Methodology based on genetic optimisation to develop overall parsimony models for predicting temperature settings on annealing furnace

Developing better prediction models is crucial for the steelmaking industry to improve the continuous hot dip galvanising line (HDGL). This paper presents a genetic based methodology whereby a wrapper based scheme is optimised to generate overall parsimony models for predicting temperature set points in a continuous annealing furnace on an HDGL. This optimisation includes a dynamic penalty function to control model complexity and an early stopping criterion during the optimisation phase. The resulting models (multilayer perceptron neural networks) were trained using a database obtained from an HDGL operating in the north of Spain. The number of neurons in the unique hidden layer, the inputs selected and the training parameters were adjusted to achieve the lowest validation and mean testing errors. Finally, a comparative evaluation is reported to highlight our proposal's range of applicability, developing models with lower prediction errors, higher generalisation capacity and less complexity than a standard method. © 2014 Institute of Materials, Minerals and Mining

Fine tuning straightening process using genetic algorithms and finite element methods

The process of straightening steel sections is used not only to actually straighten the product but also to reduce its internal residual stresses. Fine tuning this process within an industrial plant is complicated because of the time needed for conducting the tests and the difficulties in measuring the final residual stresses. This paper presents a methodology based on genetic algorithms and finite element analysis that seeks the best position of the rollers to produce a straightened product with the minimum amount of residual stresses. The process consists of simulating multiple roller positions using a previously validated finite element model and analysing the resulting residual stresses. Genetic programming is used to choose the best solutions that will give rise to the next generation of individuals. For several generations, the system combines a series of optimum solutions in which residual solutions are minimised. The best solutions obtained enable the rollers to be positioned in a way that guarantees a good end quality for the product. © 2010 Institute of Materials, Minerals and Mining

TAO-robust backpropagation learning algorithm

In several fields, as industrial modelling, multilayer feedforward neural networks are often used as universal function approximations. These supervised neural networks are commonly trained by a traditional backpropagation learning format, which minimises the mean squared error (mse) of the training data. However, in the presence of corrupted data (outliers) this training scheme may produce wrong models. We combine the benefits of the non-linear regression model -estimates [introduced by Tabatabai, M. A., Argyros, I. K. Robust Estimation and testing for general nonlinear regression models. Applied Mathematics and Computation. 58 (1993) 85-101] with the backpropagation algorithm to produce the TAO-robust learning algorithm, in order to deal with the problems of modelling with outliers. The cost function of this approach has a bounded influence function given by the weighted average of two functions, one corresponding to a very robust estimate and the other to a highly efficient estimate. The advantages of the proposed algorithm are studied with an example. © 2005 Elsevier Ltd. All rights reserved

Advanced predictive system using artificial intelligence for cleaning of steel coils

This paper presents a system based on data mining and statistical modelling tools that permits the prediction of the development of oxide scale defects in high quality flat products after the steel industrys hot strip mill process (HSM), but before the coil becomes processed on the pickling line (PL). The economic impact of the improvement provided by such a system can be valued at several million US dollars per year, because it makes it possible to downgrade materials at an early stage, avoiding additional processes like coating, etc. It also enables the speed of the PL, which is usually seen as a bottleneck in these facilities, to be increased. The learning process of the model presented here is based on automatic surface-inspection systems, as well as processing parameters at the HSM and PL to capture the essentials of the cleaning process itself, and also the main factors in scale production. The system proposed currently which is configured as a multi-agent system, is the first for this particular purpose, although the steel industry uses many other models and systems to predict other properties (e.g., mechanical properties) or the best operating parameters (e.g., forces, temperatures) for processe

Single and blended models for day-ahead photovoltaic power forecasting

Solar power forecasts are gaining continuous importance as the penetration of solar energy into the grid rises. The natural variability of the solar resource, joined to the difficulties of cloud movement modeling, endow solar power forecasts with a certain level of uncertainty. Important efforts have been carried out in the field to reduce as much as possible the errors. Various approaches have been followed, being the predominant nowadays the use of statistical techniques to model production. In this study, we have performed a comparison study between two extensively used statistical techniques, support vector regression (SVR) machines and random forests, and two other techniques that have been scarcely applied to solar forecasting, deep neural networks and extreme gradient boosting machines. Best results were obtained with the SVR technique, showing a nRMSE of 22.49%. To complete the assessment, a weighted blended model consisting on an average weighted combination of individual predictions was created. This blended model outperformed all the models studied, with a nRMSE of 22.24%. © Springer International Publishing AG 2017

Combining genetic algorithms and the finite element method to improve steel industrial processes

Most of the times the optimal control of steel industrial processes is a very complicated task because of the elevated number of parameters to adjust. For that reason, in steel plants, engineers must estimate the best values of the operational parameters of processes, and sometimes, it is also necessary to obtain the appropriate model for steel material behaviour. This article deals with three successful experiences gained from genetic algorithms and the finite element method in order to solve engineering optimisation problems. On one hand, a fully automated method for determining the best material behaviour laws is described, and on the other hand we present a common methodology to find the most appropriate settings for two cases of improvement in steel industrial processes. The study of the three reported cases allowed us to show the reliability and effectiveness of combining both techniques. © 2012 Elsevier B.V