151 research outputs found

    Forecast of Carbon Consumption of a Blast Furnace Using Extreme Learning Machine and Probabilistic Reasoning

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    Blast furnaces are chemical metallurgical reactors for the production of pig iron and slag. The raw materials used (metallic feedstock) are sinter, granulated ore and pellets. The main fuel is metallurgical coke. Considering the existing difficulties in the field of simulation of complex processes, the application of solutions based on neural networks has gained space due to its diversity of application and increase in the reliability of responses. The Extreme Learning Machine is a way to train an artificial neural network (ANN) with only one hidden layer. The database used for numerical simulation corresponds to 3.5 years of reactor operation. Big Data contains 94875 pieces of information divided into 75 variables. The input of the ELM neural network is composed of 72 variables and the output of 3 variables. The selected output variables were coke rate, PCI rate and fuel rate. Artificial neural networks using extreme learning machines and using Big Data are able to predict fuel consumption based on the parameters of the reduction process in blast furnaces, and this can be verified by the accuracy of the model

    Robust Extreme Learning Machine for Modeling with Unknown Noise

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    Extreme learning machine (ELM) is an emerging machine learning technique for training single hidden layer feedforward networks (SLFNs). During the training phase, ELM model can be created by simultaneously minimizing the modeling errors and norm of the output weights. Usually, squared loss is widely utilized in the objective function of ELMs, which is theoretically optimal for the Gaussian error distribution. However, in practice, data collected from uncertain and heterogeneous environments trivially result in unknown noise, which may be very complex and cannot be described well using any single distribution. In order to tackle this issue, in this paper, a robust ELM (R-ELM) is proposed for improving the modeling capability and robustness with Gaussian and non-Gaussian noise. In R-ELM, a modified objective function is constructed to fit the noise using mixture of Gaussian (MoG) to approximate any continuous distribution. In addition, the corresponding solution for the new objective function is developed based on expectation maximization (EM) algorithm. Comprehensive experiments, both on selected benchmark datasets and real world applications, demonstrate that the proposed R-ELM has better robustness and generalization performance than state-of-the-art machine learning approaches

    Process Modeling in Pyrometallurgical Engineering

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    The Special Issue presents almost 40 papers on recent research in modeling of pyrometallurgical systems, including physical models, first-principles models, detailed CFD and DEM models as well as statistical models or models based on machine learning. The models cover the whole production chain from raw materials processing through the reduction and conversion unit processes to ladle treatment, casting, and rolling. The papers illustrate how models can be used for shedding light on complex and inaccessible processes characterized by high temperatures and hostile environment, in order to improve process performance, product quality, or yield and to reduce the requirements of virgin raw materials and to suppress harmful emissions

    Data-driven method for enhanced corrosion assessment of reinforced concrete structures

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    Corrosion is a major problem affecting the durability of reinforced concrete structures. Corrosion related maintenance and repair of reinforced concrete structures cost multibillion USD per annum globally. It is often triggered by the ingression of carbon dioxide and/or chloride into the pores of concrete. Estimation of these corrosion causing factors using the conventional models results in suboptimal assessment since they are incapable of capturing the complex interaction of parameters. Hygrothermal interaction also plays a role in aggravating the corrosion of reinforcement bar and this is usually counteracted by applying surface protection systems. These systems have different degree of protection and they may even cause deterioration to the structure unintentionally. The overall objective of this dissertation is to provide a framework that enhances the assessment reliability of the corrosion controlling factors. The framework is realized through the development of data-driven carbonation depth, chloride profile and hygrothermal performance prediction models. The carbonation depth prediction model integrates neural network, decision tree, boosted and bagged ensemble decision trees. The ensemble tree based chloride profile prediction models evaluate the significance of chloride ingress controlling variables from various perspectives. The hygrothermal interaction prediction models are developed using neural networks to evaluate the status of corrosion and other unexpected deteriorations in surface-treated concrete elements. Long-term data for all models were obtained from three different field experiments. The performance comparison of the developed carbonation depth prediction model with the conventional one confirmed the prediction superiority of the data-driven model. The variable importance measure revealed that plasticizers and air contents are among the top six carbonation governing parameters out of 25. The discovered topmost chloride penetration controlling parameters representing the composition of the concrete are aggregate size distribution, amount and type of plasticizers and supplementary cementitious materials. The performance analysis of the developed hygrothermal model revealed its prediction capability with low error. The integrated exploratory data analysis technique with the hygrothermal model had identified the surfaceprotection systems that are able to protect from corrosion, chemical and frost attacks. All the developed corrosion assessment models are valid, reliable, robust and easily reproducible, which assist to define proactive maintenance plan. In addition, the determined influential parameters could help companies to produce optimized concrete mix that is able to resist carbonation and chloride penetration. Hence, the outcomes of this dissertation enable reduction of lifecycle costs

    Optimising Carbon Type Differentiation Techniques to Reduce Dust Emissions in Blast Furnace Ironmaking

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    The manufacturing process of iron, using the blast furnace (BF) generates dust as a by-product, which is recycled, however, the generation of the dust in excess is undesirable. A comprehensive review of the dust has determined that each of the raw materials for blast furnace ironmaking contributes to its formation, including several forms of carbon thus addressing the hypothesis ‘The raw materials that feed the blast furnace are expelled into the gas stream and all influence the blast furnace dust.’ The current technique for quantifying coal originating carbon type mostly in the form of coal char, referred to as the nominal term Low Order Carbon (LOC) within BF dust consists of thermogravimetric analysis (TGA) however, this technique does not allow for samples of dust to be analysed in a timely manner, in line with the ever-changing conditions of the blast furnace. In this work, the TGA method has been trialled for use with BF dust, with improvements offered to the heating profile, allowing for faster analysis. Moreover, alternative techniques have been trialled, in combination with various characterisation methods such as X-ray diffraction, Scanning Electron Microscopy, total carbon and Optical Emission Spectroscopy. The ‘Winkler Method’ which was originally designed to quantify charcoal in soil sediment has been successfully adapted and optimised to suit LOC quantification in BF dust, showing a good correlation with the original benchmark technique. This answered the hypothesis, ‘Thermal techniques can be used to differentiate carbon sources in dust generated in blast furnaces that use granulated coal injection.’ The techniques for LOC quantification were applied to dust samples spanning a 9 month period. to determine the process parameters that influence the LOC presence within the dust. It was found that the resolution of sampling is key to identify relationships between process parameters and LOC within the dust. A novel technique to continuously monitor the dust output of the furnace found that the dust output and the LOC within the dust are related, where the increasing dust output leads to increasing concentrations of LOC within the carbon profile of the dust itself. Process parameters including blast pressure, blast volume, and production rate were considered to increase the dust output from the furnace based on the work of the dust probe, thus answering the hypothesis ‘Coal combustion in the raceway can be impacted by process parameters and the evidence can be found in the fingerprint of blast furnace dust.’ A node mapping exercise was used to model an ideal set of process conditions for low dust operations. The foundations to make macro advances in carbon and dust output reduction in blast furnace ironmaking are laid out in this thesis

    Advanced deep regression models for smart operation of the oil and gas industry

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    The first industrial revolution in the early 18th century largely exploited steam power to replace animal labor. Since then, there has been rapid development in industrial operations. Now, the world has come to the brink of the fifth industrial revolution, a.k.a. industry 5.0, where industries invest in building intelligent systems to perform complex actions more efficiently by leveraging technological advancements, including big data, and high-performance computing (HPC) platforms. Thus, modern artificial intelligence (AI), particularly deep neural networks (DNNs) has emerged as a powerful tool in industries for informed operational control, real-time fault and anomaly detection, and maintenance. In this direction, this research aims to develop advanced regression models using artificial neural network (ANN), 1-D convolutional neural network (CNN), and long short-term memory (LSTM) units for key operations in the oil and gas industries. More specifically, this study focuses on three stages, namely drilling, transportation, and production, and proposes robust regressors for accurate prediction of void fraction, the temperature of internal components of electric motors, and the production level of hydrocarbon extracts. A precise prediction of these factors will increase resource efficiency, energy saving, and product quality, and decrease environmental pollution. An exhaustive experimental study conducted on benchmark datasets demonstrates the practicability of the proposed solutions and their robustness. It is worth mentioning that Canada is the world’s fifth-largest oil producer and has one of the world’s largest oil reserves and the world’s third-largest proven oil reserves

    Computational intelligence image processing for precision farming on-site nitrogen analysis in plants

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    PhD ThesisNitrogen is one of the macronutrients which is essentially required by plants. To support the precision farming, it is important to analyse nitrogen status in plants in order to prevent excessive fertilisation as well as to reduce production costs. Image-based analysis has been widely utilised to estimate nitrogen content in plants. Such research, however, is commonly conducted in a controlled environment with artificial lighting systems. This thesis proposes three novel computational intelligence systems to evaluate nitrogen status in wheat plants by analysing plant images captured on field and are subject to variation in lighting conditions. In the first proposed method, a fusion of regularised neural networks (NN) has been employed to normalise plant images based on the RGB colour of the 24-patch Macbeth colour checker. The colour normalisation results are then optimised using genetic algorithm (GA). The regularised neural network has also been effectively utilised to distinguish wheat leaves from other unwanted parts. This method gives improved results compared to the Otsu algorithm. Furthermore, several neural networks with different number of hidden layer nodes are combined using committee machines and optimised by GA to estimate nitrogen content. In the second proposed method, the utilisation of regularised NN has been replaced by deep sparse extreme learning machine (DSELM). In general the utilisation of DSELM in the three research steps is as effective as that of the developed regularised NN as proposed in the first method. However, the learning speed of DSELM is extremely faster than the regularised NN and the standard backpropagation multilayer perceptron (MLP). In the third proposed method, a novel approach has been developed to fine tune the colour normalisation based on the nutrient estimation errors and analyse the effect of genetic algorithm based global optimisation on the nitrogen estimation results. In this method, an ensemble of deep learning MLP (DL-MLP) has been employed in the three research steps, i.e. colour normalisation, image segmentation and nitrogen estimation. The performance of the three proposed methods has been compared with the intrusive SPAD meter and the results show that all the proposed methods are superior to the SPAD based estimation. The nutrient estimation errors of the proposed methods are less than 3%, while the error using the renowned SPAD meter method is 8.48%. As a comparison, nitrogen prediction using other methods, i.e. Kawashima greenness index () and PCA-based greenness index () are also calculated. The prediction errors by means of and methods are 9.84% and 9.20%, respectively.Indonesia Ministry of Research, Technology and Higher Education and Jenderal Soedirman Univerist

    Future Trends in Advanced Materials and Processes

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    The Special Issue “Future Trends in Advanced Materials and Processes” contains original high-quality research papers and comprehensive reviews addressing the relevant state-of-the-art topics in the area of materials focusing on relevant or innovative applications such as radiological hazard evaluations of non-metallic materials, composite materials' characterization, geopolymers, metallic biomaterials, etc

    Mining Safety and Sustainability I

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    Safety and sustainability are becoming ever bigger challenges for the mining industry with the increasing depth of mining. It is of great significance to reduce the disaster risk of mining accidents, enhance the safety of mining operations, and improve the efficiency and sustainability of development of mineral resource. This book provides a platform to present new research and recent advances in the safety and sustainability of mining. More specifically, Mining Safety and Sustainability presents recent theoretical and experimental studies with a focus on safety mining, green mining, intelligent mining and mines, sustainable development, risk management of mines, ecological restoration of mines, mining methods and technologies, and damage monitoring and prediction. It will be further helpful to provide theoretical support and technical support for guiding the normative, green, safe, and sustainable development of the mining industry

    NASA SBIR abstracts of 1991 phase 1 projects

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    The objectives of 301 projects placed under contract by the Small Business Innovation Research (SBIR) program of the National Aeronautics and Space Administration (NASA) are described. These projects were selected competitively from among proposals submitted to NASA in response to the 1991 SBIR Program Solicitation. The basic document consists of edited, non-proprietary abstracts of the winning proposals submitted by small businesses. The abstracts are presented under the 15 technical topics within which Phase 1 proposals were solicited. Each project was assigned a sequential identifying number from 001 to 301, in order of its appearance in the body of the report. Appendixes to provide additional information about the SBIR program and permit cross-reference of the 1991 Phase 1 projects by company name, location by state, principal investigator, NASA Field Center responsible for management of each project, and NASA contract number are included
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