Microbial processes and bacterial populations associated to anaerobic treatment of sulfate-rich wastewater

A pilot-scale (1.2 m(3)) anaerobic sequencing batch biofilm reactor (ASBBR) containing mineral coal for biomass attachment was fed with sulfate-rich wastewater at increasing sulfate concentrations. Ethanol was used as the main organic source. Tested COD/sulfate ratios were of 1.8 and 1.5 for sulfate loading rates of 0.65-1.90 kgSO(4)(2-)/cycle (48 h-cycle) or of 1.0 in the trial with 3.0 gSO(4)(2-) l(-1). Sulfate removal efficiencies observed in all trials were as high as 99%. Molecular inventories indicated a shift on the microbial composition and a decrease on species diversity with the increase of sulfate concentration. Beta-proteobacteria species affiliated with Aminomonas spp. and Thermanaerovibrio spp. predominated at 1.0 gSO(4)(2-) l(-1). At higher sulfate concentrations the predominant bacterial group was Delta-proteobacteria mainly Desulfovibrio spp. and Desulfomicrobium spp. at 2.0 gSO(4)(2-) l(-1), whereas Desulfurella spp. and Coprothermobacter spp. predominated at 3.0 gSO(4)(2-) l(-1). These organisms have been commonly associated with sulfate reduction producing acetate, sulfide and sulfur. Methanogenic archaea(Methanosaeta spp.)was found at 1.0 and 2.0 gSO(4)(2-) l(-1). Additionally, a simplified mathematical model was used to infer on metabolic pathways of the biomass involved in sulfate reduction. (C) 2009 Elsevier Ltd. All rights reserved.

Feasibility of a Sequencing Reactor Operated in Batch and Fed-batch Mode Applied to Nitrification and Denitrification Processes

The objective of this work was to study the operational feasibility of nitrification and denitrification processes in a mechanically stirred sequencing batch reactor (SBR) operated in batch and fed-batch mode. The reactor was equipped with a draft-tube to improve mass transfer and contained dispersed (aerobic) and granulated (anaerobic) biomass. The following reactor variables were adjusted: aeration time during the nitrification step; dissolved oxygen concentration, feed time defining batch and fed-batch phases, concentration of external carbon source used as electron donor during the denitrification stage and volumetric ammonium nitrogen load in the influent. The reactor (5 L volume) was maintained at 30 +/- 1 degrees C and treated either 1.0 or 1.5 L wastewater in 8-h cycles. Ammonium nitrogen concentrations assessed were: 50 (condition 1) and 100 mgN-NH(4)(+).L(-1) (condition 2), resulting in 29 and 67 mgN-NH(4)(+).L-1-d(-1), respectively. A synthetic medium and ethanol were used as external carbon sources (ECS). Total nitrogen removal efficiencies were 94.4 and 95.9% when the reactor was operated under conditions 1 and 2, respectively. Low nitrite (0.2 and 0.3 mgN-NO(2)(-).L(-1), respectively) and nitrate (0.01 and 0.3 mgN-NO(3)(-).L(-1), respectively) concentrations were detected in the effluent and ammonium nitrogen removal efficiencies were 97.6% and 99.6% under conditions 1 and 2, respectively.

Learning Profile Identification Based on the Analysis of the User Context of Interaction

One of the e-learning environment goal is to attend the individual needs of students during the learning process. The adaptation of contents, activities and tools into different visualization or in a variety of content types is an important feature of this environment, bringing to the user the sensation that there are suitable workplaces to his profile in the same system. Nevertheless, it is important the investigation of student behaviour aspects, considering the context where the interaction happens, to achieve an efficient personalization process. The paper goal is to present an approach to identify the student learning profile analyzing the context of interaction. Besides this, the learning profile could be analyzed in different dimensions allows the system to deal with the different focus of the learning.

HYBRID AND INCREMENTAL FUZZY LEARNING FOR HUMAN SKIN DETECTION

In this paper, a framework for detection of human skin in digital images is proposed. This framework is composed of a training phase and a detection phase. A skin class model is learned during the training phase by processing several training images in a hybrid and incremental fuzzy learning scheme. This scheme combines unsupervised-and supervised-learning: unsupervised, by fuzzy clustering, to obtain clusters of color groups from training images; and supervised to select groups that represent skin color. At the end of the training phase, aggregation operators are used to provide combinations of selected groups into a skin model. In the detection phase, the learned skin model is used to detect human skin in an efficient way. Experimental results show robust and accurate human skin detection performed by the proposed framework.

Accelerating autonomous learning by using heuristic selection of actions

This paper investigates how to make improved action selection for online policy learning in robotic scenarios using reinforcement learning (RL) algorithms. Since finding control policies using any RL algorithm can be very time consuming, we propose to combine RL algorithms with heuristic functions for selecting promising actions during the learning process. With this aim, we investigate the use of heuristics for increasing the rate of convergence of RL algorithms and contribute with a new learning algorithm, Heuristically Accelerated Q-learning (HAQL), which incorporates heuristics for action selection to the Q-Learning algorithm. Experimental results on robot navigation show that the use of even very simple heuristic functions results in significant performance enhancement of the learning rate.

Equivalent Carbon Dioxide Capture and Storage Processes in Offshore Petroleum Production Facilities

The present study approaches the economic and technical evaluation of equivalent carbon dioxide (CO(2) eqv.) capture and storage processes, considered in a proposal case compared to a base case. The base case considers an offshore petroleum production facility, with high CO(2) content (4 vol%) in the composition of the produced gas and both CO(2) and natural gas emissions to the atmosphere, called CO(2) eqv. emissions. The results obtained with this study, by using a Hysys process simulator, showed a CO(2) emission reduction of 65% comparing the proposal case in relation to the base case.

Knowledge transfer in product development processes: A case study in small and medium enterprises (SMEs) of the metal-mechanic sector from Sao Paulo, Brazil

This paper reports a research that evaluated the product development methodologies used in Brazilian small and medium-sized metal-mechanic enterprises (SMEs), in a specific region of Sao Paulo. The tool used for collecting the data was a questionnaire, which was developed and applied through interviews conducted by the researchers in 32 companies. The main focus of this paper can be condensed in the synthesis-question ""Is only the company responsible for the development?"" which was analyzed thoroughly. The results obtained from this analysis were evaluated directly (through the respective percentages of answers) and statistically (through the search of an index which demonstrates if two questions are related). The results point to a degree of maturity in SMEs, which allows product development to be conducted in cooperation networks. (C) 2007 Elsevier Ltd. All rights reserved.

Learning about knowledge management for improving environmental impact assessment in a government agency: The Western Australian experience

How does knowledge management (KM) by a government agency responsible for environmental impact assessment (EIA) potentially contribute to better environmental assessment and management practice? Staff members at government agencies in charge of the EIA process are knowledge workers who perform judgement-oriented tasks highly reliant on individual expertise, but also grounded on the agency`s knowledge accumulated over the years. Part of an agency`s knowledge can be codified and stored in an organizational memory, but is subject to decay or loss if not properly managed. The EIA agency operating in Western Australia was used as a case study. Its KM initiatives were reviewed, knowledge repositories were identified and staff surveyed to gauge the utilisation and effectiveness of such repositories in enabling them to perform EIA tasks. Key elements of KM are the preparation of substantive guidance and spatial information management. It was found that treatment of cumulative impacts on the environment is very limited and information derived from project follow-up is not properly captured and stored, thus not used to create new knowledge and to improve practice and effectiveness. Other opportunities for improving organizational learning include the use of after-action reviews. The learning about knowledge management in EIA practice gained from Western Australian experience should be of value to agencies worldwide seeking to understand where best to direct their resources for their own knowledge repositories and environmental management practice. (C) 2011 Elsevier Ltd. All rights reserved.

MODELLING AND CONTROL OF ERSW PROCESSES BY NEURONAL NETWORK

A study on the use of artificial intelligence (AI) techniques for the modelling and subsequent control of an electric resistance spot welding process (ERSW) is presented. The ERSW process is characterized by the coupling of thermal, electrical, mechanical, and metallurgical phenomena. For this reason, early attempts to model it using computational methods established as the methods of finite differences, finite element, and finite volumes, ask for simplifications that lead the model obtained far from reality or very costly in terms of computational costs, to be used in a real-time control system. In this sense, the authors have developed an ERSW controller that uses fuzzy logic to adjust the energy transferred to the weld nugget. The proposed control strategies differ in the speed with which it reaches convergence. Moreover, their application for a quality control of spot weld through artificial neural networks (ANN) is discussed.

Microstructure and texture assessment of Al-Mn-Fe-Si (3003) aluminum alloy produced by continuous and semicontinuous casting processes

Aluminum sheets are currently produced by the direct-chill process (DC). The need for low-cost aluminum sheets is a challenge for the development of new materials produced by the twin roll caster (TRC) process. It is expected that sheets produced from these different casting procedures will differ in their microstructure. These differences in microstructure and in the crystallographic texture have great impact on sheet mechanical properties and formability. The present study investigated microstructure and evaluated texture of two strips of Al-Mn-Fe-Si (3003) aluminum alloy produced by TRC and by hot-rolling processes. It was possible to notice that the microstructure, morphology, and grain size of the TRC sample were more homogenous than those found in hot-rolled samples. Both strips, obtained by the two processes, showed strong texture gradient across the thickness.

Degradation of Poly(ethylene glycol) in Aqueous Solution by Photo-Fenton and H(2)O(2)/UV Processes

This article reports experimental results obtained in a laboratory-scale photochemical reactor on the photodegradation of poly(ethylene glycol) (PEG) in aqueous solutions by means of the photo-Fenton and H(2)O(2)/UV processes. Dilute water solutions of PEG were fed to a batch reactor, mixed with pertinent reactants, and allowed to react under different conditions. Reaction progress was evaluated by sampling and analyzing the concentration of the total organic carbon (TOC) in solution as a function of the reaction time. Organic acids formed during oxidation were determined by HPLC analyses. The main acids detected in both processes were acetic and formic. Glycolic acid was detected only in the photo-Fenton process, and malonic acid was detected only in the H(2)O(2)/UV treatment, indicating that different reaction paths occur in these processes. The characteristics of both processes are discussed, based on the evolution of the TOC-time curves and the concentration profiles of the monitored organic acids. The experimental results constitute a contribution to the design of industrial processes for the treatment of wastewaters containing soluble polymers with similar properties.

Treatment of Aqueous Effluents Containing Phenol by the O(3), O(3)-UV, and O(3)-H(2)O(2) Processes: Experimental Study and Neural Network Modeling

In this work, the oxidation of the model pollutant phenol has been studied by means of the O(3), O(3)-UV, and O(3)-H(2)O(2) processes. Experiments were carried out in a fed-batch system to investigate the effects of initial dissolved organic carbon concentration, initial, ozone concentration in the gas phase, the presence or absence of UVC radiation, and initial hydrogen peroxide concentration. Experimental results were used in the modeling of the degradation processes by neural networks in order to simulate DOC-time profiles and evaluate the relative importance of process variables.

Investigation of the mass transfer processes during the desalination of water containing phenol and sodium chloride by electrodialysis

Oxidation processes are used in wastewater treatment when conventional processes are not effective due to the presence of recalcitrant organic contaminants, like phenol. However, the presence of ionic compounds associated with organic pollutants may retard the oxidation. In this work the transport of species contained in an aqueous solution of phenol containing sodium chloride was evaluated in an electrodialysis (ED) system. An experimental study was carried out in which the influence of the process variables on the phenol loss and sodium chloride removal was investigated. Experiments were also performed without current, in order to determine the phenol transfer due to diffusion. The phenol and salt concentration variations in the ED compartments were measured over time, using dedicated procedures and an experimental design to determine the global characteristic parameters. A phenomenological approach was used to relate the phenol, salt and water fluxes with the driving forces (concentration and electric potential gradients). Under ED conditions, two contributions were pointed out for the phenol transport, i.e. diffusion and convection, this latter coming from the water flux due to electroosmosis related to the migration of salts. The fitting of the parameters of the transport equations resulted in good agreement with the experimental results over the range of conditions investigated. (c) 2008 Elsevier B.V. All rights reserved.

USE OF SOLAR ENERGY IN THE TREATMENT OF WATER CONTAMINATED WITH PHENOL BY PHOTOCHEMICAL PROCESSES

The solar driven photo-Fenton process for treating water containing phenol as a contaminant has been evaluated by means of pilot-scale experiments with a parabolic trough solar reactor (PTR). The effects of Fe(II) (0.04-1.0 mmol L(-1)), H(2)O(2) (7-270 mmol L(-1)), initial phenol concentration (100 and 500 mg C L(-1)), solar radiation, and operation mode (batch and fed-batch) on the process efficiency were investigated. More than 90% of the dissolved organic carbon (DOC) was removed within 3 hours of irradiation or less, a performance equivalent to that of artificially-irradiated reactors, indicating that solar light can be used either as an effective complementary or as an alternative source of photons for the photo-Fenton degradation process. A non-linear multivariable model based on a neural network was fit to the experimental results of batch-mode experiments in order to evaluate the relative importance of the process variables considered on the DOC removal over the reaction time. This included solar radiation, which is not a controlled variable. The observed behavior of the system in batch-mode was compared with fed-batch experiments carried out under similar conditions. The main contribution of the study consists of the results from experiments under different conditions and the discussion of the system behavior. Both constitute important information for the design and scale-up of solar radiation-based photodegradation processes.

On Building a Memory Evolutive System for Application to Learning and Cognition Modeling

We address here aspects of the implementation of a memory evolutive system (MES), based on the model proposed by A. Ehresmann and J. Vanbremeersch (2007), by means of a simulated network of spiking neurons with time dependent plasticity. We point out the advantages and challenges of applying category theory for the representation of cognition, by using the MES architecture. Then we discuss the issues concerning the minimum requirements that an artificial neural network (ANN) should fulfill in order that it would be capable of expressing the categories and mappings between them, underlying the MES. We conclude that a pulsed ANN based on Izhikevich`s formal neuron with STDP (spike time-dependent plasticity) has sufficient dynamical properties to achieve these requirements, provided it can cope with the topological requirements. Finally, we present some perspectives of future research concerning the proposed ANN topology.