Search results for: sand production prediction

Authors: Jason West

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Low profitability in agriculture production along with increasing scrutiny over environmental effects is limiting food production at scale. In contrast, the mining sector offers access to resources including energy, water, transport and chemicals for food production at low marginal cost. Scalable agricultural production can benefit from the nexus of resources (water, energy, transport) offered by mining activity in remote locations. A decision support bioeconomic model for controlled environment vertical farms was used. Four submodels were used: crop structure, nutrient requirements, resource-crop integration, and economic. They escalate to a macro mathematical model. A demonstrable dynamic systems framework is needed to prove productive outcomes are feasible. We demonstrate a generalized bioeconomic macro model for controlled environment production systems in minesites using systems dynamics modeling methodology. Despite the complexity of bioeconomic modelling of resource-agricultural dynamic processes and interactions, the economic potential greater than general economic models would assume. Scalability of production as an input becomes a key success feature.

Keywords: crop production systems, mathematical model, mining, agriculture, dynamic systems

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Authors: Yi-Ling Chen, Dung-Ying Lin

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In the manufacturing process, computer numerical control (CNC) machining plays a crucial role. CNC enables precise machinery control through computer programs, achieving automation in the production process and significantly enhancing production efficiency. However, traditional CNC production lines often require manual intervention for loading and unloading operations, which limits the production line's operational efficiency and production capacity. Additionally, existing CNC automation systems frequently lack sufficient intelligence and fail to achieve optimal configuration efficiency, resulting in the need for substantial time to reconfigure production lines when producing different products, thereby impacting overall production efficiency. Using the NSGA-II algorithm, we generate production line layout configurations that consider field constraints and select robotic arm specifications from an arm list. This allows us to calculate loading and unloading times for each job order, perform demand allocation, and assign processing sequences. The NSGA-II algorithm is further employed to determine the optimal processing sequence, with the aim of minimizing demand completion time and maximizing average machine utilization. These objectives are used to evaluate the performance of each layout, ultimately determining the optimal layout configuration. By employing this method, it enhance the configuration efficiency of CNC production lines and establish an adaptive capability that allows the production line to respond promptly to changes in demand. This will minimize production losses caused by the need to reconfigure the layout, ensuring that the CNC production line can maintain optimal efficiency even when adjustments are required due to fluctuating demands.

Keywords: evolutionary algorithms, multi-objective optimization, pareto optimality, layout optimization, operations sequence

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Authors: Petra Gajdačová

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The presented research deals with a complex evaluation of after-harvest remnants utilization for particleboard production. Agricultural crops that are in the Czech Republic widely grown are in the scope of interest. Researches dealing with composites from agricultural rests solved mostly physical and mechanical properties of produced materials. For the commercialization of these results, however, one another step is essential. It is needed to evaluate the composites production from agricultural rests more comprehensive, take into account all aspects that affect their production, not only material characteristics of produced composites. In this study, descriptive, comparative and synthesis methods were used. Results of this research include a supply stability forecast, technical and technological differences of production of particleboards from agricultural rests and quantification of an economical potential of the agricultural rests.

Keywords: agricultural crops, annual plant, composite material, particleboard

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Authors: Khaled R. Khater

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The paper theme is soil retaining structures. Cantilever secant-pile wall is triggering scientific point of curiosity. Specially the capping beams structural analysis and its interaction with secant piles as one integrated matrix. It is believed that straining actions of this integrated matrix are most probably induced due to a combination of induced line load and non-uniform horizontal pile tips displacement. The strategy that followed throughout this study starts by converting the pile head horizontal displacements generated by Plaxis-2D model to a system of concentrated line load acting per meter run along the capping beam. Then, those line loads are the input data of Staad-Pro 3D-model. Those models tailored to allow the capping beam and the secant piles interacting as one matrix, i.e. a unit. It is believed that the suggested strategy presents close to real structural simulation. The above is the paper thought and methodology. Three sand densities, one pile rigidity and one excavation depth, “h = 4.0-m,” are completely sufficient to achieve the paper’s objective.

Keywords: secant piles, capping beam, analysis, design, plaxis 2D, staad pro 3D

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Authors: Rashini Maduka, C. R. Wijesinghe, A. R. Weerasinghe

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Drug-drug interactions (DDIs) can happen when two or more drugs are taken together. Today DDIs have become a serious health issue due to adverse drug effects. In vivo and in vitro methods for identifying DDIs are time-consuming and costly. Therefore, in-silico-based approaches are preferred in DDI identification. Most machine learning models for DDI prediction are used chemical and biological drug properties as features. However, some drug features are not available and costly to extract. Therefore, it is better to make automatic feature engineering. Furthermore, people who have diabetes already suffer from other diseases and take more than one medicine together. Then adverse drug effects may happen to diabetic patients and cause unpleasant reactions in the body. In this study, we present a model with a graph convolutional autoencoder and a graph decoder using a dataset from DrugBank version 5.1.3. The main objective of the model is to identify unknown interactions between antidiabetic drugs and the drugs taken by diabetic patients for other diseases. We considered automatic feature engineering and used Known DDIs only as the input for the model. Our model has achieved 0.86 in AUC and 0.86 in AP.

Keywords: drug-drug interaction prediction, graph embedding, graph convolutional networks, adverse drug effects

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Authors: Mohammadali Abedini Sanigy, Jiangang Fei

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In this paper, a mathematical optimization model was developed to maximize the profit in a vegetable production planning problem. It serves as a decision support system that assists farmers in land allocation to crops and harvest scheduling decisions. The developed model can handle different rotation rules in two consecutive cycles of production, which is a common practice in organic production system. Moreover, different production methods of the same crop were considered in the model formulation. The main strength of the model is that it is not restricted to predetermined production periods, which makes the planning more flexible. The model is classified as a mixed integer programming (MIP) model and formulated in PYOMO -a Python package to formulate optimization models- and solved via Gurobi and CPLEX optimizer packages. The model was tested with secondary data from 'Australian vegetable growing farms', and the results were obtained and discussed with the computational test runs. The results show that the model can successfully provide reliable solutions for real size problems.

Keywords: crop rotation, harvesting, mathematical model formulation, vegetable production

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Authors: Ayşen Melda Çolak, Volkan Okatan, Ercan Yıldız

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In our country, fruit production was determined as 17.2 million tons in 2011 according to official data. Turkey fig, apricot, cherry and quince production ranks first in the world. Almost all the regions of our country, despite the growing of fruit 54% of the total fruit production occur in the Mediterranean and the Aegean Region. However, fruit production in the country is consumed in the domestic market and export rates are often very low. In this study, a questionnaire to 100 farmers face-to-face interview. According to the survey, 40% of those in fruit and 7 da of 7 hectares land are small. 30% of soil testing for manufacturers, testing for 20% of the water. Manufacturers who deliberately fertilization rate of only 10%.

Keywords: fruit, generation, potential, Sivasli survey

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Authors: Ravija Gunawardana, Banuka Athuraliya

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

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

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Authors: Claudiu Mazilu, Ion Robu, Radu Deju

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At the end of 2011 worldwide there were 124 power reactors shut down, from which: 16 fully decommissioned, 50 power reactors in a decommissioning process, 49 reactors in “safe enclosure mode”, 3 reactors “entombed”, for other 6 reactors it was not yet have specified the decommissioning strategy. The concrete radioactive waste that will be generated from dismantled structures of VVR-S nuclear research reactor from Magurele (e.g.: biological shield of the reactor core and hot cells) represents an estimated amount of about 70 tons. Until now the solid low activity radioactive waste (LLW) was pre-placed in containers and cementation with mortar made from cement and natural fine aggregates, providing a fill ratio of the container of approximately 50 vol. % for concrete. In this paper is presented an innovative technology in which radioactive concrete is crushed and the mortar made from recycled radioactive sand, cement, water and superplasticizer agent is poured in container with radioactive rubble (that is pre-placed in container) for cimentation. Is achieved a radioactive waste package in which the degree of filling of radioactive waste increases substantially. The tests were carried out on non-radioactive material because the radioactive concrete was not available in a good time. Waste concrete with maximum size of 350 mm were crushed in the first stage with a Liebhher type jaw crusher, adjusted to nominal size of 50 mm. Crushed concrete less than 50 mm was sieved in order to obtain useful sort for preplacement, 10 to 50 mm. The rest of the screening > 50 mm obtained from primary crushing of concrete was crushed in the second stage, with different working principles crushers at size < 2.5 mm, in order to produce recycled fine aggregate (sand) for the filler mortar and which fulfills the technical specifications proposed: –jaw crusher, Retsch type, model BB 100; –hammer crusher, Buffalo Shuttle model WA-12-H; presented a series of characteristics of recycled concrete aggregates by predefined class (the granulosity, the granule shape, the absorption of water, behavior to the Los Angeles test, the content of attached mortar etc.), most in comparison with characteristics of natural aggregates. Various mortar recipes were used in order to identify those that meet the proposed specification (flow-rate: 16-50s, no bleeding, min. 30N/mm2 compressive strength of the mortar after 28 days, the proportion of recycled sand used in mortar: min. 900kg/m3) and allow obtaining of the highest fill ratio for mortar. In order to optimize the mortars following compositional factors were varied: aggregate nature, water/cement (W/C) ratio, sand/cement (S/C) ratio, nature and proportion of additive. To confirm the results obtained on a small scale, it made an attempt to fill the mortar in a container that simulates the final storage drums. Was measured the mortar fill ratio (98.9%) compared with the results of laboratory tests and targets set out in the proposed specification. Although fill ratio obtained on the mock-up is lower by 0.8 vol. % compared to that obtained in the laboratory tests (99.7%), the result meets the specification criteria.

Keywords: characteristics, radioactive recycled concrete aggregate, mortars, fill ratio

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Authors: Sariah Abang, S. M. Anisuzzaman, Awang Bono, D. Krishnaiah, S. Rasmih

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Biodiesel is a potential renewable energy due to biodegradable and non-toxic. The challenge of its commercialization is associated with high production cost due to its feedstock also useful in various food products. Non-competitive feedstock such as waste cooking oils normally contains a large amount of free fatty acids (FFAs). Large amount of fatty acid degrades the alkaline catalyst in the biodiesel production, thereby decreasing the biodiesel production rate. Generally, biodiesel production processes including esterification and trans-esterification are conducting in a mixed system, in which the hydrodynamic effect on the reaction could not be completely defined. The aim of this study was to investigate the effect of variation rate constant and activation energy on energy consumption of biodiesel production. Usually, the changes of rate constant and activation energy depend on the operating temperature and the degradation of catalyst. By varying the activation energy and kinetic rate constant, the effects can be seen on the energy consumption of biodiesel production. The result showed that the energy consumption of biodiesel is dependent on the changes of rate constant and activation energy. Furthermore, this study was simulated using Aspen HYSYS.

Keywords: methanol, palm oil, simulation, transesterification, triolein

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Authors: Evy Sulistianingsih, Mariatul Kiftiah, Dedi Rosadi, Heni Wahyuni

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This research attempts to analyse palm oil production and rubber production to prosperity of the community of ten districts in West Kalimantan namely Sanggau, Sintang, Sambas, Ketapang, Bengkayang, Landak, Singkawang, Kapuas Hulu, Melawi and Sekadau by panel regression. Gross Domestic Product (GDP) of the districts will be used to be a prosperity indicator on this research. Based on the result of analysis, it can be concluded that palm oil and rubber production statistically give contribution to GDP. Adjusted coefficient determination of Fixed Effect Model indicates that 76% of GDP’s variation can be explained by palm oil and rubber production. In another point of view, there should be a district’s government intervention to regulate the plantations. In addition, there is an obligation of the government to monitor regularly the plantations and to conduct researches in order to govern better planning of lands that have been used to the plantations. So that, the environmental effects that have been caused by the plantation can be diminished.

Keywords: gross domestic product (GDP), panel, palm, welfare

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Authors: Asra Yousuf, Ajaykumar Tannirkulum

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Inferring rural-urban migration trends can help design effective policies that promote better urban planning and rural development. In this paper, we describe how machine learning algorithms can be applied to predict internal migration decisions of people. We consider data collected from household surveys in Tamil Nadu to train our model. To measure the performance of the model, we use data on past migration from National Sample Survey Organisation of India. The factors for training the model include socioeconomic characteristic of each individual like age, gender, place of residence, outstanding loans, strength of the household, etc. and his past migration history. We perform a comparative analysis of the performance of a number of machine learning algorithm to determine their prediction accuracy. Our results show that machine learning algorithms provide a stronger prediction accuracy as compared to statistical models. Our goal through this research is to propose the use of data science techniques in understanding human decisions and behaviour in developing countries.

Keywords: development, migration, internal migration, machine learning, prediction

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Authors: Mahmud Abba Tahir

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This is report on experiment out to compare the sulphate resistance of sand mortar made with five different pozzolanic cement. The pozzolanic cement were prepared by blending powered burnt bricks from the Adamawa, Makurdi, Kano, Kaduna and Niger bricks factories with ordinary Portland cement in the ratio 1:4. Sand –pozzolanic cement mortars of mix ratio 1:6 and 1:3 with water-cement ratio of 0.65 and 0.40 respectively were used to prepare cubes and bars specimens. 150 mortar cubes of size 70mm x 70mm x 70mm and 35 mortar bars of 15mm x 15mm x 100mm dimensions were cast and cured for 28 days. The cured specimens then immersed in the solutions of K₂SO₄, (NH₄)₂SO₄ and water for 28 days and then tested. The compressive strengths of cubes in water increased by 34% while those in the sulphate solutions decreased. Strength decreases of the cubes, cracking and warping of bars immersed in K₂SO₄ were less than those in (NH₄)₂SO_4. Specimens made with Niger and Makurdi pulverized burnt bricks experienced less effect of the sulphates and can therefore be used as pozzolan in mortar and concrete to resist sulphate.

Keywords: burnt bricks powder, comparative, pozzolanic cement, sulphates

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Authors: Florian Gschösser, Walter Purrer

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The paper shows examples for the (environmental) optimization of production processes for asphalt mixtures applied for typical road pavements in Austria and Switzerland. The conducted “from-cradle-to-gate” LCA firstly analyzes the production one cubic meter of asphalt and secondly all material production processes for exemplary highway pavements applied in Austria and Switzerland. It is shown that environmental impacts can be reduced by the application of reclaimed asphalt pavement (RAP) and by the optimization of specific production characteristics, e.g. the reduction of the initial moisture of the mineral aggregate and the reduction of the mixing temperature by the application of low-viscosity and foam bitumen. The results of the LCA study demonstrate reduction potentials per cubic meter asphalt of up to 57 % (Global Warming Potential–GWP) and 77 % (Ozone depletion–ODP). The analysis per square meter of asphalt pavement determined environmental potentials of up to 40 % (GWP) and 56 % (ODP).

Keywords: asphalt mixtures, environmental potentials, life cycle assessment, material production

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Authors: Queen Suraajini Rajendran, Sai Hung Cheung

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Statistical down scaling models are required to connect the global climate model outputs and the local weather variables for climate change impact prediction. For reliable climate change impact studies, the uncertainty associated with the model including natural variability, uncertainty in the climate model(s), down scaling model, model inadequacy and in the predicted results should be quantified appropriately. In this work, a new approach is developed by the authors for statistical classification, statistical down scaling and uncertainty assessment and is applied to Singapore rainfall. It is a robust Bayesian uncertainty analysis methodology and tools based on coupling dependent modeling error with classification and statistical down scaling models in a way that the dependency among modeling errors will impact the results of both classification and statistical down scaling model calibration and uncertainty analysis for future prediction. Singapore data are considered here and the uncertainty and prediction results are obtained. From the results obtained, directions of research for improvement are briefly presented.

Keywords: statistical downscaling, global climate model, climate change, uncertainty

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Authors: Adrien Chatel, Ehsan Askari Mahvelati, Laurent Fitschy

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Once-Through-Steam-Generators are commonly used in the oil-sand industry in the heavy fuel oil extraction process. They are composed of three main parts: the burner, the radiant and convective sections. Natural gas is burned through staged diffusive flames stabilized by the burner. The heat generated by the combustion is transferred to the water flowing through the piping system in the radiant and convective sections. The steam produced within the pipes is then directed to the ground to reduce the oil viscosity and allow its pumping. With the rapid development of the oil-sand industry, the number of OTSG in operation has increased as well as the associated emissions of environmental pollutants, especially the Nitrous Oxides (NOₓ). To limit the environmental degradation, various international environmental agencies have established regulations on the pollutant discharge and pushed to reduce the NOₓ release. To meet these constraints, OTSG constructors have to rely on more and more advanced tools to study and predict the NOₓ emission. With the increase of the computational resources, Computational Fluid Dynamics (CFD) has emerged as a flexible tool to analyze the combustion and pollutant formation process. Moreover, to optimize the burner operating condition regarding the NOx emission, field characterization and measurements are usually accomplished. However, these kinds of experimental campaigns are particularly time-consuming and sometimes even impossible for industrial plants with strict operation schedule constraints. Therefore, the application of CFD seems to be more adequate in order to provide guidelines on the NOₓ emission and reduction problem. In the present work, two different software are employed to simulate the combustion process in an OTSG, namely the commercial software ANSYS Fluent and the open source software OpenFOAM. RANS (Reynolds-Averaged Navier–Stokes) equations combined with the Eddy Dissipation Concept to model the combustion and closed by the k-epsilon model are solved. A mesh sensitivity analysis is performed to assess the independence of the solution on the mesh. In the first part, the results given by the two software are compared and confronted with experimental data as a mean to assess the numerical modelling. Flame temperatures and chemical composition are used as reference fields to perform this validation. Results show a fair agreement between experimental and numerical data. In the last part, OpenFOAM is employed to simulate several operating conditions, and an Emission Characteristic Map of the combustion system is generated. The sources of high NOₓ production inside the OTSG are pointed and correlated to the physics of the flow. CFD is, therefore, a useful tool for providing an insight into the NOₓ emission phenomena in OTSG. Sources of high NOₓ production can be identified, and operating conditions can be adjusted accordingly. With the help of RANS simulations, an Emission Characteristics Map can be produced and then be used as a guide for a field tune-up.

Keywords: combustion, computational fluid dynamics, nitrous oxides emission, once-through-steam-generators

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Authors: C. Njoku Paul

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This paper deals with systems engineering applications design for offshore oil drilling production platform in the Nigerian Marine Environment. Engineering Design model of the distribution and accumulation of petroleum hydrocarbons discharged into marine environment production platform and sources of impact of an offshore is treated.

Keywords: design of offshore oil drilling production platform, marine, environment, petroleum hydrocarbons

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Authors: Yulna Yulnafatmawita, Syafrimen Yasin, Lusi Maira

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Irrigation source is one of the factors affecting physical properties of rice field. This research was aimed to determine the impact of polluted irrigation wáter on soil physical properties of rice field. The study site was located in Koto Nan IV, Dharmasraya Regency, West Sumatra, Indonesia. The rice field was irrigated with wáter from Momongan river in which people do gold mining. The soil was sampled vertically from the top to 100 cm depth with 20 cm increment of soil profile from 2 year-fallowed rice field, as well as from the top 20 cm of cultivated rice field from the terrace-1 (the highest terrace) to terrace-5 (the lowest terrace) position. Soil samples were analysed in laboratory. For comparison, rice field receiving irrigation wáter from non-polluted source was also sampled at the top 20 cm and anaysed for the physical properties. The result showed that there was a change in soil physical properties of rice field after 9 years of getting irrigation from the river. Based on laboratory analyses, the total suspended solid (TSS) in the tailing reached 10,736 mg/L. The texture of rice field at polluted rice field (PRF) was dominated (>55%) by sand particles at the top 100 cm soil depth, and it tended to linearly decrease (R2=0.65) from the top 20 cm to 100 cm depth. Likewise, the sand particles also linearly decreased (R2=0.83), but clay particles linearly increased (R2=0.74) horizontally as the distance from the wáter input (terrace-1) was fartherst. Compared to nonpolluted rice field (NPRF), percentage of sand was higher, and clay was lower at PRF. This sandy texture of soil in PRF increased soil hydraulic conductivity (up to 19.1 times), soil bulk density (by 38%), and sharply decreased SOM (by 88.5 %), as well as soil total pore (by 22.1%) compared to the NPRF at the top 20 cm soil. The rice field was suggested to be reclaimed before reusing it. Otherwise the soil characteristics requirement, especially soil wáter retention, for rice field could not be fulfilled.

Keywords: gold mine tailing, polluted irrigation, rice field, soil physical properties

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Authors: B. A. Mir, A. Juneja

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In this paper, shear behavior of reconstituted clay reinforced with varying diameter of sand compaction piles with area replacement-ratio (a_s) of 6.25, 10.24, 16, 20.25 and 64% in 100mm diameter and 200mm long clay specimens is modeled using consolidated drained and undrained triaxial tests under different confining pressures ranging from 50kPa to 575kPa. The test results show that the stress-strain behavior of the clay was highly influenced by the presence of SCP. The insertion of SCPs into soft clay has shown to have a positive effect on the load carrying capacity of the clay, resulting in a composite soil mass that has greater shear strength and improved stiffness compared to the unreinforced clay due to increased reinforcement area ratio. In addition, SCP also acts as vertical drain in the clay thus accelerating the dissipation of excess pore water pressures that are generated during loading by shortening the drainage path and activating radial drainage, thereby reducing post-construction settlement. Thus, sand compaction piles currently stand as one of the most viable and practical techniques for improving the mechanical properties of soft clays.

Keywords: reconstituted clay, SCP, shear strength, stress-strain response, triaxial tests

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Authors: Cristian Păuna

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After the widespread release of electronic trading, automated trading systems have become a significant part of the business intelligence system of any modern financial investment company. An important part of the trades is made completely automatically today by computers using mathematical algorithms. The trading decisions are taken almost instantly by logical models and the orders are sent by low-latency automatic systems. This paper will present a real-time price prediction methodology designed especially for algorithmic trading. Based on the price cyclicality function, the methodology revealed will generate price cyclicality bands to predict the optimal levels for the entries and exits. In order to automate the trading decisions, the cyclicality bands will generate automated trading signals. We have found that the model can be used with good results to predict the changes in market behavior. Using these predictions, the model can automatically adapt the trading signals in real-time to maximize the trading results. The paper will reveal the methodology to optimize and implement this model in automated trading systems. After tests, it is proved that this methodology can be applied with good efficiency in different timeframes. Real trading results will be also displayed and analyzed in order to qualify the methodology and to compare it with other models. As a conclusion, it was found that the price prediction model using the price cyclicality function is a reliable trading methodology for algorithmic trading in the financial market.

Keywords: algorithmic trading, automated trading systems, financial markets, high-frequency trading, price prediction

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Authors: Joanna Kochanska, Dagmara Gornicka, Anna Burduk

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The article examines the process of implementing a new assembly line in a manufacturing enterprise of the household appliances industry area. At the initial stages of the project, a decision was made that one of its foundations should be the concept of lean management. Because of that, eliminating as many errors as possible in the first phases of its functioning was emphasized. During the start-up of the line, there were identified and documented all production losses (from serious machine failures, through any unplanned downtime, to micro-stops and quality defects). During 6 weeks (line start-up period), all errors resulting from problems in various areas were analyzed. These areas were, among the others, production, logistics, quality, and organization. The aim of the work was to analyze the occurrence of production failures during the initial phase of starting up the line and to propose a method for determining their critical level during its full functionality. There was examined the repeatability of the production losses in various areas and at different levels at such an early stage of implementation, by using the methods of statistical process control. Based on the Pareto analysis, there were identified the weakest points in order to focus improvement actions on them. The next step was to examine the effectiveness of the actions undertaken to reduce the level of recorded losses. Based on the obtained results, there was proposed a method for determining the critical failures level in the studied areas. The developed coefficient can be used as an alarm in case of imbalance of the production, which is caused by the increased failures level in production and production support processes in the period of the standardized functioning of the line.

Keywords: production failures, level of production losses, new production line implementation, assembly line, statistical process control

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Authors: Man Fung Ho, Lap So, Jiaqi Zhang, Yuheng Zhao, Huiyang Lu, Tat Shing Choi, K. Y. Michael Wong

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Nowadays, a tremendous amount of data is available in the transportation system, enabling the development of various machine learning approaches to make short-term latency predictions. A natural question is then the choice of relevant information to enable accurate predictions. Using traffic data collected from the Taiwan Freeway System, we consider the prediction of short-term latency of a freeway segment with a length of 17 km covering 5 measurement points, each collecting vehicle-by-vehicle data through the electronic toll collection system. The processed data include the past latencies of the freeway segment with different time lags, the traffic conditions of the individual segments (the accumulations, the traffic fluxes, the entrance and exit rates), the total accumulations, and the weekday latency profiles obtained by Gaussian process regression of past data. We arrive at several important conclusions about how data should be refined to obtain accurate predictions, which have implications for future system-wide latency predictions. (1) We find that the prediction of median latency is much more accurate and meaningful than the prediction of average latency, as the latter is plagued by outliers. This is verified by machine-learning prediction using XGBoost that yields a 35% improvement in the mean square error of the 5-minute averaged latencies. (2) We find that the median latency of the segment 15 minutes ago is a very good baseline for performance comparison, and we have evidence that further improvement is achieved by machine learning approaches such as XGBoost and Long Short-Term Memory (LSTM). (3) By analyzing the feature importance score in XGBoost and calculating the mutual information between the inputs and the latencies to be predicted, we identify a sequence of inputs ranked in importance. It confirms that the past latencies are most informative of the predicted latencies, followed by the total accumulation, whereas inputs such as the entrance and exit rates are uninformative. It also confirms that the inputs are much less informative of the average latencies than the median latencies. (4) For predicting the latencies of segments composed of two or three sub-segments, summing up the predicted latencies of each sub-segment is more accurate than the one-step prediction of the whole segment, especially with the latency prediction of the downstream sub-segments trained to anticipate latencies several minutes ahead. The duration of the anticipation time is an increasing function of the traveling time of the upstream segment. The above findings have important implications to predicting the full set of latencies among the various locations in the freeway system.

Keywords: data refinement, machine learning, mutual information, short-term latency prediction

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Authors: E. P. Doku-Asare, A. Essuman

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The materials used for the production of holders are mainly metals and plastic, and these materials are difficult and expensive to process; therefore, the need to explore other materials such as clay for the production of holders. Clay is a viable material for the production of holders due to its plastic nature. Using ceramic materials as a medium for the production of holders does not only serve its purpose but also economically cheaper since the material is mined in Ghana. The study also examines the aesthetic nature of the holders due to the properties found in the material used. Six holders were chosen and were made in a manner that would not take a lot of space. They are Pin holders, Paper holders, Penholders, Paperweight and Umbrella holders. The production technique employed in the execution of this project were the slab method, throwing, and press mold. Results indicated that ceramic holders are durable and long-lasting and can serve the purpose of metallic and plastic holders. The study also found that clay holders are durable due to the fact that clay is from a natural source which ensures permanence and resistance to stress. It is recommended that press molds be used in the production of holders. Clay holders last longer due to the useful properties of clay including very high hardness and strength.

Keywords: ceramics, interior design, Ghana, production technique

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Authors: Marian Sorin Ionescu, Olivia Negoita, Cosmin Dobrin

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This scientific communication reports and discusses learning models adaptable to modern business problems and models specific to digital concepts and paradigms. In the PAC (probably approximately correct) learning model approach, in which the learning process begins by receiving a batch of learning examples, the set of learning processes is used to acquire a hypothesis, and when the learning process is fully used, this hypothesis is used in the prediction of new operational examples. For complex business models, a lot of models should be introduced and evaluated to estimate the induced results so that the totality of the results are used to develop a predictive rule, which anticipates the choice of new models. In opposition, for online learning-type processes, there is no separation between the learning (training) and predictive phase. Every time a business model is approached, a test example is considered from the beginning until the prediction of the appearance of a model considered correct from the point of view of the business decision. After choosing choice a part of the business model, the label with the logical value "true" is known. Some of the business models are used as examples of learning (training), which helps to improve the prediction mechanisms for future business models.

Keywords: machine learning, business models, convex analysis, online learning

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Authors: Imad Eddine Charif, Wafaa Benchouk, Sidi Mohamed Mekelleche

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The regioselectivity of a series of 16 1,3-dipolar cycloaddition reactions of nitrile oxides with 2(5H)-furanones has been analysed by means of global and local electrophilic and nucleophilic reactivity indices using density functional theory at the B3LYP level together with the 6-31G(d) basis set. The local electrophilicity and nucleophilicity indices, based on Fukui and Parr functions, have been calculated for the terminal sites, namely the C1 and O3 atoms of the 1,3-dipole and the C4 and C5 atoms of the dipolarophile. These local indices were calculated using both Mulliken and natural charges and spin densities. The results obtained show that the C5 atom of the 2(5H)-furanones is the most electrophilic site whereas the O3 atom of the nitrile oxides is the most nucleophilic centre. It turns out that the experimental regioselectivity is correctly reproduced, indicating that both Fukui- and Parr-based indices are efficient tools for the prediction of the regiochemistry of the studied reactions and could be used for the prediction of newly designed reactions of the same kind.

Keywords: 1, 3-dipolar cycloaddition, density functional theory, nitrile oxides, regioselectivity, reactivity indices

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Authors: Khelfaoui Rachid, Chekifi Tawfiq, Dennai Brahim, Maazouzi A. Hak

Abstract:

A numerical and experimental studies of passive micro drops production have been presented. This paper focuses on the modeling of micro-oscillators systems which are composed by passive amplifier without moving part. The micro-system modeling is based on geometrical oscillators form. An asymmetric micro-oscillator design that is based on a bistable fluidic amplifier is proposed. The characteristic size of the channels is generally about 35 microns of depth. The numerical results indicate that the production and manipulation of microdrops are possible with passive device within a typical oscillators chamber of 2.25 mm diameter and 0.20 mm length when the Reynolds number is Re = 490. The novel micro drops method that is presented in this study provides a simple solution about the production of microdrops problems in micro system. We undertake an experimental step. The first part is based on the realisation of sample oscillator; the second part is consisted of visualization, production and manipulation of microdrops.

Keywords: modelling, miscible, micro drops, production, oscillator sample, capillary

Procedia PDF Downloads 373

Authors: Hasan Keshavarzian, Tayebeh Nesari

Abstract:

Bolted rail joint is one of the most vulnerable areas in railway track. A comprehensive approach was developed for studying the reliability of fatigue crack initiation of railroad bolt hole under random axle loads and random material properties. The operation condition was also considered as stochastic variables. In order to obtain the comprehensive probability model of fatigue crack initiation life prediction in railroad bolt hole, we used FEM, response surface method (RSM), and reliability analysis. Combined energy-density based and critical plane based fatigue concept is used for the fatigue crack prediction. The dynamic loads were calculated according to the axle load, speed, and track properties. The results show that axle load is most sensitive parameter compared to Poisson’s ratio in fatigue crack initiation life. Also, the reliability index decreases slowly due to high cycle fatigue regime in this area.

Keywords: rail-wheel tribology, rolling contact mechanic, finite element modeling, reliability analysis

Procedia PDF Downloads 377

Authors: M. Cizmesija, N. Erjavec, V. Bahovec

Abstract:

While the European Union (EU) harmonized methodology is a benchmark of worldwide used business survey (BS) methodology, the choice of variables that are components of the confidence indicators, as the leading indicators, is not strictly determined and unique. Therefore, the aim of this paper is to investigate and to quantify the relationship between all business survey variables in manufacturing industry and industrial production as a reference macroeconomic series in Croatia. The assumption is that there are variables in the business survey, that are not components of Industrial Confidence Indicator (ICI) and which can accurately (and sometimes better then ICI) predict changes in Croatian industrial production. Empirical analyses are conducted using quarterly data of BS variables in manufacturing industry and Croatian industrial production over the period from the first quarter 2005 to the first quarter 2013. Research results confirmed the assumption: three BS variables which is not components of ICI (competitive position, demand and liquidity) are the best leading indicator then ICI, in forecasting changes in Croatian industrial production instantaneously, with one, two or three quarter ahead.

Keywords: balance, business survey, confidence indicators, industrial production, forecasting

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Authors: Marlies Achenbach

Abstract:

System competencies play a key role to ensure an effective and efficient improvement of production systems. Thus, there can be observed an increasing demand for developing system competencies in industry as well as in engineering education. System competencies consist of the following two main abilities: Evaluating the current state of a production system and developing a target state. The innovative course ‘multi-perspective learning in a real production plant (multi real)’ is developed to create a learning setting that supports the development of these system competencies. Therefore, the setting combines two innovative aspects: First, the Learning takes place in heterogeneous groups formed by students as well as professionals and managers from industry. Second, the learning takes place in a real production plant. This paper presents the innovative didactic concept of ‘multi real’ in detail, which will initially be implemented in October/November 2016 in the industrial engineering, logistics and mechanical master’s program at TU Dortmund University.

Keywords: experiential learning, heterogeneous groups, improving production systems, system competencies

Procedia PDF Downloads 422

Authors: M. Arifuzzaman, H. S. Kim

Abstract:

Multiple sets of variables associated with expanded perlite particle consolidation in foam manufacturing were analyzed to develop a model for predicting perlite foam density. The consolidation of perlite particles based on the flotation method and compaction involves numerous variables leading to the final perlite foam density. The variables include binder content, compaction ratio, perlite particle size, various perlite particle densities and porosities, and various volumes of perlite at different stages of process. The developed model was found to be useful not only for prediction of foam density but also for optimization between compaction ratio and binder content to achieve a desired density. Experimental verification was conducted using a range of foam densities (0.15–0.5 g/cm3) produced with a range of compaction ratios (1.5-3.5), a range of sodium silicate contents (0.05–0.35 g/ml) in dilution, a range of expanded perlite particle sizes (1-4 mm), and various perlite densities (such as skeletal, material, bulk, and envelope densities). A close agreement between predictions and experimental results was found.

Keywords: expanded perlite, flotation method, foam density, model, prediction, sodium silicate

Procedia PDF Downloads 405