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

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

DEEP ADVERSARIAL APPROACHES IN RELIABILITY

Reliability engineering has long been proposed with the problem of predicting failures using all available data. As modeling techniques have become more sophisticated, so too have the data sources from which reliability engineers can draw conclusions. The Internet of Things (IoT) and cheap sensing technologies have ushered in a new expansive set of multi-dimensional big machinery data in which previous reliability engineering modeling techniques remain ill-equipped to handle. Therefore, the objective of this dissertation is to develop and advance reliability engineering research by proposing four comprehensive deep learning methodologies to handle these big machinery data sets. In this dissertation, a supervised fault diagnostic deep learning approach with applications to the rolling element bearings incorporating a deep convolutional neural network on time-frequency images was developed. A semi-supervised generative adversarial networks-based approach to fault diagnostics using the same time-frequency images was proposed. The time-frequency images were used again in the development of an unsupervised generative adversarial network-based methodology for fault diagnostics. Finally, to advance the studies of remaining useful life prediction, a mathematical formulation and subsequent methodology to combine variational autoencoders and generative adversarial networks within a state-space modeling framework to achieve both unsupervised and semi-supervised remaining useful life estimation was proposed. All four proposed contributions showed state of the art results for both fault diagnostics and remaining useful life estimation. While this research utilized publicly available rolling element bearings and turbofan engine data sets, this research is intended to be a comprehensive approach such that it can be applied to a data set of the engineer’s chosen field. This research highlights the potential for deep learning-based approaches within reliability engineering problems