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No description available.

Climate change is one of the greatest and probably most challenging environmental, social and economical threats facing the world this century. Human activities have altered the chemical composition of the atmosphere through the buildup of significant quantities of greenhouse gases (GHGs), which remain in the atmosphere for long periods of time and intensify the natural greenhouse effect. Increasing concentrations of greenhouse gases, mainly CO2, are likely to accelerate the rate of climate change. Concerns are growing about how increases in CO2 caused by human activities are contributing to the natural greenhouse effect and raising the Earth's average temperature. Electricity generation, especially from fossil fuel, and petroleum industries contribute the most to greenhouse gases emissions in Canada. As of 2004, they contributed to about 37% of total (GHGs). Risks of climate change and subsequent future environmental regulations are pressing electricity and petroleum refining industries to minimize their greenhouse gas emissions, mainly CO2. Fossil fuel power plants and refineries are now being challenged to comply with the Kyoto protocol by the United Nations Framework Convention and Climate Change (UNFCC). Canada???s target is a reduction in CO2 emissions of 6% from 1990 level. In this thesis, an optimization approach for integrating planning and CO2 reduction is developed for electricity and refinery sectors. Three different CO2 mitigation options are considered in each case. For the electricity sector, these mitigation options were 1) fuel balancing (optimal adjustment of the operation of existing generating stations to reduce CO2 emissions without making structural changes to the fleet), 2) fuel switching (switching from carbon intensive fuel to less carbon intensive fuel, essentially switching from coal to natural gas) and 3) implementing different technologies for efficiency improvement. The optimization model takes into account meeting electricity demand and achieving a certain CO2 reduction target at a minimum overall cost. The model was formulated as a Mixed Integer Non Linear Program (MINLP) and was implemented in GAMS (General Algebraic Modeling System). Exact linearization techniques were employed to facilitate solution development. The computer program was capable of determining the best strategy or mix of strategies to meet a certain CO2 reduction target at minimum cost. The model was illustrated on a case study for Ontario Power Generation (OPG) fleet. The results showed that for 1% CO2 reduction target, only fuel balancing need to be applied and even a decrease of about 1.3% in overall cost was obtained. The optimizer chose to increase production from all non fossil fuel power plants and to decrease production from natural gas power plant. This is because natural gas is the most expensive fuel that OPG uses. For higher reduction targets, it was necessary to implement fuel switching. For 30% reduction, for example, 11 boilers out of 27 (4 are already natural gas) are switched from coal to natural gas and the cost increases by about 13%. Applying efficiency improvement technologies such as installing new turbine blades was a good option only at small reduction targets. As the reduction target increases, the optimizer chose not to implement efficiency improvement technologies and only fuel switching was the best option to select in addition to fuel balancing. For the refinery sector, a similar strategy was applied. An optimization model was developed to maximize profit from selling final products and to meet a given CO2 reduction target with products demand and specifications. Three CO2 mitigation options were considered and these were: 1) balancing that implies the increase in production from units that emit less CO2 emissions provided that demand is met, 2) fuel switching that involves switching from current carbon intensive fuel to less carbon intensive fuel such as natural gas, 3) implementation of CO2 capture technologies. Chemical absorption (MEA) process was used as the capture process. Prior to the development of the refinery planning model, a sub-model was developed for each unit in a refinery layout. Then, the sub-models were integrated into a master planning model to meet final products demand and specifications with the objective of maximizing profit without CO2 mitigation options. The model was solved first as a Non Linear Program (NLP). Then, binary variables representing the existence or no existence of fuel switching option and CO2 capture processes were introduced into the model. The model was formulated as a Mixed Integer Non Linear Program (MINLP), coded in GAMS, and applied to different case studies. The results showed that the refinery planning model tends to produce more from the most profitable product, which is gasoline, and chose to blend products into the most profitable pool unless the demand needs to be satisfied for certain other products. The model, for example, chose to send kerosene from the diesel hydrotreater to the kerosene pool and not to the diesel pool since kerosene has higher selling value than diesel. When CO2 mitigation options were introduced into the model, only 0.4% CO2 reduction was achieved by simply decreasing production from the hydrocracker (HC) unit and increasing production from the fluidized catalytic cracking (FCC) unit. This was done because the FCC unit tends to emit less CO2 compared to the HC unit. At higher reduction target such as 1%, fuel switching was implemented by choosing the FCC to run with natural gas. The profit decreased slightly because of the retrofit cost of switching. It was noticed also that fuel switching can achieve a maximum of 30% reduction in CO2 emissions. This was achieved by switching all units to run with natural gas that emits less CO2 emissions. For a reduction target higher than 30%, CO2 capture technologies need to be applied. For 60% reduction, the optimization chose to switch three units (out of 8) and to capture CO2 emissions coming from four units. Only the FCC remained unchanged. A decrease in the profit was noticed as the reduction target increases since more units need to be switched and more CO2 need to be captured. The results showed that adding sequestration cost further decreased the profit. However, it was noticed that the selling price of final products had the most effect on the profit. An increase of 20%, for example, in final products??? prices, leads to a 10% increase in profit even when the CO2 reduction target was as high as 80%. When the retrofit cost for switching and capture was decreased by 30%, the effect on the profit was noticed only at higher reduction targets since more units were switched and more CO2 capture units were implemented

Climate change is one of the greatest and probably most challenging environmental, social and economical threats facing the world this century. Human activities have altered the chemical composition of the atmosphere through the buildup of significant quantities of greenhouse gases (GHGs), which remain in the atmosphere for long periods of time and intensify the natural greenhouse effect. Increasing concentrations of greenhouse gases, mainly CO2, are likely to accelerate the rate of climate change. Concerns are growing about how increases in CO2 caused by human activities are contributing to the natural greenhouse effect and raising the Earth's average temperature. Electricity generation, especially from fossil fuel, and petroleum industries contribute the most to greenhouse gases emissions in Canada. As of 2004, they contributed to about 37% of total (GHGs). Risks of climate change and subsequent future environmental regulations are pressing electricity and petroleum refining industries to minimize their greenhouse gas emissions, mainly CO2. Fossil fuel power plants and refineries are now being challenged to comply with the Kyoto protocol by the United Nations Framework Convention and Climate Change (UNFCC). Canada’s target is a reduction in CO2 emissions of 6% from 1990 level. In this thesis, an optimization approach for integrating planning and CO2 reduction is developed for electricity and refinery sectors. Three different CO2 mitigation options are considered in each case. For the electricity sector, these mitigation options were 1) fuel balancing (optimal adjustment of the operation of existing generating stations to reduce CO2 emissions without making structural changes to the fleet), 2) fuel switching (switching from carbon intensive fuel to less carbon intensive fuel, essentially switching from coal to natural gas) and 3) implementing different technologies for efficiency improvement. The optimization model takes into account meeting electricity demand and achieving a certain CO2 reduction target at a minimum overall cost. The model was formulated as a Mixed Integer Non Linear Program (MINLP) and was implemented in GAMS (General Algebraic Modeling System). Exact linearization techniques were employed to facilitate solution development. The computer program was capable of determining the best strategy or mix of strategies to meet a certain CO2 reduction target at minimum cost. The model was illustrated on a case study for Ontario Power Generation (OPG) fleet. The results showed that for 1% CO2 reduction target, only fuel balancing need to be applied and even a decrease of about 1.3% in overall cost was obtained. The optimizer chose to increase production from all non fossil fuel power plants and to decrease production from natural gas power plant. This is because natural gas is the most expensive fuel that OPG uses. For higher reduction targets, it was necessary to implement fuel switching. For 30% reduction, for example, 11 boilers out of 27 (4 are already natural gas) are switched from coal to natural gas and the cost increases by about 13%. Applying efficiency improvement technologies such as installing new turbine blades was a good option only at small reduction targets. As the reduction target increases, the optimizer chose not to implement efficiency improvement technologies and only fuel switching was the best option to select in addition to fuel balancing. For the refinery sector, a similar strategy was applied. An optimization model was developed to maximize profit from selling final products and to meet a given CO2 reduction target with products demand and specifications. Three CO2 mitigation options were considered and these were: 1) balancing that implies the increase in production from units that emit less CO2 emissions provided that demand is met, 2) fuel switching that involves switching from current carbon intensive fuel to less carbon intensive fuel such as natural gas, 3) implementation of CO2 capture technologies. Chemical absorption (MEA) process was used as the capture process. Prior to the development of the refinery planning model, a sub-model was developed for each unit in a refinery layout. Then, the sub-models were integrated into a master planning model to meet final products demand and specifications with the objective of maximizing profit without CO2 mitigation options. The model was solved first as a Non Linear Program (NLP). Then, binary variables representing the existence or no existence of fuel switching option and CO2 capture processes were introduced into the model. The model was formulated as a Mixed Integer Non Linear Program (MINLP), coded in GAMS, and applied to different case studies. The results showed that the refinery planning model tends to produce more from the most profitable product, which is gasoline, and chose to blend products into the most profitable pool unless the demand needs to be satisfied for certain other products. The model, for example, chose to send kerosene from the diesel hydrotreater to the kerosene pool and not to the diesel pool since kerosene has higher selling value than diesel. When CO2 mitigation options were introduced into the model, only 0.4% CO2 reduction was achieved by simply decreasing production from the hydrocracker (HC) unit and increasing production from the fluidized catalytic cracking (FCC) unit. This was done because the FCC unit tends to emit less CO2 compared to the HC unit. At higher reduction target such as 1%, fuel switching was implemented by choosing the FCC to run with natural gas. The profit decreased slightly because of the retrofit cost of switching. It was noticed also that fuel switching can achieve a maximum of 30% reduction in CO2 emissions. This was achieved by switching all units to run with natural gas that emits less CO2 emissions. For a reduction target higher than 30%, CO2 capture technologies need to be applied. For 60% reduction, the optimization chose to switch three units (out of 8) and to capture CO2 emissions coming from four units. Only the FCC remained unchanged. A decrease in the profit was noticed as the reduction target increases since more units need to be switched and more CO2 need to be captured. The results showed that adding sequestration cost further decreased the profit. However, it was noticed that the selling price of final products had the most effect on the profit. An increase of 20%, for example, in final products’ prices, leads to a 10% increase in profit even when the CO2 reduction target was as high as 80%. When the retrofit cost for switching and capture was decreased by 30%, the effect on the profit was noticed only at higher reduction targets since more units were switched and more CO2 capture units were implemented

In most cases, data from petroleum industry wetland studies indicate that treatment wetlands are equally or more effective at removing pollutants from petroleum industry wastewaters than from other types of wastewater. In this study, we discussed the treatment efficiencies of oil-refinery industry wastewater by pilot-scale constructed wetland systems .The constructed wetland systems were one free water surface system filled with the sandy media and one subsurface flow system filled with the gravel media operated in parallel. Each system planted with Phragmites communis. The hydraulic retention time for the treatment wetland was controlled in turn at 0.96, 0.48, and 0.72 days. The experimental results showed that all of these contaminants could be reliably removed from wastewater by treatment wetland, especially the FWS. The effluents from the constructed wetland systems reusing and recovering were feasible.

Petroleum leakage from storage tanks, underground pipelines during exploration and production facilities is the reason of hydrocarbon migration into the groundwater. Petroleum companies use various LNAPL (Light Non-Aqueous Phase Liquids) recovery techniques to prevent lateral migration of hydrocarbon through the offsite of a facility. A petroleum refinery facility in the Gulf Coast region of South Texas was selected to evaluate ongoing LNAPL recovery system. Three analyses were carried out in this study. First, hydrogeologic conditions were determined using DGP (Diagnostic Gauge Plots). The concept of why ANT (Apparent LNAPL Thickness) is not a good metric to estimate recovery rates was explained based on hydrogeologic conditions of LNAPL. LNAPL and groundwater surface contour maps were built to have information about the direction of flow. All map illustrations were created using ArcGIS software. Well configurations were used to determine hydrogeologic condition in case of lack sufficient data for DGP. Second, LNAPL transmissivity were estimated using API (American Petroleum Institute) LNAPL Transmissivity Workbook. LNAPL condition was required in estimating LNAPL transmissivity values with API workbook, where methods of analysis are dependent of LNAPL condition. Total fluids recovery data were also used to estimate transmissivity values in the study wells. 0.08 ft2/d transmissivity value was arbitrarily chosen to determine the endpoint of recovery. Third, LNAPL recovery rates were predicted using LDRM (LNAPL Distribution and Recovery Model) for 11 recovery wells in the study region. Single phase –water- extraction method was used for LNAPL recovery under atmospheric conditions. Soil and fluid properties along with recovery system data were required for LNAPL recovery estimation. Some of these data were available from the dataset provided by oil company and some of them were estimated using API and Rosetta databases. Soil properties, radius of recovery values, and water production rates were calibrated in order to fit the LDRM recovery and transmissivity results with the actual field data. The modeled recovery rates and transmissivity values were consistent with actual data. Projections for future in a well were made. The model can be used for the endpoint of recovery projections. / text

As refinarias de petróleo utilizam grandes quantidades de água em seus processos e por isso são impelidas a desenvolver fortes programas de redução de seu consumo. O objetivo do presente estudo é a aplicação de metodologia de otimização em problemas de alocação ótima e reuso de água em refinaria de petróleo. Diferentemente dos enfoques tradicionais que buscam tratar os efluentes gerados para atender às legislações ambientais ou para algum tipo de reuso na planta, a metodologia utilizada avalia os processos que usam água, questionando as causas da geração do efluente. Essa abordagem envolve o levantamento de dados industriais, análise dos processos de produção, identificação de oportunidades para aplicar as estratégias de otimização da alocação de água: racionalização, reuso e/ou reciclagem do efluente e, em seguida, a otimização da rede de água. Visando obter uma rede de água que seja aplicável na indústria, desenvolveu-se um procedimento específico baseado em regras heurísticas que representam as restrições reais dos processos. Através deste procedimento, o consumo de água fresca atingido é próximo do valor ótimo encontrado na literatura. As redes de água geradas são simples, com poucas interconexões e, consequentemente, os investimentos necessários para implementação são menores. / Oil refineries consume a large amount of water in their processes and because of that they are urged to develop strong programs to reduce their water consumption. The aim of this study is to apply an water optimization methodology in an oil refinery. Unlike traditional approaches that only search for treating the effluents in order to meet environmental legislation or to reuse in the plant, this methodology evaluates the processes that use water, questioning the causes of the wastewater generation. This approach involves the industrial data collection, analysis of production processes, identification of opportunities to optimize water use: rationalization, reuse and/or recycling of the effluent and, lastly, the water network optimization. Looking for a water network that is applicable in the industry, in this study it was also developed a specific optimization procedure based on heuristic rules representing the constraints of the actual processes. Through this procedure, freshwater consumption was found to be close to that of the theories found in the literature. The water networks generated are simple, with few interconnections and, consequently, the investments required to implement them are small.

The biodesulfurization 4S-pathway is a promising complementary enzymatic approach to remove sulfur from recalcitrant thiophenic derivatives in petroleum products that remain from conventional hydrodesulfurization method without diminishing the calorific value of oil. The final step of this pathway involves the carbon-sulfur bond cleavage from HBPS, and the production of the final products 2-hydroxybiphenyl (HBP) and sulfite, has been recognized as the rate-limiting step, partially as a result of product inhibition. However, the mechanisms and factors responsible for product inhibition in the last step have not been fully understood. In this work, we proposed a computational investigation using molecular dynamic simulations and free energy calculations on 2’-hydroxybiphenyl-2-sulfinate (HBPS) desulfinase (DszB) with different bound ligands as well as different solvent conditions to develop a fundamental understanding of the molecular-level mechanism responsible for product inhibition. Based on available crystal structures of DszB and biochemical characterization, we proposed a “gate” area close to substrate binding site of DszB is responsible for ligand egress and plays a role in product inhibition. We have conducted biphasic molecular dynamic simulations to evaluate the proposed gate area functionality. Non-bonded interaction energy analysis shows that hydrophobic residues around the gate area produce van der Waals interactions inhibiting translocation through the gate channel, and therefore, the molecules are easily trapped inside the binding site. Umbrella sampling molecular dynamics was performed to obtain the energy penalty associated with gate conformational change from open to close, which was 2.4 kcal/mol independent of solvent conditions as well as bound ligands. Free energy perturbation calculations were conducted for a group of six selected molecules bound to DszB. The selections were based on functional group representation and to calculate binding free energies that were directly comparable to experimental inhibition constants, KI. Our work provides a fundamental molecular-level analysis on product inhibition for the biodesulfurization 4S-pathway.

The objective of the work reported in this thesis was to design and implement an ecological effects environmental monitoring program which would: 1) Collect baseline biological information on sessile epibiotic fouling communities from an area adjacent to a petroleum refinery located on Corio Bay, Victoria, to allow comparison with results of future monitoring for the assessment of long term temporal water quality trends. 2) Detect and — if possible - estimate the magnitude of any influence on epibiotic fouling communities within the Corio Bay marine ecosystem attributable to operations at the Shell Petroleum Refinery. 3) Investigate the extent of thermal stratification and rate of dispersal of the petroleum refinery main cooling-water outfall plume (discharging up to 350,000 tonnes of warmed seawater per day), and its effect on epibiotic communities within the EPA-defined mixing zone. A major component of the work undertaken was the design and development of artificial-substrate biological sampling stations suitable for use under the conditions prevailing in Corio Bay, and the development of appropriate quantitative underwater photographic sampling techniques to fulfil the experimental criteria outlined above. Experimental and other constraints imposed on the design of the stations precluded the simple suspension of frames from jetties or pylons, a technique widely used in previous work of this type. Artificial substrate panels were deployed along three radial transects centred within and extending beyond the petroleum refinery main cooling-water mixing zone. Identical substrate panels were deployed at a number of control sites located throughout Corio Bay, each chosen for differences in their degree of exposure to such factors as water movement, depth, shipping traffic and/or comparable industrial activity. The rate of colonisation (space utilisation) and the development of epibiotic fouling communities on artificial substrate panels was monitored over two twelve-month sampling periods using quantitative underwater photographic sampling techniques. Sampling was conducted at 4-8 week intervals with the rate of panel colonisation and community structure determined via coverage measurements. Various species of marine algae, polychaete tubeworms, hydroids, barnacles, simple and colonial ascidians, sponges, bivalve molluscs and encrusting bryozoans were all detected growing on panels. Communities which established on panels within the cooling-water mixing-zone and those at control sites were compared using statistical procedures including agglomerative hierarchical cluster analysis. A photographic sample archive has been established to allow comparison with similar future studies.

Adopting new technologies enable enterprises to improve employees¡¦ performance and competitive advantages. The differences in natures of adopting processes of organizations and individuals need further clarify for better understandings regarding to their framework in adopting new technologies. This paper based on the Theory of Reasoned Action (TRA) and existed research to construct the relations amongst the effective factors which affect the adopting new technologies in either individual or organization perspectives. In the individual level, the constructed research framework indicated employees¡¦ e-Learning satisfaction could be measured by three major dimensions, the perceived e-Learning qualities, individual internal beliefs (usefulness and ease of use), and social influence. Eight proposed hypothesis were confirmed by Structured Equation Modeling analysis of 428 valid samples. Path analyses verified the original path in TRA, TAM, and D&M ISS Model. The perceived e-Learning qualities and social influence cause significantly influence to employees¡¦ e-Learning satisfaction in both directly and indirectly, which by way of individual internal beliefs, positive paths. The results also showed that perceived information quality, usefulness, system quality, social influence, ease of use, and than service quality positively affect employees¡¦ satisfaction of e-Learning in descend sequences. Where, in the organization level, decision framework of adopting new technology of oil refinery was composed by modified Delphi method and was verified by Analytic Network Process from the survey of 15 experts. The consistency opinions confirmed four inter-depended dimensions and seventeen criteria were included. The results suggested that process fitness, environmental fitness, actors¡¦ organizational fitness, and new technology characteristics are important dimensions of adopting new technology in descend sequences. On the other hand, economic feasibility, relative advantages, government, environment acceptance, and engineering feasibility are the top five important factors to be evaluated during the adopting process. The different natures of adopting processes of organizations and individuals cause their different framework in adopting new technologies. This paper concluded that new technology, actors¡¦, environmental characteristics are three interdepended dimensions which influence the adopting behavior no matter in individual or organization context. In organization level of oil refinery case, actors¡¦ characteristics consist not only of actors¡¦ organizational fitness but also process fitness, which is the most important dimension while adopting new technology. In final, the implications of findings were discussed and directions were also suggested for future research.