-Shiang Cheng, Wei-Jun Hong, Jheng-Syun Wang, Kawuu W. Lin

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Users rely increasingly on Location-Based Services (LBS) and automated navigation/guidance systems nowadays. However, while such services are easily implemented in outdoor environments using Global Positioning System (GPS) technology, a requirement still exists for accurate localization and guidance schemes in indoor settings. Accordingly, the present study presents a methodology based on GPS, Bluetooth Low Energy (BLE) beacons, and Near Field Communication (NFC) technology. Through establishing graphic information and the design of algorithm, this study develops a guidance system for indoor and outdoor on smartphones, with aim to provide users a smart life through this system. The presented system is implemented on a smartphone and evaluated on a student campus environment. The experimental results confirm the ability of the presented app to switch automatically from an outdoor mode to an indoor mode and to guide the user to the requested target destination via the shortest possible route.

Keywords: beacon, indoor, BLE, Dijkstra algorithm

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2926 The Next Generation’s Learning Ability, Memory, as Well as Cognitive Skills Is under the Influence of Paternal Physical Activity (An Intergenerational and Trans-Generational Effect): A Systematic Review and Meta-Analysis

Authors: Parvin Goli, Amirhosein Kefayat, Rezvan Goli

Abstract:

Background: It is well established that parents can influence their offspring's neurodevelopment. It is shown that paternal environment and lifestyle is beneficial for the progeny's fitness and might affect their metabolic mechanisms; however, the effects of paternal exercise on the brain in the offspring have not been explored in detail. Objective: This study aims to review the impact of paternal physical exercise on memory and learning, neuroplasticity, as well as DNA methylation levels in the off-spring's hippocampus. Study design: In this systematic review and meta-analysis, an electronic literature search was conducted in databases including PubMed, Scopus, and Web of Science. Eligible studies were those with an experimental design, including an exercise intervention arm, with the assessment of any type of memory function, learning ability, or any type of brain plasticity as the outcome measures. Standardized mean difference (SMD) and 95% confidence intervals (CI) were computed as effect size. Results: The systematic review revealed the important role of environmental enrichment in the behavioral development of the next generation. Also, offspring of exercised fathers displayed higher levels of memory ability and lower level of brain-derived neurotrophic factor. A significant effect of paternal exercise on the hippocampal volume was also reported in the few available studies. Conclusion: These results suggest an intergenerational effect of paternal physical activity on cognitive benefit, which may be associated with hippocampal epigenetic programming in offspring. However, the biological mechanisms of this modulation remain to be determined.

Keywords: hippocampal plasticity, learning ability, memory, parental exercise

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2925 An Efficient Algorithm for Solving the Transmission Network Expansion Planning Problem Integrating Machine Learning with Mathematical Decomposition

Authors: Pablo Oteiza, Ricardo Alvarez, Mehrdad Pirnia, Fuat Can

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To effectively combat climate change, many countries around the world have committed to a decarbonisation of their electricity, along with promoting a large-scale integration of renewable energy sources (RES). While this trend represents a unique opportunity to effectively combat climate change, achieving a sound and cost-efficient energy transition towards low-carbon power systems poses significant challenges for the multi-year Transmission Network Expansion Planning (TNEP) problem. The objective of the multi-year TNEP is to determine the necessary network infrastructure to supply the projected demand in a cost-efficient way, considering the evolution of the new generation mix, including the integration of RES. The rapid integration of large-scale RES increases the variability and uncertainty in the power system operation, which in turn increases short-term flexibility requirements. To meet these requirements, flexible generating technologies such as energy storage systems must be considered within the TNEP as well, along with proper models for capturing the operational challenges of future power systems. As a consequence, TNEP formulations are becoming more complex and difficult to solve, especially for its application in realistic-sized power system models. To meet these challenges, there is an increasing need for developing efficient algorithms capable of solving the TNEP problem with reasonable computational time and resources. In this regard, a promising research area is the use of artificial intelligence (AI) techniques for solving large-scale mixed-integer optimization problems, such as the TNEP. In particular, the use of AI along with mathematical optimization strategies based on decomposition has shown great potential. In this context, this paper presents an efficient algorithm for solving the multi-year TNEP problem. The algorithm combines AI techniques with Column Generation, a traditional decomposition-based mathematical optimization method. One of the challenges of using Column Generation for solving the TNEP problem is that the subproblems are of mixed-integer nature, and therefore solving them requires significant amounts of time and resources. Hence, in this proposal we solve a linearly relaxed version of the subproblems, and trained a binary classifier that determines the value of the binary variables, based on the results obtained from the linearized version. A key feature of the proposal is that we integrate the binary classifier into the optimization algorithm in such a way that the optimality of the solution can be guaranteed. The results of a study case based on the HRP 38-bus test system shows that the binary classifier has an accuracy above 97% for estimating the value of the binary variables. Since the linearly relaxed version of the subproblems can be solved with significantly less time than the integer programming counterpart, the integration of the binary classifier into the Column Generation algorithm allowed us to reduce the computational time required for solving the problem by 50%. The final version of this paper will contain a detailed description of the proposed algorithm, the AI-based binary classifier technique and its integration into the CG algorithm. To demonstrate the capabilities of the proposal, we evaluate the algorithm in case studies with different scenarios, as well as in other power system models.

Keywords: integer optimization, machine learning, mathematical decomposition, transmission planning

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2924 SVID: Structured Vulnerability Intelligence for Building Deliberated Vulnerable Environment

Authors: Wenqing Fan, Yixuan Cheng, Wei Huang

Abstract:

The diversity and complexity of modern IT systems make it almost impossible for internal teams to find vulnerabilities in all software before the software is officially released. The emergence of threat intelligence and vulnerability reporting policy has greatly reduced the burden on software vendors and organizations to find vulnerabilities. However, to prove the existence of the reported vulnerability, it is necessary but difficult for security incident response team to build a deliberated vulnerable environment from the vulnerability report with limited and incomplete information. This paper presents a structured, standardized, machine-oriented vulnerability intelligence format, that can be used to automate the orchestration of Deliberated Vulnerable Environment (DVE). This paper highlights the important role of software configuration and proof of vulnerable specifications in vulnerability intelligence, and proposes a triad model, which is called DIR (Dependency Configuration, Installation Configuration, Runtime Configuration), to define software configuration. Finally, this paper has also implemented a prototype system to demonstrate that the orchestration of DVE can be automated with the intelligence.

Keywords: DIR triad model, DVE, vulnerability intelligence, vulnerability recurrence

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2923 Social Perspective of Gender Biasness Among Rural Children in Haryna State of India

Authors: Kamaljeet Kaur, Vinod Kumari, Jatesh Kathpalia, Bas Kaur

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A gender bias towards girl child is pervasive across the world. It is seen in all the strata of the society and manifests in various forms. However nature and extent of these inequalities are not uniform. Generally these inequalities are more prevalent in patriarchal society. Despite emerging and increasing opportunities for women, there are still inequalities between men and women in each and every sphere like education, health, economy, polity and social sphere. Patriarchal ideology as a cultural norm enforces gender construction which is oriented toward hierarchical relations between the sexes and neglect of women in Indian society. Discrimination to girls may also vary by their age and be restricted to the birth order and sex composition of her elder surviving siblings. The present study was conducted to know the gender discrimination among rural children in India. The respondents were selected from three generations as per AICRP age group viz, 18-30 years (3rd generation), 31-60 years (2nd generation) and above 60 years (1st generation). A total sample size was 600 respondents from different villages of two districts of Haryana state comprising of half males and half females. Data were collected using personal interview schedule and analysed by SPSS software. Among the total births 46.35 per cent were girl child and 53.64 % were male child. Dropout rate was more in female children as compared to male children i.e. near about one third (31.09%) female children dropped school followed by 21.17 % male children. It was quite surprising that near about two-third (61.16%) female children and more than half (59.22%) of the male children dropped school. Cooking was mainly performed by adult female with overall mean scores 2.0 and ranked first which was followed by female child (1.7 mean scores) clearly indicating that cooking was the activity performed mainly by females while activity related to purchase of fruits and vegetable, cereals and pulses was mainly done by adult male. First preference was given to male child for serving of costly and special food. Regarding professional aspiration of children of the respondents’ families, it was observed that 20.10% of the male children wanted to become engineer, whereas only 3.89 % female children wanted to become engineer. Ratio of male children was high in both generations irrespective of the districts. School dropouts were more in case of female in both the 1st and 2 nd generations. The main reasons of school dropout were lack of interest, lack of resources and early marriage in both the generations. Female enrolment was more in faculty of arts, whereas in case of male percentage it was more in faculty of non-medical and medical which showed that female children were getting traditional type of education. It is suggested to provide equal opportunities to girls and boys in home as well as outside the home for smooth functioning of society.

Keywords: gender biasness, male child, female child, education, home

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2922 Unreliable Production Lines with Simultaneously Unbalanced Operation Time Means, Breakdown, and Repair Rates

Authors: Sabry Shaaban, Tom McNamara, Sarah Hudson

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This paper investigates the benefits of deliberately unbalancing both operation time means (MTs) and unreliability (failure and repair rates) for non-automated production lines.The lines were simulated with various line lengths, buffer capacities, degrees of imbalance and patterns of MT and unreliability imbalance. Data on two performance measures, namely throughput (TR) and average buffer level (ABL) were gathered, analyzed and compared to a balanced line counterpart. A number of conclusions were made with respect to the ranking of configurations, as well as to the relationships among the independent design parameters and the dependent variables. It was found that the best configurations are a balanced line arrangement and a monotone decreasing MT order, coupled with either a decreasing or a bowl unreliability configuration, with the first generally resulting in a reduced TR and the second leading to a lower ABL than those of a balanced line.

Keywords: unreliable production lines, unequal mean operation times, unbalanced failure and repair rates, throughput, average buffer level

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2921 Impact of Transitioning to Renewable Energy Sources on Key Performance Indicators and Artificial Intelligence Modules of Data Center

Authors: Ahmed Hossam ElMolla, Mohamed Hatem Saleh, Hamza Mostafa, Lara Mamdouh, Yassin Wael

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Artificial intelligence (AI) is reshaping industries, and its potential to revolutionize renewable energy and data center operations is immense. By harnessing AI's capabilities, we can optimize energy consumption, predict fluctuations in renewable energy generation, and improve the efficiency of data center infrastructure. This convergence of technologies promises a future where energy is managed more intelligently, sustainably, and cost-effectively. The integration of AI into renewable energy systems unlocks a wealth of opportunities. Machine learning algorithms can analyze vast amounts of data to forecast weather patterns, solar irradiance, and wind speeds, enabling more accurate energy production planning. AI-powered systems can optimize energy storage and grid management, ensuring a stable power supply even during intermittent renewable generation. Moreover, AI can identify maintenance needs for renewable energy infrastructure, preventing costly breakdowns and maximizing system lifespan. Data centers, which consume substantial amounts of energy, are prime candidates for AI-driven optimization. AI can analyze energy consumption patterns, identify inefficiencies, and recommend adjustments to cooling systems, server utilization, and power distribution. Predictive maintenance using AI can prevent equipment failures, reducing energy waste and downtime. Additionally, AI can optimize data placement and retrieval, minimizing energy consumption associated with data transfer. As AI transforms renewable energy and data center operations, modified Key Performance Indicators (KPIs) will emerge. Traditional metrics like energy efficiency and cost-per-megawatt-hour will continue to be relevant, but additional KPIs focused on AI's impact will be essential. These might include AI-driven cost savings, predictive accuracy of energy generation and consumption, and the reduction of carbon emissions attributed to AI-optimized operations. By tracking these KPIs, organizations can measure the success of their AI initiatives and identify areas for improvement. Ultimately, the synergy between AI, renewable energy, and data centers holds the potential to create a more sustainable and resilient future. By embracing these technologies, we can build smarter, greener, and more efficient systems that benefit both the environment and the economy.

Keywords: data center, artificial intelligence, renewable energy, energy efficiency, sustainability, optimization, predictive analytics, energy consumption, energy storage, grid management, data center optimization, key performance indicators, carbon emissions, resiliency

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2920 Rapid, Automated Characterization of Microplastics Using Laser Direct Infrared Imaging and Spectroscopy

Authors: Andreas Kerstan, Darren Robey, Wesam Alvan, David Troiani

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Over the last 3.5 years, Quantum Cascade Lasers (QCL) technology has become increasingly important in infrared (IR) microscopy. The advantages over fourier transform infrared (FTIR) are that large areas of a few square centimeters can be measured in minutes and that the light intensive QCL makes it possible to obtain spectra with excellent S/N, even with just one scan. A firmly established solution of the laser direct infrared imaging (LDIR) 8700 is the analysis of microplastics. The presence of microplastics in the environment, drinking water, and food chains is gaining significant public interest. To study their presence, rapid and reliable characterization of microplastic particles is essential. Significant technical hurdles in microplastic analysis stem from the sheer number of particles to be analyzed in each sample. Total particle counts of several thousand are common in environmental samples, while well-treated bottled drinking water may contain relatively few. While visual microscopy has been used extensively, it is prone to operator error and bias and is limited to particles larger than 300 µm. As a result, vibrational spectroscopic techniques such as Raman and FTIR microscopy have become more popular, however, they are time-consuming. There is a demand for rapid and highly automated techniques to measure particle count size and provide high-quality polymer identification. Analysis directly on the filter that often forms the last stage in sample preparation is highly desirable as, by removing a sample preparation step it can both improve laboratory efficiency and decrease opportunities for error. Recent advances in infrared micro-spectroscopy combining a QCL with scanning optics have created a new paradigm, LDIR. It offers improved speed of analysis as well as high levels of automation. Its mode of operation, however, requires an IR reflective background, and this has, to date, limited the ability to perform direct “on-filter” analysis. This study explores the potential to combine the filter with an infrared reflective surface filter. By combining an IR reflective material or coating on a filter membrane with advanced image analysis and detection algorithms, it is demonstrated that such filters can indeed be used in this way. Vibrational spectroscopic techniques play a vital role in the investigation and understanding of microplastics in the environment and food chain. While vibrational spectroscopy is widely deployed, improvements and novel innovations in these techniques that can increase the speed of analysis and ease of use can provide pathways to higher testing rates and, hence, improved understanding of the impacts of microplastics in the environment. Due to its capability to measure large areas in minutes, its speed, degree of automation and excellent S/N, the LDIR could also implemented for various other samples like food adulteration, coatings, laminates, fabrics, textiles and tissues. This presentation will highlight a few of them and focus on the benefits of the LDIR vs classical techniques.

Keywords: QCL, automation, microplastics, tissues, infrared, speed

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2919 Influence of Convective Boundary Condition on Chemically Reacting Micropolar Fluid Flow over a Truncated Cone Embedded in Porous Medium

Authors: Pradeepa Teegala, Ramreddy Chitteti

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This article analyzes the mixed convection flow of chemically reacting micropolar fluid over a truncated cone embedded in non-Darcy porous medium with convective boundary condition. In addition, heat generation/absorption and Joule heating effects are taken into consideration. The similarity solution does not exist for this complex fluid flow problem, and hence non-similarity transformations are used to convert the governing fluid flow equations along with related boundary conditions into a set of nondimensional partial differential equations. Many authors have been applied the spectral quasi-linearization method to solve the ordinary differential equations, but here the resulting nonlinear partial differential equations are solved for non-similarity solution by using a recently developed method called the spectral quasi-linearization method (SQLM). Comparison with previously published work on special cases of the problem is performed and found to be in excellent agreement. The effect of pertinent parameters namely, Biot number, mixed convection parameter, heat generation/absorption, Joule heating, Forchheimer number, chemical reaction, micropolar and magnetic field on physical quantities of the flow are displayed through graphs and the salient features are explored in detail. Further, the results are analyzed by comparing with two special cases, namely, vertical plate and full cone wherever possible.

Keywords: chemical reaction, convective boundary condition, joule heating, micropolar fluid, mixed convection, spectral quasi-linearization method

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2918 Dual Metal Organic Framework Derived N-Doped Fe3C Nanocages Decorated with Ultrathin ZnIn2S4 Nanosheets for Efficient Photocatalytic Hydrogen Generation

Authors: D. Amaranatha Reddy

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Highly efficient and stable co-catalysts materials is of great important for boosting photo charge carrier’s separation, transportation efficiency, and accelerating the catalytic reactive sites of semiconductor photocatalysts. As a result, it is of decisive importance to fabricate low price noble metal free co-catalysts with high catalytic reactivity, but it remains very challenging. Considering this challenge here, dual metal organic frame work derived N-Doped Fe3C nanocages have been rationally designed and decorated with ultrathin ZnIn2S4 nanosheets for efficient photocatalytic hydrogen generation. The fabrication strategy precisely integrates co-catalyst nanocages with ultrathin two-dimensional (2D) semiconductor nanosheets by providing tightly interconnected nano-junctions and helps to suppress the charge carrier’s recombination rate. Furthermore, constructed highly porous hybrid structures expose ample active sites for catalytic reduction reactions and harvest visible light more effectively by light scattering. As a result, fabricated nanostructures exhibit superior solar driven hydrogen evolution rate (9600 µmol/g/h) with an apparent quantum efficiency of 3.6 %, which is relatively higher than the Pt noble metal co-catalyst systems and earlier reported ZnIn2S4 based nanohybrids. We believe that the present work promotes the application of sulfide based nanostructures in solar driven hydrogen production.

Keywords: photocatalysis, water splitting, hydrogen fuel production, solar-driven hydrogen

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2917 Development of a Methodology for Surgery Planning and Control: A Management Approach to Handle the Conflict of High Utilization and Low Overtime

Authors: Timo Miebach, Kirsten Hoeper, Carolin Felix

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In times of competitive pressures and demographic change, hospitals have to reconsider their strategies as a company. Due to the fact, that operations are one of the main income and one of the primary cost drivers otherwise, a process-oriented approach and an efficient use of resources seems to be the right way for getting a consistent market position. Thus, the efficient operation room occupancy planning is an important cause variable for the success and continued the existence of these institutions. A high utilization of resources is essential. This means a very high, but nevertheless sensible capacity-oriented utilization of working systems that can be realized by avoiding downtimes and a thoughtful occupancy planning. This engineering approach should help hospitals to reach her break-even point. Firstly, the aim is to establish a strategy point, which can be used for the generation of a planned throughput time. Secondly, the operation planning and control should be facilitated and implemented accurately by the generation of time modules. More than 100,000 data records of the Hannover Medical School were analyzed. The data records contain information about the type of conducted operation, the duration of the individual process steps, and all other organizational-specific data such as an operating room. Based on the aforementioned data base, a generally valid model was developed by an analysis to define a strategy point which takes the conflict of capacity utilization and low overtime into account. Furthermore, time modules were generated in this work, which allows a simplified and flexible operation planning and control for the operation manager. By the time modules, it is possible to reduce a high average value of the idle times of the operation rooms. Furthermore, the potential is used to minimize the idle time spread.

Keywords: capacity, operating room, surgery planning and control, utilization

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2916 Unauthorized License Verifier and Secure Access to Vehicle

Authors: G. Prakash, L. Mohamed Aasiq, N. Dhivya, M. Jothi Mani, R. Mounika, B. Gomathi

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In our day to day life, many people met with an accident due to various reasons like over speed, overload in the vehicle, violation of the traffic rules, etc. Driving license system is difficult task for the government to monitor. To prevent non-licensees from driving who are causing most of the accidents, a new system is proposed. The proposed system consists of a smart card capable of storing the license details of a particular person. Vehicles such as cars, bikes etc., should have a card reader capable of reading the particular license. A person, who wishes to drive the vehicle, should insert the card (license) in the vehicle and then enter the password in the keypad. If the license data stored in the card and database about the entire license holders in the microcontroller matches, he/she can proceed for ignition after the automated opening of the fuel tank valve, otherwise the user is restricted to use the vehicle. Moreover, overload detector in our proposed system verifies and then prompts the user to avoid overload before driving. This increases the security of vehicles and also ensures safe driving by preventing accidents.

Keywords: license, verifier, EEPROM, secure, overload detection

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2915 The Grammatical Dictionary Compiler: A System for Kartvelian Languages

Authors: Liana Lortkipanidze, Nino Amirezashvili, Nino Javashvili

Abstract:

The purpose of the grammatical dictionary is to provide information on the morphological and syntactic characteristics of the basic word in the dictionary entry. The electronic grammatical dictionaries are used as a tool of automated morphological analysis for texts processing. The Georgian Grammatical Dictionary should contain grammatical information for each word: part of speech, type of declension/conjugation, grammatical forms of the word (paradigm), alternative variants of basic word/lemma. In this paper, we present the system for compiling the Georgian Grammatical Dictionary automatically. We propose dictionary-based methods for extending grammatical lexicons. The input lexicon contains only a few number of words with identical grammatical features. The extension is based on similarity measures between features of words; more precisely, we add words to the extended lexicons, which are similar to those, which are already in the grammatical dictionary. Our dictionaries are corpora-based, and for the compiling, we introduce the method for lemmatization of unknown words, i.e., words of which neither full form nor lemma is in the grammatical dictionary.

Keywords: acquisition of lexicon, Georgian grammatical dictionary, lemmatization rules, morphological processor

Procedia PDF Downloads 136
2914 Transparency of Algorithmic Decision-Making: Limits Posed by Intellectual Property Rights

Authors: Olga Kokoulina

Abstract:

Today, algorithms are assuming a leading role in various areas of decision-making. Prompted by a promise to provide increased economic efficiency and fuel solutions for pressing societal challenges, algorithmic decision-making is often celebrated as an impartial and constructive substitute for human adjudication. But in the face of this implied objectivity and efficiency, the application of algorithms is also marred with mounting concerns about embedded biases, discrimination, and exclusion. In Europe, vigorous debates on risks and adverse implications of algorithmic decision-making largely revolve around the potential of data protection laws to tackle some of the related issues. For example, one of the often-cited venues to mitigate the impact of potentially unfair decision-making practice is a so-called 'right to explanation'. In essence, the overall right is derived from the provisions of the General Data Protection Regulation (‘GDPR’) ensuring the right of data subjects to access and mandating the obligation of data controllers to provide the relevant information about the existence of automated decision-making and meaningful information about the logic involved. Taking corresponding rights and obligations in the context of the specific provision on automated decision-making in the GDPR, the debates mainly focus on efficacy and the exact scope of the 'right to explanation'. In essence, the underlying logic of the argued remedy lies in a transparency imperative. Allowing data subjects to acquire as much knowledge as possible about the decision-making process means empowering individuals to take control of their data and take action. In other words, forewarned is forearmed. The related discussions and debates are ongoing, comprehensive, and, often, heated. However, they are also frequently misguided and isolated: embracing the data protection law as ultimate and sole lenses are often not sufficient. Mandating the disclosure of technical specifications of employed algorithms in the name of transparency for and empowerment of data subjects potentially encroach on the interests and rights of IPR holders, i.e., business entities behind the algorithms. The study aims at pushing the boundaries of the transparency debate beyond the data protection regime. By systematically analysing legal requirements and current judicial practice, it assesses the limits of the transparency requirement and right to access posed by intellectual property law, namely by copyrights and trade secrets. It is asserted that trade secrets, in particular, present an often-insurmountable obstacle for realising the potential of the transparency requirement. In reaching that conclusion, the study explores the limits of protection afforded by the European Trade Secrets Directive and contrasts them with the scope of respective rights and obligations related to data access and portability enshrined in the GDPR. As shown, the far-reaching scope of the protection under trade secrecy is evidenced both through the assessment of its subject matter as well as through the exceptions from such protection. As a way forward, the study scrutinises several possible legislative solutions, such as flexible interpretation of the public interest exception in trade secrets as well as the introduction of the strict liability regime in case of non-transparent decision-making.

Keywords: algorithms, public interest, trade secrets, transparency

Procedia PDF Downloads 122
2913 Fully Autonomous Vertical Farm to Increase Crop Production

Authors: Simone Cinquemani, Lorenzo Mantovani, Aleksander Dabek

Abstract:

New technologies in agriculture are opening new challenges and new opportunities. Among these, certainly, robotics, vision, and artificial intelligence are the ones that will make a significant leap, compared to traditional agricultural techniques, possible. In particular, the indoor farming sector will be the one that will benefit the most from these solutions. Vertical farming is a new field of research where mechanical engineering can bring knowledge and know-how to transform a highly labor-based business into a fully autonomous system. The aim of the research is to develop a multi-purpose, modular, and perfectly integrated platform for crop production in indoor vertical farming. Activities will be based both on hardware development such as automatic tools to perform different activities on soil and plants, as well as research to introduce an extensive use of monitoring techniques based on machine learning algorithms. This paper presents the preliminary results of a research project of a vertical farm living lab designed to (i) develop and test vertical farming cultivation practices, (ii) introduce a very high degree of mechanization and automation that makes all processes replicable, fully measurable, standardized and automated, (iii) develop a coordinated control and management environment for autonomous multiplatform or tele-operated robots in environments with the aim of carrying out complex tasks in the presence of environmental and cultivation constraints, (iv) integrate AI-based algorithms as decision support system to improve quality production. The coordinated management of multiplatform systems still presents innumerable challenges that require a strongly multidisciplinary approach right from the design, development, and implementation phases. The methodology is based on (i) the development of models capable of describing the dynamics of the various platforms and their interactions, (ii) the integrated design of mechatronic systems able to respond to the needs of the context and to exploit the strength characteristics highlighted by the models, (iii) implementation and experimental tests performed to test the real effectiveness of the systems created, evaluate any weaknesses so as to proceed with a targeted development. To these aims, a fully automated laboratory for growing plants in vertical farming has been developed and tested. The living lab makes extensive use of sensors to determine the overall state of the structure, crops, and systems used. The possibility of having specific measurements for each element involved in the cultivation process makes it possible to evaluate the effects of each variable of interest and allows for the creation of a robust model of the system as a whole. The automation of the laboratory is completed with the use of robots to carry out all the necessary operations, from sowing to handling to harvesting. These systems work synergistically thanks to the knowledge of detailed models developed based on the information collected, which allows for deepening the knowledge of these types of crops and guarantees the possibility of tracing every action performed on each single plant. To this end, artificial intelligence algorithms have been developed to allow synergistic operation of all systems.

Keywords: automation, vertical farming, robot, artificial intelligence, vision, control

Procedia PDF Downloads 35
2912 Design and Analysis of a Combined Cooling, Heating and Power Plant for Maximum Operational Flexibility

Authors: Salah Hosseini, Hadi Ramezani, Bagher Shahbazi, Hossein Rabiei, Jafar Hooshmand, Hiwa Khaldi

Abstract:

Diversity of energy portfolio and fluctuation of urban energy demand establish the need for more operational flexibility of combined Cooling, Heat, and Power Plants. Currently, the most common way to achieve these specifications is the use of heat storage devices or wet operation of gas turbines. The current work addresses using variable extraction steam turbine in conjugation with a gas turbine inlet cooling system as an alternative way for enhancement of a CCHP cycle operating range. A thermodynamic model is developed and typical apartments building in PARDIS Technology Park (located at Tehran Province) is chosen as a case study. Due to the variable Heat demand and using excess chiller capacity for turbine inlet cooling purpose, the mentioned steam turbine and TIAC system provided an opportunity for flexible operation of the cycle and boosted the independence of the power and heat generation in the CCHP plant. It was found that the ratio of power to the heat of CCHP cycle varies from 12.6 to 2.4 depending on the City heating and cooling demands and ambient condition, which means a good independence between power and heat generation. Furthermore, selection of the TIAC design temperature is done based on the amount of ratio of power gain to TIAC coil surface area, it was found that for current cycle arrangement the TIAC design temperature of 15 C is most economical. All analysis is done based on the real data, gathered from the local weather station of the PARDIS site.

Keywords: CCHP plant, GTG, HRSG, STG, TIAC, operational flexibility, power to heat ratio

Procedia PDF Downloads 275
2911 All-Optical Gamma-Rays and Positrons Source by Ultra-Intense Laser Irradiating an Al Cone

Authors: T. P. Yu, J. J. Liu, X. L. Zhu, Y. Yin, W. Q. Wang, J. M. Ouyang, F. Q. Shao

Abstract:

A strong electromagnetic field with E>1015V/m can be supplied by an intense laser such as ELI and HiPER in the near future. Exposing in such a strong laser field, laser-matter interaction enters into the near quantum electrodynamics (QED) regime and highly non-linear physics may occur during the laser-matter interaction. Recently, the multi-photon Breit-Wheeler (BW) process attracts increasing attention because it is capable to produce abundant positrons and it enhances the positron generation efficiency significantly. Here, we propose an all-optical scheme for bright gamma rays and dense positrons generation by irradiating a 1022 W/cm2 laser pulse onto an Al cone filled with near-critical-density plasmas. Two-dimensional (2D) QED particle-in-cell (PIC) simulations show that, the radiation damping force becomes large enough to compensate for the Lorentz force in the cone, causing radiation-reaction trapping of a dense electron bunch in the laser field. The trapped electrons oscillate in the laser electric field and emits high-energy gamma photons in two ways: (1) nonlinear Compton scattering due to the oscillation of electrons in the laser fields, and (2) Compton backwardscattering resulting from the bunch colliding with the reflected laser by the cone tip. The multi-photon Breit-Wheeler process is thus initiated and abundant electron-positron pairs are generated with a positron density ~1027m-3. The scheme is finally demonstrated by full 3D PIC simulations, which indicate the positron flux is up to 109. This compact gamma ray and positron source may have promising applications in future.

Keywords: BW process, electron-positron pairs, gamma rays emission, ultra-intense laser

Procedia PDF Downloads 258
2910 Feasibility Study of Tidal Current of the Bay of Bengal to Generate Electricity as a Renewable Energy

Authors: Myisha Ahmad, G. M. Jahid Hasan

Abstract:

Electricity is the pinnacle of human civilization. At present, the growing concerns over significant climate change have intensified the importance of the use of renewable energy technologies for electricity generation. The interest is primarily due to better energy security, smaller environmental impact and providing a sustainable alternative compared to the conventional energy sources. Solar power, wind, biomass, tidal power, and wave power are some of the most reliable sources of renewable energy. Ocean approximately holds 2×10³ TW of energy and has the largest renewable energy resource on the planet. Ocean energy has many forms namely, encompassing tides, ocean circulation, surface waves, salinity and thermal gradients. Ocean tide in particular, associates both potential and kinetic energy. The study is focused on the latter concept that deals with tidal current energy conversion technologies. Tidal streams or marine currents generate kinetic energy that can be extracted by marine current energy devices and converted into transmittable energy form. The principle of technology development is very comparable to that of wind turbines. Conversion of marine tidal resources into substantial electrical power offers immense opportunities to countries endowed with such resources and this work is aimed at addressing such prospects of Bangladesh. The study analyzed the extracted current velocities from numerical model works at several locations in the Bay of Bengal. Based on current magnitudes, directions and available technologies the most fitted locations were adopted and possible annual generation capacity was estimated. The paper also examines the future prospects of tidal current energy along the Bay of Bengal and establishes a constructive approach that could be adopted in future project developments.

Keywords: bay of Bengal, energy potential, renewable energy, tidal current

Procedia PDF Downloads 371
2909 Automation Test Method and HILS Environment Configuration for Hydrogen Storage System Management Unit Verification

Authors: Jaejeogn Kim, Jeongmin Hong, Jungin Lee

Abstract:

The Hydrogen Storage System Management Unit (HMU) is a controller that manages hydrogen charging and storage. It detects hydrogen leaks and tank pressure and temperature, calculates the charging concentration and remaining amount, and controls the opening and closing of the hydrogen tank valve. Since this role is an important part of the vehicle behavior and stability of Fuel Cell Electric Vehicles (FCEV), verifying the HMU controller is an essential part. To perform verification under various conditions, it is necessary to increase time efficiency based on an automated verification environment and increase the reliability of the controller by applying numerous test cases. To this end, we introduce the HMU controller automation verification method by applying the HILS environment and an automation test program with the ASAM XIL standard.

Keywords: HILS, ASAM, fuel cell electric vehicle, automation test, hydrogen storage system

Procedia PDF Downloads 64
2908 High-Rise Building with PV Facade

Authors: Jiří Hirš, Jitka Mohelnikova

Abstract:

A photovoltaic system integrated into a high-rise building façade was studied. The high-rise building is located in the Central Europe region with temperate climate and dominant partly cloudy and overcast sky conditions. The PV façade has been monitored since 2013. The three-year monitoring of the façade energy generation shows that the façade has an important impact on the building energy efficiency and sustainable operation.

Keywords: buildings, energy, PV façade, solar radiation

Procedia PDF Downloads 302
2907 Modeling of Building a Conceptual Scheme for Multimodal Freight Transportation Information System

Authors: Gia Surguladze, Nino Topuria, Lily Petriashvili, Giorgi Surguladze

Abstract:

Modeling of building processes of a multimodal freight transportation support information system is discussed based on modern CASE technologies. Functional efficiencies of ports in the eastern part of the Black Sea are analyzed taking into account their ecological, seasonal, resource usage parameters. By resources, we mean capacities of berths, cranes, automotive transport, as well as work crews and neighbouring airports. For the purpose of designing database of computer support system for Managerial (Logistics) function, using Object-Role Modeling (ORM) tool (NORMA – Natural ORM Architecture) is proposed, after which Entity Relationship Model (ERM) is generated in automated process. The software is developed based on Process-Oriented and Service-Oriented architecture, in Visual Studio.NET environment.

Keywords: seaport resources, business-processes, multimodal transportation, CASE technology, object-role model, entity relationship model, SOA

Procedia PDF Downloads 425
2906 Design and Development of Fleet Management System for Multi-Agent Autonomous Surface Vessel

Authors: Zulkifli Zainal Abidin, Ahmad Shahril Mohd Ghani

Abstract:

Agent-based systems technology has been addressed as a new paradigm for conceptualizing, designing, and implementing software systems. Agents are sophisticated systems that act autonomously across open and distributed environments in solving problems. Nevertheless, it is impractical to rely on a single agent to do all computing processes in solving complex problems. An increasing number of applications lately require multiple agents to work together. A multi-agent system (MAS) is a loosely coupled network of agents that interact to solve problems that are beyond the individual capacities or knowledge of each problem solver. However, the network of MAS still requires a main system to govern or oversees the operation of the agents in order to achieve a unified goal. We had developed a fleet management system (FMS) in order to manage the fleet of agents, plan route for the agents, perform real-time data processing and analysis, and issue sets of general and specific instructions to the agents. This FMS should be able to perform real-time data processing, communicate with the autonomous surface vehicle (ASV) agents and generate bathymetric map according to the data received from each ASV unit. The first algorithm is developed to communicate with the ASV via radio communication using standard National Marine Electronics Association (NMEA) protocol sentences. Next, the second algorithm will take care of the path planning, formation and pattern generation is tested using various sample data. Lastly, the bathymetry map generation algorithm will make use of data collected by the agents to create bathymetry map in real-time. The outcome of this research is expected can be applied on various other multi-agent systems.

Keywords: autonomous surface vehicle, fleet management system, multi agent system, bathymetry

Procedia PDF Downloads 266
2905 Depollution of the Pinheiros River in the City of São Paulo: Mapping the Dynamics of Conflicts and Coalitions between Actors in Two Recent Depollution Projects

Authors: Adalberto Gregorio Back

Abstract:

Historically, the Pinheiros River, which crosses the urban area of the largest South American metropolis, the city of São Paulo, has been the subject of several interventions involving different interests and multiple demands, including the implementation of road axes and industrial occupation in the city, following its floodplains. the dilution of sewers; generation of electricity, with the reversal of its waters to the Billings Dam; and urban drainage. These processes, together with the exclusionary and peripheral urban sprawl with high population density in the peripheries, result in difficulties for the collection and treatment of household sewage, which flow into the tributaries and the Pinheiros River itself. In the last 20 years, two separate projects have been undertaken to clean up its waters. The first one between 2001-2011 was the flotation system, aimed at cleaning the river in its own gutter with equipment installed near the Bilings Dam; and, more recently, from 2019 to 2022, the proposal to connect about 74 thousand dwellings to the sewage collection and treatment system, as well as to install treatment plants in the tributaries of Pinheiros where the connection to the system is impracticable, given the irregular occupations. The purpose of this paper is to make a comparative analysis on the dynamics of conflicts, interests and opportunities of coalitions between the actors involved in the two referred projects of pollution of the Pinheiros River. For this, we use the analysis of documents produced by the state government; as well as documents related to the legal disputes that occurred in the first attempt of decontamination involving the sanitation company; the Billings Dam management company interested in power generation; the city hall and regular and irregular dwellings not linked to the sanitation system.

Keywords: depollution of the Pinheiros River, interests groups, São Paulo, water energy nexus

Procedia PDF Downloads 104
2904 Characterization of Surface Suction Grippers for Continuous-Discontinuous Fiber Reinforced Semi-Finished Parts of an Automated Handling and Preforming Operation

Authors: Jürgen Fleischer, Woramon Pangboonyanon, Dominic Lesage

Abstract:

Non-metallic lightweight materials such as fiber reinforced plastics (FRP) become very significant at present. Prepregs e.g. SMC and unidirectional tape (UD-tape) are one of raw materials used to produce FRP. This study concerns with the manufacturing steps of handling and preforming of this UD-SMC and focuses on the investigation of gripper characteristics regarding gripping forces in normal and lateral direction, in order to identify suitable operating pressures for a secure gripping operation. A reliable handling and preforming operation results in a higher adding value of the overall process chain. As a result, the suitable operating pressures depending on travelling direction for each material type could be shown. Moreover, system boundary conditions regarding allowable pulling force in normal and lateral directions during preforming could be measured.

Keywords: continuous-discontinuous fiber reinforced plastics, UD-SMC-prepreg, handling, preforming, prepregs, sheet moulding compounds, surface suction gripper

Procedia PDF Downloads 220