Selection of desulfurizing agents and optimization of operational variables in hot metal desulfurization

The growing demand for steels with tighter compositional specifications led the Companhia Siderúrgica Nacional (CSN) to develop more efficient processes. To solve this problem this paper aims to identify the operational variables more impacting in the desulfurization process, specifically in torpedo car, as well as its causes and solutions. Then select and test, with laboratorial and industrial tests, desulfurizing agents based of CaC 2, CaO, CaCO3, and Mg to assess the cost per quantity of product desulfurized. The mixture with best results was not that one with highest content of CaC2. It is believed that this mixture showed better efficiency because of the increased agitation of the bath, produced by the releasing of gas from compound CaCO3 present in this mixture. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Probabilistic neural network to predict cracks in taphole mud used in blast furnaces

One of the major problems facing Blast Furnaces is the occurrence of cracks in taphole mud, as the underlying causes are not easily identifiable. The absence of this knowledge makes it difficult the use of conventional techniques for predictability and mitigation. This paper will address the application of Probabilistic Neural Network using the Matlab software as a means to detect and control such cracks. The most relevant BF operational variables were picked through the statistic tool "Principal Component Analysis - PCA." Based upon the selection of these variables a probabilistic neural network was built. A set of BF operational data, consisting of 30 controlling variables, was divided into 2 groups, one of which for network training, and the other one to validate the neural network. The neural network got 98% of the cases right. The results show the effectiveness of this tool for crack prediction in relation to clay intrinsic properties and as a result of the fluctuation in operational variables.

Applying a genetic neuro-model reference adaptive controller in drilling optimization

Motivated by rising drilling operation costs, the oil industry has shown a trend toward real-time measurements and control. In this scenario, drilling control becomes a challenging problem for the industry, especially due to the difficulty associated with parameters modeling. One of the drillbit performance evaluators, the Rate Of Penetration (ROP), has been used as a drilling control parameter. However, relationships between operational variables affecting the ROP are complex and not easily modeled. This work presents a neuro-genetic adaptive controller to treat this problem. It is based on an auto-regressive with extra input signals, or ARX model and on a Genetic Algorithm (GA) to control the ROP. © [2006] IEEE.

A genetic neuro-model reference adaptive controller for petroleum wells drilling operations

Motivated by rising drilling operation costs, the oil industry has shown a trend towards real-time measurements and control. In this scenario, drilling control becomes a challenging problem for the industry, especially due to the difficulty associated to parameters modeling. One of the drill-bit performance evaluators, the Rate of Penetration (ROP), has been used in the literature as a drilling control parameter. However, the relationships between the operational variables affecting the ROP are complex and not easily modeled. This work presents a neuro-genetic adaptive controller to treat this problem. It is based on the Auto-Regressive with Extra Input Signals model, or ARX model, to accomplish the system identification and on a Genetic Algorithm (GA) to provide a robust control for the ROP. Results of simulations run over a real offshore oil field data, consisted of seven wells drilled with equal diameter bits, are provided. © 2006 IEEE.

Operational parameters of soybean seeding in santa fe system

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

A modified Primal-Dual Logarithmic-Barrier Method for solving the Optimal Power Flow problem with discrete and continuous control variables

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

The sugar cane vegetal residues unloaded in the sugar mill: Operational costs and physical characteristics

Brazil produced in 2002/03 season 317.87×106tons of sugar cane stalks and 36.88×106tons of vegetal residues (green leaves, dry leaves and tops) in a planted area of 4.61×106 hectares (ha). These residues have a useful heat of 3,613.14Mcal.t-1. Currently most of this biomass is burned as a pre-harvest practice. The doubt persists in the system type that it must be adopted to pick up, load, transport and unload this biomass at the sugar mill boilers. This study analyzed 22 variables related to operational costs and physical characteristics of these residues in a field situation using a JOHN DEERE® 6850 forage harvester with two different treatments: T1 and T2 (two types of rakes) with 6 repetitions each one. The geographic location of the studied area that belongs to COSTA PINTO MILL (COSAN® Group) is: Latitude 22°40'30S and Longitude 47°36'38W. The adopted methodology was proposed by Ripoli et al. (2002). The obtained results at a 5% level of significance showed that both treatments did not differed significantly between them. Some of the results were, where EBP stands for Oil Equivalent Barrel: Windrowing (T1=US$0.17.EBP-1 and US$9.59.ha-1, T2=US$0.08.EBP-1 and US$4.27.ha-1); Pick up (T1=US$1.31.EBP-1 and US$44.29.ha-1, T2 =US$1.37.EBP-1 and US$48.36.ha-1); Transportation (T1=US$1.27.EBP-1 and US$14,30.ha -1, T2=US$1.33.EBP-1 and US$14,80.ha -1), Unloading at the sugar mill (T1=US$0.30.EBP-1 and US$3.39.ha-1, T2=US$0.32.EBP-1 and US$3.51.ha-1); Total (T1=US$3.05.EBP-1 and US$71.57.ha-1, T2=US$3.10.EBP-1 and US$70.94.ha-1). Confronting the obtained data with the ones in the bibliography, this system revealed itself more expensive than the baling system or the integral harvest system using combines.