961 resultados para water use optimization


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Mestrado em Engenharia Química - Ramo Optimização Energética na Indústria Química

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Mestrado em Engenharia Química - Ramo Tecnologias de Protecção Ambiental

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Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies

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Mestrado em Engenharia Química

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Glass fibre-reinforced plastics (GFRP), nowadays commonly used in the construction, transportation and automobile sectors, have been considered inherently difficult to recycle due to both: cross-linked nature of thermoset resins, which cannot be remolded, and complex composition of the composite itself, which includes glass fibres, matrix and different types of inorganic fillers. Presently, most of the GFRP waste is landfilled leading to negative environmental impacts and supplementary added costs. With an increasing awareness of environmental matters and the subsequent desire to save resources, recycling would convert an expensive waste disposal into a profitable reusable material. There are several methods to recycle GFR thermostable materials: (a) incineration, with partial energy recovery due to the heat generated during organic part combustion; (b) thermal and/or chemical recycling, such as solvolysis, pyrolisis and similar thermal decomposition processes, with glass fibre recovering; and (c) mechanical recycling or size reduction, in which the material is subjected to a milling process in order to obtain a specific grain size that makes the material suitable as reinforcement in new formulations. This last method has important advantages over the previous ones: there is no atmospheric pollution by gas emission, a much simpler equipment is required as compared with ovens necessary for thermal recycling processes, and does not require the use of chemical solvents with subsequent environmental impacts. In this study the effect of incorporation of recycled GFRP waste materials, obtained by means of milling processes, on mechanical behavior of polyester polymer mortars was assessed. For this purpose, different contents of recycled GFRP waste materials, with distinct size gradings, were incorporated into polyester polymer mortars as sand aggregates and filler replacements. The effect of GFRP waste treatment with silane coupling agent was also assessed. Design of experiments and data treatment were accomplish by means of factorial design and analysis of variance ANOVA. The use of factorial experiment design, instead of the one factor at-a-time method is efficient at allowing the evaluation of the effects and possible interactions of the different material factors involved. Experimental results were promising toward the recyclability of GFRP waste materials as polymer mortar aggregates, without significant loss of mechanical properties with regard to non-modified polymer mortars.

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The elastic behavior of the demand consumption jointly used with other available resources such as distributed generation (DG) can play a crucial role for the success of smart grids. The intensive use of Distributed Energy Resources (DER) and the technical and contractual constraints result in large-scale non linear optimization problems that require computational intelligence methods to be solved. This paper proposes a Particle Swarm Optimization (PSO) based methodology to support the minimization of the operation costs of a virtual power player that manages the resources in a distribution network and the network itself. Resources include the DER available in the considered time period and the energy that can be bought from external energy suppliers. Network constraints are considered. The proposed approach uses Gaussian mutation of the strategic parameters and contextual self-parameterization of the maximum and minimum particle velocities. The case study considers a real 937 bus distribution network, with 20310 consumers and 548 distributed generators. The obtained solutions are compared with a deterministic approach and with PSO without mutation and Evolutionary PSO, both using self-parameterization.

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Demand response can play a very relevant role in the context of power systems with an intensive use of distributed energy resources, from which renewable intermittent sources are a significant part. More active consumers participation can help improving the system reliability and decrease or defer the required investments. Demand response adequate use and management is even more important in competitive electricity markets. However, experience shows difficulties to make demand response be adequately used in this context, showing the need of research work in this area. The most important difficulties seem to be caused by inadequate business models and by inadequate demand response programs management. This paper contributes to developing methodologies and a computational infrastructure able to provide the involved players with adequate decision support on demand response programs and contracts design and use. The presented work uses DemSi, a demand response simulator that has been developed by the authors to simulate demand response actions and programs, which includes realistic power system simulation. It includes an optimization module for the application of demand response programs and contracts using deterministic and metaheuristic approaches. The proposed methodology is an important improvement in the simulator while providing adequate tools for demand response programs adoption by the involved players. A machine learning method based on clustering and classification techniques, resulting in a rule base concerning DR programs and contracts use, is also used. A case study concerning the use of demand response in an incident situation is presented.

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The massification of electric vehicles (EVs) can have a significant impact on the power system, requiring a new approach for the energy resource management. The energy resource management has the objective to obtain the optimal scheduling of the available resources considering distributed generators, storage units, demand response and EVs. The large number of resources causes more complexity in the energy resource management, taking several hours to reach the optimal solution which requires a quick solution for the next day. Therefore, it is necessary to use adequate optimization techniques to determine the best solution in a reasonable amount of time. This paper presents a hybrid artificial intelligence technique to solve a complex energy resource management problem with a large number of resources, including EVs, connected to the electric network. The hybrid approach combines simulated annealing (SA) and ant colony optimization (ACO) techniques. The case study concerns different EVs penetration levels. Comparisons with a previous SA approach and a deterministic technique are also presented. For 2000 EVs scenario, the proposed hybrid approach found a solution better than the previous SA version, resulting in a cost reduction of 1.94%. For this scenario, the proposed approach is approximately 94 times faster than the deterministic approach.

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Applications of niclosamide at three-monthly intervals were undertaken for 14 years in foci of Biomphalaria glabrata in the water sources of Peri-Peri (Capim Branco, MG). All the residents of the area were submitted to an annual fecal examination (Kato/Katz) and those individuals eliminating Schistosoma mansoni eggs were treated with oxamniquine. A malacological survey was undertaken at three-monthly intervals by means of ten scoops with a perforated ladle each ten metres along the two banks of the ditches and streams of the region. Where snails were found, molluscicide was applied by means of dripping or aspersion using a 3 ppm aqueous suspension of niclosamide. Initially, a mean of 14.3% of snails in the region were found to be eliminating cercariae. Following the first four applications of molluscicide, this was reduced to 0.0% and maintained at about 1.5% throughout the program. Thus, there was a continued possibility of schistosomiasis transmission in the area and it was observed that the population of snails reestablished itself within three months of molluscicide application. The results obtained in this study do not encourage the continual use of niclosamide as the only method of control of schistosomiasis.

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Coffee silverskin is a major roasting by-product that could be valued as a source of antioxidant compounds. The effect of the major variables (solvent polarity, temperature and extraction time) affecting the extraction yields of bioactive compounds and antioxidant activity of silverskin extracts was evaluated. The extracts composition varied significantly with the extraction conditions used. A factorial experimental design showed that the use of a hydroalcoholic solvent (50%:50%) at 40 °C for 60 min is a sustainable option to maximize the extraction yield of bioactive compounds and the antioxidant capacity of extracts. Using this set of conditions it was possible to obtain extracts containing total phenolics (302.5 ± 7.1 mg GAE/L), tannins (0.43 ± 0.06 mg TAE/L), and flavonoids (83.0 ± 1.4 mg ECE/L), exhibiting DPPHradical dot scavenging activity (326.0 ± 5.7 mg TE/L) and ferric reducing antioxidant power (1791.9 ± 126.3 mg SFE/L). These conditions allowed, in comparison with other “more effective” for some individual parameters, a cost reduction, saving time and energy.

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The optimal design of cold-formed steel columns is addressed in this paper, with two objectives: maximize the local-global buckling strength and maximize the distortional buckling strength. The design variables of the problem are the angles of orientation of cross-section wall elements the thickness and width of the steel sheet that forms the cross-section are fixed. The elastic local, distortional and global buckling loads are determined using Finite Strip Method (CUFSM) and the strength of cold-formed steel columns (with given length) is calculated using the Direct Strength Method (DSM). The bi-objective optimization problem is solved using the Direct MultiSearch (DMS) method, which does not use any derivatives of the objective functions. Trade-off Pareto optimal fronts are obtained separately for symmetric and anti-symmetric cross-section shapes. The results are analyzed and further discussed, and some interesting conclusions about the individual strengths (local-global and distortional) are found.

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Master Thesis to obtain the Master degree in Chemical Engineering - Branch Chemical Processes

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Leishmania braziliensis is a causative agent of American Cutaneous Leishmaniasis (ACL). The 034-JCG strain, isolated from a patient from the northern region of Paraná State, Brazil, was cultivated in Blood Agar Base medium, lyophilized and submitted to phenol-water extraction. The extract was treated with RNase I. The carbohydrate containing-antigen (Ag-CHO) was immunogenic to rabbits and showed at least a fraction with some negative charge at pH 8.2. This antigen showed cross-reactivity with the phenol-water extract of the growth medium used for the culture of promastigotes and with the surface antigens of promastigotes. Its composition is: 24.3% of total sugars, from which 11.2% of galactose, 7.5% of mannose and 5.6% of ribose. Protein content was 5.4% and phosphate 18.5%. The antigenic activity was maintained after: repeated freezing-thawing; lyophilization; heating at 100ºC for 30 minutes; treatment with RNase, trichloroacetic acid and sodium metaperiodate. The precipitin line obtained is Periodic Acid Schiff positive. The application of the Ag-CHO in counterimmunoelectrophoresis reaction for the immunodiagnosis of ACL showed 60% sensitivity, and no cross-reaction with the five sera of Chagas' disease patients tested. The use of this antigen in a more sensitive technique, with more samples of sera, may improve these results.

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The ORF strain of Cysticercus longicollis represents an important model for the study of heterologous antigens in the immunodiagnosis of neurocysticercosis (NC). The immunoperoxidase (IP) technique was standardized using a particulate antigen suspension of Cysticercus longicollis (Cl) and Cysticercus cellulosae (Cc). Cerebrospinal fluid (CSF) samples were incubated on the antigen fixed to microscopy slides; the conjugate employed was anti-IgG-peroxidase and the enzymatic reaction was started by covering the slides with chromogen solution (diaminobenzidine/H2O2). After washing with distilled water, the slide was stained with 2% malachite green in water. Of the CSF samples from 21 patients with NC, 19 (90.5%) were positive, whereas the 8 CSF samples from the control group (100%) were negative. The results of the IP-Cl test applied to 127 CSF samples from patients with suspected NC showed 28.3% reactivity as opposed to 29.1 % for the IP-Cc test. The agreement index for the IP test (Cl x Cc) was 94.2%, with no significant difference between the two antigens.

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This paper presents a methodology for multi-objective day-ahead energy resource scheduling for smart grids considering intensive use of distributed generation and Vehicle- To-Grid (V2G). The main focus is the application of weighted Pareto to a multi-objective parallel particle swarm approach aiming to solve the dual-objective V2G scheduling: minimizing total operation costs and maximizing V2G income. A realistic mathematical formulation, considering the network constraints and V2G charging and discharging efficiencies is presented and parallel computing is applied to the Pareto weights. AC power flow calculation is included in the metaheuristics approach to allow taking into account the network constraints. A case study with a 33-bus distribution network and 1800 V2G resources is used to illustrate the performance of the proposed method.