The efficiency of colloidal gas aphron (CGA) flotation for separation of algae from growth media was investigated. Anionic, cationic and non-ionic surfactants were used to generate CGAs. Preliminary batch studies showed that two CGA flotation procedures could be successfully used for algae flotation. CGA flotation without pretreatment of algae was only successful using cationic surfactants. All three types of surfactants yielded promising results while combining CGA flotation with alum flocculation as a pretreatment step. Observed removal efficiencies were above ninety percent for batch applications. “Pure CGA flotation” was studied in countercurrent continuous flow operations. Satisfactory removal of algae could be achieved even at substantially reduced volume ratios of CGAs to algae solution. However, TOC concentrations increased with higher volume ratios and higher flowrates due to carryover of CGA bubbles and diffusion of surfactant molecules into the bulk solution. The cationic surfactant Cetyl Pyrimidinium Chloride, present at very low concentrations in the raffinate and the froth algae mixture, exhibited biostatic and biocidal properties in the microbial activity test. / Master of Science / incomplete_metadata

Lake restoration and management strategies focus on reducing the negative impacts of enriched or polluted inflows. These strategies become of paramount importance when lakes are used for recreational and/or drinking water purposes. Long term control of eutrophication and turbidity problems associated with large inflow loads is usually oriented to catchment management. Although it has been suggested that this is the correct long term approach, public concerns usually require a short term solution. In addition, due to political and economic costs related to changes in catchment management, in-lake restoration technologies have been emerging as a viable pretreatment option, complementary to water treatment plants, both reducing the operational costs of the water treatment plant and ameliorating the water residing in the lakes. This research investigates the effects of two in-lake technologies on the dynamics of inflowing rivers, where basin shape plays a significant role. The three lakes in this study suffer from eutrophication combined with a distinctive water quality problem: from turbidity in Silvan Reservoir (Australia), to heavy metal loads in Coeur d'Alene Lake (USA) and industrial wastes in Lake Como (Italy). Firstly, the influence of basin morphology, wind speed, and wind direction on the fate and transport of two rivers flowing into the L-shaped Coeur d'Alene Lake was examined, and it was shown that transport and mixing patterns in a lake can be greatly influenced by the shape of the lake, leading to important consequences for the plankton ecology in the lake. Secondly, in Silvan Reservoir we investigated the potential to modify the basin shape using vertical barriers, increasing the retention time and hence the barrier capacity to microbial pollution. A final in-lake technology was tested for Lake Como, using a downward pointing impeller to remove polluted water from the coastal margin. Lessons from these three examples indicate that there is significant potential for in-lake remediation at relatively low cost, over relatively short timescales.

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Operational planning of water resource systems like reservoirs and hydropower plants calls for real-time forecasting of reservoir inflow. Reservoir daily inflow forecasts provide a warning of impending floods or drought conditions and help to optimize operating policies for reservoir management based on a fine time scale. The aim of this study was to determine the best model for daily reservoir inflow prediction through linear regression, exponential smoothing and artificial neural network (ANN) techniques. The Hedi reservoir, the third largest reservoir in south China with a 1.144 x 109 m 3, was selected as the study site. The performance of these forecasting models, in terms of forecasting accuracy, efficiency of model development and adaptability for future prediction, were compared to one another. All models performed well during the dry season (inflow with low variability), while the non-linear ANNs were superior to other models in frontal rainy season and typhoon season (inflow with high variability). The performance of ANN models were hardly affected by the high degree of uncertainty and variability inherent to the rainy season. Stepwise selection was very helpful in identifying significant variables for regression models and ANNs. This procedure reduced ANN's size and greatly improved forecasting accuracy for ANN models. The impact of training data series, model architecture and network internal parameters on ANNs performances were also addressed in this study. The overall evaluation indicates that ANNs are an effective and robust tool for input-output mapping under more extreme and variable conditions. ANNs provide an alternative forecasting approach to conventional time series forecasting models for daily reservoir inflow prediction.

The diploma thesis deals with the topic of small reservoirs. In the theoretical part the history, division of the reservoirs and technical compositions are described. Furthermore the thesis deals with the legislation and the possible technical solution within the frame of landscaping. The state of purpose reservoirs is settled in the basin area of Zborovský stream. The work propose the repairs of: dam, drain or the spillway. and the maintenance of bank vegetation and state of the mud amount in the basin as well. The erosion threat and the soil washes from the surrounding agricultural grounds are also described. The Universal soil loss equation - USLE according to Wischmeier and Smith (1978) was used for the calculation.

Orientador: Denis José Schiozer / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecânica e Instituto de Geociências / Made available in DSpace on 2018-08-18T02:24:49Z (GMT). No. of bitstreams: 1 Mello_SamuelFerreirade_M.pdf: 5532119 bytes, checksum: bd96341bedac10788b77f2035e64d4b3 (MD5) Previous issue date: 2011 / Resumo: O aumento significativo do volume potencial de petróleo se deve às descobertas recentes de reservatórios de petróleo localizados em profundidades de camada pré-sal que contém CO2, em alguns casos, em quantidade superior a 10% e frações voláteis, em condições de grande desvio do comportamento dos gases ideais. A literatura sugere que nesses casos é necessário o uso de modelos composicionais de simulação de reservatórios. Neste trabalho procurou-se através de estudos do comportamento de fluidos diagnosticar fatores decisivos do tratamento de fluidos para a aplicação confiável da simulação composicional em reservatórios de petróleo similares aos da camada pré-sal. Ênfase especial foi dada ao estudo do comportamento de fases e à simulação de método de recuperação melhorada de óleo baseados na injeção de CO2. Para isto, foram obtidos da literatura um modelo geológico de reservatório heterogêneo, dados de permeabilidade relativa e dados PVT da caracterização de óleos leves (acima de 20º API de densidade) e ricos em CO2 (3% a 20% da fração molar). Foi feita uma modelagem pelo ajuste termodinâmico de equações de estado aplicadas ao petróleo, onde foram testadas diferentes representações termodinâmicas (de 5 a 24 componentes), dois diferentes métodos de ajustes de equações de estado e dois modelos geológicos. Os resultados obtidos foram analisados e comparados em diferentes combinações para o estudo da sensibilidade de parâmetros críticos de fluido para a simulação composicional, como a escolha de pseudocomponentes, a escolha de equações de estado e a escolha de técnicas de ajuste de equações de estado. Os resultados ressaltam a importância não só da qualidade de dados experimentais adequados, como da escolha cautelosa de modelos teóricos termodinâmicos adequados, de técnicas de ajuste de dados experimentais e do uso cuidadoso da pseudoização. O estudo conclui que a alteração indiscriminada em qualquer uma destas variáveis pode comprometer a confiabilidade de um modelo de simulação de reservatórios. O estudo conclui que diferentes técnicas de regressão de equações de estado não são equivalentes e também mostra que a relação entre a pseudoização e a redução do tempo computacional não é direta. Os resultados deste trabalho são importantes para estudos futuros de simulação composicional / Abstract: The Brazilian expected oil potential has increased substantially after the discoveries of subsalt located deep oil reservoirs and they are regarded with huge economic importance for Brazil. Some of these fields contain above 10% molar CO2 and volatile fractions, submitted to conditions that differ greatly from that described by ideal gas models. Compositional reservoir simulation is fundamental for the description of these phenomena. Although this technique improves the reservoir management, the modeling of fluid data is needed due to computational limitation and it adds uncertainties that affect the quality of the compositional simulation and are not studied very often. The objective of this work is to identify key factors in order to allow the reliable application of compositional simulation to petroleum reservoir analogous to the Brazilian subsalt reservoirs. Special highlighting was given to the phase behavior and simulation of enhanced oil recovery methods based on the CO2 injection applied to heterogeneous reservoirs. This required the acquisition from literature of a heterogeneous reservoir geological model, of a singular set of relative permeability data and of several sets of PVT data characterizing light oils (over 20º API gravity) that are also CO2 enriched (from 3% to 20% of molar fraction). From these PVT sets of data it was necessary to perform the thermodynamic match of equations of state applied to complex hydrocarbons mixtures. An oil with different extended and lumped thermodynamic versions was tested with two different equations of state matching methods from literature and two different geological models. The results obtained were analyzed and compared under different combinations for the sensitivity study of critical fluid parameters for the compositional simulation. This work emphasizes the importance not only of experimental data quality, but also of the equation of state choice, regression method choice and the careful use of lumping. The study concludes that the indiscriminate alteration in any of these variables may harm the model reliability of reservoir simulation. The study concludes that different PVT tuning techniques are not equivalent and also shows that the relation between lumping and computational time reduction is not direct. The results of this work are important for future compositional simulation studies / Mestrado / Reservatórios e Gestão / Mestre em Ciências e Engenharia de Petróleo

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Optimal operation of reservoir systems is necessary for better utilizing the limited water resources and to justify the high capital investments associated with reservoir projects. However, finding optimal policies for real-life problems of reservoir systems operation (RSO) is a challenging task as the available analytical methods can not handle the arbitrary functions of the problem and almost all methods employed are numerical or iterative type that are computer dependent. Since the computer resources in terms of memory and CPU time are limited, a limit exists for the size of the problem, in terms of arithmetic and memory involved, that can be handled. This limit is approached quickly as the dimension and the nonlinearity of the problem increases. In encountering the complex aspects of the problem all the traditionally employed methods have their own drawbacks. Linear programming (LP), though very efficient in dealing with linear functions, can not handle nonlinear functions which is the case mostly in real-life problems. Attempting to approximate nonlinear functions to linear ones results in the problem size growing enormously. Dynamic programming (DP), though suitable for most of the RSO problems, requires exponentially increasing computer resources as the dimension of the problem increases and at present many high dimensional real-life problems can not be solved using DP. Nonlinear programming (NLP) methods are not known to be efficient in RSO problems due to slow rate of convergence and inability to handle stochastic problems. Simulation methods can, practically, explore only a small portion of the search region. Many simplifications in formulations and adoption of approximate methods in literature still fall short in addressing the most critical aspects, namely multidimensionality, stochasticity, and additional complexity in conjunctive operation, of the problem. As the problem complexity increases and the possibility of arriving at the solution recedes, a near optimal solution with the best use of computational resources can be very valuable. In this context, genetic algorithms (GA) can be a promising technique which is believed to have an advantage in terms of efficient use of computer resources. GA is a random search method which find, in general, near optimal solutions using evolutionary mechanism of natural selection and natural genetics. When a pool of feasible solutions, represented in a coded form, are given fitness according to a objective function and explored by genetic operators for obtaining new pools of solutions, then the ensuing trajectories of solutions come closer and closer to the optimal solution which has the greatest fitness associated with it. GA can be applied to arbitrary functions and is not excessively sensitive to the dimension of the problem. Though in general GA finds only the near optimal solutions trapping in local optima is not a serious problem due to global look and random search. Since GA is not fully explored for RSO problems two such problems are selected here to study the usefulness and efficiency of GA in obtaining near optimal solutions. One problem is conjunctive operation of a system consisting of a surface reservoir and an aquifer, taken from the literature for which deterministic and stochastic models are solved. Another problem is real-time operation of a multipurpose reservoir, operated for irrigation (primary purpose) and hydropower production, which is in the form of a case study. The conjunctive operation problem consists of determining optimal policy for a combined system of a surface reservoir and an aquifer. The surface reservoir releases water to an exclusive area for irrigation and to a recharge facility from which it reaches the aquifer in the following period. Another exclusive area is irrigated by water pumped from the aquifer. The objective is to maximize the total benefit from the two irrigated areas. The inflow to the surface reservoir is treated as constant in deterministic model and taken at 6 different classes in stochastic model. The hydrological interactions between aquifer and reservoir are described using a lumped parameter model in which the average aquifer water table is arrived at based on the quantity of water in the aquifer, and local drawdown in pumping well is neglected. In order to evaluate the GA solution both deterministic and stochastic models are solved using DP and stochastic DP (SDP) techniques respectively. In the deterministic model, steady state (SS) cyclic (repetitive) solution is identified in DP as well as in GA. It is shown that the benefit from GA solution converges to as near as 95% of the benefit from exact DP solution at a highly discounted CPU time. In the stochastic model, the steady state solution obtained with SDP consists of converged first stage decisions, which took a 8-stage horizon, for any combination of components of the system state. The GA solution is obtained after simplifying the model to reduce the number of decision variables. Unlike SDP policy which gives decisions considering the state of the system in terms of storages, at reservoir, aquifer, and recharge facility, and previous inflow at the beginning of that period, GA gives decisions for each period of the horizon considering only the past inflow state of the period. In arriving at these decisions the effect of neglected state information is approximately reflected in the decisions by the process of refinement of the decisions, to conform to feasibility of storages in reservoir and aquifer, carried out in a simplified simulation process. Moreover, the validity of the solution is confirmed by simulating the operation with all possible inflow sequences for which the 8-stages benefit converged up to 90 % of the optimum. However, since 8 stages are required for convergence to SS, a 16-stage process is required for GA method in which the first 8 stages policy is valid. Results show that GA convergence to the optimum is satisfactory, justifying the approximations, with significant savings in CPU time. For real-time operation of a multipurpose reservoir, a rule curve (RC) based monthly operation is formulated and applied on a real-life problem involving releases for irrigation as well as power production. The RC operation is based on the target storages that have to be maintained, at each season of the year, in the reservoir during normal hydrological conditions. Exceptions to target storages are allowed when the demands have to be met or for conserving water during the periods of high inflows. The reservoir in the case study supplies water to irrigation fields through two canals where a set of turbines each at the canal heads generate hydropower. A third set of turbines operate on the river bed with the water let out downstream from the dam. The problem consists of determining the the RC target storages that facilitate maximum power production while meeting the irrigation demands up to a given reliability level. The RC target storages are considered at three different levels, corresponding to dry, normal, and wet conditions, according to the system state in terms of actual (beginning of period) storage of the reservoir. That is, if the actual beginning storage of the reservoir is less than some coefficient, dry-coe, times the normal target storage the target for the end of the period storage is taken at the dry storage target (of the three sets of storages). Similarly the wet level is taken for the end of the period target if the actual beginning storage is greater than some coefficient, wet-coe, times the normal storage. For other conditions the target is the normal storage level. The dry-coe and wet-coe parameters are obtained by trial and error analysis working on a small sequence of inflows. The three sets of targets are obtained from optimization over a 1000 year generated inflow sequence. With deterministic DP solutions, for small sequences of inflows, the optimization capability of GA-RC approach, in terms of objective function convergence, and generalization or robustness capability of GA-RC approach, for which the GA-RC benefit is obtained by simulating the reservoir operation using the previously obtained GA-RC solution, are evaluated. In both the cases GA-RC approach proves to be promising. Finally a 15 year real-time simulation of the reservoir is carried out using historical inflows and demands and the comparison with the historical operation shows significant improvement in benefit, i.e. power produced, without compromising irrigation demands throughout the simulation period.

Patterns in 0+ fish distribution along longitudinal and depth profiles were studied in five canyon-shaped reservoirs in the Czech Republic. In two canyon-shaped and one shallow well-mixed reservoirs the efficiency of fry trawls was investigated during late summer for juveniles and in one canyon-shaped reservoir the efficiency of ichthyoplankton trawls was investigated during spring for larvae and early juveniles.