Search results for: multidimensional process mining

Authors: Nieto Bernal Wilson, Carmona Suarez Edgar

Abstract:

The organizations have structured and unstructured information in different formats, sources, and systems. Part of these come from ERP under OLTP processing that support the information system, however these organizations in OLAP processing level, presented some deficiencies, part of this problematic lies in that does not exist interesting into extract knowledge from their data sources, as also the absence of operational capabilities to tackle with these kind of projects.  Data Warehouse and its applications are considered as non-proprietary tools, which are of great interest to business intelligence, since they are repositories basis for creating models or patterns (behavior of customers, suppliers, products, social networks and genomics) and facilitate corporate decision making and research. The following paper present a structured methodology, simple, inspired from the agile development models as Scrum, XP and AUP. Also the models object relational, spatial data models, and the base line of data modeling under UML and Big data, from this way sought to deliver an agile methodology for the developing of data warehouses, simple and of easy application. The methodology naturally take into account the application of process for the respectively information analysis, visualization and data mining, particularly for patterns generation and derived models from the objects facts structured.

Keywords: data warehouse, model data, big data, object fact, object relational fact, process developed data warehouse

Procedia PDF Downloads 409

Authors: M. Inmaculada Alvarez-Fernández, Celestino González-Nicieza, M. Belén Prendes-Gero, Fernando López-Gayarre

Abstract:

Among many factors affecting the stability of mining excavations, rock-bursts and tremors play a special role. These dynamic loads occur practically always and have different sources of generation. The most important of them is the commonly used mining technique, which disintegrates a certain area of the rock mass not only in the area of the planned mining, but also creates waves that significantly exceed this area affecting the structural elements. In this work it is analysed the consequences of dynamic loads over the structural elements in an underground room and pillar mine to avoid roof instabilities. With this end, dynamic loads were evaluated through in situ and laboratory tests and simulated with numerical modelling. Initially, the geotechnical characterization of all materials was carried out by mean of large-scale tests. Then, drill holes were done on the roof of the mine and were monitored to determine possible discontinuities in it. Three seismic stations and a triaxial accelerometer were employed to measure the vibrations from blasting tests, establish the dynamic behaviour of roof and pillars and develop the transmission laws. At last, computer simulations by FLAC3D software were done to check the effect of vibrations on the stability of the roofs. The study shows that in-situ tests have a greater reliability than laboratory samples because of eliminating the effect of heterogeneities, that the pillars work decreasing the amplitude of the vibration around them, and that the tensile strength of a beam and depending on its span is overcome with waves in phase and delayed. The obtained transmission law allows designing a blasting which guarantees safety and prevents the risk of future failures.

Keywords: dynamic modelling, long term instability risks, room and pillar, seismic collapse

Procedia PDF Downloads 138

Authors: Louise H. Gracioso, Marcela P.G. Baltazar, Ingrid R. Avanzi, Bruno Karolski, Luciana J. Gimenes, Claudio O. Nascimento, Elen A. Perpetuo

Abstract:

Introduction: Higher copper concentrations promote a selection pressure on organisms such as plants, fungi and bacteria, which allows surviving only the resistant organisms to the contaminated site. This selective pressure keeps only the organisms most resistant to a specific condition and subsequently increases their bioremediation potential. Despite the bacteria importance for biosphere maintenance, it is estimated that only a small fraction living microbial species has been described and characterized. Due to the molecular biology development, tools based on analysis 16S ribosomal RNA or another specific gene are making a new scenario for the characterization studies and identification of microorganisms in the environment. News identification of microorganisms methods have also emerged like Biotyper (MALDI / TOF), this method mass spectrometry is subject to the recognition of spectroscopic patterns of conserved and features proteins for different microbial species. In view of this, this study aimed to isolate bacteria resistant to copper present in a Copper Processing Area (Sossego Mine, Canaan, PA) and identifies them in two different methods: Recent (spectrometry mass) and conventional. This work aimed to use them for a future bioremediation of this Mining. Material and Methods: Samples were collected at fifteen different sites of five periods of times. Microorganisms were isolated from mining wastes by culture enrichment technique; this procedure was repeated 4 times. The isolates were inoculated into MJS medium containing different concentrations of chloride copper (1mM, 2.5mM, 5mM, 7.5mM and 10 mM) and incubated in plates for 72 h at 28 ºC. These isolates were subjected to mass spectrometry identification methods (Biotyper – MALDI/TOF) and 16S gene sequencing. Results: A total of 105 strains were isolated in this area, bacterial identification by mass spectrometry method (MALDI/TOF) achieved 74% agreement with the conventional identification method (16S), 31% have been unsuccessful in MALDI-TOF and 2% did not obtain identification sequence the 16S. These results show that Biotyper can be a very useful tool in the identification of bacteria isolated from environmental samples, since it has a better value for money (cheap and simple sample preparation and MALDI plates are reusable). Furthermore, this technique is more rentable because it saves time and has a high performance (the mass spectra are compared to the database and it takes less than 2 minutes per sample).

Keywords: copper mining area, bioremediation, microorganisms, identification, MALDI/TOF, RNA 16S

Procedia PDF Downloads 377

Authors: Felicite Ann Fairer-Wessels

Abstract:

There has always been a disparity between mining and tourism mainly due to the socio-economic and environmental impacts of mines on both the adjacent resident communities and the areas taken up by the mining operation. Although heritage mining tourism has been actively and successfully pursued and developed in the UK, largely Wales, and Scandinavian countries, the debate whether active mining and tourism can have a mutually beneficial relationship remains imminent. This pilot study explores the relationship between the ‘to be developed’ future Nokeng Mine and its adjacent community, the rural community of Moloto, will be investigated in terms of whether sustainable tourism and livelihood activities can potentially be developed with the support of the mine. Concepts such as social entrepreneur, corporate social responsibility, sustainable development and triple bottom line are discussed. Within the South African context as a mineral rich developing country, the government has a statutory obligation to empower disenfranchised communities through social and labour plans and policies. All South African mines must preside over a Social and Labour Plan according to the Mineral and Petroleum Resources Development Act, No 28 of 2002. The ‘social’ component refers to the ‘social upliftment’ of communities within or adjacent to any mine; whereas the ‘labour’ component refers to the mine workers sourced from the specific community. A qualitative methodology is followed using the case study as research instrument for the Nokeng Mine and Moloto community with interviews and focus group discussions. The target population comprised of the Moloto Tribal Council members (8 in-depth interviews), the Moloto community members (17: focus groups); and the Nokeng Mine representatives (4 in-depth interviews). In this pilot study two disparate ‘worlds’ are potentially linked: on the one hand, the mine as social entrepreneur that is searching for feasible and sustainable ideas; and on the other hand, the community adjacent to the mine, with potentially sustainable tourism entrepreneurs that can tap into the resources of the mine should their ideas be feasible to build their businesses. Being an exploratory study the findings are limited but indicate that the possible success of tourism and sustainable livelihood activities lies in the fact that both the Mine and Community are keen to work together – the mine in terms of obtaining labour and profit; and the community in terms of improved and sustainable social and economic conditions; with both parties realizing the importance to mitigate negative environmental impacts. In conclusion, a relationship of trust is imperative between a mine and a community before a long term liaison is possible. However whether tourism is a viable solution for the community to engage in is debatable. The community could initially rather pursue the sustainable livelihoods approach and focus on life-supporting activities such as building, gardening, etc. that once established could feed into possible sustainable tourism activities.

Keywords: community development, mining tourism, sustainability, South Africa

Procedia PDF Downloads 302

Authors: Syamala Krishnannair

Abstract:

A multivariate statistical process monitoring methodology using dissimilarity scale-based singular spectrum analysis (SSA) is proposed for the detection and diagnosis of process faults in the base metal flotation plant. Process faults are detected based on the multi-level decomposition of process signals by SSA using the dissimilarity structure of the process data and the subsequent monitoring of the multiscale signals using the unified monitoring index which combines T² with SPE. Contribution plots are used to identify the root causes of the process faults. The overall results indicated that the proposed technique outperformed the conventional multivariate techniques in the detection and diagnosis of the process faults in the flotation plant.

Keywords: fault detection, fault diagnosis, process monitoring, dissimilarity scale

Procedia PDF Downloads 209

Authors: Abraham Addo-Bediako

Abstract:

Many freshwater ecosystems are facing immense pressure from anthropogenic activities, such as agricultural, industrial and mining. Trace metal pollution in freshwater ecosystems has become an issue of public health concern due to its toxicity and persistence in the environment. Trace elements pose a serious risk not only to the environment and aquatic biota but also humans. Chromium is one of such trace elements and its pollution in surface waters and groundwaters represents a serious environmental problem. In South Africa, agriculture, mining, industrial and domestic wastes are the main contributors to chromium discharge in rivers. The common forms of chromium are chromium (III) and chromium (VI). The latter is the most toxic because it can cause damage to human health. The aim of the study was to assess the contamination of chromium in the water and sediments of two rivers in the Steelpoort River sub-catchment of the Olifants River Basin, South Africa and human health risk. The concentration of Cr was analyzed using inductively coupled plasma–optical emission spectrometry (ICP-OES). The concentration of the metal was found to exceed the threshold limit, mainly in areas of high human activities. The hazard quotient through ingestion exposure did not exceed the threshold limit of 1 for adults and children and cancer risk for adults and children computed did not exceed the threshold limit of 10-4. Thus, there is no potential health risk from chromium through ingestion of drinking water for now. However, with increasing human activities, especially mining, the concentration could increase and become harmful to humans who depend on rivers for drinking water. It is recommended that proper management strategies should be taken to minimize the impact of chromium on the rivers and water from the rivers should properly be treated before domestic use.

Keywords: land use, health risk, metal pollution, water quality

Procedia PDF Downloads 87

Authors: Hamed Homaei, Iraj Mahdavi, Ali Tajdin

Abstract:

One of the main concerns of miners is to increase the quality level of their products because the mining metals price depends on their quality level; however, increasing the quality level of these products has different costs at different levels of the supply chain. These costs usually increase after extractor level. This paper studies the coordination issue of a decentralized three-level supply chain with one supplier (extractor), one mineral processor and one manufacturer in which the increasing product quality level cost at the processor level is higher than the supplier and at the level of the manufacturer is more than the processor. We identify the optimal product quality level for each supply chain member by designing a revenue sharing contract. Finally, numerical examples show that the designed contract not only increases the final product quality level but also provides a win-win condition for all supply chain members and increases the whole supply chain profit.

Keywords: three-stage supply chain, product quality improvement, channel coordination, revenue sharing

Procedia PDF Downloads 183

Authors: Javier Sedano-Cibrián, Julio Manuel de Luis-Ruiz, Rubén Pérez-Álvarez, Raúl Pereda-García, Beatriz Malagón-Picón

Abstract:

Structural inspection activities are necessary to ensure the correct functioning of infrastructures. Unmanned Aerial Vehicle (UAV) techniques have become more popular than traditional techniques. Specifically, UAV Photogrammetry allows time and cost savings. The development of this technology has permitted the use of low-cost thermal sensors in UAVs. The representation of 3D thermal models with this type of equipment is in continuous evolution. The direct processing of thermal images usually leads to errors and inaccurate results. A methodology is proposed for the generation of 3D thermal models using dual sensors, which involves the application of visible Red-Blue-Green (RGB) and thermal images in parallel. Hence, the RGB images are used as the basis for the generation of the model geometry, and the thermal images are the source of the surface temperature information that is projected onto the model. Mining/industrial facilities representations that are obtained can be used for inspection activities.

Keywords: aerial thermography, data processing, drone, low-cost, point cloud

Procedia PDF Downloads 143

Authors: Tayyaba Azim, Bibi Amina

Abstract:

The increase in the popularity of opinion mining with the rapid growth in the availability of social networks has attracted a lot of opportunities for research in the various domains of Sentiment Analysis and Natural Language Processing (NLP) using Artificial Intelligence approaches. The latest trend allows the public to actively use the internet for analyzing an individual’s opinion and explore the effectiveness of published facts. The main theme of this research is to account the public opinion on the most crucial and extensively discussed development projects, China Pakistan Economic Corridor (CPEC), considered as a game changer due to its promise of bringing economic prosperity to the region. So far, to the best of our knowledge, the theme of CPEC has not been analyzed for sentiment determination through the ML approach. This research aims to demonstrate the use of ML approaches to spontaneously analyze the public sentiment on Twitter tweets particularly about CPEC. Support Vector Machine SVM is used for classification task classifying tweets into positive, negative and neutral classes. Word2vec and TF-IDF features are used with the SVM model, a comparison of the trained model on manually labelled tweets and automatically generated lexicon is performed. The contributions of this work are: Development of a sentiment analysis system for public tweets on CPEC subject, construction of an automatic generation of the lexicon of public tweets on CPEC, different themes are identified among tweets and sentiments are assigned to each theme. It is worth noting that the applications of web mining that empower e-democracy by improving political transparency and public participation in decision making via social media have not been explored and practised in Pakistan region on CPEC yet.

Keywords: machine learning, natural language processing, sentiment analysis, support vector machine, Word2vec

Procedia PDF Downloads 148

Authors: O. E. Obayemi, M. A. Ayoade, O. O. Komolafe

Abstract:

Fish survey was carried out for an annual cycle covering both rainy and dry seasons using cast nets, gill nets and traps at two different reservoirs. The objective was to examined the fish assemblages of the reservoirs and provide more additional information on the reservoir. The fish species in the reservoirs comprised of twelve species of six families. The results of the study also showed that five species of fish were caught in reservoir five while ten fish species were captured in reservoir six. Species such as Malapterurus electricus, Ctenopoma kingsleyae, Mormyrus rume, Parachanna obscura, Sarotherodon galilaeus, Tilapia mariae, C. guntheri, Clarias macromystax, Coptodon zilii and Clarias gariepinus were caught during the sampling period. There was a significant difference (p=0.014, t = 1.711) in the abundance of fish species in the two reservoirs. Seasonally, reservoirs five (p=0.221, t = 1.859) and six (p=0.453, t = 1.734) showed there was no significant difference in their fish populations. Also, despite being impacted with gold mining the diversity indices were high when compared to less disturbed waterbodies. The study concluded that the environments recorded low abundant fish species which suggests the influence of mining on the abundance and diversity of fish species.

Keywords: Igun, fish, Shannon-Wiener Index, Simpson index, Pielou index

Procedia PDF Downloads 106

Authors: Katalina Grigorova, Kaloyan Mironov

Abstract:

The paper focuses on the benefits of business process modeling. Although this discipline is developing for many years, there is still necessity of creating new opportunities to meet the ever-increasing users’ needs. Because one of these needs is related to the conversion of business process models from one standard to another, the authors have developed a converter between BPMN and EPC standards using workflow patterns as intermediate tool. Nowadays there are too many systems for business process modeling. The variety of output formats is almost the same as the systems themselves. This diversity additionally hampers the conversion of the models. The presented study is aimed at discussing problems due to differences in the output formats of various modeling environments.

Keywords: business process modeling, business process modeling standards, workflow patterns, converting models

Procedia PDF Downloads 584

Authors: Amir Mirzaei

Abstract:

The powertrain system is one of the most basic and essential components in a machine. The occurrence of motion is practically impossible without the presence of this system. When power is generated by the engine, it is transmitted by the powertrain system to the wheels, which are the last parts of the system. Powertrain system has different components according to the type of use and design. When the force generated by the engine reaches to the wheels, the amount of frictional force between the tire and the ground determines the amount of traction and non-slip or the amount of slip. At various levels, such as icy, muddy, and snow-covered ground, the amount of friction coefficient between the tire and the ground decreases dramatically and considerably, which in turn increases the amount of force loss and the vehicle traction decreases drastically. This condition is caused by the phenomenon of slipping, which, in addition to the waste of energy produced, causes the premature wear of driving tires. It also causes the temperature of the transmission oil to rise too much, as a result, causes a reduction in the quality and become dirty to oil and also reduces the useful life of the clutches disk and plates inside the transmission. this issue is much more important in road construction and mining machinery than passenger vehicles and is always one of the most important and significant issues in the design discussion, in order to overcome. One of these methods is the automatic spin regulator system which is abbreviated as ASR. The importance of this method and its structure and function have solved one of the biggest challenges of the powertrain system in the field of construction and mining machinery. That this research is examined.

Keywords: automatic spin regulator, ASR, methods of reducing slipping, methods of preventing the reduction of the useful life of clutches disk and plate, methods of preventing the premature dirtiness of transmission oil, method of preventing the reduction of the useful life of tires

Procedia PDF Downloads 79

Authors: Fiona S. Motswaiso, Kengo Nakamura, Takeshi Komai

Abstract:

Heavy metals may occur naturally in rocks and soils, but elevated quantities of them are being gradually released into the environment by anthropogenic activities such as mining. In order to address issues of heavy metal water and soil pollution, a distinction needs to be made between natural and anthropogenic anomalies. The current study aims at characterizing the spatial distribution of trace elements and evaluate site-specific geochemical background concentrations of trace elements in the mine soils examined, and also to discriminate between lithogenic and anthropogenic sources of enrichment around a copper-nickel mining town in Selibe-Phikwe, Botswana. A total of 20 Soil samples, 11 river sediment, and 9 river water samples were collected from an area of 625m² within the precincts of the mine and the smelter. The concentrations of metals (Cu, Ni, Pb, Zn, Cr, Ni, Mn, As, Pb, and Co) were determined by using an ICP-MS after digestion with aqua regia. Major elements were also determined using ED-XRF. Water pH and EC were measured on site and recorded while soil pH and EC were also determined in the laboratory after performing water elution tests. The highest Cu and Ni concentrations in soil are 593mg/kg and 453mg/kg respectively, which is 3 times higher than the crustal composition values and 2 times higher than the South African minimum allowable levels of heavy metals in soils. The level of copper contamination was higher than that of nickel and other contaminants. Water pH levels ranged from basic (9) to very acidic (3) in areas closer to the mine/smelter. There is high variation in heavy metal concentration, eg. Cu suggesting that some sites depict regional natural background concentrations while other depict anthropogenic sources.

Keywords: contamination, geochemical baseline, heavy metals, soils

Procedia PDF Downloads 159

Authors: Alexandre Chagnes

Abstract:

Lithium-ion battery is the technology of choice in the development of electric vehicles. This technology is now mature, although there are still many challenges to increase their energy density while ensuring an irreproachable safety of use. For this goal, it is necessary to develop new cathodic materials that can be cycled at higher voltages and electrolytes compatible with these materials. But the challenge does not only concern the production of efficient batteries for the electrochemical storage of energy since lithium-ion battery technology relies on the use of critical and/or strategic value resources. It is, therefore, crucial to include Lithium-ion batteries development in a circular economy approach very early. In particular, optimized recycling and reuse of battery components must both minimize their impact on the environment and limit geopolitical issues related to tensions on the mineral resources necessary for lithium-ion battery production. Although recycling will never replace mining, it reduces resource dependence by ensuring the presence of exploitable resources in the territory, which is particularly important for countries like France, where exploited or exploitable resources are limited. This conference addresses the development of a new hydrometallurgical process combining leaching of cathodic material from spent lithium-ion battery in acidic chloride media and solvent extraction process. Most of recycling processes reported in the literature rely on the sulphate route, and a few studies investigate the potentialities of the chloride route despite many advantages and the possibility to develop new chemistry, which could get easier the metal separation. The leaching mechanisms and the solvent extraction equilibria will be presented in this conference. Based on the comprehension of the physicochemistry of leaching and solvent extraction, the present study will introduce a new hydrometallurgical process for the production of cobalt, nickel, manganese and lithium from spent cathodic materials.

Keywords: lithium-ion battery, recycling, hydrometallurgy, leaching, solvent extraction

Procedia PDF Downloads 80

Authors: Farida Mellah, Abdelhak Boutaleb, Bachir Henni, Dalila Berdous, Abdelhamid Mellah

Abstract:

Context/Purpose: One of the largest mining operations for lead and zinc deposits in northwestern Algeria in more than thirty years, El Abed is now the abandoned mine that has been inactive since 2004, leaving large amounts of accumulated mining waste under the influence of Wind, erosion, rain, and near agricultural lands. Materials & Methods: This study aims to verify the concentrations and sources of heavy metals for surface samples containing randomly taken soil. Chemical analyses were performed using iCAP 7000 Series ICP-optical emission spectrometer, using a set of environmental quality indicators by calculating the enrichment factor using iron and aluminum references, geographic accumulation index and geographic information system (GIS). On the basis of the spatial distribution. Results: The results indicated that the average metal concentration was: (As = 30,82),(Pb = 1219,27), (Zn = 2855,94), (Cu = 5,3), mg/Kg,based on these results, all metals except Cu passed by GBV in the Earth's crust. Environmental quality indicators were calculated based on the concentrations of trace metals such as lead, arsenic, zinc, copper, iron and aluminum. Interpretation: This study investigated the concentrations and sources of trace metals, and by using quality indicators and statistical methods, lead, zinc, and arsenic were determined from human sources, while copper was a natural source. And based on the spatial analysis on the basis of GIS, many hot spots were identified in the El-Abed region. Conclusion: These results could help in the development of future treatment strategies aimed primarily at eliminating materials from mining waste.

Keywords: soil contamination, trace metals, geochemical indices, El Abed mine, Algeria

Procedia PDF Downloads 71

Authors: Vaclav Sadilek, Miroslav Vorechovsky

Abstract:

The paper deals with the classical fiber bundle model of equal load sharing, sometimes referred to as the Daniels' bundle or the democratic bundle. Daniels formulated a multidimensional integral and also a recursive formula for evaluation of the strength cumulative distribution function. This paper describes three algorithms for evaluation of the recursive formula and also their implementations with source codes in high-level programming language Python. A comparison of the algorithms are provided with respect to execution time. Analysis of orders of magnitudes of addends in the recursion is also provided.

Keywords: equal load sharing, mpmath, python, strength of Daniels' bundle

Procedia PDF Downloads 404

Authors: Hamd Rezaeifar, Hamid Reza Sahriari

Abstract:

Accidents are considered to be one of the challenges of modern life. Due to the fact that the victims of this problem and also internal transportations are getting increased day by day in Iran, studying effective factors of accidents and identifying suitable models and parameters about this issue are absolutely essential. The main purpose of this research has been studying the factors and spatial data affecting accidents of Mashhad during 2007- 2008. In this paper it has been attempted to – through matching spatial layers on each other and finally by elaborating them with the place of accident – at the first step by adding landmarks of the accident and through adding especial fields regarding the existence or non-existence of effective phenomenon on accident, existing information banks of the accidents be completed and in the next step by means of data mining tools and analyzing by neural network, the relationship between these data be evaluated and a logical model be designed for predicting accident-prone spots with minimum error. The model of this article has a very accurate prediction in low-accident spots; yet it has more errors in accident-prone regions due to lack of primary data.

Keywords: accident, data mining, neural network, GIS

Procedia PDF Downloads 47

Authors: Mohd Asim, K. Nageswara Rao

Abstract:

The present work was carried out on River Yamuna passing through Delhi- National Capital Region (Delhi-NCR) of India for a stretch of about 130 km to assess the anthropogenic impact on the channel migration process for a period of 200 years with the help of satellite data and topographical maps with integration of geographic information system environment. Digital Shoreline Analysis System (DSAS) application was used to quantify river channel migration in ArcGIS environment. The average river channel migration was calculated to be 22.8 m/year for the entire study area. River channel migration was found to be moving in westward and eastward direction. Westward migration is more than 4 km maximum in length and eastward migration is about 4.19 km. The river has migrated a total of 32.26 sq. km of area. The results reveal that the river is being impacted by various human activities. The impact indicators include engineering structures, sand mining, embankments, urbanization, land use/land cover, canal network. The DSAS application was also used to predict the position of river channel in future for 2032 and 2042 by analyzing the past and present rate and direction of movement. The length of channel in 2032 and 2042 will be 132.5 and 141.6 km respectively. The channel will migrate maximum after crossing Okhla Barrage near Faridabad for about 3.84 sq. km from 2022 to 2042 from west to east.

Keywords: river migration, remote sensing, river Yamuna, anthropogenic impacts, DSAS, Delhi-NCR

Procedia PDF Downloads 123

Authors: Vidit Mohan, Ashwin P. Ramesh, Anirudh Toshniwal

Abstract:

Alkali-Surfactant-Alternate-Gas(ASAG) injection, a novel EOR process has the potential to improve displacement efficiency over Surfactant-Alternate-Gas(SAG) by addressing the problem of surfactant adsorption by clay minerals in rock matrix. A detailed laboratory investigation on ASAG injection process was carried out with encouraging results. To further enhance recovery over WAG injection process, SAG injection was investigated at laboratory scale. SAG injection yielded marginal incremental displacement efficiency over WAG process. On investigation, it was found that, clay minerals in rock matrix adsorbed the surfactants and were detrimental for SAG process. Hence, ASAG injection was conceptualized using alkali as a clay stabilizer. The experiment of ASAG injection with surfactant concentration of 5000 ppm and alkali concentration of 0.5 weight% yields incremental displacement efficiency of 5.42% over WAG process. The ASAG injection is a new process and has potential to enhance efficiency of WAG/SAG injection process.

Keywords: alkali surfactant alternate gas (ASAG), surfactant alternate gas (SAG), laboratory investigation, EOR process

Procedia PDF Downloads 479

Authors: Siti Nur Izati Azmi, Zatil Afifah Omar, Kathi Swaran, Navin Kumar

Abstract:

Organophilic Clays, also known as Organoclays, is used as a viscosifier in Oil based Drilling fluids. Most often, Organophilic clay are produced from modified Sodium and Calcium based Bentonite. Many studies and data show that Organophilic Clay using Hectorite based clays provide the best yield and good fluid loss properties in an oil-based drilling fluid at a higher cost. In terms of the manufacturing process, the two common methods of manufacturing organophilic clays are a Wet Process and a Dry Process. Wet process is known to produce better performance product at a higher cost while Dry Process shorten the production time. Hence, the purpose of this study is to evaluate the various formulation of an organophilic clay and its performance vs. the cost, as well as to determine the most efficient and cost-effective method of manufacturing organophilic clays.

Keywords: organophilic clay, viscosifier, wet process, dry process

Procedia PDF Downloads 226

Authors: Fahd Sabry Esmail, M. Badr Senousy, Mohamed Ragaie

Abstract:

In recent years, there has been an explosion in the rate of using technology that help discovering the diseases. For example, DNA microarrays allow us for the first time to obtain a "global" view of the cell. It has great potential to provide accurate medical diagnosis, to help in finding the right treatment and cure for many diseases. Various classification algorithms can be applied on such micro-array datasets to devise methods that can predict the occurrence of Leukemia disease. In this study, we compared the classification accuracy and response time among eleven decision tree methods and six rule classifier methods using five performance criteria. The experiment results show that the performance of Random Tree is producing better result. Also it takes lowest time to build model in tree classifier. The classification rules algorithms such as nearest- neighbor-like algorithm (NNge) is the best algorithm due to the high accuracy and it takes lowest time to build model in classification.

Keywords: data mining, classification techniques, decision tree, classification rule, leukemia diseases, microarray data

Procedia PDF Downloads 320

Authors: Kemal Polat

Abstract:

In this paper, an attribute weighting method called fuzzy C-means clustering based attribute weighting (FCMAW) for classification of Diabetes disease dataset has been used. The aims of this study are to reduce the variance within attributes of diabetes dataset and to improve the classification accuracy of classifier algorithm transforming from non-linear separable datasets to linearly separable datasets. Pima Indians Diabetes dataset has two classes including normal subjects (500 instances) and diabetes subjects (268 instances). Fuzzy C-means clustering is an improved version of K-means clustering method and is one of most used clustering methods in data mining and machine learning applications. In this study, as the first stage, fuzzy C-means clustering process has been used for finding the centers of attributes in Pima Indians diabetes dataset and then weighted the dataset according to the ratios of the means of attributes to centers of theirs. Secondly, after weighting process, the classifier algorithms including support vector machine (SVM) and k-NN (k- nearest neighbor) classifiers have been used for classifying weighted Pima Indians diabetes dataset. Experimental results show that the proposed attribute weighting method (FCMAW) has obtained very promising results in the classification of Pima Indians diabetes dataset.

Keywords: fuzzy C-means clustering, fuzzy C-means clustering based attribute weighting, Pima Indians diabetes, SVM

Procedia PDF Downloads 413

Authors: Samar M. Alqhtani, Suhuai Luo, Brian Regan

Abstract:

Data fusion technology can be the best way to extract useful information from multiple sources of data. It has been widely applied in various applications. This paper presents a data fusion approach in multimedia data for event detection in twitter by using Dempster-Shafer evidence theory. The methodology applies a mining algorithm to detect the event. There are two types of data in the fusion. The first is features extracted from text by using the bag-ofwords method which is calculated using the term frequency-inverse document frequency (TF-IDF). The second is the visual features extracted by applying scale-invariant feature transform (SIFT). The Dempster - Shafer theory of evidence is applied in order to fuse the information from these two sources. Our experiments have indicated that comparing to the approaches using individual data source, the proposed data fusion approach can increase the prediction accuracy for event detection. The experimental result showed that the proposed method achieved a high accuracy of 0.97, comparing with 0.93 with texts only, and 0.86 with images only.

Keywords: data fusion, Dempster-Shafer theory, data mining, event detection

Procedia PDF Downloads 410

Authors: Davit Mirzoyan, Ararat Khachatryan

Abstract:

A process corner monitoring circuit (PCMC) is presented in this work. The circuit generates a signal, the logical value of which depends on the process corner only. The signal can be used in both digital and analog circuits for testing and compensation of process variations (PV). The presented circuit uses only metal-oxide-semiconductor (MOS) transistors, which allow increasing its detection accuracy, decrease power consumption and area. Due to its simplicity the presented circuit can be easily modified to monitor parametrical variations of only n-type and p-type MOS (NMOS and PMOS, respectively) transistors, resistors, as well as their combinations. Post-layout simulation results prove correct functionality of the proposed circuit, i.e. ability to monitor the process corner (equivalently die-to-die variations) even in the presence of within-die variations.

Keywords: detection, monitoring, process corner, process variation

Procedia PDF Downloads 524

Authors: Xiangtuo Chen, Paul-Henry Cournéde

Abstract:

Crop yield prediction is a paramount issue in agriculture. The main idea of this paper is to find out efficient way to predict the yield of corn based meteorological records. The prediction models used in this paper can be classified into model-driven approaches and data-driven approaches, according to the different modeling methodologies. The model-driven approaches are based on crop mechanistic modeling. They describe crop growth in interaction with their environment as dynamical systems. But the calibration process of the dynamic system comes up with much difficulty, because it turns out to be a multidimensional non-convex optimization problem. An original contribution of this paper is to propose a statistical methodology, Multi-Scenarios Parameters Estimation (MSPE), for the parametrization of potentially complex mechanistic models from a new type of datasets (climatic data, final yield in many situations). It is tested with CORNFLO, a crop model for maize growth. On the other hand, the data-driven approach for yield prediction is free of the complex biophysical process. But it has some strict requirements about the dataset. A second contribution of the paper is the comparison of these model-driven methods with classical data-driven methods. For this purpose, we consider two classes of regression methods, methods derived from linear regression (Ridge and Lasso Regression, Principal Components Regression or Partial Least Squares Regression) and machine learning methods (Random Forest, k-Nearest Neighbor, Artificial Neural Network and SVM regression). The dataset consists of 720 records of corn yield at county scale provided by the United States Department of Agriculture (USDA) and the associated climatic data. A 5-folds cross-validation process and two accuracy metrics: root mean square error of prediction(RMSEP), mean absolute error of prediction(MAEP) were used to evaluate the crop prediction capacity. The results show that among the data-driven approaches, Random Forest is the most robust and generally achieves the best prediction error (MAEP 4.27%). It also outperforms our model-driven approach (MAEP 6.11%). However, the method to calibrate the mechanistic model from dataset easy to access offers several side-perspectives. The mechanistic model can potentially help to underline the stresses suffered by the crop or to identify the biological parameters of interest for breeding purposes. For this reason, an interesting perspective is to combine these two types of approaches.

Keywords: crop yield prediction, crop model, sensitivity analysis, paramater estimation, particle swarm optimization, random forest

Procedia PDF Downloads 231

Authors: S. Kreitlein, N. Eder, J. Franke

Abstract:

The importance of energy efficiency within the production process increases steadily. Unfortunately, so far no tools for a comprehensive assessment of energy efficiency within the production process exist. Therefore the Institute for Factory Automation and Production Systems of the Friedrich-Alexander-University Erlangen-Nuremberg has developed two methods with the goal of achieving transparency and a quantitative assessment of energy efficiency: EEV (Energy Efficiency Value) and EPE (Energetic Process Efficiency). This paper describes the basics and state of the art as well as the developed approaches.

Keywords: energy efficiency, energy efficiency value, energetic process efficiency, production

Procedia PDF Downloads 733

Authors: Samuel Munaf, Kevin Swingler, Franz Brülisauer, Anthony O’Hare, George Gunn, Aaron Reeves

Abstract:

Background: Avian influenza, more commonly known as Bird flu, is a viral zoonotic respiratory disease stemming from various species of poultry, including pets and migratory birds. Researchers have purported that the accessibility of health information online, in addition to the low-cost data collection methods the internet provides, has revolutionized the methods in which epidemiological and disease surveillance data is utilized. This paper examines the feasibility of using internet data sources, such as Twitter and livestock forums, for the early detection of the avian flu outbreak, through the use of text mining algorithms and social network analysis. Methods: Social media mining was conducted on Twitter between the period of 01/01/2021 to 31/12/2021 via the Twitter API in Python. The results were filtered firstly by hashtags (#avianflu, #birdflu), word occurrences (avian flu, bird flu, H5N1), and then refined further by location to include only those results from within the UK. Analysis was conducted on this text in a time-series manner to determine keyword frequencies and topic modeling to uncover insights in the text prior to a confirmed outbreak. Further analysis was performed by examining clinical signs (e.g., swollen head, blue comb, dullness) within the time series prior to the confirmed avian flu outbreak by the Animal and Plant Health Agency (APHA). Results: The increased search results in Google and avian flu-related tweets showed a correlation in time with the confirmed cases. Topic modeling uncovered clusters of word occurrences relating to livestock biosecurity, disposal of dead birds, and prevention measures. Conclusions: Text mining social media data can prove to be useful in relation to analysing discussed topics for epidemiological surveillance purposes, especially given the lack of applied research in the veterinary domain. The small sample size of tweets for certain weekly time periods makes it difficult to provide statistically plausible results, in addition to a great amount of textual noise in the data.

Keywords: veterinary epidemiology, disease surveillance, infodemiology, infoveillance, avian influenza, social media

Procedia PDF Downloads 105

Authors: Xiaohui Zhao, Shahan Mafuz

Abstract:

Context-aware technologies provide system applications with the awareness of environmental conditions, customer behaviour, object movements, etc. Further, with such capability system applications can be smart to adapt intelligently their responses to the changing conditions. Concerning business operations, this promises businesses that their business processes can run more intelligently, adaptively and flexibly, and thereby either improve customer experience, enhance reliability of service delivery, or lower operational cost, to make the business more competitive and sustainable. Aiming at realizing such context-aware business process management, this paper firstly explores its potential benefit and then identifies some gaps between the current business process management support and the expected. In addition, some preliminary solutions are also discussed with context definition, rule-based process execution, run-time process evolution, etc. A framework is also presented to give a conceptual architecture of context-aware business process management system to guide system implementation.

Keywords: business process adaptation, business process evolution, business process modelling, and context awareness

Procedia PDF Downloads 412

Authors: Marcos Devaner, Marcela Alves, Cledson Braga, Fabiano Alves, Wilton Bezerra

Abstract:

This article discusses the inclusion of people with disabilities in the process of testing an accessible system solution for distance education. The accessible system, team profile, methodologies and techniques covered in the testing process are presented. The testing process shown in this paper was designed from the experience with user. The testing process emerged from lessons learned from past experiences and the end user is present at all stages of the tests. Also, lessons learned are reported and how it was possible the maturing of the team and the methods resulting in a simple, productive and effective process.

Keywords: experience report, accessible systems, software testing, testing process, systems, e-learning

Procedia PDF Downloads 396

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

Abstract:

Manufacturing companies are facing global competition and enormous cost pressure. The use of machine learning applications can help reduce production costs and create added value. Predictive quality enables the securing of product quality through data-supported predictions using machine learning models as a basis for decisions on test results. Furthermore, machine learning methods are able to process large amounts of data, deal with unfavourable row-column ratios and detect dependencies between the covariates and the given target as well as assess the multidimensional influence of all input variables on the target. Real production data are often subject to highly fluctuating boundary conditions and unbalanced data sets. Changes in production data manifest themselves in trends, systematic shifts, and seasonal effects. Thus, Machine learning applications require intensive pre-processing and feature selection. Data preprocessing includes rule-based data cleaning, the application of dimensionality reduction techniques, and the identification of comparable data subsets. Within the used real data set of Bosch hydraulic valves, the comparability of the same production conditions in the production of hydraulic valves within certain time periods can be identified by applying the concept drift method. Furthermore, a classification model is developed to evaluate the feature importance in different subsets within the identified time periods. By selecting comparable and stable features, the number of features used can be significantly reduced without a strong decrease in predictive power. The use of cross-process production data along the value chain of hydraulic valves is a promising approach to predict the quality characteristics of workpieces. In this research, the ada boosting classifier is used to predict the leakage of hydraulic valves based on geometric gauge blocks from machining, mating data from the assembly, and hydraulic measurement data from end-of-line testing. In addition, the most suitable methods are selected and accurate quality predictions are achieved.

Keywords: classification, achine learning, predictive quality, feature selection

Procedia PDF Downloads 162