Effects of dredging operations on sediment quality: contaminant mobilization in dredged sediments from the Port of Santos, SP, Brazil

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

Contamination of port zone sediments by metals from Large Marine Ecosystems of Brazil

Sediment contamination by metals poses risks to coastal ecosystems and is considered to be problematic to dredging operations. In Brazil, there are differences in sedimentology along the Large Marine Ecosystems in relation to the metal distributions. We aimed to assess the extent of Al, Fe, Hg, Cd, Cr, Cu, Ni, Pb and Zn contamination in sediments from port zones in northeast (Mucuripe and Pecem) and southeast (Santos) Brazil through geochemical analyses and sediment quality ratings. The metal concentrations found in these port zones were higher than those observed in the continental shelf or the background values in both regions. In the northeast, metals were associated with carbonate, while in Santos, they were associated with mud. Geochemical analyses showed enrichments in Hg, Cd, Cu, Ni and Zn, and a simple application of international sediment quality guidelines failed to predict their impacts, whereas the use of site-specific values that were derived by geochemical and ecotoxicological approaches seemed to be more appropriate in the management of the dredged sediments. (C) 2012 Elsevier Ltd. All rights reserved.

Ecotoxicological assessment of sediments from the Port of Santos and the disposal sites of dredged material

A dragagem dos sedimentos do Canal de Santos é necessária para permitir o trânsito de navios que operam no Porto de Santos. As áreas de disposição do material dragado estão situadas na zona costeira, em frente à Baía de Santos. Este estudo visou avaliar a qualidade dos sedimentos do Canal de Santos e das áreas de disposição atuais e antigas, utilizando testes de toxicidade de sedimento integral com anfípodos e de toxicidade de elutriatos com embriões de ouriço do mar. As amostras do Canal de Santos foram consideradas as mais tóxicas: todas as amostras dessa área foram consideradas significativamente tóxicas. Além disso, algumas amostras das áreas de disposição exibiram toxicidade. Os resultados mostraram, portanto, que os sedimentos apresentam evidências de degradação em sua qualidade, porém novos estudos devem ser conduzidos visando determinar as relações entre contaminação e toxicidade. Os resultados sugerem ainda que a disposição dos sedimentos dragados deva ser reavaliada.

Modeling and analysis of artificial neural networks applied in operations research

Artificial neural networks are dynamic systems consisting of highly interconnected and parallel nonlinear processing elements. Systems based on artificial neural networks have high computational rates due to the use of a massive number of these computational elements. Neural networks with feedback connections provide a computing model capable of solving a rich class of optimization problems. In this paper, a modified Hopfield network is developed for solving problems related to operations research. The internal parameters of the network are obtained using the valid-subspace technique. Simulated examples are presented as an illustration of the proposed approach. Copyright (C) 2000 IFAC.

The minimization of open stacks problem: A review of some properties and their use in pre-processing operations

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Artificial immune systems for classification of petroleum well drilling operations

This paper presents two approaches of Artificial Immune System for Pattern Recognition (CLONALG and Parallel AIRS2) to classify automatically the well drilling operation stages. The classification is carried out through the analysis of some mud-logging parameters. In order to validate the performance of AIS techniques, the results were compared with others classification methods: neural network, support vector machine and lazy learning.

Classification of petroleum well drilling operations using Support Vector Machine (SVM)

During the petroleum well drilling operation many mechanical and hydraulic parameters are monitored by an instrumentation system installed in the rig called a mud-logging system. These sensors, distributed in the rig, monitor different operation parameters such as weight on the hook and drillstring rotation. These measurements are known as mud-logging records and allow the online following of all the drilling process with well monitoring purposes. However, in most of the cases, these data are stored without taking advantage of all their potential. On the other hand, to make use of the mud-logging data, an analysis and interpretationt is required. That is not an easy task because of the large volume of information involved. This paper presents a Support Vector Machine (SVM) used to automatically classify the drilling operation stages through the analysis of some mud-logging parameters. In order to validate the results of SVM technique, it was compared to a classification elaborated by a Petroleum Engineering expert. © 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.

Classification of petroleum well drilling operations with a hybrid particle swarm/ant colony algorithm

This paper describes an investigation of the hybrid PSO/ACO algorithm to classify automatically the well drilling operation stages. The method feasibility is demonstrated by its application to real mud-logging dataset. The results are compared with bio-inspired methods, and rule induction and decision tree algorithms for data mining. © 2009 Springer Berlin Heidelberg.

Environmental solutions of recovery of Amazonian forest mining operations

In order for the projects of recovery of degraded areas to be successful, it is necessary to have a perfect recovery of the soil where the revegetation will be implanted as an initial action in the recovery of the whole process. The use of native forest species fully adapted to these types of terrain is another aspect of great importance, once the non-selection of these species, even if abundant in the surrounding areas, as it is in our case, implies great mortality of individuals during the planting and their low fixation during the process. The establishment of a monitoring program that contemplates the advancements obtained in the soil, the vegetation and the return of wild animals also collaborate in the evaluation of the success of the process. And, finally, the effective participation of the mining company, accepting and applying the techniques tested and indicated by research, even if, initially, the return time is longer than expected, also guarantees the success of the process. The mining company not only implemented a partnership with important universities in Brazil to obtain solutions for the environmental problems but also applied the developed techniques and the monitoring program. In the present work, we have attempted to summarize important aspects to evaluate the advancements in the rehabilitation plan for those areas, being here presented some results of the monitoring of areas under different levels of recovery, in accordance with the techniques adopted. Biological parameters of the soil were used to verify the efficiency of these different techniques in the recovery process. This work is part of the monitoring program of areas in rehabilitation by the mining company, implemented as of 1999 and in partnership with universities. The microbial activity was determined through the quantification of the carbon and nitrogen microbial biomass (BMC and BMN) and the activity of the dehydrogenase evaluated in the mining floor and tailing areas in different levels of soil preparation and planting of native species. The analysis of the parameters studied revealed that the preparation of the soil, following the three years proposed by the methodology, was important for the success in establishing the rehabilitation process. Some of the areas analyzed already show some parameters with values close or superior to those found in the capoeira (secondary forest), the latter being the non-treated area. © 2010 WIT Press.

Operations strategy and supply chain management: A Brazilian case study

The objective of this study is to understand how an assembly company, that is considered a focal company in the chain of Brazilian white goods sector, can influence the supply chain management established with its first tier suppliers. This is an exploratory qualitative study in which the information was gathered through direct observations, documents' retention, and data from interviews held with management-level employees of the sales and product development areas of the focal company and of the production area of the suppliers' companies. This study indicates that the operations strategy of the focal company influences the supply chain management and that the common business processes shared by its suppliers are a way to verify the truth of such statement. The suppliers cooperate closely with the focal company when complementing their business processes and consequently supporting the company to pursue its operations strategy. A set of mechanisms to aid the comprehension of how the operations strategy can affect the business processes and therefore to achieve the result of this research were adopted. © EuroJournals Publishing, Inc. 2012.