Temperature Prediction Using Multivariate Time Series Deep Learning in the Lining of an Electric Arc Furnace for Ferronickel Production.

The analysis of data from sensors in structures subjected to extreme conditions such as the ones used in smelting processes is a great decision tool that allows knowing the behavior of the structure under different operational conditions. In this industry, the furnaces and the different elements are fully instrumented, including sensors to measure variables such as temperature, pressure, level, flow, power, electrode positions, among others. From the point of view of engineering and data analytics, this quantity of data presents an opportunity to understand the operation of the system under normal conditions or to explore new ways of operation by using information from models provided by using deep learning approaches. Although some approaches have been developed with application to this industry, it is still an open research area. As a contribution, this paper presents an applied deep learning temperature prediction model for a 75 MW electric arc furnace, which is used for ferronickel production. In general, the methodology proposed considers two steps: first, a data cleaning process to increase the quality of the data, eliminating both redundant information as well as atypical and unusual data, and second, a multivariate time series deep learning model to predict the temperatures in the furnace lining. The developed deep learning model is a sequential one based on GRU (gated recurrent unit) layer plus a dense layer. The GRU + Dense model achieved an average root mean square error (RMSE) of 1.19 °C in the test set of 16 different thermocouples radially distributed on the furnace.

Decision-Making Model for Adaptive Impedance Control of Teleoperation Systems.

This paper presents a haptic assistance strategy for teleoperation that makes a task and situation-specific compromise between improving tracking performance or human-machine interaction in partially structured environments via the scheduling of the parameters of an admittance controller. The proposed assistance strategy builds on decision-making models and combines one of them with impedance control techniques that are standard in bilateral teleoperation systems. Even though several decision-making models have been proposed in cognitive science, their application to assisted teleoperation and assisted robotics has hardly been explored yet. Experimental data supports the Drift-Diffusion model as a suitable scheduling strategy for haptic shared control, in which the assistance mechanism can be adapted via the parameters of reward functions. Guidelines to tune the decision making model are presented. The influence of the reward structure on the realized haptic assistances is evaluated in a user study and results are compared to the no assistance and human assistance case.