Search results for: irrigationaxial flux machines

Authors: Fernanda M. Assef, Maria Teresinha A. Steiner

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The aim of this paper is to compare and discuss better classifier algorithm options for credit risk assessment by applying different Machine Learning techniques. Using records from a Brazilian financial institution, this study uses a database of 5,432 companies that are clients of the bank, where 2,600 clients are classified as non-defaulters, 1,551 are classified as defaulters and 1,281 are temporarily defaulters, meaning that the clients are overdue on their payments for up 180 days. For each case, a total of 15 attributes was considered for a one-against-all assessment using four different techniques: Artificial Neural Networks Multilayer Perceptron (ANN-MLP), Artificial Neural Networks Radial Basis Functions (ANN-RBF), Logistic Regression (LR) and finally Support Vector Machines (SVM). For each method, different parameters were analyzed in order to obtain different results when the best of each technique was compared. Initially the data were coded in thermometer code (numerical attributes) or dummy coding (for nominal attributes). The methods were then evaluated for each parameter and the best result of each technique was compared in terms of accuracy, false positives, false negatives, true positives and true negatives. This comparison showed that the best method, in terms of accuracy, was ANN-RBF (79.20% for non-defaulter classification, 97.74% for defaulters and 75.37% for the temporarily defaulter classification). However, the best accuracy does not always represent the best technique. For instance, on the classification of temporarily defaulters, this technique, in terms of false positives, was surpassed by SVM, which had the lowest rate (0.07%) of false positive classifications. All these intrinsic details are discussed considering the results found, and an overview of what was presented is shown in the conclusion of this study.

Keywords: artificial neural networks (ANNs), classifier algorithms, credit risk assessment, logistic regression, machine Learning, support vector machines

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Authors: Samain Sabrin, Joshua Pratt, Joshua Bryk, Maryam Karimi

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Globally, extreme heat events have led to a surge in the number of heat-related moralities. These incidents are further exacerbated in high-density population centers due to the Urban Heat Island (UHI) effect. Varieties of anthropogenic activities such as unsupervised land surface modifications, expansion of impervious areas, and lack of use of vegetation are all contributors to an increase in the amount of heat flux trapped by an urban canopy which intensifies the UHI effect. This project aims to propose a systematic approach to measure the impact of air quality and increased temperature based on urban morphology in the selected metropolitan cities. This project will measure the impact of build environment for urban and regional planning using human biometeorological evaluations (mean radiant temperature, Tmrt). We utilized the Rayman model (capable of calculating short and long wave radiation fluxes affecting the human body) to estimate the Tmrt in an urban environment incorporating location and height of buildings and trees as a supplemental tool in urban planning, and street design. Our current results suggest a strong correlation between building height and increased surface temperature in megacities. This model will help with; 1. Quantify the impacts of the built environment and surface properties on surrounding temperature, 2. Identify priority urban neighborhoods by analyzing Tmrt and air quality data at pedestrian level, 3. Characterizing the need for urban green infrastructure or better urban planning- maximizing the cooling benefit from existing Urban Green Infrastructure (UGI), and 4. Developing a hierarchy of streets for new UGI integration and propose new UGI based on site characteristics and cooling potential.

Keywords: air quality, heat mitigation, human-biometeorological indices, increased temperature, mean radiant temperature, radiation flux, sustainable development, thermal comfort, urban canopy, urban planning

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Authors: S. B. Q. Tran

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Drop condensation is the phenomenon that the tiny drops form when the oversaturated vapour present in the environment condenses on a substrate and makes the droplet growth. Recently, this subject has received much attention due to its applications in many fields such as thin film growth, heat transfer, recovery of atmospheric water and polymer templating. In literature, many papers investigated theoretically and experimentally in macro droplet growth with the size of millimeter scale of radius. However few papers about nanodroplet condensation are found in the literature especially theoretical work. In order to understand the droplet growth in nanoscale, we perform the numerical simulation work to study nanodroplet growth. We investigate and discuss the role of the droplet shape and monomer diffusion on drop growth and their effect on growth law. The effect of droplet shape is studied by doing parametric studies of contact angle and disjoining pressure magnitude. Besides, the effect of pinning and de-pinning behaviours is also studied. We investigate the axisymmetric homogeneous growth of 10–100 nm single water nanodroplet on a substrate surface. The main mechanism of droplet growth is attributed to the accumulation of laterally diffusing water monomers, formed by the absorption of water vapour in the environment onto the substrate. Under assumptions of quasi-steady thermodynamic equilibrium, the nanodroplet evolves according to the augmented Young–Laplace equation. Using continuum theory, we model the dynamics of nanodroplet growth including the coupled effects of disjoining pressure, contact angle and monomer diffusion with the assumption of constant flux of water monomers at the far field. The simulation result is validated by comparing with the published experimental result. For the case of nanodroplet growth with constant contact angle, our numerical results show that the initial droplet growth is transient by monomer diffusion. When the flux at the far field is small, at the beginning, the droplet grows by the diffusion of initially available water monomers on the substrate and after that by the flux at the far field. In the steady late growth rate of droplet radius and droplet height follow a power law of 1/3, which is unaffected by the substrate disjoining pressure and contact angle. However, it is found that the droplet grows faster in radial direction than high direction when disjoining pressure and contact angle increase. The simulation also shows the information of computational domain effect in the transient growth period. When the computational domain size is larger, the mass coming in the free substrate domain is higher. So the mass coming in the droplet is also higher. The droplet grows and reaches the steady state faster. For the case of pinning and de-pinning droplet growth, the simulation shows that the disjoining pressure does not affect the droplet radius growth law 1/3 in steady state. However the disjoining pressure modifies the growth rate of the droplet height, which then follows a power law of 1/4. We demonstrate how spatial depletion of monomers could lead to a growth arrest of the nanodroplet, as observed experimentally.

Keywords: augmented young-laplace equation, contact angle, disjoining pressure, nanodroplet growth

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Authors: Anwar Beg, Sireetorn Kuharat, Rashid Mehmood, Rabil Tabassum, Meisam Babaie

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Nano-polymeric solar paints and sol-gels have emerged as a major new development in solar cell/collector coatings offering significant improvements in durability, anti-corrosion and thermal efficiency. They also exhibit substantial viscosity variation with temperature which can be exploited in solar collector designs. Modern manufacturing processes for such nano-rheological materials frequently employ stagnation flow dynamics under high temperature which invokes radiative heat transfer. Motivated by elaborating in further detail the nanoscale heat, mass and momentum characteristics of such sol gels, the present article presents a mathematical and computational study of the steady, two-dimensional, non-aligned thermo-fluid boundary layer transport of copper metal-doped water-based nano-polymeric sol gels under radiative heat flux. To simulate real nano-polymer boundary interface dynamics, thermal slip is analysed at the wall. A temperature-dependent viscosity is also considered. The Tiwari-Das nanofluid model is deployed which features a volume fraction for the nanoparticle concentration. This approach also features a Maxwell-Garnet model for the nanofluid thermal conductivity. The conservation equations for mass, normal and tangential momentum and energy (heat) are normalized via appropriate transformations to generate a multi-degree, ordinary differential, non-linear, coupled boundary value problem. Numerical solutions are obtained via the stable, efficient Runge-Kutta-Fehlberg scheme with shooting quadrature in MATLAB symbolic software. Validation of solutions is achieved with a Variational Iterative Method (VIM) utilizing Langrangian multipliers. The impact of key emerging dimensionless parameters i.e. obliqueness parameter, radiation-conduction Rosseland number (Rd), thermal slip parameter (α), viscosity parameter (m), nanoparticles volume fraction (ϕ) on non-dimensional normal and tangential velocity components, temperature, wall shear stress, local heat flux and streamline distributions is visualized graphically. Shear stress and temperature are boosted with increasing radiative effect whereas local heat flux is reduced. Increasing wall thermal slip parameter depletes temperatures. With greater volume fraction of copper nanoparticles temperature and thermal boundary layer thickness is elevated. Streamlines are found to be skewed markedly towards the left with positive obliqueness parameter.

Keywords: non-orthogonal stagnation-point heat transfer, solar nano-polymer coating, MATLAB numerical quadrature, Variational Iterative Method (VIM)

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Authors: Adya Uluwita, Fernando Pedrosa, Georgi Georgiev, Christian Bernard, Raoul Masterson

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CERN is an international organisation funded by 23 countries that provide the particle physics community with excellence in particle accelerators and other related facilities. Founded in 1954, CERN has a wide range of accelerators that allow groundbreaking science to be conducted. Accelerators bring particles to high levels of energy and make them collide with each other or with fixed targets, creating specific conditions that are of high interest to physicists. A chain of accelerators is used to ramp up the energy of particles and eventually inject them into the largest and most recent one: the Large Hadron Collider (LHC). Among this chain of machines is, for instance the Proton Synchrotron, which was started in 1959 and is still in operation. These machines, called "injectors”, keep evolving over time, as well as the related infrastructure. Massive decommissioning of obsolete cables started in 2015 at CERN in the frame of the so-called "injectors de-cabling project phase 1". Its goal was to replace aging cables and remove unused ones, freeing space for new cables necessary for upgrades and consolidation campaigns. To proceed with the de-cabling, a project co-ordination team was assembled. The start of this project led to the investigation of legacy cables throughout the organisation. The identification of cables stacked over half a century proved to be arduous. Phase 1 of the injectors de-cabling was implemented for 3 years with success after overcoming some difficulties. Phase 2, started 3 years later, focused on improving safety and structure with the introduction of a quality assurance procedure. This paper discusses the implementation of this quality assurance procedure throughout phase 2 of the project and the transition between the two phases. Over hundreds of kilometres of cable were removed in the injectors complex at CERN from 2015 to 2023.

Keywords: CERN, de-cabling, injectors, quality assurance procedure

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Authors: Ali Albadri

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The logistics equation has never been used or studied in scientific fields outside the field of ecology. It has never been used to understand the behavior of a dynamic system of mechanical machines, like an escalator. We have studied the compatibility of the logistic map against real measurements from an escalator. This study has proven that there is good compatibility between the logistics equation and the experimental measurements. It has discovered the potential of a relationship between the fractal dimension and the non-linearity parameter, R, in the logistics equation. The fractal dimension increases as the R parameter (non-linear parameter) increases. It implies that the fractal dimension increases as the phase of the life span of the machine move from the steady/stable phase to the periodic double phase to a chaotic phase. The fractal dimension and the parameter R can be used as a tool to verify and check the health of machines. We have come up with a theory that there are three areas of behaviors, which they can be classified during the life span of a machine, a steady/stable stage, a periodic double stage, and a chaotic stage. The level of attention to the machine differs depending on the stage that the machine is in. The rate of faults in a machine increases as the machine moves through these three stages. During the double period and the chaotic stages, the number of faults starts to increase and become less predictable. The rate of predictability improves as our monitoring of the changes in the fractal dimension and the parameter R improves. The principles and foundations of our theory in this work have and will have a profound impact on the design of systems, on the way of operation of systems, and on the maintenance schedules of the systems. The systems can be mechanical, electrical, or electronic. The discussed methodology in this paper will give businesses the chance to be more careful at the design stage and planning for maintenance to control costs. The findings in this paper can be implied and used to correlate the three stages of a mechanical system to more in-depth mechanical parameters like wear and fatigue life.

Keywords: logistcs map, bifurcation map, fractal dimension, logistics equation

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Authors: Gaurav Prabhudesai, Gaetan Brill

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The sun's energy source comes from a hydrogen-to-helium thermonuclear reaction, generating a temperature of about 5760 K on its outer layer. On account of this high temperature, energy is radiated by the sun, a part of which reaches the earth. This sunlight, even after losing part of its energy en-route to scattering and absorption, provides a time and space averaged solar flux of 174.7 W/m^2 striking the earth’s surface. According to one study, the solar energy striking earth’s surface in one and a half hour is more than the energy consumption that was recorded in the year 2001 from all sources combined. Thus, technology for extraction of solar energy holds much promise for solving energy crisis. Of the many technologies developed in this regard, Concentrating Solar Power (CSP) plants with central solar tower and receiver system are very impressive because of their capability to provide a renewable energy that can be stored in the form of heat. One design of central receiver towers is an open cavity where sunlight is concentrated into by using mirrors (also called heliostats). This concentrated solar flux produces high temperature inside the cavity which can be utilized in an energy conversion process. The amount of energy captured is reduced by losses occurring at the cavity through all three modes viz., radiation to the atmosphere, conduction to the adjoining structure and convection. This study investigates the natural convection losses to the environment from the receiver. Computational fluid dynamics were used to simulate the fluid flow and heat transfer of the receiver; since no analytical solution can be obtained and no empirical correlations exist for the given geometry. The results provide guide lines for predicting natural convection losses for hexagonal and circular shaped open cavities. Additionally, correlations are given for various inclination angles and aspect ratios. These results provide methods to minimize natural convection through careful design of receiver geometry and modification of the inclination angle, and aspect ratio of the cavity.

Keywords: concentrated solar power (CSP), central receivers, natural convection, CFD, open cavities

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Authors: Mark Salisbury

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The arrival of artificially intelligent avatars and the automation they bring is worrying many of us, not only for our livelihood but for the jobs that may be lost to our kids. We worry about what our place will be as human beings in this new economy where much of it will be conducted online in the metaverse – in a network of 3D virtual worlds – working with intelligent machines. The Future of Leadership was written to address these fears and show what our place will be – the right place – in this new economy of AI avatars, automation, and 3D virtual worlds. But to be successful in this new economy, our job will be to bring wisdom to our workplace and the marketplace. And we will use AI avatars and 3D virtual worlds to do it. However, this book is about more than AI and the avatars that we will work with in the metaverse. It’s about building Organizational intelligence (OI) -- the capability of an organization to comprehend and create knowledge relevant to its purpose; in other words, it is the intellectual capacity of the entire organization. To increase organizational intelligence requires a new kind of knowledge worker, a wisdom worker, that requires a new kind of leadership. This book begins your story for how to become a leader of wisdom workers and be successful in the emerging wisdom economy. After this presentation, conference participants will be able to do the following: Recognize the characteristics of the new generation of wisdom workers and how they differ from their predecessors. Recognize that new leadership methods and techniques are needed to lead this new generation of wisdom workers. Apply personal and professional values – personal integrity, belief in something larger than yourself, and keeping the best interest of others in mind – to improve your work performance and lead others. Exhibit an attitude of confidence, courage, and reciprocity of sharing knowledge to increase your productivity and influence others. Leverage artificial intelligence to accelerate your ability to learn, augment your decision-making, and influence others.Utilize new technologies to communicate with human colleagues and intelligent machines to develop better solutions more quickly.

Keywords: metaverse, generative artificial intelligence, automation, leadership, organizational intelligence, wisdom worker

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Authors: S. W. Lo, S.-H. Lu, Y. H. Guo, L. C. Hsu

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In this paper, a novel design of aerostatic thrust bearing is proposed and is analyzed numerically. The capillary restrictor and bearing disk are made of elastomer like silicone and PU. The viscoelasticity of elastomer helps the capillary expand for more air flux and at the same time, allows conicity of the bearing surface to form when the air pressure is enhanced. Therefore, the bearing has the better ability of passive compensation. In the present example, as compared with the typical model, the new designs can nearly double the load capability and offer four times static stiffness.

Keywords: aerostatic, bearing, elastomer, static stiffness

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Authors: Liang Cheng Chang, Wei Ju Huang, You Cheng Chen

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Investigation and research are keys for conjunctive use of surface and groundwater resources. The hydrogeological structure is an important base for groundwater analysis and simulation. Traditionally, the hydrogeological structure is artificially determined based on geological drill logs, the structure of wells, groundwater levels, and so on. In Taiwan, groundwater observation network has been built and a large amount of groundwater-level observation data are available. The groundwater level is the state variable of the groundwater system, which reflects the system response combining hydrogeological structure, groundwater injection, and extraction. This study applies analytical tools to the observation database to develop a methodology for the identification of confined and unconfined aquifers. These tools include frequency analysis, cross-correlation analysis between rainfall and groundwater level, groundwater regression curve analysis, and decision tree. The developed methodology is then applied to groundwater layer identification of two groundwater systems: Zhuoshui River alluvial fan and Pingtung Plain. The abovementioned frequency analysis uses Fourier Transform processing time-series groundwater level observation data and analyzing daily frequency amplitude of groundwater level caused by artificial groundwater extraction. The cross-correlation analysis between rainfall and groundwater level is used to obtain the groundwater replenishment time between infiltration and the peak groundwater level during wet seasons. The groundwater regression curve, the average rate of groundwater regression, is used to analyze the internal flux in the groundwater system and the flux caused by artificial behaviors. The decision tree uses the information obtained from the above mentioned analytical tools and optimizes the best estimation of the hydrogeological structure. The developed method reaches training accuracy of 92.31% and verification accuracy 93.75% on Zhuoshui River alluvial fan and training accuracy 95.55%, and verification accuracy 100% on Pingtung Plain. This extraordinary accuracy indicates that the developed methodology is a great tool for identifying hydrogeological structures.

Keywords: aquifer identification, decision tree, groundwater, Fourier transform

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Authors: Soumava Boral, Sanjay K. Chaturvedi, Ian Howard, Kristoffer McKee, V. N. A. Naikan

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Due to strict regulations from government to enhance the possibilities of sustainability practices in industries, and noting the advances in sustainable manufacturing practices, it is necessary that the associated processes are also sustainable. Maintenance of large scale and complex machines is a pivotal task to maintain the uninterrupted flow of manufacturing processes. Appropriate maintenance practices can prolong the lifetime of machines, and prevent associated breakdowns, which subsequently reduces different cost heads. Selection of the best maintenance strategies for such machines are considered as a burdensome task, as they require the consideration of multiple technical criteria, complex mathematical calculations, previous fault data, maintenance records, etc. In the era of the fourth industrial revolution, organizations are rapidly changing their way of business, and they are giving their utmost importance to sensor technologies, artificial intelligence, data analytics, automations, etc. In this work, the effectiveness of several maintenance strategies (e.g., preventive, failure-based, reliability centered, condition based, total productive maintenance, etc.) related to a large scale and complex gearbox, operating in a steel processing plant is evaluated in terms of economic, social, environmental and technical criteria. As it is not possible to obtain/describe some criteria by exact numerical values, these criteria are evaluated linguistically by cross-functional experts. Fuzzy sets are potential soft-computing technique, which has been useful to deal with linguistic data and to provide inferences in many complex situations. To prioritize different maintenance practices based on the identified sustainable criteria, multi-criteria decision making (MCDM) approaches can be considered as potential tools. Multi-Attributive Ideal Real Comparative Analysis (MAIRCA) is a recent addition in the MCDM family and has proven its superiority over some well-known MCDM approaches, like TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and ELECTRE (ELimination Et Choix Traduisant la REalité). It has a simple but robust mathematical approach, which is easy to comprehend. On the other side, due to some inherent drawbacks of Intuitionistic Fuzzy Sets (IFS) and Pythagorean Fuzzy Sets (PFS), recently, the use of Fermatean Fuzzy Sets (FFSs) has been proposed. In this work, we propose the novel concept of FF-MAIRCA. We obtain the weights of the criteria by experts’ evaluation and use them to prioritize the different maintenance practices according to their suitability by FF-MAIRCA approach. Finally, a sensitivity analysis is carried out to highlight the robustness of the approach.

Keywords: Fermatean fuzzy sets, Fermatean fuzzy MAIRCA, maintenance strategy selection, sustainable manufacturing, MCDM

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Authors: G. G. Kagramanov, E. N. Farnosova, Linn Maung Maung

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The technologies of ion exchange and nanofiltration can be used for treatment of wastewater containing copper and other heavy metal ions to decrease the environmental risks. Nanofiltration characteristics under water treatment of heavy metals have been studied. The influence of main technical process parameters - pressure, temperature, concentration and pH value of the initial solution on flux and rejection of nanofiltration membranes has been considered. And ion exchange capacities of resins in removal of heavy metal ions from wastewater have been determined.

Keywords: exchange capacity, heavy metals, ion exchange, membrane separation, nanofiltration

Procedia PDF Downloads 278

Authors: Anthony I. Otuonye, Juliet N. Odii, Perpetual N. Ibe

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The obvious challenges faced by most commercial bank customers while using the services of ATMs (Automated Teller Machines) across developing countries have triggered the need for an improved system with better security features. Current ATM systems are password-based, and research has proved the vulnerabilities of these systems to heinous attacks and manipulations. We have discovered by research that the security of current ATM-assisted banking services in most developing countries of the world is easily broken and maneuvered by fraudsters, majorly because it is quite difficult for these systems to identify an impostor with privileged access as against the authentic bank account owner. Again, PIN (Personal Identification Number) code passwords are easily guessed, just to mention a few of such obvious limitations of traditional ATM operations. In this research work also, we have developed a system of fingerprint biometrics with PIN code Authentication that seeks to improve the security features of traditional ATM installations as well as other Banking Services. The aim is to ensure better security at all ATM installations and raise the confidence of bank customers. It is hoped that our system will overcome most of the challenges of the current password-based ATM operation if properly applied. The researchers made use of the OOADM (Object-Oriented Analysis and Design Methodology), a software development methodology that assures proper system design using modern design diagrams. Implementation and coding were carried out using Visual Studio 2010 together with other software tools. Results obtained show a working system that provides two levels of security at the client’s side using a fingerprint biometric scheme combined with the existing 4-digit PIN code to guarantee the confidence of bank customers across developing countries.

Keywords: fingerprint biometrics, banking operations, verification, ATMs, PIN code

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Authors: Grace E. Santella, Shawn G. Gallaher, Joseph P. Smith

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In order to determine the importance of small-system riverine heat flux on regional landfast sea ice breakup, our study explores the annual spring freshet of the Sagavanirktok River from 2014-2019. Seasonal heat cycling ultimately serves as the driving mechanism behind the freshet; however, as an emerging area of study, the extent to which inland thermodynamics influence coastal tundra geomorphology and connected landfast sea ice has not been extensively investigated in relation to small-scale Arctic river systems. The Sagavanirktok River is a small-to-midsized river system that flows south-to-north on the Alaskan North Slope from the Brooks mountain range to the Beaufort Sea at Prudhoe Bay. Seasonal warming in the spring rapidly melts snow and ice in a northwards progression from the Brooks Range and transitional tundra highlands towards the coast and when coupled with seasonal precipitation, results in a pulsed freshet that propagates through the Sagavanirktok River. The concentrated presence of newly exposed vegetation in the transitional tundra region due to spring melting results in higher absorption of solar radiation due to a lower albedo relative to snow-covered tundra and/or landfast sea ice. This results in spring flood runoff that advances over impermeable early-season permafrost soils with elevated temperatures relative to landfast sea ice and sub-ice flow. We examine the extent to which interannual temporal variability influences the onset and magnitude of river discharge by analyzing field measurements from the United States Geological Survey (USGS) river and meteorological observation sites. Rapid influx of heat to the Arctic Ocean via riverine systems results in a noticeable decay of landfast sea ice independent of ice breakup seaward of the shear zone. Utilizing MODIS imagery from NASA’s Terra satellite, interannual variability of river discharge is visualized, allowing for optical validation that the discharge flow is interacting with landfast sea ice. Thermal erosion experienced by sediment fast ice at the arrival of warm overflow preconditions the ice regime for rapid thawing. We investigate the extent to which interannual heat flux from the Sagavanirktok River’s freshet significantly influences the onset of local landfast sea ice breakup. The early-season warming of atmospheric temperatures is evidenced by the presence of storms which introduce liquid, rather than frozen, precipitation into the system. The resultant decreased albedo of the transitional tundra supports the positive relationship between early-season precipitation events, inland thermodynamic cycling, and degradation of landfast sea ice. Early removal of landfast sea ice increases coastal erosion in these regions and has implications for coastline geomorphology which stress industrial, ecological, and humanitarian infrastructure.

Keywords: Albedo, freshet, landfast sea ice, riverine heat flux, seasonal heat cycling

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Authors: Rui Wu

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In the volatile modern manufacturing environment, new orders randomly occur at any time, while the pre-emptive methods are infeasible. This leads to a real-time scheduling method that can produce a reasonably good schedule quickly. The dynamic Flexible Job Shop problem is an NP-hard scheduling problem that hybrid the dynamic Job Shop problem with the Parallel Machine problem. A Flexible Job Shop contains different work centres. Each work centre contains parallel machines that can process certain operations. Many algorithms, such as genetic algorithms or simulated annealing, have been proposed to solve the static Flexible Job Shop problems. However, the time efficiency of these methods is low, and these methods are not feasible in a dynamic scheduling problem. Therefore, a dynamic rule selection scheduling system based on the reinforcement learning method is proposed in this research, in which the dynamic Flexible Job Shop problem is divided into several parallel machine problems to decrease the complexity of the dynamic Flexible Job Shop problem. Firstly, the features of jobs, machines, work centres, and flexible job shops are selected to describe the status of the dynamic Flexible Job Shop problem at each decision point in each work centre. Secondly, a framework of reinforcement learning algorithm using a double-layer deep Q-learning network is applied to select proper composite dispatching rules based on the status of each work centre. Then, based on the selected composite dispatching rule, an available operation is selected from the waiting buffer and assigned to an available machine in each work centre. Finally, the proposed algorithm will be compared with well-known dispatching rules on objectives of mean tardiness, mean flow time, mean waiting time, or mean percentage of waiting time in the real-time Flexible Job Shop problem. The result of the simulations proved that the proposed framework has reasonable performance and time efficiency.

Keywords: dynamic scheduling problem, flexible job shop, dispatching rules, deep reinforcement learning

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Authors: Pranab Kumar Goswami, Elena Gurung

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Small and medium enterprises (SME) are considered as the driving force for the economic growth of a developing country like India. Most of the SMEs are located in residential/non-industrial areas to avoid legal obligations of occupational safety and health (OSH) provisions. This study was conducted in Delhiwith a view to analyze the accidents that occurredduringthe year 2019 & 2020. The objective of the study was to find out the accident prone SMEs in Delhi and major causes of such accidents. Methods: Survey and comprehensive data analysis methods, followed by applying simple statistical techniques, were used for this study. The accident reports for the study period collected from the labour department and police stations were analyzed for the study. The injured workers were interviewed to ascertain safety compliances, training and awareness programs, etc. The study was completed in March2021. Results: It was found that most of the accidents took place in SMEs located in residential/non- industrial areas in Delhi. The accident-prone machines were found to be power presses (42%) and injection moulding machines (37%). Predominantly unsafe machinery or unsafe working conditions and lack of training of worker were observed to be the major causes of accidents in such industries. Conclusions: It was concluded from the study that unsafe machinery/equipment and lack of proper training to the workers were two main reasons for increase in accidents.It was also concluded that the industries located in industrial areas were better placed in terms of workplace compliances. The managements who were running their operations from residential/non-industrial areaswere found to be less aware on health and safety issues. Lack of enforcement by government agencies in such areas has escalated this problem. Adequate training to workers, managing safe & healthy workplace, and sustained enforcement can reduce accidents in such industries.

Keywords: SME, accident prevention, cause of accident, unorganised

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Authors: Sen-Yung Lee, Li-Kuo Chou, Chao-Kuang Chen

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In this study, the problem of temperature transient response of a spiral fin, with its end insulated, is analyzed with base end subjected to a variation of fluid temperature. The hybrid method of Laplace transforms/Adomian decomposed method-Padé, is applied to the temperature transient response of the fin, the result of the temperature distribution and the heat flux at the base of the spiral fin are obtained, show a good agreement in the physical phenomenon.

Keywords: Laplace transforms, Adomian decomposed method- Padé, transient response, heat transfer

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Authors: Danica Maljkovic, Igor Balen, Bojana Dalbelo Basic

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Following the regulation of EU Directive on Energy Efficiency, specifically Article 9, individual metering in district heating systems has to be introduced by the end of 2016. These directions have been implemented in member state’s legal framework, Croatia is one of these states. The directive allows installation of both heat metering devices and heat cost allocators. Mainly due to bad communication and PR, the general public false image was created that the heat cost allocators are devices that save energy. Although this notion is wrong, the aim of this work is to develop a model that would precisely express the influence of installation heat cost allocators on potential energy savings in each unit within multifamily buildings. At the same time, in recent years, a science of machine learning has gain larger application in various fields, as it is proven to give good results in cases where large amounts of data are to be processed with an aim to recognize a pattern and correlation of each of the relevant parameter as well as in the cases where the problem is too complex for a human intelligence to solve. A special method of machine learning, decision tree method, has proven an accuracy of over 92% in prediction general building consumption. In this paper, a machine learning algorithms will be used to isolate the sole impact of installation of heat cost allocators on a single building in multifamily houses connected to district heating systems. Special emphasises will be given regression analysis, logistic regression, support vector machines, decision trees and random forest method.

Keywords: district heating, heat cost allocator, energy efficiency, machine learning, decision tree model, regression analysis, logistic regression, support vector machines, decision trees and random forest method

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Authors: Maxim Glushenkov, Alexander Kronberg

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Isobaric expansion (IE) process is an alternative to conventional gas/vapor expansion accompanied by a pressure decrease typical of all state-of-the-art heat engines. The elimination of the expansion stage accompanied by useful work means that the most critical and expensive parts of ORC systems (turbine, screw expander, etc.) are also eliminated. In many cases, IE heat engines can be more efficient than conventional expansion machines. In addition, IE machines have a very simple, reliable, and inexpensive design. They can also perform all the known operations of existing heat engines and provide usable energy in a very convenient hydraulic or pneumatic form. This paper reports measurement made with the engine operating as a heat-to-shaft-power or electricity converter and a comparison of the experimental results to a thermodynamic model. Experiments were carried out at heat source temperature in the range 30–85 °C and heat sink temperature around 20 °C; refrigerant R134a was used as the engine working fluid. The pressure difference generated by the engine varied from 2.5 bar at the heat source temperature 40 °C to 23 bar at the heat source temperature 85 °C. Using a differential piston, the generated pressure was quadrupled to pump hydraulic oil through a hydraulic motor that generates shaft power and is connected to an alternator. At the frequency of about 0.5 Hz, the engine operates with useful powers up to 1 kW and an oil pumping flowrate of 7 L/min. Depending on the temperature of the heat source, the obtained efficiency was 3.5 – 6 %. This efficiency looks very high, considering such a low temperature difference (10 – 65 °C) and low power (< 1 kW). The engine’s observed performance is in good agreement with the predictions of the model. The results are very promising, showing that the engine is a simple and low-cost alternative to ORC plants and other known energy conversion systems, especially at low temperatures (< 100 °C) and low power range (< 500 kW) where other known technologies are not economic. Thus low-grade solar, geothermal energy, biomass combustion, and waste heat with a temperature above 30 °C can be involved into various energy conversion processes.

Keywords: isobaric expansion, low-grade heat, heat engine, renewable energy, waste heat recovery

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Authors: Rohini Hariharan, Yazhini R, Bhamidipati Naga Shrikarti

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In the realm of cricket, particularly within the context of the Indian Premier League (IPL), the ability to predict team scores accurately holds significant importance for both cricket enthusiasts and stakeholders alike. This paper presents a comprehensive study on IPL score prediction utilizing various machine learning algorithms, including Support Vector Machines (SVM), XGBoost, Multiple Regression, Linear Regression, K-nearest neighbors (KNN), and Random Forest. Through meticulous data preprocessing, feature engineering, and model selection, we aimed to develop a robust predictive framework capable of forecasting team scores with high precision. Our experimentation involved the analysis of historical IPL match data encompassing diverse match and player statistics. Leveraging this data, we employed state-of-the-art machine learning techniques to train and evaluate the performance of each model. Notably, Multiple Regression emerged as the top-performing algorithm, achieving an impressive accuracy of 77.19% and a precision of 54.05% (within a threshold of +/- 10 runs). This research contributes to the advancement of sports analytics by demonstrating the efficacy of machine learning in predicting IPL team scores. The findings underscore the potential of advanced predictive modeling techniques to provide valuable insights for cricket enthusiasts, team management, and betting agencies. Additionally, this study serves as a benchmark for future research endeavors aimed at enhancing the accuracy and interpretability of IPL score prediction models.

Keywords: indian premier league (IPL), cricket, score prediction, machine learning, support vector machines (SVM), xgboost, multiple regression, linear regression, k-nearest neighbors (KNN), random forest, sports analytics

Procedia PDF Downloads 35

Authors: L. Richter, J. Lübkemann, P. Nyhuis

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In order to remain competitive in what is a turbulent environment; businesses must be able to react rapidly to change. The past response to volatile market conditions was to introduce an element of flexibility to production. Nowadays, what is often required is a redesign of factory structures in order to cope with the state of constant flux. The Institute of Production Systems and Logistics is currently developing a descriptive and causal model for the redesign of plant structures as part of an ongoing research project. This article presents the first research findings attained in devising this model.

Keywords: change driven factory redesign, factory planning, plant structure, flexibility

Procedia PDF Downloads 260

Authors: Saule Mussabekova

Abstract:

Recent advances in genetics have allowed increasing acutely the capacities of the formation of reliable evidence in conducting forensic examinations. Thus, traces of biological origin are important sources of information about a crime. Currently, around the world, sexual offenses have increased, and among them are those in which the criminals use various detergents to remove traces of their crime. A feature of modern synthetic detergents is the presence of biological additives - enzymes. Enzymes purposefully destroy stains of biological origin. To study the nature and extent of the impact of modern washing powders on saliva stains on the physical evidence, specially prepared test specimens of different types of tissues to which saliva was applied have been examined. Materials and Methods: Washing machines of famous manufacturers of household appliances have been used with different production characteristics and advertised brands of washing powder for test washing. Over 3,500 experimental samples were tested. After washing, the traces of saliva were identified using modern research methods of forensic medicine. Results: The influence was tested and the dependence of the use of different washing programs, types of washing machines and washing powders in the process of establishing saliva trace and identify of the stains on the physical evidence while washing was revealed. The results of experimental and practical expert studies have shown that in most cases it is not possible to draw the conclusions in the identification of saliva traces on physical evidence after washing. This is a consequence of the effect of biological additives and other additional factors on traces of saliva during washing. Conclusions: On the basis of the results of the study, the feasibility of saliva traces of the stains on physical evidence after washing is established. The use of modern molecular genetic methods makes it possible to partially solve the problems arising in the study of unlaundered evidence. Additional study of physical evidence after washing facilitates detection and investigation of sexual offenses against women and children.

Keywords: saliva research, modern synthetic detergents, laundry detergents, forensic medicine

Procedia PDF Downloads 212

Authors: E. Ramprasath, P. Manojkumar, P. Veena

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DC motors have been widely used in the past centuries which are proudly known as the workhorse of industrial systems until the invention of the AC induction motors which makes a huge revolution in industries. Since then, the use of DC machines have been decreased due to enormous factors such as reliability, robustness and complexity but it lost its fame due to the losses. A new methodology is proposed to construct a DC motor through the simulation in LabVIEW to get an idea about its real time performances, if a change in parameter might have bigger improvement in losses and reliability.

Keywords: analysis, characteristics, direct current motor, LabVIEW software, simulation

Procedia PDF Downloads 540

Authors: Lopez J., Mehrvar M., Quinones E., Suarez A., Romero C.

Abstract:

Membrane Distillation is an emerging technology that uses thermal and membrane steps for the desalination process to get drinking water. In this study, silver nanoparticles (AgNP) were deposited by dip-coating process over Polyvinylidene Fluoride, Fiberglass hydrophilic, and Polytetrafluoroethylene hydrophobic commercial membranes as substrate. Membranes were characterized and used in a Vacuum Membrane Distillation cell under Ultraviolet light with sea salt feed solution. The presence of AgNP increases the absorption of energy on the membrane, which improves the transmembrane flux.

Keywords: silver nanoparticles, membrane distillation, desalination technologies, heat deliver

Procedia PDF Downloads 160

Authors: S. Al-Sowayan

Abstract:

Rogowski coils are increasingly used for measurement of AC and transient electric currents. Mostly used Rogowski coils now are with circular or rectangular cores. In order to increase the sensitivity of the measurement of Rogowski coil and perform smooth wire winding, this paper studies the effect of increasing the mutual inductance in order to increase the coil sensitivity by presenting the calculation and simulation of a Rogowski coil with equilateral hexagonal shaped core and comparing the resulted mutual inductance with commonly used core shapes.

Keywords: Rogowski coil, mutual inductance, magnetic flux density, communication engineering

Procedia PDF Downloads 361

Authors: T. Praveenkumar, Kulpreet Singh, Divy Bhanpuriya, M. Saimurugan

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This study analysed the classification accuracy for gearbox faults using Machine Learning Techniques. Gearboxes are widely used for mechanical power transmission in rotating machines. Its rotating components such as bearings, gears, and shafts tend to wear due to prolonged usage, causing fluctuating vibrations. Increasing the dependability of mechanical components like a gearbox is hampered by their sealed design, which makes visual inspection difficult. One way of detecting impending failure is to detect a change in the vibration signature. The current study proposes various machine learning algorithms, with aid of these vibration signals for obtaining the fault classification accuracy of an automotive 4-Speed synchromesh gearbox. Experimental data in the form of vibration signals were acquired from a 4-Speed synchromesh gearbox using Data Acquisition System (DAQs). Statistical features were extracted from the acquired vibration signal under various operating conditions. Then the extracted features were given as input to the algorithms for fault classification. Supervised Machine Learning algorithms such as Support Vector Machines (SVM) and unsupervised algorithms such as Deep Feed Forward Neural Network (DFFNN), Deep Belief Networks (DBN) algorithms are used for fault classification. The fusion of DBN & DFFNN classifiers were architected to further enhance the classification accuracy and to reduce the computational complexity. The fault classification accuracy for each algorithm was thoroughly studied, tabulated, and graphically analysed for fused and individual algorithms. In conclusion, the fusion of DBN and DFFNN algorithm yielded the better classification accuracy and was selected for fault detection due to its faster computational processing and greater efficiency.

Keywords: deep belief networks, DBN, deep feed forward neural network, DFFNN, fault diagnosis, fusion of algorithm, vibration signal

Procedia PDF Downloads 105

Authors: Liang Qin, Hanan M. D. Habbi

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A robust sensorless speed for permanent magnet synchronous motor (PMSM) has been presented for estimation of stator flux components and rotor speed based on The Extended Kalman Filter (EKF). The model of PMSM and its EKF models are modeled in Matlab /Sirnulink environment. The proposed EKF speed estimation method is also proved insensitive to the PMSM parameter variations. Simulation results demonstrate a good performance and robustness.

Keywords: DTC, Extended Kalman Filter (EKF), PMSM, sensorless control, anti-windup PI

Procedia PDF Downloads 654

Authors: Luvindran Sugumaran, Mohd Nashrul Mohd Zubir, Kazi Md Salim Newaz, Tuan Zaharinie Tuan Zahari, Suazlan Mt Aznam, Aiman Mohd Halil

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With the fast growth of integrated circuits and the trend towards making electronic devices smaller, the heat dissipation load of electronic devices has continued to go over the limit. The high heat flux element would not only harm the operation and lifetime of the equipment but would also impede the performance upgrade brought about by the iteration of technological updates, which would have a direct negative impact on the economic and production cost benefits of rising industries. Hence, in high-tech industries like radar, information and communication, electromagnetic power, and aerospace, the development and implementation of effective heat dissipation technologies were urgently required. Pool boiling is favored over other cooling methods because of its capacity to dissipate a high heat flux at a low wall superheat without the usage of mechanical components. Enhancing the pool boiling performance by increasing the heat transfer coefficient via surface modification techniques has received a lot of attention. There are several surface modification methods feasible today, but the stability and durability of surface modification are the greatest priority. Thus, laser machining is an interesting choice for surface modification due to its low production cost, high scalability, and repeatability. In this study, different patterns of laser-processed copper surfaces are fabricated to investigate the nucleate pool boiling heat transfer performance of distilled water. The investigation showed that there is a significant enhancement in the pool boiling heat transfer performance of the laser-processed surface compared to the reference surface due to the notable increase in nucleation frequency and nucleation site density. It was discovered that the heat transfer coefficients increased when both the surface area ratio and the ratio of peak-to-valley height of the microstructure were raised. It is believed that the development of microstructures on the surface as a result of laser processing is the primary factor in the enhancement of heat transfer performance.

Keywords: heat transfer coefficient, laser processing, micro structured surface, pool boiling

Procedia PDF Downloads 77

Authors: Haroon Jan Khan, Xian-Feng Xu, Syed Nasir Shah, Anooshay Niazi

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CNC machines are not only used on a large scale but also now become a prominent necessity among households and smaller businesses. Printed Circuit Boards manufactured by the chemical process are not only risky and unsafe but also expensive and time-consuming. A 3-axis precise CNC machine has been developed, which not only fabricates PCB but has also been used for multi-tasks just by changing the materials used and tools, making it versatile. The advanced CNC machine takes data from CAM software. The TB-6560 controller is used in the CNC machine to adjust variation in the X, Y, and Z axes. The advanced machine is efficient in automatic drilling, engraving, and cutting.

Keywords: CNC, G-code, CAD, CAM, Proteus, FLATCAM, Easel

Procedia PDF Downloads 150

Authors: Zahir Bakiri, Saci Nacefa

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In this paper a comparison in made between two models, with and without dispersion term, and focused on the characterization of the movement of the sludge blanket in the secondary sedimentation tank using the solid flux theory and the velocity settling. This allowed us develop a one-dimensional models, with and without dispersion based on a thorough experimental study carried out in situ and the application of online data which are the mass load flow, transfer concentration, and influent characteristic. On the other hand, in the proposed model, the new settling velocity law (double-exponential function) used is based on the Vesilind function.

Keywords: wastewater, activated sludge, sedimentation, settling velocity, settling models

Procedia PDF Downloads 379