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 global energy and industrial market, petroleum refining industry accounts for a major share. Through proper planning and the use of adequate mathematical models for the different processing units, many profit improving opportunities can be realized. The increasing crude oil price has also made refining of crude oil blends to be a common practice. This thesis aims to provide useful insight for planning of the integrated refinery subsystems. The main subsystems referred to are (1) The crude oil unloading subsystem (2) The production and product blending subsystem and (3) The product distribution subsystem. Aspen HYSYS® was first used to develop a rigorous model for crude distillation unit (CDU) and vacuum distillation unit (VDU). The rigorous model was validated with pilot plant data from literature. The information obtained from the rigorous model is further used to develop a model for planning of the CDU and VDU. This was combined with models (obtained from empirical correlations) for fluid catalytic cracker (FCC) and hydrotreater (HDT) units to form a mathematical programming planning model used for refinery production and product blending subsystem planning. Since two different types of crude were considered, the optimum volumetric mixing ratio, the sulphur content at that mixing ratio and the CDU flow rate were determined. The yields fraction obtained from the rigorous model were then used to generate regression model using least square method. The sulphur composition of the crude oil was used as independent variable in the regression model. The generated regression models were then used to replace the regular fixed yield approach in a refinery planning model and the results compared. From the results obtained, the proposed method provided an alternative and convenient means for estimating yields from CDU and VDU than the regular fixed yield approach. The proposed aggregate model for the production and products blending subsystem was integrated with the modified scheduling model for the crude unloading subsystem developed by Lee et al. (1996) and products distribution model developed by Alabi and Castro (2009) for refinery planning. It was found that the regression model could be integrated in a refinery planning model and that the CDU flow rate was maximised as compared to the non- integrated system.

In global energy and industrial market, petroleum refining industry accounts for a major share. Through proper planning and the use of adequate mathematical models for the different processing units, many profit improving opportunities can be realized. The increasing crude oil price has also made refining of crude oil blends to be a common practice. This thesis aims to provide useful insight for planning of the integrated refinery subsystems. The main subsystems referred to are (1) The crude oil unloading subsystem (2) The production and product blending subsystem and (3) The product distribution subsystem. Aspen HYSYS® was first used to develop a rigorous model for crude distillation unit (CDU) and vacuum distillation unit (VDU). The rigorous model was validated with pilot plant data from literature. The information obtained from the rigorous model is further used to develop a model for planning of the CDU and VDU. This was combined with models (obtained from empirical correlations) for fluid catalytic cracker (FCC) and hydrotreater (HDT) units to form a mathematical programming planning model used for refinery production and product blending subsystem planning. Since two different types of crude were considered, the optimum volumetric mixing ratio, the sulphur content at that mixing ratio and the CDU flow rate were determined. The yields fraction obtained from the rigorous model were then used to generate regression model using least square method. The sulphur composition of the crude oil was used as independent variable in the regression model. The generated regression models were then used to replace the regular fixed yield approach in a refinery planning model and the results compared. From the results obtained, the proposed method provided an alternative and convenient means for estimating yields from CDU and VDU than the regular fixed yield approach. The proposed aggregate model for the production and products blending subsystem was integrated with the modified scheduling model for the crude unloading subsystem developed by Lee et al. (1996) and products distribution model developed by Alabi and Castro (2009) for refinery planning. It was found that the regression model could be integrated in a refinery planning model and that the CDU flow rate was maximised as compared to the non- integrated system.

The petroleum refining industry is considered to be one of the most important industries affecting daily life. However, this industry is facing many new and challenging situations, including such new trends as increased heavy crude markets, a shrinking market for fuel oils, clean-fuel legislation that encourages production of ultra low-sulfur (ULS) gasoline and diesel fuels, and strict green house gas (GHG) regulations to reduce CO2 emissions into the atmosphere. Refineries thus face a serious need to increase the capacity of their conversion units, such as the hydrocracker and fluid catalytic cracking units (FCCs), and to increase their consumption of hydrogen to meet the new requirements. These increases should be planned with reference to allowable CO2 emission limits. Refineries therefore need an appropriate tool for planning their operations and production. This research focuses on refinery planning under hydrogen and carbon management considerations. A systematic method that uses mathematical programming techniques to integrate the management of hydrogen and CO2 for refinery planning is proposed. Three different models for refinery planning, hydrogen management, and CO2 management, are prepared and then properly integrated. Firstly, a Nonlinear Programming (NLP) model that provides a more accurate representation of the refinery processes and which is able to optimize the operating variables such as the Crude Distillation Unit (CDU) cut-point temperatures and the conversion of the FCC unit is developed. The model is able to evaluate properties of the final products to meet market specifications as well as required product demands, thereby achieving maximum refinery profit. A systematic methodology for modeling the integration of hydrogen management and refinery planning was considered next. This resulted in a Mixed Integer Nonlinear Programming (MINLP) model that consists of two main building blocks: a set of nonlinear processing unit models and a hydrogen balance framework. The two blocks are integrated to produce a refinery-wide planning model with hydrogen management. The hydrogen alternatives considered in this research are hydrogen balancing, compressors, and purification processes. The model was illustrated on representative case studies and lead to an improvement in the hidden hydrogen unavailability that prevents refineries from achieving their maximum production and profit. It was found that an additional annual profit equivalent to $7 million could be achieved with a $13 million investment in a new purification unit. The consideration of CO2 management and the integration with refinery planning and the hydrogen network required the formulation of a CO2 management model. This model focused on the refinery emission sources and the mitigation options. The refinery emissions sources are the fuel system, hydrogen plant, and FCC unit, and the mitigation options considered are load shifting, fuel switching, and capturing technology. The model performance was tested on different case studies with various reduction targets. The optimization results showed that CO2 mitigation options worked successfully together to meet a given reduction target. The results show that load shifting can contribute up to a 3% reduction of CO2 emissions, and fuel switching can provide up to 20% reduction. To achieve greater than 30% reductions, a refinery must employ capturing technology solutions. The proposed model provides an efficient tool for assisting production planning in refineries and at the same time determines the optimum hydrogen and CO2 emissions strategies.

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

The petroleum refining industry is considered to be one of the most important industries affecting daily life. However, this industry is facing many new and challenging situations, including such new trends as increased heavy crude markets, a shrinking market for fuel oils, clean-fuel legislation that encourages production of ultra low-sulfur (ULS) gasoline and diesel fuels, and strict green house gas (GHG) regulations to reduce CO2 emissions into the atmosphere. Refineries thus face a serious need to increase the capacity of their conversion units, such as the hydrocracker and fluid catalytic cracking units (FCCs), and to increase their consumption of hydrogen to meet the new requirements. These increases should be planned with reference to allowable CO2 emission limits. Refineries therefore need an appropriate tool for planning their operations and production. This research focuses on refinery planning under hydrogen and carbon management considerations. A systematic method that uses mathematical programming techniques to integrate the management of hydrogen and CO2 for refinery planning is proposed. Three different models for refinery planning, hydrogen management, and CO2 management, are prepared and then properly integrated. Firstly, a Nonlinear Programming (NLP) model that provides a more accurate representation of the refinery processes and which is able to optimize the operating variables such as the Crude Distillation Unit (CDU) cut-point temperatures and the conversion of the FCC unit is developed. The model is able to evaluate properties of the final products to meet market specifications as well as required product demands, thereby achieving maximum refinery profit. A systematic methodology for modeling the integration of hydrogen management and refinery planning was considered next. This resulted in a Mixed Integer Nonlinear Programming (MINLP) model that consists of two main building blocks: a set of nonlinear processing unit models and a hydrogen balance framework. The two blocks are integrated to produce a refinery-wide planning model with hydrogen management. The hydrogen alternatives considered in this research are hydrogen balancing, compressors, and purification processes. The model was illustrated on representative case studies and lead to an improvement in the hidden hydrogen unavailability that prevents refineries from achieving their maximum production and profit. It was found that an additional annual profit equivalent to $7 million could be achieved with a $13 million investment in a new purification unit. The consideration of CO2 management and the integration with refinery planning and the hydrogen network required the formulation of a CO2 management model. This model focused on the refinery emission sources and the mitigation options. The refinery emissions sources are the fuel system, hydrogen plant, and FCC unit, and the mitigation options considered are load shifting, fuel switching, and capturing technology. The model performance was tested on different case studies with various reduction targets. The optimization results showed that CO2 mitigation options worked successfully together to meet a given reduction target. The results show that load shifting can contribute up to a 3% reduction of CO2 emissions, and fuel switching can provide up to 20% reduction. To achieve greater than 30% reductions, a refinery must employ capturing technology solutions. The proposed model provides an efficient tool for assisting production planning in refineries and at the same time determines the optimum hydrogen and CO2 emissions strategies.

The pooling problem is an important optimization problem that is encountered in operation and scheduling of important industrial processes within petroleum refineries. The key objective of pooling is to mix various intermediate products to achieve desired properties and quantities of products. First, intermediate streams from various processing units are mixed and stored in intermediate tanks referred to as pools. The stored streams in pools are subsequently allowed to mix to meet varying market demands. While these pools enhance the operational flexibility of the process, they complicate the decisionmaking process needed for optimization. The problem to find the least costly mixing recipe from intermediate streams to pools and then from pools to sale products is referred to as the pooling problem. The research objective is to contribute an approach to solve this problem. The pooling problem can be formulated as an optimization program whose objective is to minimize cost or maximize profit while determining the optimal allocation of intermediate streams to pools and the blending of pools to final products. Because of the presence of bilinear terms, the resulting formulation is nonconvex which makes it very difficult to attain the global solution. Consequently, there is a need to develop computationally-efficient and easy-to-implement global-optimization techniques to solve the pooling problem. In this work, a new approach is introduced for the global optimization of pooling problems. The approach is based on three concepts: linearization by discretizing nonlinear variables, pre-processing using implicit enumeration of the discretization to form a convex-hull which limits the size of the search space, and application of integer cuts to ensure compatibility between the original problem and the discretized formulation. The continuous quality variables contributing to bilinear terms are first discretized. The discretized problem is a mixed integer linear program (MILP) and can be globally solved in a computationally effective manner using branch and bound method. The merits of the proposed approach are illustrated by solving test case studies from literature and comparison with published results.

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.

Abstract The research of this project is to discuss the governmental enterprise¡¦s employee reaction when they are face the extremely change of the governmental company to become the privation. In this privatization process, the company have used the enterprise¡¦s re-engineering and change the human resource planning to fit the privatization. The object of this research is the Refinery Business Unit (RBU) of Chinese petroleum corp., the biggest governmental enterprise belongs to the Ministry of the Economic Affairs. In order to strengthen their competition, the Chinese Petroleum Corp. established the Refinery Business Unit at the end of the year 2000. They try to simplify and consolidate the organization and reorganize functional organization to business unit. The main purpose is hope that will be change the organizational culture, reduce the working process and revolute the employees mental. It also wish that re-engineering process which by through simplified the administrative levels and rationalized the human resources planning would produce enterprise¡¦s operation efficiently. The researcher is working in the Chinese Petroleum Corp. and have experienced in the establishment of RBU. After fully understanding the response of various levels of the employees to the human resource planning in this re-engineering process, he make the conclusion of the research is as following: 1. There is no significant difference among employees with different attributes against ¡§Manpower Transfer Planning¡¨. 2. There is no significant difference among employees with different attributes against ¡§Strengthening of Manpower Employment¡¨. 3. There is significant positive correlation between Manpower Transfer Planning and Strengthening of Manpower Employment as expressed by employees of different attributes. 4. Empirical outcomes of employees of different attributes toward the impact of Manpower Transfer Planning and Strengthening of Manpower Employment are: a. The manpower transfer and core manpower have significant positive impact. b. There is significant positive impact of human resources supply and demand adjustment and core manpower. c. There is significant positive impact of Manpower Supply and Demand Adjustment and manpower subject to transfer. d. There is significant positive impact of Manpower Supply and Demand Adjustment and Manpower to be developed.

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