Search results for: vehicle following models

Authors: Heiko Diefenbach, Christoph H. Glock

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Order picking, i.e., the order-based retrieval of items in a warehouse, is an important time- and cost-intensive process for many logistic systems. Despite the ongoing trend of automation, most order picking systems are still manual picker-to-parts systems, where human pickers walk through the warehouse to collect ordered items. Human work in warehouses is not free from errors, and order pickers may at times pick the wrong or the incorrect number of items. Errors can cause additional costs and significant correction efforts. Moreover, age might increase a person’s likelihood to make mistakes. Hence, the negative impact of picking errors might increase for an aging workforce currently witnessed in many regions globally. A significant amount of research has focused on making order picking systems more efficient. Among other factors, storage assignment, i.e., the assignment of items to storage locations (e.g., shelves) within the warehouse, has been subject to optimization. Usually, the objective is to assign items to storage locations such that order picking times are minimized. Surprisingly, there is a lack of research concerned with picking errors and respective prevention approaches. This paper hypothesize that the storage assignment of items can affect the probability of pick errors. For example, storing similar-looking items apart from one other might reduce confusion. Moreover, storing items that are hard to count or require a lot of counting at easy-to-access and easy-to-comprehend self heights might reduce the probability to pick the wrong number of items. Based on this hypothesis, the paper discusses how to incorporate error-prevention measures into mathematical models for storage assignment optimization. Various approaches with respective benefits and shortcomings are presented and mathematically modeled. To investigate the newly developed models further, they are compared to conventional storage assignment strategies in a computational study. The study specifically investigates how the importance of error prevention increases with pickers being more prone to errors due to age, for example. The results suggest that considering error-prevention measures for storage assignment can reduce error probabilities with only minor decreases in picking efficiency. The results might be especially relevant for an aging workforce.

Keywords: an aging workforce, error prevention, order picking, storage assignment

Procedia PDF Downloads 204

Authors: R. F. Vieira, D. Lopes, I. Baptista, S. A. Figueiredo, V. F. Domingues, R. Jorge, C. Delerue-matos, O. M. Freitas

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Odours are generated in municipal solid wastes management plants as a result of decomposition of organic matter, especially when anaerobic degradation occurs. Information was collected about the substances and respective concentration in the surrounding atmosphere of some management plants. The main components which are associated with these unpleasant odours were identified: ammonia, hydrogen sulfide and mercaptans. The first is the most common and the one that presents the highest concentrations, reaching values of 700 mg/m3. Biofiltration, which involves simultaneously biodegradation, absorption and adsorption processes, is a sustainable technology for the treatment of these odour emissions when a natural packing material is used. The packing material should ideally be cheap, durable, and allow the maximum microbiological activity and adsorption/absorption. The presence of nutrients and water is required for biodegradation processes. Adsorption and absorption are enhanced by high specific surface area, high porosity and low density. The main purpose of this work is the exploitation of natural waste materials, locally available, as packing media: heather (Erica lusitanica), chestnut bur (from Castanea sativa), peach pits (from Prunus persica) and eucalyptus bark (from Eucalyptus globulus). Preliminary batch tests of ammonia removal were performed in order to select the most interesting materials for biofiltration, which were then characterized. The following physical and chemical parameters were evaluated: density, moisture, pH, buffer and water retention capacity. The determination of equilibrium isotherms and the adjustment to Langmuir and Freundlich models was also performed. Both models can fit the experimental results. Based both in the material performance as adsorbent and in its physical and chemical characteristics, eucalyptus bark was considered the best material. It presents a maximum adsorption capacity of 0.78±0.45 mol/kg for ammonia. The results from its characterization are: 121 kg/m3 density, 9.8% moisture, pH equal to 5.7, buffer capacity of 0.370 mmol H+/kg of dry matter and water retention capacity of 1.4 g H2O/g of dry matter. The application of natural materials locally available, with little processing, in biofiltration is an economic and sustainable alternative that should be explored.

Keywords: ammonia removal, biofiltration, natural materials, odour control

Procedia PDF Downloads 369

Authors: German Osma, Gabriel Ordonez

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The production of metal-rubber spares for vehicles is a sequential process that consists in the transformation of raw material through cutting activities and chemical and thermal treatments, which demand electricity and fossil fuels. The energy efficiency analysis for these cases is mostly focused on studying of each machine or production step, but is not common to study of the quality of the production process achieves from aggregated value viewpoint, which can be used as a quality measurement for determining of impact on the environment. In this paper, the theory of exergetic cost is used for determining of aggregated exergy to three metal-rubber spares, from an exergy analysis and thermoeconomic analysis. The manufacturing processing of these spares is based into batch production technique, and therefore is proposed the use of this theory for discontinuous flows from of single models of workstations; subsequently, the complete exergy model of each product is built using flowcharts. These models are a representation of exergy flows between components into the machines according to electrical, mechanical and/or thermal expressions; they determine the demanded exergy to produce the effective transformation in raw materials (aggregated exergy value), the exergy losses caused by equipment and irreversibilities. The energy resources of manufacturing process are electricity and natural gas. The workstations considered are lathes, punching presses, cutters, zinc machine, chemical treatment tanks, hydraulic vulcanizing presses and rubber mixer. The thermoeconomic analysis was done by workstation and by spare; first of them describes the operation of the components of each machine and where the exergy losses are; while the second of them estimates the exergy-aggregated value for finished product and wasted feedstock. Results indicate that exergy efficiency of a mechanical workstation is between 10% and 60% while this value in the thermal workstations is less than 5%; also that each effective exergy-aggregated value is one-thirtieth of total exergy required for operation of manufacturing process, which amounts approximately to 2 MJ. These troubles are caused mainly by technical limitations of machines, oversizing of metal feedstock that demands more mechanical transformation work, and low thermal insulation of chemical treatment tanks and hydraulic vulcanizing presses. From established information, in this case, it is possible to appreciate the usefulness of theory of exergetic cost for analyzing of aggregated value in manufacturing processes.

Keywords: exergy-aggregated value, exergy efficiency, thermoeconomics, exergy modeling

Procedia PDF Downloads 171

Authors: Christian Neunzig, Simon Fahle, Jürgen Schulz, Matthias Möller, Bernd Kuhlenkötter

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Due to increasing cost pressure in global markets, artificial intelligence is becoming a technology that is decisive for competition. Predictive quality enables machinery and plant manufacturers to ensure product quality by using data-driven forecasts via machine learning models as a decision-making basis for test results. The use of cross-process Bosch production data along the value chain of hydraulic valves is a promising approach to classifying the quality characteristics of workpieces.

Keywords: predictive quality, hydraulics, machine learning, classification, supervised learning

Procedia PDF Downloads 231

Authors: M. Amparo Núñez-Andrés, Felipe Buill, Albert Prades

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More and more frequently, geomatics techniques such as terrestrial laser scanning or digital photogrammetry, either terrestrial or from drones, are being used to obtain digital terrain models (DTM) used for the monitoring of geological phenomena that cause natural disasters, such as landslides, rockfalls, debris-flow. One of the main multitemporal analyses developed from these models is the quantification of volume changes in the slopes and hillsides, either caused by erosion, fall, or land movement in the source area or sedimentation in the deposition zone. To carry out this task, it is necessary to filter the point clouds of all those elements that do not belong to the slopes. Among these elements, vegetation stands out as it is the one we find with the greatest presence and its constant change, both seasonal and daily, as it is affected by factors such as wind. One of the best-known indexes to detect vegetation on the image is the NVDI (Normalized Difference Vegetation Index), which is obtained from the combination of the infrared and red channels. Therefore it is necessary to have a multispectral camera. These cameras are generally of lower resolution than conventional RGB cameras, while their cost is much higher. Therefore we have to look for alternative indices based on RGB. In this communication, we present the results obtained in Georisk project (PID2019‐103974RB‐I00/MCIN/AEI/10.13039/501100011033) by using the GLI (Green Leaf Index) and ExG (Excessive Greenness), as well as the change to the Hue-Saturation-Value (HSV) color space being the H coordinate the one that gives us the most information for vegetation filtering. These filters are applied both to the images, creating binary masks to be used when applying the SfM algorithms, and to the point cloud obtained directly by the photogrammetric process without any previous filter or the one obtained by TLS (Terrestrial Laser Scanning). In this last case, we have also tried to work with a Riegl VZ400i sensor that allows the reception, as in the aerial LiDAR, of several returns of the signal. Information to be used for the classification on the point cloud. After applying all the techniques in different locations, the results show that the color-based filters allow correct filtering in those areas where the presence of shadows is not excessive and there is a contrast between the color of the slope lithology and the vegetation. As we have advanced in the case of using the HSV color space, it is the H coordinate that responds best for this filtering. Finally, the use of the various returns of the TLS signal allows filtering with some limitations.

Keywords: RGB index, TLS, photogrammetry, multispectral camera, point cloud

Procedia PDF Downloads 154

Authors: Mohammad Daud Ali

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For the past decade, marketing ethics has been a central point for academic researchers and practitioners. In particular, the development of frameworks and models to help in the analysis of marketing decisions are the focus of research. The current study aims at finding whether ethical decisions (honesty, fairness, responsibility, and respect) affect organizational marketing decisions. A selection of 250 respondents was purposely made from the telecommunication industry of Pakistan, out of which 204 responses were induced at an acceptable rate of 81.6%. A five-point Likert Scale, itemized with 12 items, was adopted from Taylor-Dunlop & Lester (2000) and used to draw responses regarding ethics.

Keywords: marketing, ethics, decisions making, telecommunication, Pakistan

Procedia PDF Downloads 97

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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Authors: Kayne Uriel K. Rodrigo, Jerriane Hillary Heart S. Marcial, Samuel C. Brillo

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Rice leaf diseases significantly impact yield production in rice-dependent countries, affecting their agricultural sectors. As part of precision agriculture, early and accurate detection of these diseases is crucial for effective mitigation practices and minimizing crop losses. Hence, this study proposes an enhancement to the Convolutional Neural Network (CNN), a widely-used method for Rice Leaf Disease Image Classification, by incorporating MobileViTV2—a recently advanced architecture that combines CNN and Vision Transformer models while maintaining fewer parameters, making it suitable for broader deployment on edge devices. Our methodology utilizes a publicly available rice disease image dataset from Kaggle, which was validated by a university structural biologist following the guidelines provided by the Philippine Rice Institute (PhilRice). Modifications to the dataset include renaming certain disease categories and augmenting the rice leaf image data through rotation, scaling, and flipping. The enhanced dataset was then used to train the MobileViTV2 model using the Timm library. The results of our approach are as follows: the model achieved notable performance, with 98% accuracy in both training and validation, 6% training and validation loss, and a Receiver Operating Characteristic (ROC) curve ranging from 95% to 100% for each label. Additionally, the F1 score was 97%. These metrics demonstrate a significant improvement compared to a conventional CNN-based approach, which, in a previous 2022 study, achieved only 78% accuracy after using 5 convolutional layers and 2 dense layers. Thus, it can be concluded that MobileViTV2, with its fewer parameters, outperforms traditional CNN models, particularly when applied to Rice Leaf Disease Image Identification. For future work, we recommend extending this model to include datasets validated by international rice experts and broadening the scope to accommodate biotic factors such as rice pest classification, as well as abiotic stressors such as climate, soil quality, and geographic information, which could improve the accuracy of disease prediction.

Keywords: convolutional neural network, MobileViTV2, rice leaf disease, precision agriculture, image classification, vision transformer

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Authors: Meysam Naeimi, Zili Li, Roumen Petrov, Rolf Dollevoet, Jilt Sietsma, Jun Wu

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The substantial similarity of fatigue mechanism in a new test rig for rolling contact fatigue (RCF) has been investigated. A new reduced-scale test rig is designed to perform controlled RCF tests in wheel-rail materials. The fatigue mechanism of the rig is evaluated in this study using a combined finite element-fatigue prediction approach. The influences of loading conditions on fatigue crack initiation have been studied. Furthermore, the effects of some artificial defects (squat-shape) on fatigue lives are examined. To simulate the vehicle-track interaction by means of the test rig, a three-dimensional finite element (FE) model is built up. The nonlinear material behaviour of the rail steel is modelled in the contact interface. The results of FE simulations are combined with the critical plane concept to determine the material points with the greatest possibility of fatigue failure. Based on the stress-strain responses, by employing of previously postulated criteria for fatigue crack initiation (plastic shakedown and ratchetting), fatigue life analysis is carried out. The results are reported for various loading conditions and different defect sizes. Afterward, the cyclic mechanism of the test rig is evaluated from the operational viewpoint. The results of fatigue life predictions are compared with the expected number of cycles of the test rig by its cyclic nature. Finally, the estimative duration of the experiments until fatigue crack initiation is roughly determined.

Keywords: fatigue, test rig, crack initiation, life, rail, squats

Procedia PDF Downloads 515

Authors: Shafaq Rubab

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The primary reason for dropping out of school is associated with students’ lack of motivation in class, especially in mathematics. Digital game-based learning is an approach that is being actively explored; there are very few learning games based on proven instructional design models or frameworks due to which the effectiveness of the learning games suffers. The purpose of this research was twofold: first, developing an appropriate instructional design model and second, evaluating the impact of the instructional design model on students’ motivation. This research contributes significantly to the existing literature in terms of student motivation and the impact of instructional design model in digital game-based learning. The sample size for this study consists of two hundred out-of-school students between the age of 6 and 12 years. The research methodology used for this research was a quasi-experimental approach and data was analyzed by using the instructional material motivational survey questionnaire which is adapted from the Keller Arcs model. Control and experimental groups consisting of two hundred students were analyzed by utilizing instructional material motivational survey (IMMS), and comparison of result from both groups showed the difference in the level of motivation of the students. The result of the research showed that the motivational level of student in the experimental group who were taught by the game was higher than the student in control group (taught by conventional methodology). The mean score of the experimental group against all subscales (attention, relevance, confidence, and satisfaction) of IMMS survey was higher; however, no statistical significance was found between the motivational scores of control and experimental group. The positive impact of game-based learning on students’ level of motivation, as measured in this study, strengthens the case for the use of pedagogically sound instructional design models in the design of interactive learning applications. In addition, the present study suggests learning from interactive, immersive applications as an alternative solution for children, especially in Third World countries, who, for various reasons, do not attend school. The mean score of experimental group against all subscales of IMMS survey was higher; however, no statistical significance was found between motivational scores of control and experimental group.

Keywords: digital game-based learning, students’ motivation, and instructional designing, instructional material motivational survey

Procedia PDF Downloads 420

Authors: Alexander I. Dovgyallo, Evgeniy A. Zinoviev, Svetlana O. Nekrasova

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The most of works applied for the pulse tube converters contain the workflow description implemented through the use of mathematical models on stationary modes. However, the study of the thermoacoustic systems unsteady behavior in the start, stop, and acoustic load changes modes is in the particular interest. The aim of the present study was to develop a mathematical thermal excitation model of acoustic oscillations in pulse tube engine (PTE) as a small-scale scheme of pulse tube engine operating at atmospheric air. Unlike some previous works this standing wave configuration is a fully closed system. The improvements over previous mathematical models are the following: the model allows specifying any values of porosity for regenerator, takes into account the piston weight and the friction in the cylinder and piston unit, and determines the operating frequency. The numerical method is based on the relation equations between the pressure and volume velocity variables at the ends of each element of PTE which is recorded through the appropriate transformation matrix. A solution demonstrates that the PTE operation frequency is the complex value, and it depends on the piston mass and the dynamic friction due to its movement in the cylinder. On the basis of the determined frequency thermoacoustically induced heat transport and generation of acoustic power equations were solved for channel with temperature gradient on its ends. The results of numerical simulation demonstrate the features of the initialization process of oscillation and show that that generated acoustic power more than power on the steady mode in a factor of 3…4. But doesn`t mean the possibility of its further continuous utilizing due to its existence only in transient mode which lasts only for a 30-40 sec. The experiments were carried out on small-scale PTE. The results shows that the value of acoustic power is in the range of 0.7..1.05 W for the defined frequency range f = 13..18 Hz and pressure amplitudes 11..12 kPa. These experimental data are satisfactorily correlated with the numerical modeling results. The mathematical model can be straightforwardly applied for the thermoacoustic devices with variable temperatures of thermal reservoirs and variable transduction loads which are expected to occur in practical implementations of portable thermoacoustic engines.

Keywords: nonlinear processes, pulse tube engine, thermal excitation, standing wave

Procedia PDF Downloads 377

Authors: Arianna Sacerdoti

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There is a rich and frequent representation of uniqueness in Statius’ poem called Thebaid. This topos interweave with a psychoanalytical study about groups and individual but is also a literary device. This paper will analyze all the passages in the 'Thebaid' referred to uniqueness and exceptionality. Antigone, Adrastus and other characters are, in fact, often characterized as the only ones to behave in a specific way or to do something. Also, the insomniac characters are often the only ones who do not sleep. The material of such a tòpos is very rich throughout the 'Thebaid'. The methodology will be text-oriented. Conclusions will enlighten Statius’ specific use of this tòpos, as related to his models, and will be interdisciplinary. In concluding, this is a study linking philology and psychoanalysis and focused on a topic which deserves a specific analysis.

Keywords: statius, Silius Italicus, uniqueness, epic

Procedia PDF Downloads 182

Authors: Navid Mirzaei Varzeghani, Mahmoud Saffarzadeh, Ali Naderan, Amirhossein Taheri

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Countries are progressing, and the world's busiest airports see year-on-year increases in travel demand. Passenger acceptability of an airport depends on the airport's appeals, which may include one of these routes between the city and the airport, as well as the facilities to reach them. One of the critical roles of transportation planners is to predict future transportation demand so that an integrated, multi-purpose system can be provided and diverse modes of transportation (rail, air, and land) can be delivered to a destination like an airport. In this study, 356 questionnaires were filled out in person over six days. First, the attraction of business and non-business trips was studied using data and a linear regression model. Lower travel costs, a range of ages more significant than 55, and other factors are essential for business trips. Non-business travelers, on the other hand, have prioritized using personal vehicles to get to the airport and ensuring convenient access to the airport. Business travelers are also less price-sensitive than non-business travelers regarding airport travel. Furthermore, carrying additional luggage (for example, more than one suitcase per person) undoubtedly decreases the attractiveness of public transit. Afterward, based on the manner and purpose of the trip, the locations with the highest trip generation to the airport were identified. The most famous district in Tehran was District 2, with 23 visits, while the most popular mode of transportation was an online taxi, with 12 trips from that location. Then, significant variables in separation and behavior of travel methods to access the airport were investigated for all systems. In this scenario, the most crucial factor is the time it takes to get to the airport, followed by the method's user-friendliness as a component of passenger preference. It has also been demonstrated that enhancing public transportation trip times reduces private transportation's market share, including taxicabs. Based on the responses of personal and semi-public vehicles, the desire of passengers to approach the airport via public transportation systems was explored to enhance present techniques and develop new strategies for providing the most efficient modes of transportation. Using the binary model, it was clear that business travelers and people who had already driven to the airport were the least likely to change.

Keywords: multimodal transportation, demand modeling, travel behavior, statistical models

Procedia PDF Downloads 173

Authors: Saul Emilio Rivero Mejia, Estefanya Marin Tabares, Carlos Andres Rodriguez Toro, Katherine Bolano Restrepo, Sarita Santa Cortes

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Medellin is the Colombian city with the best public transportation system in the country, which is composed of two subway lines, five metro cables, two Bus Rapid Transit lines, and a streetcar. But despite the above, the Aburra Valley, the area in which the city is located, comparatively speaking, has a lower number of urban roads per inhabitant built, compared to the national average. In addition, since there is approximately one vehicle for every three inhabitants in Medellin, the problems of congestion and environmental pollution have become more acute over the years, and it has even been necessary to implement restrictive measures to the use of private vehicles on a permanent basis. In that sense, due to the limitations of physical space, the low public investment in road infrastructure, it is necessary to opt for mobility alternatives according to the above. Within the options for the city, there is what is known as micromobility. Micromobility is understood as those small and light means of transport used to travel short distances, which use electrical energy, such as skateboards and bicycles. These transport alternatives have a high potential for use by the city's young population, but this requires an adequate infrastructure and also state regulation. Taking into account the above, this paper will analyze the current state and future of micro mobility in the city of Medellin, making a prospective analysis, supported by a PEST (political, economic, social and technological) analysis. Based on the above, it is expected to identify the growth of demand for these alternative means and its impact on the mobility of the city in the medium and short term.

Keywords: electric, micromobility, transport, sustainable

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Authors: Peggy Näser

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Factory planning is a systematic, objective-oriented process for planning a factory, structured into a sequence of phases, each of which is dependent on the preceding phase and makes use of particular methods and tools, and extending from the setting of objectives to the start of production. The digital factory, on the other hand, is the generic term for a comprehensive network of digital models, methods, and tools – including simulation and 3D visualisation – integrated by a continuous data management system. Its aim is the holistic planning, evaluation and ongoing improvement of all the main structures, processes and resources of the real factory in conjunction with the product. Digital factory planning has already become established in factory planning. The application of Building Information Modeling has not yet been established in factory planning but has been used predominantly in the planning of public buildings. Furthermore, this concept is limited to the planning of the buildings and does not include the planning of equipment of the factory (machines, technical equipment) and their interfaces to the building. BIM is a cooperative method of working, in which the information and data relevant to its lifecycle are consistently recorded, managed and exchanged in a transparent communication between the involved parties on the basis of digital models of a building. Both approaches, the planning approach of Building Information Modeling and the methodical approach of the Digital Factory, are based on the use of a comprehensive data model. Therefore it is necessary to examine how the approach of Building Information Modeling can be extended in the context of factory planning in such a way that an integration of the equipment planning, as well as the building planning, can take place in a common digital model. For this, a number of different perspectives have to be investigated: the equipment perspective including the tools used to implement a comprehensive digital planning process, the communication perspective between the planners of different fields, the legal perspective, that the legal certainty in each country and the quality perspective, on which the quality criteria are defined and the planning will be evaluated. The individual perspectives are examined and illustrated in the article. An approach model for the integration of factory planning into the BIM approach, in particular for the integrated planning of equipment and buildings and the continuous digital planning is developed. For this purpose, the individual factory planning phases are detailed in the sense of the integration of the BIM approach. A comprehensive software concept is shown on the tool. In addition, the prerequisites required for this integrated planning are presented. With the help of the newly developed approach, a better coordination between equipment and buildings is to be achieved, the continuity of the digital factory planning is improved, the data quality is improved and expensive implementation errors are avoided in the implementation.

Keywords: building information modeling, digital factory, digital planning, factory planning

Procedia PDF Downloads 267

Authors: Mustafa A. Al-Refai

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This paper investigates the energy storage technologies that can potentially enhance the use of solar energy. Water electrolysis systems are seen as the principal means of producing a large amount of hydrogen in the future. Starting from the analysis of the models of the system components, a complete simulation model was realized in the Matlab-Simulink environment. Results of the numerical simulations are provided. The operation of electrolysis and photovoltaic array combination is verified at various insulation levels. It is pointed out that solar cell arrays and electrolysers are producing the expected results with solar energy inputs that are continuously varying.

Keywords: electrolyzer, simulink, solar energy, storage system

Procedia PDF Downloads 435

Authors: Chen Zhang, Yongpeng Gu, Xiaotian Li

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Quasi-zero-stiffness (QZS) vibration isolation technology has garnered significant attention in both academia and industry, which enables ultra-low-frequency vibration isolation. In modern industries, such as shipbuilding and aerospace, rotating machinery generates vibrations over a wide frequency range, thus imposing more stringent requirements on vibration isolation technologies. These technologies must not only achieve ultra-low starting isolation frequencies but also provide effective isolation across mid- to high-frequency ranges. However, existing research on QZS vibration isolators primarily focuses on frequency ranges below 50 Hz. Moreover, studies have shown that in the mid-to high-frequency ranges, QZS isolators tend to generate resonance peaks that adversely affect their isolation performance. This limitation significantly restricts the practical applicability of QZS isolation technology. To address this issue, the present study investigates the high-frequency isolation performance of two typical QZS isolators: the mechanism type three-spring QZS isolator mechanism and the structure and bowl-shaped QZS isolator structure. First, the parameter conditions required to achieve quasi-zero stiffness characteristics for two isolators are derived based on static mechanical analysis. The theoretical transmissibility characteristics are then calculated using the harmonic balance method. Three-dimensional finite element models of two QZS isolators are developed using ABAQUS simulation software, and transmissibility curves are computed for the 0-500 Hz frequency range. The results indicate that the three-spring QZS mechanism exhibits multiple higher-order resonance peaks in the mid-to high-frequency ranges due to the higher-order models of the springs. Springs with fewer coils and larger diameters can shift the higher-order modals to higher frequencies but cannot entirely eliminate their occurrence. In contrast, the bowl-shaped QZS isolator, through shape optimization using a spline-based representation, effectively mitigates the generation of higher-order resonance modes, resulting in superior isolation performance in the mid-to high-frequency ranges. This study provides essential theoretical insights for optimizing the vibration isolation performance of QZS technologies in complex, wide-frequency vibration environments, offering significant practical value for their application.

Keywords: quasi-zero-stiffness, wide-frequency vibration, vibration isolator, transmissibility

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Authors: Kingsley Oturu, Winifred Ijomah, Wale Coker, Chibueze Achi

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With the impact of BREXIT and the need for cross-border collaboration, this international research investigated the use of a conceptual model for remanufacturing medical equipment (with a focus on anesthetic machines and baby incubators). Early findings of the research suggest that contextual factors need to be taken into consideration, as well as an emphasis on cleaning (e.g., sterilization) during the process of remanufacturing medical equipment. For example, copper tubings may be more important in the remanufacturing of anesthetic equipment in tropical climates than in cold climates.

Keywords: medical equipment remanufacture, sustainability, circular business models, remanufacture process model

Procedia PDF Downloads 172

Authors: Kaewkarn Phuangsombat, Arthit Phuangsombat, Anupun Terdwongworakul

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Hard seeds will not grow and can cause mold in sprouting process. Thus, the hard seeds need to be separated from the normal seeds. Near infrared hyperspectral imaging in a range of 900 to 1700 nm was implemented to develop a model by partial least squares discriminant analysis to discriminate the hard seeds from the normal seeds. The orientation of the seeds was also studied to compare the performance of the models. The model based on hilum-up orientation achieved the best result giving the coefficient of determination of 0.98, and root mean square error of prediction of 0.07 with classification accuracy was equal to 100%.

Keywords: mung bean, near infrared, germinatability, hard seed

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Authors: Jaya Mathew

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Many organizations are faced with the challenge of how to analyze and build Machine Learning models using their sensitive telemetry data. In this paper, we discuss how users can leverage the power of R without having to move their big data around as well as a cloud based solution for organizations willing to host their data in the cloud. By using ScaleR technology to benefit from parallelization and remote computing or R Services on premise or in the cloud, users can leverage the power of R at scale without having to move their data around.

Keywords: predictive maintenance, machine learning, big data, cloud based, on premise solution, R

Procedia PDF Downloads 379

Authors: Aboagye Boye Gilbert

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Teaching and learning (Education), not only in Ghana but the whole world is regarded as the (Stepping stone) vehicle to accelerate the country’s economy, development and social growth. Basically the ingredients for human development and the country in general is Vocational and Technical education and this has been stressed in Ghana’s education system since Pre-independence. To this effect, this research seeks to determine using demonstration method of Teachings sewing to improve the skills of form 3 Fashion Designing students of Baworo Integrated Community Centre for Employable Skills. In this research, reviewed literature on opinions of other researchers and what other people have done and said on related articles or topics, analyzed the research design used, translate the data gathered in the study. The study was design to gather information from the school on how they use Teaching methods to teach sewing. The targeted respondent contacted to give assistance Consist of students from BICCES, fashion teachers and tailored garment makers. The sample size consisted of 5 teachers, 20 students and 5 tailors were selected to answer questionnaire items that were used to gather the data for the study. The study revealed that most teachers and students agreed to the fact that demonstration, teaching and learning materials had a positive attitude towards the students in learning sewing. The study recommends that there should be more mechanisms in place to serve as a guide.

Keywords: VOTEC, BECE, BICCES, SHS

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Authors: Kinnsley Travis, Camerron M. Crowder

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Mapk8ip3 (Mitogen-Activated Protein Kinase 8 Interacting Protein 3) is a gene that codes for the JIP3 protein, which is a part of the JIP scaffolding protein family. This protein is involved in axonal vesicle transport, elongation and regeneration. Variants in the Mapk8ip3 gene are associated with a rare-genetic condition that results in a neurodevelopmental disorder that can cause a range of phenotypes including global developmental delay and intellectual disability. Currently, there are 18 known individuals diagnosed to have sequenced confirmed Mapk8ip3 genetic disorders. This project focuses on examining the impact of a subset of missense patient variants on the Jip3 protein function by overexpressing the mRNA of these variants in a zebrafish knockout model for Jip3. Plasmids containing cDNA with individual missense variants were reverse transcribed, purified, and injected into single-cell zebrafish embryos (Wild Type, Jip3 -/+, and Jip3 -/-). At 6-days post mRNA microinjection, morphological, behavioral, and microscopic phenotypes were examined in zebrafish larvae. Morphologically, we compared the size and shape of the zebrafish during their development over a 5-day period. Total locomotive activity was assessed using the Microtracker assay and patterns of movement over time were examined using the DanioVision assay. Lastly, we used confocal microscopy to examine sensory axons for swelling and shortened length, which are phenotypes observed in the loss-of-function knockout Jip3 zebrafish model. Using these assays during embryonic development, we determined the impact of various missense variants on Jip3 protein function, compared to knockout and wild-type zebrafish embryo models. Variants in the gene Mapk8ip3 cause rare-neurodevelopmental disorders due to an essential role in axonal vesicle transport, elongation and regeneration. A subset of missense variants was examined by overexpressing the mRNA of these variants in a Jip3 knock-out zebrafish. Morphological, behavioral, and microscopic phenotypes were examined in zebrafish larvae. Using these assays, the spectrum of disorders can be phenotypically determined and the impact of variant location can be compared to knockout and wild-type zebrafish embryo models.

Keywords: rare disease, neurodevelopmental disorders, mrna overexpression, zebrafish research

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Authors: Ihsanullah, Muataz Ali Atieh

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In this study, a low-cost adsorbent carbon fly ash (CFA) was used for the removal of Phenol from the water. The adsorbent characteristics were observed by the Thermogravimetric Analysis (TGA), BET specific surface area analyzer, Zeta Potential and Field Emission Scanning Electron Microscopy (FE-SEM). The effect of pH, agitation speed, contact time, adsorbent dosage, and initial concentration of phenol were studied on the removal of phenol from the water. The optimum values of these variables for maximum removal of phenol were also determined. Both Freundlich and Langmuir isotherm models were successfully applied to describe the experimental data. Results showed that low-cost adsorbent phenol can be successfully applied for the removal of Phenol from the water.

Keywords: phenol, fly ash, adsorption, carbon adsorbents

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Authors: Ahmet Karakuş, Akif Can Kilic, Emre Alptekin

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A growing number of studies have been conducted to determine how well-being may be predicted using well-designed models. It is necessary to investigate the backgrounds of features in order to construct a viable Subjective Well-Being (SWB) model. We have picked the suitable variables from the literature on SWB that are acceptable for real-world data instructions. The goal of this work is to evaluate the model by feeding it with SWB characteristics and then categorizing the stress levels using machine learning methods to see how well it performs on a real dataset. Despite the fact that it is a multiclass classification issue, we have achieved significant metric scores, which may be taken into account for a specific task.

Keywords: machine learning, multiclassification problem, subjective well-being, perceived stress scale

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Authors: Chun-Wei Yeh, Santhanamoorthi Nachimuthu, Jyh-Chiang Jiang

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Reducing greenhouse gases or converting them into fuels and chemicals with added value is vital for the environment. Given the enhanced techniques for hydrocarbon extraction in this context, the catalytic conversion of methane to methanol is particularly intriguing for future applications as vehicle fuels and/or bulk chemicals. Metal-organic frameworks (MOFs) have received much attention recently for the oxidation of methane to methanol. In addition, biomimetic material, particulate methane monooxygenase (pMMO), has been reported to convert methane using copper oxide clusters as active sites. Inspired by these, in this study, we considered the well-known MIL-53(Al) MOF as support for copper oxide clusters (Cu2Ox, Cu3Ox) to investigate their reactivity towards methane oxidation using Density Functional Theory (DFT) calculations. The copper oxide clusters (Cu2O2, Cu3O2) are modeled by oxidizing copper clusters (Cu2, Cu3) with two oxidizers, O2 and N2O. The initial C-H bond activation barriers on Cu2O2/MIL-53(Al) and Cu3O2/MIL-53(Al) catalysts are 0.70 eV and 0.64 eV, respectively, and are the rate-determining steps in the overall methane conversion to methanol reactions. The desorption energy of the methanol over the Cu2O/MIL-53(Al) and Cu3O/MIL-53(Al) is 0.71eV and 0.75 eV, respectively. Furthermore, to explore the prospect of catalyst reusability, we considered the different oxidants and proposed the different reaction pathways for completing the reaction cycle and regenerating the active copper oxide clusters. To know the reason for the difference between bi-copper and tri-cooper systems, we also did an electronic analysis. Finally, we calculate the Microkinetic Simulation. The result shows that the reaction can happen at room temperature.

Keywords: DFT study, copper oxide cluster, MOFs, methane conversion

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Authors: Karisma Karisma, Pardis Moslemzadeh Tehrani

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This study aims to conduct a high-level strategic dialogue on the lack of consensus, consistency, and legal certainty regarding blockchain-based energy prosumerism so that appropriate institutional and governance structures can be put in place to address the inadequacies and gaps in the legal and regulatory framework. The drive to achieve national and global decarbonization targets is a driving force behind climate goals and policies under the Paris Agreement. In recent years, efforts to ‘demonopolize’ and ‘decentralize’ energy generation and distribution have driven the energy transition toward decentralized systems, invoking concepts such as ownership, sovereignty, and autonomy of RE sources. The emergence of individual and collective forms of prosumerism and the rapid diffusion of blockchain is expected to play a critical role in the decarbonization and democratization of energy systems. However, there is a ‘regulatory void’ relating to individual and collective forms of prosumerism that could prevent the rapid deployment of blockchain systems and potentially stagnate the operationalization of blockchain-enabled energy sharing and trading activities. The application of broad and facile regulatory fixes may be insufficient to address the major regulatory gaps. First, to the authors’ best knowledge, the concepts and elements circumjacent to individual and collective forms of prosumerism have not been adequately described in the legal frameworks of many countries. Second, there is a lack of legal certainty regarding the creation and adaptation of business models in a highly regulated and centralized energy system, which inhibits the emergence of prosumer-driven niche markets. There are also current and prospective challenges relating to the legal status of blockchain-based platforms for facilitating energy transactions, anticipated with the diffusion of blockchain technology. With the rise of prosumerism in the energy sector, the areas of (a) network charges, (b) energy market access, (c) incentive schemes, (d) taxes and levies, and (e) licensing requirements are still uncharted territories in many countries. The uncertainties emanating from this area pose a significant hurdle to the widespread adoption of blockchain technology, a complementary technology that offers added value and competitive advantages for energy systems. The authors undertake a conceptual and theoretical investigation to elucidate the lack of consensus, consistency, and legal certainty in the study of blockchain-based prosumerism. In addition, the authors set an exploratory tone to the discussion by taking an analytically eclectic approach that builds on multiple sources and theories to delve deeper into this topic. As an interdisciplinary study, this research accounts for the convergence of regulation, technology, and the energy sector. The study primarily adopts desk research, which examines regulatory frameworks and conceptual models for crucial policies at the international level to foster an all-inclusive discussion. With their reflections and insights into the interaction of blockchain and prosumerism in the energy sector, the authors do not aim to develop definitive regulatory models or instrument designs, but to contribute to the theoretical dialogue to navigate seminal issues and explore different nuances and pathways. Given the emergence of blockchain-based energy prosumerism, identifying the challenges, gaps and fragmentation of governance regimes is key to facilitating global regulatory transitions.

Keywords: blockchain technology, energy sector, prosumer, legal and regulatory.

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Authors: Varun Dongre, Stefan Pirker, Stefan Heinrich

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Over the last decades, the numerical modelling of fluidized bed processes has become feasible even for industrial processes. Commonly, continuous two-fluid models are applied to describe large-scale fluidization. In order to allow for coarse grids novel two-fluid models account for unresolved sub-grid heterogeneities. However, computational efforts remain high – in the order of several hours of compute-time for a few seconds of real-time – thus preventing the representation of long-term phenomena such as heating or particle conversion processes. In order to overcome this limitation, data-based recurrence computational fluid dynamics (rCFD) has been put forward in recent years. rCFD can be regarded as a data-based method that relies on the numerical predictions of a conventional short-term simulation. This data is stored in a database and then used by rCFD to efficiently time-extrapolate the flow behavior in high spatial resolution. This study will compare the numerical predictions of rCFD simulations with those of corresponding full CFD reference simulations for lab-scale and pilot-scale fluidized beds. In assessing the predictive capabilities of rCFD simulations, we focus on solid mixing and secondary gas holdup. We observed that predictions made by rCFD simulations are highly sensitive to numerical parameters such as diffusivity associated with face swaps. We achieved a computational speed-up of four orders of magnitude (10,000 time faster than classical TFM simulation) eventually allowing for real-time simulations of fluidized beds. In the next step, we apply the checkerboarding technique by introducing gas tracers subjected to convection and diffusion. We then analyze the concentration profiles by observing mixing, transport of gas tracers, insights about the convective and diffusive pattern of the gas tracers, and further towards heat and mass transfer methods. Finally, we run rCFD simulations and calibrate them with numerical and physical parameters compared with convectional Two-fluid model (full CFD) simulation. As a result, this study gives a clear indication of the applicability, predictive capabilities, and existing limitations of rCFD in the realm of fluidization modelling.

Keywords: multiphase flow, recurrence CFD, two-fluid model, industrial processes

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Authors: Homayon Ahmad Panahi, Amir Hesam Hassani, Akram Torki, Elham Moniri

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A new sorbent was synthesized through chemical modification of clinoptilolite zeolite using 2-naphtol, and characterized with fourier transform infrared spectroscopy and elemental analysis methods and applied for the removal and elimination of trace naphthalene from water samples. The optimum pH value for sorption of the naphthalene by modified zeolite was in acidic pH. The sorption capacity of modified zeolite was 142 mg. g−1. Isotherm models, Langmuir, Frendlich and Temkin were employed to analyze the adsorption capacity of modified zeolite, which revealed that naphthalene adsorption by this zeolite follows Langmuir model.

Keywords: zeolite, clinoptilolite, modification, naphthalene

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Authors: Bahar Hazal Yalçınkaya, Bayram Yılmaz, Mustafa Özilgen

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Brain information transmission in the neuronal network occurs in the form of electrical signals. Neural work transmits information between the neurons or neurons and target cells by moving charged particles in a voltage field; a fraction of the energy utilized in this process is dissipated via entropy generation. Exergy loss and entropy generation models demonstrate the inefficiencies of the communication along the dendritic trees. In this study, neurons of 4 different animals were analyzed with one dimensional cable model with N=6 identical dendritic trees and M=3 order of symmetrical branching. Each branch symmetrically bifurcates in accordance with the 3/2 power law in an infinitely long cylinder with the usual core conductor assumptions, where membrane potential is conserved in the core conductor at all branching points. In the model, exergy loss and entropy generation rates are calculated for each branch of equivalent cylinders of electrotonic length (L) ranging from 0.1 to 1.5 for four different dendritic branches, input branch (BI), and sister branch (BS) and two cousin branches (BC-1 & BC-2). Thermodynamic analysis with the data coming from two different cat motoneuron studies show that in both experiments nearly the same amount of exergy is lost while generating nearly the same amount of entropy. Guinea pig vagal motoneuron loses twofold more exergy compared to the cat models and the squid exergy loss and entropy generation were nearly tenfold compared to the guinea pig vagal motoneuron model. Thermodynamic analysis show that the dissipated energy in the dendritic tress is directly proportional with the electrotonic length, exergy loss and entropy generation. Entropy generation and exergy loss show variability not only between the vertebrate and invertebrates but also within the same class. Concurrently, single action potential Na⁺ ion load, metabolic energy utilization and its thermodynamic aspect contributed for squid giant axon and mammalian motoneuron model. Energy demand is supplied to the neurons in the form of Adenosine triphosphate (ATP). Exergy destruction and entropy generation upon ATP hydrolysis are calculated. ATP utilization, exergy destruction and entropy generation showed differences in each model depending on the variations in the ion transport along the channels.

Keywords: ATP utilization, entropy generation, exergy loss, neuronal information transmittance

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Authors: Eze R. Onukwugha

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The presence of cyanide in wastewater is known to inhibit the normal functioning of bio-reactors since it has the tendency to poison reactor micro-organisms. Bench scale models of activated sludge reactors with varying aspect ratios were operated for the treatment of cassava wastewater at several values of hydraulic retention time (HRT). The different values of HRT were achieved by the use of a peristaltic pump to vary the rate of introduction of the wastewater into the reactor. The main parameters monitored are the cyanide concentration and respective COD values of the influent and effluent. These observed values were then transformed into a mathematical model for the prediction of treatment efficiency.

Keywords: wastewater, aspect ratio, cyanide-inhibited wastewater, modeling

Procedia PDF Downloads 78