Energy Savings in EAF Steelmaking by Process Simulation and Data-Science Modeling on the Reproduced Results

Electric-Arc-Furnace (EAF)-based process route in modern steelmaking for the production of plates and special quality bars requires a series of stations for the secondary metallurgy treatment (Ladle-Furnace, and potentially Vacuum-Degasser), till the final casting for the production of slabs and blooms in the corresponding continuous casting machines. However, since every steel grade has its own melting characteristics, the melting (liquidus) temperature per grade is generally different and plays an important role in the final casting temperature, which has to exceed by somewhat the melting temperature by an amount called superheat. The superheat is adjusted at the ladle-furnace (LF) station by the operator who decides mostly on personal experience but, since the ladle has to pass from downstream processes, the liquid steel loses temperature not only due to the duration of the processes till casting but also due to the ladle refractory history. Simulation software was developed in order to reproduce the phenomena involved in a meltshop and influence downstream superheats. Data science models were deployed in order to check the potential of controlling casting temperatures by adjusting liquid-steel exit temperatures at LF

Quality Prediction and Control of Reducing Pipe Based on EOS-ELM-RPLS Mathematics Modeling Method

The inspection of inhomogeneous transverse and longitudinal wall thicknesses, which determines the quality of reducing pipe during the production of seamless steel reducing pipe, is lags and difficult to establish its mechanism model. Aiming at the problems, we proposed the quality prediction model of reducing pipe based on EOS-ELM-RPLS algorithm, which taking into account the production characteristics of its time-varying, nonlinearity, rapid intermission, and data echelon distribution. Key contents such as analysis of data time interval, solving of mean value, establishment of regression model, and model online prediction were introduced and the established prediction model was used in the quality prediction and iteration control of reducing pipe. It is shown through experiment and simulation that the prediction and iteration control method based on EOS-ELM-RPLS model can effectively improve the quality of steel reducing pipe, and, moreover, its maintenance cost was low and it has good characteristics of real time, reliability, and high accuracy

Computational intelligence image processing for precision farming on-site nitrogen analysis in plants

PhD ThesisNitrogen is one of the macronutrients which is essentially required by plants. To support the precision farming, it is important to analyse nitrogen status in plants in order to prevent excessive fertilisation as well as to reduce production costs. Image-based analysis has been widely utilised to estimate nitrogen content in plants. Such research, however, is commonly conducted in a controlled environment with artificial lighting systems. This thesis proposes three novel computational intelligence systems to evaluate nitrogen status in wheat plants by analysing plant images captured on field and are subject to variation in lighting conditions. In the first proposed method, a fusion of regularised neural networks (NN) has been employed to normalise plant images based on the RGB colour of the 24-patch Macbeth colour checker. The colour normalisation results are then optimised using genetic algorithm (GA). The regularised neural network has also been effectively utilised to distinguish wheat leaves from other unwanted parts. This method gives improved results compared to the Otsu algorithm. Furthermore, several neural networks with different number of hidden layer nodes are combined using committee machines and optimised by GA to estimate nitrogen content. In the second proposed method, the utilisation of regularised NN has been replaced by deep sparse extreme learning machine (DSELM). In general the utilisation of DSELM in the three research steps is as effective as that of the developed regularised NN as proposed in the first method. However, the learning speed of DSELM is extremely faster than the regularised NN and the standard backpropagation multilayer perceptron (MLP). In the third proposed method, a novel approach has been developed to fine tune the colour normalisation based on the nutrient estimation errors and analyse the effect of genetic algorithm based global optimisation on the nitrogen estimation results. In this method, an ensemble of deep learning MLP (DL-MLP) has been employed in the three research steps, i.e. colour normalisation, image segmentation and nitrogen estimation. The performance of the three proposed methods has been compared with the intrusive SPAD meter and the results show that all the proposed methods are superior to the SPAD based estimation. The nutrient estimation errors of the proposed methods are less than 3%, while the error using the renowned SPAD meter method is 8.48%. As a comparison, nitrogen prediction using other methods, i.e. Kawashima greenness index () and PCA-based greenness index () are also calculated. The prediction errors by means of and methods are 9.84% and 9.20%, respectively.Indonesia Ministry of Research, Technology and Higher Education and Jenderal Soedirman Univerist

Evolutionary Computation

This book presents several recent advances on Evolutionary Computation, specially evolution-based optimization methods and hybrid algorithms for several applications, from optimization and learning to pattern recognition and bioinformatics. This book also presents new algorithms based on several analogies and metafores, where one of them is based on philosophy, specifically on the philosophy of praxis and dialectics. In this book it is also presented interesting applications on bioinformatics, specially the use of particle swarms to discover gene expression patterns in DNA microarrays. Therefore, this book features representative work on the field of evolutionary computation and applied sciences. The intended audience is graduate, undergraduate, researchers, and anyone who wishes to become familiar with the latest research work on this field

Natural and Technological Hazards in Urban Areas

Natural hazard events and technological accidents are separate causes of environmental impacts. Natural hazards are physical phenomena active in geological times, whereas technological hazards result from actions or facilities created by humans. In our time, combined natural and man-made hazards have been induced. Overpopulation and urban development in areas prone to natural hazards increase the impact of natural disasters worldwide. Additionally, urban areas are frequently characterized by intense industrial activity and rapid, poorly planned growth that threatens the environment and degrades the quality of life. Therefore, proper urban planning is crucial to minimize fatalities and reduce the environmental and economic impacts that accompany both natural and technological hazardous events