Synthesis of Optimal Enterprise Networks between Bio-refinery and Petroleum Refinery

The production of transportation fuels from the conversion of biomass into gasoline and diesel in a bio-refinery is an attractive, clean, carbon neutral and sustainable process. The economics of standalone bio-refinery can be improved via integration with an existing petroleum refinery, whereby bio-refinery intermediates can be upgraded using the existing petroleum refinery infrastructure. The current literature considered only limited materials integration between bio-refinery and petroleum refinery. In this work, an efficient mathematical modeling approach for synthesizing optimum enterprise networks between bio-refinery and petroleum refinery is proposed for the utilization of materials and hydrogen across the enterprise

Petroleum refinery scheduling with consideration for uncertainty

Scheduling refinery operation promises a big cut in logistics cost, maximizes efficiency, organizes allocation of material and resources, and ensures that production meets targets set by planning team. Obtaining accurate and reliable schedules for execution in refinery plants under different scenarios has been a serious challenge. This research was undertaken with the aim to develop robust methodologies and solution procedures to address refinery scheduling problems with uncertainties in process parameters. The research goal was achieved by first developing a methodology for short-term crude oil unloading and transfer, as an extension to a scheduling model reported by Lee et al. (1996). The extended model considers real life technical issues not captured in the original model and has shown to be more reliable through case studies. Uncertainties due to disruptive events and low inventory at the end of scheduling horizon were addressed. With the extended model, crude oil scheduling problem was formulated under receding horizon control framework to address demand uncertainty. This work proposed a strategy called fixed end horizon whose efficiency in terms of performance was investigated and found out to be better in comparison with an existing approach. In the main refinery production area, a novel scheduling model was developed. A large scale refinery problem was used as a case study to test the model with scheduling horizon discretized into a number of time periods of variable length. An equivalent formulation with equal interval lengths was also presented and compared with the variable length formulation. The results obtained clearly show the advantage of using variable timing. A methodology under self-optimizing control (SOC) framework was then developed to address uncertainty in problems involving mixed integer formulation. Through case study and scenarios, the approach has proven to be efficient in dealing with uncertainty in crude oil composition

Integration of Hydrogen and CO2 Management within Refinery Planning

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

Global optimisation of large-scale quadratic programs: application to short-term planning of industrial refinery-petrochemical complexes

This thesis is driven by an industrial problem arising in the short-term planning of an integrated refinery-petrochemical complex (IRPC) in Colombia. The IRPC of interest is composed of 60 industrial plants and a tank farm for crude mixing and fuel blending consisting of 30 additional units. It considers both domestic and imported crude oil supply, as well as refined product imports such as low sulphur diesel and alkylate. This gives rise to a large-scale mixed-integer quadratically constrained quadratic program (MIQCQP) comprising about 7,000 equality constraints with over 35,000 bilinear terms and 280 binary variables describing operating modes for the process units. Four realistic planning scenarios are recreated to study the performance of the algorithms developed through the thesis and compare them to commercial solvers. Local solvers such as SBB and DICOPT cannot reliably solve such large-scale MIQCQPs. Usually, it is challenging to even reach a feasible solution with these solvers, and a heuristic procedure is required to initialize the search. On the other hand, global solvers such as ANTIGONE and BARON determine a feasible solution for all the scenarios analysed, but they are unable to close the relaxation gap to less than 40% on average after 10h of CPU runtime. Overall, this industrial-size problem is thus intractable to global optimality in a monolithic way. The first main contribution of the thesis is a deterministic global optimisation algorithm based on cluster decomposition (CL) that divides the network into groups of process units according to their functionality. The algorithm runs through the sequences of clusters and proceeds by alternating between: (i) the (global) solution of a mixed-integer linear program (MILP), obtained by relaxing the bilinear terms based on their piecewise McCormick envelopes and a dynamic partition of their variable ranges, in order to determine an upper bound on the maximal profit; and (ii) the local solution of a quadratically-constrained quadratic program (QCQP), after fixing the binary variables and initializing the continuous variables to the relaxed MILP solution point, in order to determine a feasible solution (lower bound on the maximal profit). Applied to the base case scenario, the CL approach reaches a best solution of 2.964 MMUSD/day and a relaxation gap of 7.5%, a remarkable result for such challenging MIQCQP problem. The CL approach also vastly outperforms both ANTIGONE (2.634 MMUSD/day, 32% optimality gap) and BARON (2.687 MMUSD/day, 40% optimality gap). The second main contribution is a spatial Lagrangean decomposition, which entails decomposing the IRPC short-term planning problem into a collection of smaller subproblems that can be solved independently to determine an upper bound on the maximal profit. One advantage of this strategy is that each sub-problem can be solved to global optimality, potentially providing good initial points for the monolithic problem itself. It furthermore creates a virtual market for trading crude blends and intermediate refined–petrochemical streams and seeks an optimal trade-off in such a market, with the Lagrange multipliers acting as transfer prices. A decomposition over two to four is considered, which matches the crude management, refinery, petrochemical operations, and fuel blending sections of the IRPC. An optimality gap below 4% is achieved in all four scenarios considered, which is a significant improvement over the cluster decomposition algorithm.Open Acces

An Optimization Approach for Integrating Planning and CO2 Mitigation in the Power and Refinery Sectors

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 implemente

Assessment of Lagrangean decomposition for short-term planning of integrated refinery-petrochemical operations

We present an integrated methodology for optimal short-term planning of integrated refinery-petrochemical complexes (IRPCs) and demonstrate it on a full-scale industrial case study under four realistic planning scenarios. The large-scale mixed-integer quadratically constrained optimization models are amenable to a spatial Lagrangean decomposition through dividing the IRPC into multiple subsections, which comprise crude management, refinery, fuel blending, and petrochemical production. The decomposition algorithm creates virtual markets for trading crude blends and intermediate petrochemical streams within the IRPC and seeks an optimal tradeoff in such markets, with the Lagrange multipliers acting as transfer prices. The best results are obtained for decompositions with two or three subsections, achieving optimality gaps below 4% in all four planning scenarios. The Lagrangean decomposition provides tighter primal and dual bounds than the global solvers BARON and ANTIGONE, and it also improves the dual bounds computed using piecewise linear relaxation strategies

Scheduling of crude oil and product blending and distribution operations in a refinery

Ph.DDOCTOR OF PHILOSOPH

Petroleum Refining and Petrochemical Industry Integration and Coordination under Uncertainty

Petroleum refining and the petrochemical industry account for a major share in the world energy and industrial market. In many situations, they represent the economy back-bone of industrial countries. Today, the volatile environment of the market and the continuous change in customer requirements lead to constant pressure to seek opportunities that properly align and coordinate the different components of the industry. In particular, petroleum refining and petrochemical industry coordination and integration is gaining a great deal of interest. However, previous research in the field either studied the two systems in isolation or assumed limited interactions between them. The aim of this thesis is to provide a framework for the planning, integration and coordination of multisite refinery and petrochemical networks using proper deterministic, stochastic and robust optimization techniques. The contributions of this dissertation fall into three categories; namely, a) Multisite refinery planning, b) Petrochemical industry planning, and c) Integration and coordination of multisite refinery and petrochemical networks. The first part of this thesis tackles the integration and coordination of a multisite refinery network. We first address the design and analysis of multisite integration and coordination strategies within a network of petroleum refineries through a mixed-integer linear programming (MILP) technique. The integrated network design specifically addresses intermediate material transfer between processing units at each site. The proposed model is then extended to account for model uncertainty by means of two-stage stochastic programming. Parameter uncertainty was considered and included coefficients of the objective function and right-hand-side parameters in the inequality constraints. Robustness is analyzed based on both model robustness and solution robustness, where each measure is assigned a scaling factor to analyze the sensitivity of the refinery plan and the integration network due to variations. The proposed technique makes use of the sample average approximation (SAA) method with statistical bounding techniques to give an insight on the sample size required to give adequate approximation of the problem. The second part of the thesis addresses the strategic planning, design and optimization of a network of petrochemical processes. We first set up and give an overview of the deterministic version of the petrochemical industry planning model adopted in this thesis. Then we extend the model to address the strategic planning, design and optimization of a network of petrochemical processes under uncertainty and robust considerations. Similar to the previous part, robustness is analyzed based on both model robustness and solution robustness. Parameter uncertainty considered in this part includes process yield, raw material and product prices, and lower product market demand. The Expected Value of Perfect Information (EVPI) and Value of the Stochastic Solution (VSS) are also investigated to numerically illustrate the value of including the randomness of the different model parameters. The final part of this dissertation addresses the integration between the multisite refinery system and the petrochemical industry. We first develop a framework for the design and analysis of possible integration and coordination strategies of multisite refinery and petrochemical networks to satisfy given petroleum and chemical product demand. The main feature of the work is the development of a methodology for the simultaneous analysis of process network integration within a multisite refinery and petrochemical system. Then we extend the petroleum refinery and petrochemical industry integration problem to consider different sources of uncertainties in model parameters. Parameter uncertainty considered includes imported crude oil price, refinery product price, petrochemical product price, refinery market demand, and petrochemical lower level product demand. We apply the sample average approximation (SAA) method within an iterative scheme to generate the required scenarios and provide solution quality by measuring the optimality gap of the final solution