Search results for: dataset production

Authors: Charline David, Alexandre Blondin Massé, Arnaud Zinflou

Abstract:

In 2016, Clements, Hurn, and Li proposed a multiple equation time series approach for the short-term load forecasting, reporting an average mean absolute percentage error (MAPE) of 1.36% on an 11-years dataset for the Queensland region in Australia. We present an adaptation of their model to the electrical power load consumption for the whole Quebec province in Canada. More precisely, we take into account two additional meteorological variables — cloudiness and wind speed — on top of temperature, as well as the use of multiple meteorological measurements taken at different locations on the territory. We also consider other minor improvements. Our final model shows an average MAPE score of 1:79% over an 8-years dataset.

Keywords: short-term load forecasting, special days, time series, multiple equations, parallelization, clustering

Procedia PDF Downloads 70

Authors: Thorben Kuprat, Julian Becker, Jonas Mayer, Peter Nyhuis

Abstract:

For years now, it has been recognized that logistic performance capability contributes enormously to a production enterprise’s competitiveness and as such is a critical control lever. In doing so, the orientation on customer wishes (e.g. delivery dates) represents a key parameter not only in the value-adding production but also in product regeneration. Since production and regeneration processes have different characteristics, production planning and control measures cannot be directly transferred to regeneration processes. As part of a special research project, the Institute of Production Systems and Logistics Hannover is focused on increasing the logistic performance capability of regeneration processes for complex capital goods. The aim is to ensure logistic targets are met by implementing a model specifically designed to align the capacities and load in regeneration processes.

Keywords: capacity planning, complex capital goods, logistic performance, regeneration process

Procedia PDF Downloads 460

Authors: Jason West

Abstract:

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

Procedia PDF Downloads 49

Authors: Petra Gajdačová

Abstract:

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

Procedia PDF Downloads 162

Authors: Walid Cherif

Abstract:

Data mining has, over recent years, seen big advances because of the spread of internet, which generates everyday a tremendous volume of data, and also the immense advances in technologies which facilitate the analysis of these data. In particular, classification techniques are a subdomain of Data Mining which determines in which group each data instance is related within a given dataset. It is used to classify data into different classes according to desired criteria. Generally, a classification technique is either statistical or machine learning. Each type of these techniques has its own limits. Nowadays, current data are becoming increasingly heterogeneous; consequently, current classification techniques are encountering many difficulties. This paper defines new measure functions to quantify the resemblance between instances and then combines them in a new approach which is different from actual algorithms by its reliability computations. Results of the proposed approach exceeded most common classification techniques with an f-measure exceeding 97% on the IRIS Dataset.

Keywords: data mining, knowledge discovery, machine learning, similarity measurement, supervised classification

Procedia PDF Downloads 428

Authors: Mohammadali Abedini Sanigy, Jiangang Fei

Abstract:

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

Procedia PDF Downloads 156

Authors: Ayşen Melda Çolak, Volkan Okatan, Ercan Yıldız

Abstract:

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

Procedia PDF Downloads 228

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

Abstract:

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

Procedia PDF Downloads 292

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

Abstract:

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

Procedia PDF Downloads 228

Authors: Florian Gschösser, Walter Purrer

Abstract:

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

Procedia PDF Downloads 508

Authors: C. Njoku Paul

Abstract:

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

Procedia PDF Downloads 516

Authors: Ksheeraj Sai Vepuri, Nada Attar

Abstract:

We as humans use words with accompanying visual and facial cues to communicate effectively. Classifying facial emotion using computer vision methodologies has been an active research area in the computer vision field. In this paper, we propose a simple method for facial expression recognition that enhances accuracy. We tested our method on the FER-2013 dataset that contains static images. Instead of using Histogram equalization to preprocess the dataset, we used Unsharp Mask to emphasize texture and details and sharpened the edges. We also used ImageDataGenerator from Keras library for data augmentation. Then we used Convolutional Neural Networks (CNN) model to classify the images into 7 different facial expressions, yielding an accuracy of 69.46% on the test set. Our results show that using image preprocessing such as the sharpening technique for a CNN model can improve the performance, even when the CNN model is relatively simple.

Keywords: facial expression recognittion, image preprocessing, deep learning, CNN

Procedia PDF Downloads 103

Authors: Joanna Kochanska, Dagmara Gornicka, Anna Burduk

Abstract:

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

Procedia PDF Downloads 106

Authors: Gabriele Borg, Alexei De Bono, Charlie Abela

Abstract:

There on a constant rise in the availability of high volumes of data gathered from multiple sources, resulting in an abundance of unprocessed information that can be used to monitor patterns and trends in user behaviour. Similarly, year after year, Malta is also constantly experiencing ongoing population growth and an increase in mobilization demand. This research takes advantage of data which is continuously being sourced and converting it into useful information related to the traffic problem on the Maltese roads. The scope of this paper is to provide a methodology to create a custom dataset (MalTra - Malta Traffic) compiled from multiple participants from various locations across the island to identify the most common routes taken to expose the main areas of activity. This use of big data is seen being used in various technologies and is referred to as ITSs (Intelligent Transportation Systems), which has been concluded that there is significant potential in utilising such sources of data on a nationwide scale.

Keywords: Big Data, vehicular traffic, traffic management, mobile data patterns

Procedia PDF Downloads 78

Authors: Tonislav Ivanov, Oleksii Nedashkivskyi, Denis Babeshko, Vadim Pinskiy, Matthew Putman

Abstract:

Training a machine learning model for object detection that generalizes well is known to benefit from a training dataset with diverse examples. However, training datasets usually contain many repeats of common examples of a class and lack rarely seen examples. This is due to the process commonly used during human annotation where a person would proceed sequentially through a list of images labeling a sufficiently high total number of examples. Instead, the method presented involves an active process where, after the initial labeling of several images is completed, the next subset of images for labeling is selected by an algorithm. This process of algorithmic image selection and manual labeling continues in an iterative fashion. The algorithm used for the image selection is a deep learning algorithm, based on the U-shaped architecture, which quantifies the presence of unseen data in each image in order to find images that contain the most novel examples. Moreover, the location of the unseen data in each image is highlighted, aiding the labeler in spotting these examples. Experiments performed using semiconductor wafer data show that labeling a subset of the data, curated by this algorithm, resulted in a model with a better performance than a model produced from sequentially labeling the same amount of data. Also, similar performance is achieved compared to a model trained on exhaustive labeling of the whole dataset. Overall, the proposed approach results in a dataset that has a diverse set of examples per class as well as more balanced classes, which proves beneficial when training a deep learning model.

Keywords: computer vision, deep learning, object detection, semiconductor

Procedia PDF Downloads 107

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

Abstract:

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

Procedia PDF Downloads 142

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 345

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

Procedia PDF Downloads 446

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 394

Authors: Somar Boubou, Tatsuo Narikiyo, Michihiro Kawanishi

Abstract:

In this work, we address the problem of 3D object recognition with depth sensors such as Kinect or Structure sensor. Compared with traditional approaches based on local descriptors, which depends on local information around the object key points, we propose a global features based descriptor. Proposed descriptor, which we name as Differential Histogram of Normal Vectors (DHONV), is designed particularly to capture the surface geometric characteristics of the 3D objects represented by depth images. We describe the 3D surface of an object in each frame using a 2D spatial histogram capturing the normalized distribution of differential angles of the surface normal vectors. The object recognition experiments on the benchmark RGB-D object dataset and a self-collected dataset show that our proposed descriptor outperforms two others descriptors based on spin-images and histogram of normal vectors with linear-SVM classifier.

Keywords: vision in control, robotics, histogram, differential histogram of normal vectors

Procedia PDF Downloads 251

Authors: Chaima Babi, Said Gadri

Abstract:

The rapid increase in fake news continues to grow at a very fast rate; this requires implementing efficient techniques that allow testing the re-liability of online content. For that, the current research strives to illuminate the fake news problem using deep learning DL and machine learning ML ap-proaches. We have developed the traditional LSTM (Long short-term memory), and the bidirectional BiLSTM model. A such process is to perform a training task on almost of samples of the dataset, validate the model on a subset called the test set to provide an unbiased evaluation of the final model fit on the training dataset, then compute the accuracy of detecting classifica-tion and comparing the results. For the programming stage, we used Tensor-Flow and Keras libraries on Python to support Graphical Processing Units (GPUs) that are being used for developing deep learning applications.

Keywords: machine learning, deep learning, natural language, fake news, Bi-LSTM, LSTM, multiclass classification

Procedia PDF Downloads 45

Authors: Jindong Gu, Matthias Schubert, Volker Tresp

Abstract:

In many outlier detection tasks, only training data belonging to one class, i.e., the positive class, is available. The task is then to predict a new data point as belonging either to the positive class or to the negative class, in which case the data point is considered an outlier. For this task, we propose a novel corrupted Generative Adversarial Network (CorGAN). In the adversarial process of training CorGAN, the Generator generates outlier samples for the negative class, and the Discriminator is trained to distinguish the positive training data from the generated negative data. The proposed framework is evaluated using an image dataset and a real-world network intrusion dataset. Our outlier-detection method achieves state-of-the-art performance on both tasks.

Keywords: one-class classification, outlier detection, generative adversary networks, semi-supervised learning

Procedia PDF Downloads 122

Authors: Suzan Arslanturk, Mohammad-Reza Siadat, Theophilus Ogunyemi, Ananias Diokno

Abstract:

Missing data is a common challenge in statistical analyses of most clinical survey datasets. A variety of methods have been developed to enable analysis of survey data to deal with missing values. Imputation is the most commonly used among the above methods. However, in order to minimize the bias introduced due to imputation, one must choose the right imputation technique and apply it to the correct type of missing data. In this paper, we have identified different types of missing values: missing data due to skip pattern (SPMD), undetermined missing data (UMD), and genuine missing data (GMD) and applied rough set imputation on only the GMD portion of the missing data. We have used rough set imputation to evaluate the effect of such imputation on prediction by generating several simulation datasets based on an existing epidemiological dataset (MESA). To measure how well each dataset lends itself to the prediction model (logistic regression), we have used p-values from the Wald test. To evaluate the accuracy of the prediction, we have considered the width of 95% confidence interval for the probability of incontinence. Both imputed and non-imputed simulation datasets were fit to the prediction model, and they both turned out to be significant (p-value < 0.05). However, the Wald score shows a better fit for the imputed compared to non-imputed datasets (28.7 vs. 23.4). The average confidence interval width was decreased by 10.4% when the imputed dataset was used, meaning higher precision. The results show that using the rough set method for missing data imputation on GMD data improve the predictive capability of the logistic regression. Further studies are required to generalize this conclusion to other clinical survey datasets.

Keywords: rough set, imputation, clinical survey data simulation, genuine missing data, predictive index

Procedia PDF Downloads 138

Authors: Walid Moudani, Ahmad Shahin

Abstract:

This research presents a study to handle the recruitment services system. It aims to enhance a business intelligence system by embedding data mining in its core engine and to facilitate the link between job searchers and recruiters companies. The purpose of this study is to present an intelligent management system for supporting recruitment services based on data mining methods. It consists to apply segmentation on the extracted job postings offered by the different recruiters. The details of the job postings are associated to a set of relevant features that are extracted from the web and which are based on critical criterion in order to define consistent clusters. Thereafter, we assign the job searchers to the best cluster while providing a ranking according to the job postings of the selected cluster. The performance of the proposed model used is analyzed, based on a real case study, with the clustered job postings dataset and classified job searchers dataset by using some metrics.

Keywords: job postings, job searchers, clustering, genetic algorithms, business intelligence

Procedia PDF Downloads 302

Authors: Ignatio Madanhire, Charles Mbohwa

Abstract:

This study explores to improve the resource efficiency, waste water reduction and to reduce losses of raw materials in a beverage making industry. A number of cleaner production technologies were put across in this work. It was also noted that cleaner production technology practices are not only desirable from the environmental point of view, but they also make good economic sense, in their contribution to the bottom line by conserving resources like energy, raw materials and manpower, improving yield as well as reducing treatment/disposal costs. This work is a resource in promoting adoption and implementation of CP in other industries for sustainable development.

Keywords: resource efficiency, beverages, reduce losses, cleaner production, energy, yield

Procedia PDF Downloads 391

Authors: Mauricio Quintero Angel, Andrés A. Duque Nivia, Carlos H. Fajardo Toro

Abstract:

This paper analyzes two approaches of sustainability, the weak and strong, considering a case of study of oil palm production for an industry of biodegradable detergent. In this case, a company demand the oil palm as the active element for washing and through its trademark aims to supply 10% of the Colombian market of washing powders. Under each approach the economic and ecological implications of the palm oil production and especially the implications for crop management are described. The crop production under the weak sustainability implies plantations, intensive use of agrochemicals and the inclusion of new areas of cultivation as the market grows. Under the strong sustainability the production system is limited by the productive vocation of the ecosystem, so that new approaches and creativity for making viable the nature conservancy and the business development are require.

Keywords: agriculture, environmental impacts, oil palm, strong sustainability, weak sustainability

Procedia PDF Downloads 398

Authors: Aparajita Mukherjee, D. P. Mukherjee

Abstract:

This paper critically assessed pollution problems in small and medium enterprises with unique references to foundries and sponge iron industries to survey the adverse impact on human societies and the environment. The objective of this paper was to show how cleaner production concept was implemented in one foundry through improvisation of existing technology in India. Incremental advancement of existing technology minimized environmental issues and resource utilization. This study depicted that poor fiscal help, poor enforcement of government regulations, owners’ attitude and lacking specialized technical workers were the significant hindrances towards cleaner production. The paper explored the possible ways to overcome these hindrances for cleaner production. On a more general level, findings raise important questions regarding the need for a new paradigm for the implementation of cleaner production. Improvisation of existing technology in these enterprises would be cost effective towards sustainable development.

Keywords: SME pollution, ecological crisis, sustainable development, cleaner production, training

Procedia PDF Downloads 340

Authors: P. Pötters, A. Marquet, B. Leyendecker

Abstract:

The objective of this paper is to investigate different implementation approaches for the implementation of Lean production in companies. Furthermore, a structured overview of those different approaches is to be made. Therefore, the present work is intended to answer the following research question: What differences and similarities exist between the various systematic approaches and phase models for the implementation of Lean Production? To present various approaches for the implementation of Lean Production discussed in the literature, a qualitative content analysis was conducted. Within the framework of a qualitative survey, a selection of texts dealing with lean production and its introduction was examined. The analysis presents different implementation approaches from the literature, covering the descriptive aspect of the study. The study also provides insights into similarities and differences among the implementation approaches, which are drawn from the analysis of latent text contents and author interpretations. In this study, the focus is on identifying differences and similarities among systemic approaches for implementing Lean Production. The research question takes into account the main object of consideration, objectives pursued, starting point, procedure, and endpoint of the implementation approach. The study defines the concept of Lean Production and presents various approaches described in literature that companies can use to implement Lean Production successfully. The study distinguishes between five systemic implementation approaches and seven phase models to help companies choose the most suitable approach for their implementation project. The findings of this study can contribute to enhancing transparency regarding the existing approaches for implementing Lean Production. This can enable companies to compare and contrast the available implementation approaches and choose the most suitable one for their specific project.

Keywords: implementation, lean production, phase models, systematic approaches

Procedia PDF Downloads 62

Authors: Hidir S. Nogay

Abstract:

In this study, two systems were created to predict interior temperature in induction motor. One of them consisted of a simple ANN model which has two layers, ten input parameters and one output parameter. The other one consisted of eight ANN models connected each other as cascaded. Cascaded ANN system has 17 inputs. Main reason of cascaded system being used in this study is to accomplish more accurate estimation by increasing inputs in the ANN system. Cascaded ANN system is compared with simple conventional ANN model to prove mentioned advantages. Dataset was obtained from experimental applications. Small part of the dataset was used to obtain more understandable graphs. Number of data is 329. 30% of the data was used for testing and validation. Test data and validation data were determined for each ANN model separately and reliability of each model was tested. As a result of this study, it has been understood that the cascaded ANN system produced more accurate estimates than conventional ANN model.

Keywords: cascaded neural network, internal temperature, inverter, three-phase induction motor

Procedia PDF Downloads 316

Authors: Nasiruddin Khan

Abstract:

The action of (-)-epicatechin, a cocoa (Theobroma cacao) flavanol that modulates redox/oxidative stress are contributed mainly to their antioxidant properties. The present study investigates the concentration and time dependent effect of (-)-epicatechin metabolites 3MeEc, 4MeEc, and 4SulEc on the production of ROS on BAEC using L-012, Lucigenin as chemiluminescence dye and XO/HX system. Our result demonstrates that 3MeEc shows significant (P <0.05) lowering effect of ROS production in BAEC with increasing concentration of metabolite while L-012 was used as chemiluminescence dye but not in the case of Lucigenin. In XO/HX system, using L-012 as chemiluminescence dye, 3MeEc and 4MeEc showed significant lowering effect on ROS production with increasing concentration from 100-500nM as compared to the positive control (SOD). When Lucigenin was used as chemiluminescence dye, 3MeEc exerted significant lowering effect with increasing concentration when compared to the positive control (SOD) whereas 4MeEc showed significant lowering effect in ROS production from 250 nM on as compared to positive control. For 4SulEc, a significant lowering effect of ROS production was only observed at 100 and 250 nM. Overall, although each metabolite shows considerable effect, 3MeEc exhibited more pronounced effect on decreasing the production of ROS as compared to other two metabolites.

Keywords: epicatechin metabolites, HO-1, Nrf2, ROS

Procedia PDF Downloads 200