The extraction of molten iron and slag in the liquid phase from the lower part of a blast furnace (hearth) is usually accomplished according to operational experience and involves a high degree of uncertainty, mainly because the liquid level cannot be directly measured. This study presents a methodology for obtaining multistep models to forecast the hearth liquid level by measuring a voltage generated on the blast furnace shell, which is strongly correlated with the hearth liquid level. The results show that this electrical signal is a nonstationary and nonlinear time-series that, after appropriate treatment, can be represented by a time-delay neural network (TDNN) model. Some comparisons are made with linear time-series models represented by an autoregressive moving average model and a seasonal autoregressive integrated moving average model, and the results indicate that the TDNN model provides better forecasting performance up to one hour ahead.

Silicon content forecasting models have been requested by the operational team to anticipate necessary actions during the blast furnace operation when producing molten iron, to control the quality of the product and reduce costs. This paper proposed a new algorithm to perform the silicon content time series up to 8 h ahead, immediately after the molten iron chemical analysis is delivered by the laboratory. Due to the delay of the laboratory when delivering the silicon content measurement, the proposed algorithm considers a minimum useful forecasting horizon of 3 h ahead. In a first step, it decomposes the silicon content time series into different subseries using the Maximal Overlap Discrete Wavelet Packet Transform (MODWPT). Next, all subseries forecasts were determined through Nonlinear Autoregressive (NAR) networks, and finally, these forecasts were summed to furnish the long-term forecast of silicon content. Using data from a real industry, we showed that the prediction error was within an acceptable range according to the blast furnace technical team.

This article presents the weather and power data files from renewable sources used to solve the economic dispatch problem of a microgrid that operates in the isolated and grid-connected modes. Methodology is used in the research article “Management of an island and grid-connected microgrid using hybrid economic model predictive control with weather data” (Silva et al., 2020). Automatic stations located in the Brazil's south and northeast furnished the weather data (global horizontal irradiance, temperature, and wind speed). A script generates files containing weather forecasts from one-day ahead using the geographical coordinates of the weather stations. Hybrid models, characterized by real and binary variables, use the weather forecasting data to calculate the photovoltaic and wind power forecasts. A microgrid management algorithm uses these forecasts to solve the optimal economic dispatch problem. This data-in-brief paper presents five datasets for each weather station: (i) Weather dataset downloaded from the website of the National Meteorological Institute, (ii) Weather research and forecasting (WRF) dataset derived from the raw data generated by the weather research and forecasting model, (iii) Weather dataset that joins the forecasted data with the measured data in a single file, (iv) Handled dataset that treats some gaps in the weather dataset and converts it to other formats, (v) Files containing only the temperature, global horizontal irradiance, and wind speed data, (vi) Files containing the measured and forecasted wind and solar power.