Prediction of the functional properties of ceramic materials from composition using artificial neural networks

We describe the development of artificial neural networks (ANN) for the prediction of the properties of ceramic materials. The ceramics studied here include polycrystalline, inorganic, non-metallic materials and are investigated on the basis of their dielectric and ionic properties. Dielectric materials are of interest in telecommunication applications where they are used in tuning and filtering equipment. Ionic and mixed conductors are the subjects of a concerted effort in the search for new materials that can be incorporated into efficient, clean electrochemical devices of interest in energy production and greenhouse gas reduction applications. Multi-layer perceptron ANNs are trained using the back-propagation algorithm and utilise data obtained from the literature to learn composition-property relationships between the inputs and outputs of the system. The trained networks use compositional information to predict the relative permittivity and oxygen diffusion properties of ceramic materials. The results show that ANNs are able to produce accurate predictions of the properties of these ceramic materials which can be used to develop materials suitable for use in telecommunication and energy production applications

Investigation on Rheology of Oil Well Cement Slurries

The rheology of OWC slurries is generally more complicated than that of conventional cement paste. In order to contend with bottom hole conditions (wide range of pressure and temperature), a number of additives are usually used in the OWC slurries and the slurry shows different characteristics depending on the combination of admixture used. The objective of this research is to develop a fundamental understanding on the important mechanisms that affects the rheology of cement slurry incorporating various chemical and mineral admixtures. The thesis aimed at developing cement slurries by partial replacement of oil well cement using different mineral admixtures, offering both environmental and economic benefit. The mechanisms underlying the effect of chemical admixtures on the rheology of oil well cement slurry were investigated at different temperatures using an advanced shear-stress/shear-strain controlled rheometer. The compatibility and interactions between the binder and chemical admixtures were explored. It was found that the rheological properties of oil well cement slurries are highly dependent on temperature, water/cement ratio and the type of admixture used. Coupled effects of temperature and chemical admixtures had a substantial effect on the flow properties of the slurries. The results indicated that current technical data for chemical admixtures need to be validated for oil well cementing; admixtures proven effective in normal cementing job at moderate temperature may become ineffective for oil well cementing at high temperature. The coupled effects of temperature and supplementary cementing materials on the rheology of oil well cement slurry were also investigated. Because of differences in their chemical compositions and the mechanisms by which they act, cement slurries prepared with the addition of supplementary cementitious materials exhibit different rheological behaviour than those prepared with pure oil well cement. It was found that not all minerals/supplementary cementitious materials (SCMs) act in the same way when used as replacement of cement. For example, Fly ash, owing to its spherical particle shape, reduces the water demand when used as a partial replacement of cement. On the other hand, silica fume increase the water demand by adsorbing water because of their higher surface area. However results suggested that new generation polycarboxylate-based high-range water reducing admixture (PCH) improved the rheological properties of all slurries at all temperature tested. However, lower dosage of PCH was found to be less efficient in reducing the yield stress or plastic viscosity of OWC slurries when metakaolin (MK) or rice husk ash (RHA) was used as replacement of cement. PCH was found to enhance the shear thickening behaviour of oil well cement slurries and the intensity of this behaviour varied with the type and amount of SCM such as the phenomenon was amplified with metakaolin, reduced by SF, unchanged with FA and showed irregular behaviour with RHA. Furthermore, new equations were proposed using multiple regression analysis (MRA) and design of experiments (DOE) to predict the Bingham parameters (yield stress and plastic viscosity) of cement slurries prepared in combination with or without supplementary cementitious materials considering various parameters including the ambient temperature, chemical admixture type and dosage, and superplasticizer type and dosage. An artificial neural network (ANN) model was developed to predict the rheological properties of oil well cement slurries. The results indicated that the predicted rheological parameters for cement slurries were in good agreement with corresponding experimental results. However, the ANN-based model performed better than the MRA-based model or DOE-based model in predicting the rheological properties of OWC slurries

Experimental investigation and prediction of compresive strength of concrete using soft computing techniques with different additives

High Performance Concrete (HPC) is the latest development in concrete, But HPC not only\ud demands High cement consumption, which pushes the natural resources towards depletion,\ud but also increases C02 emission on a higher extent. In the recent year’s use of Supplementary\ud Cementitious Materials (SCMs) is increased due to environment concerns, conservation of\ud resource & economy because most of them are generally Industrial waste products such as fly\ud ash, GGBS & micro silica. One of the costliest constituent of HPC is ultrafine material such\ud as micro silica, alccofine. In recent years with the advancement in technology ultrafine fly ash\ud is now being produced which is cheaper ultrafine material but, with less literature available on\ud it. In available literature on Ternary blend concrete the level of replacement was restricted up\ud to 30%-35%.\ud In this Experimental Investigation an attempt was made to investigate compressive strength\ud (100MPa) of concrete by replacing Cement on 40%, 45%, 50%, by incorporating P100 fly ash\ud as an ultrafine material and GGBS.\ud Each replacement was further divided into three sub parts (40%F.A-60%GGBS), (45%F.A-\ud 55%GGBS), (50%F.A-50%GGBS). Among which 40% replacement of cement (50%F.a-\ud 50%GGBS) gave maximum strength. Nominal mix was prepared with only OPC with w/c of\ud 0.24.and all other ternary mixes was made on w/c of 0.2 to have an edge when compared with\ud strength of nominal mix.\ud Nowadays, soft computing techniques are used to predict the properties of concrete and hence\ud reduce the experimental work. Thus, a neural network also known as a parallel distributed\ud processing network, is used as computing paradigm that is loosely modeled after structures of\ud the brain. It consists of interconnected processing elements called nodes or neurons that work\ud together to produce an output function.\ud This experimental investigation presents the application of Multiple Linear Regression (MLR)\ud and Artificial Neural Network (ANN) techniques for developing the model to predict the\ud compressive strength of the concrete with SCMs. For this purpose, a systematic laboratory\ud investigation was carried out. The compressive strength was evaluated on various mixes for 3\ud days, 7days, 14 days and 28 days of curing period. The data generated in the lab was used for development of the MLR and ANN model. The data used in the models are arranged in the\ud format of four input parameters that cover the contents of OPC, FA, GGBS and w/c ratio\ud respectively and one dependent variable as compressive strength of concrete for both MLR\ud and ANN. Networks are trained and tested for various combinations input and output data\ud sets.\ud Keywords: High Performance Concrete (HPC), Supplementary Cementitious Materials\ud (SCMs), Fly Ash (FA), Ground Granulated Blast Furnace Slag (GGBS), Artificial Neural\ud Network (ANN), Multi Linear Regression (MLR)

Application Of Artificial Neural Networks To Predict Wettability And Relative Permeability Of Sandstone Rocks

An Artificial Neural Network (ANN) model based on the back-propagation technique is trained with a number of variables from experimentally established relative permeability curves. The reservoir core input data covers an extensive range of porosities and permeabilities from different sandstone lithologies having diverse wettabilities. The trained model is then tested with only a couple of input variables such as the initial connate water saturation, S,»c and the residual oil saturation. So, . The developed model outputs, or the predictions define the relative permeability end-points and the intersection point to quantify the wettability and the shape of the relative permeability curves. A number of correlations based on empirical models and network models exist to predict the relative permeability curves and the wettability of oil bearing sandstone formations from the initial oil and water. Calculations from the ANN model were then compared with values calculated from other models currently in wide spread use

Intelligent data-driven decision-making to mitigate or stop lost circulation

”Lost circulation is a challenging problem in the oil and gas industry. Each year, millions of dollars are spent to mitigate or stop this problem. The aim of this work is to utilize machine learning and other intelligent solutions to help to make better decision to mitigate or stop lost circulation. A detailed literature review on the applications of decision tree analysis, expected monetary value, and artificial neural networks in the oil and gas industry was provided. Data for more than 3000 wells were gathered from many sources around the world. Detailed economics and probability analyses for lost circulation treatments’ strategies were conducted for three formations in southern Iraq which are the Dammam, Hartha, and Shuaiba formations. Multiple machine learning methods such as support vector machine, decision trees, logistic regression, artificial neural networks, and ensemble trees were used to create models that can predict lost circulation and recommend the best lost circulation treatment based on the type of loss and reason of loss. The results showed that the created models can predict lost circulation and recommend the best lost circulation strategy within a reasonable margin of error. The created models can be used globally which avoids the shortcoming in the literature. Intelligence solutions and machine learning have proven their applicability to solve complicated problems and make better future decisions. With the large data available in the oil and gas industry, these methods can help the decision-makers to make better future decisions that will save time and money”--Abstract, page iv

Artificial neural networks and fuzzy logic applications in modeling the compressive strength of portland cement

Thesis (Master)--Izmir Institute of Technology, Materials Science and Engineering, Izmir, 2004Includes bibliographical references (leaves: 43)Text in English; Abstract: Turkish and Englishix, 44 leavesPortland cement production is a complex process that involves the effect of several processing parameters on the quality control of 28-day cement compressive strength (CCS). There are some chemical parameters like the C3S, C2S, C3A, C4AF, and SO3 contents in addition to the physical parameters like Blaine (surface area) and particle size distribution. These factors are all effective in producing a single quantity of 28-day CCS. The long duration of 28 day CCS test provided the motivation for research on predictive models. The purpose for these studies was to be able to predict the strength instead of waiting for 28 days for the test to be complete. In this thesis, artificial intelligence (AI) methods like artificial neural networks (ANNs) and fuzzy logic were used in the modeling of the 28-day CCS. The two models were compared for their quality of fit and for the ease of application.Quality control data from a local cement plant were used in the modeling studies. The data were separated randomly into two parts: the first one contained 100 data points to be used in training and the second part had 50 data points to be used in testing stages of the models. In this study, four different AI models were created and tested (3 ANN, 1 fuzzy logic). One of the ANN models (Model A) had 20 input parameters in 20x20x1 architecture with testing average absolute percentage error (AAPE) of 2.24%. The other ANN model (Model B) had four input parameters (SO3, C3S, Blaine and total alkali amount) in 4x4x1 architecture with AAPE of 2.41%. Both of the Model A and the Model B were created in the MatLAB® environment by writinga custom computer code. The last ANN model (Model C) actually refers to 72 differentANN models created in the MatLAB® neural networks toolbox. In order to obtain a model with the lowest error, different learning algorithms, training functions and architectures in combinations were tested. The lowest AAPE among these models appeared to be 2.31%. The fuzzy logic model (Model D) which had four input parameters (SO3, C3S, Blaine and total alkali amount) was created in the MatLAB fuzzy logic toolbox. In order to write the fuzzy rules, the sensitivity analysis of the Model B was utilized. The AAPE of the Model D was 2.69%. The model was compared with the ANN models for its error levels and ease of application. The results indicated that through the application of fuzzy logic algorithm, a more user friendly andmore explicit model than the ANNs could be produced within successfully low error margins

Sustainability and New Technologies

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Prediction of the functional properties of ceramic materials from composition using artificial neural networks

We describe the development of artificial neural networks (ANN) for the prediction of the properties of ceramic materials. The ceramics studied here include polycrystalline, inorganic, non-metallic materials and are investigated on the basis of their dielectric and ionic properties. Dielectric materials are of interest in telecommunication applications, where they are used in tuning and filtering equipment. Ionic and mixed conductors are the subjects of a concerted effort in the search for new materials that can be incorporated into efficient, clean electrochemical devices of interest in energy production and greenhouse gas reduction applications. Multi-layer perceptron ANNs are trained using the back-propagation algorithm and utilise data obtained from the literature to learn composition–property relationships between the inputs and outputs of the system. The trained networks use compositional information to predict the relative permittivity and oxygen diffusion properties of ceramic materials. The results show that ANNs are able to produce accurate predictions of the properties of these ceramic materials, which can be used to develop materials suitable for use in telecommunication and energy production applications