Optimization of crude oil operations scheduling by applying a two-stage stochastic programming approach with risk management

Producción CientíficaThis paper focuses on the problem of crude oil operations scheduling carried out in a system composed of a refinery and a marine terminal, considering uncertainty in the arrival date of the ships that supply the crudes. To tackle this problem, we develop a two-stage stochastic mixed-integer nonlinear programming (MINLP) model based on continuous-time representation. Furthermore, we extend the proposed model to include risk management by considering the Conditional Value-at-Risk (CVaR) measure as the objective function, and we analyze the solutions obtained for different risk levels. Finally, to evaluate the solution obtained, we calculate the Expected Value of Perfect Information (EVPI) and the Value of the Stochastic Solution (VSS) to assess whether two-stage stochastic programming model offers any advantage over simpler deterministic approaches.Gobierno de España - proyects a-CIDiT (PID2021-123654OB-C31) and InCo4In (PGC 2018-099312-B-C31)Junta de Castilla y León - EU-FEDER (CLU 2017-09, CL-EI-2021-07, UIC 233

An overview of different approaches in hydrogen network optimization via mathematical programming

Goal: Hydrogen has shown increasing demand in oil refineries, due to the importance of its use as a sulfur capture element. As different oils and products require different amounts of hydrogen, their use optimally is an essential tool for refinery production scheduling. A comparison was made between the different approaches used in optimization via mathematical programming.Design / Methodology / Approach: One of the most used methods for hydrogen network optimization is through mathematical programming. Linear and non-linear models are discussed, positive aspects of each formulation and different initialization techniques for non-linear modeling were considered.Results: The optimization through the linear model was more satisfactory, taking into account the payback of the new proposed design, combined with the use of compressor rearrangement, which reduces the investment cost.Limitations of the investigation: The objective function chosen is based on the operational cost, but another approach to be considered would be the total annual cost. In addition, the parameters related to costs are obtained from the literature and may change over the years.Practical implications: The proposal is to discuss the main aspects of each model, showing which models more robust and easier to converge are capable of providing competitive results. Also, different initialization techniques that can be used in future works.Originality / Value: The main contribution is the relationship between hydrogen management and production scheduling and for that, a discussion is made about possible formulations. Linear model is sufficient to optimize the problem, due to its main characteristics discussed

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

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

Optimization of refinery preheat trains undergoing fouling: control, cleaning scheduling, retrofit and their integration

Crude refining is one of the most energy intensive industrial operations. The large amounts of crude processed, various sources of inefficiencies and tight profit margins promote improving energy recovery. The preheat train, a large heat exchanger network, partially recovers the energy of distillation products to heat the crude, but it suffers of the deposition of material over time – fouling – deteriorating its performance. This increases the operating cost, fuel consumption, carbon emissions and may reduce the production rate of the refinery. Fouling mitigation in the preheat train is essential for a profitable long term operation of the refinery. It aims to increase energy savings, and to reduce operating costs and carbon emissions. Current alternatives to mitigate fouling are based on heuristic approaches that oversimplify the representation of the phenomena and ignore many important interactions in the system, hence they fail to fully achieve the potential energy savings. On the other hand, predictive first principle models and mathematical programming offer a comprehensive way to mitigate fouling and optimize the performance of preheat trains overcoming previous limitations. In this thesis, a novel modelling and optimization framework for heat exchanger networks under fouling is proposed, and it is based on fundamental principles. The models developed were validated against plant data and other benchmark models, and they can predict with confidence the main effect of operating variables on the hydraulic and thermal performance of the exchangers and those of the network. The optimization of the preheat train, an MINLP problem, aims to minimize the operating cost by: 1) dynamic flow distribution control, 2) cleaning scheduling and 3) network retrofit. The framework developed allows considering these decisions individually or simultaneously, although it is demonstrated that an integrated approach exploits the synergies among decision levels and can reduce further the operating cost. An efficient formulation of the model disjunctions and time representation are developed for this optimization problem, as well as efficient solution strategies. To handle the combinatorial nature of the problem and the many binary decisions, a reformulation using complementarity constraints is proposed. Various realistic case studies are used to demonstrate the general applicability and benefits of the modelling and optimization framework. This is the first time that first principle predictive models are used to optimize various types of decisions simultaneously in industrial size heat exchanger networks. The optimization framework developed is taken further to an online application in a feedback loop. A multi-loop NMPC approach is designed to optimize the flow distribution and cleaning scheduling of preheat trains over two different time scales. Within this approach, dynamic parameter estimation problems are solved at frequent intervals to update the model parameters and cope with variability and uncertainty, while predictive first principle models are used to optimize the performance of the network over a future horizon. Applying this multi-loop optimization approach to a case study of a real refinery demonstrates the importance of considering process variability on deciding about optimal fouling mitigation approaches. Uncertainty and variability have been ignored in all previous model based fouling mitigation strategies, and this novel multi-loop NMPC approach offers a solution to it so that the economic savings are enhanced. In conclusion, the models and optimization algorithms developed in this thesis have the potential to reduce the operating cost and carbon emission of refining operations by mitigating fouling. They are based on accurate models and deterministic optimization that overcome the limitations of previous applications such as poor predictability, ignoring variability and dynamics, ignoring interactions in the system, and using inappropriate tools for decision making.Open Acces

Síntese e reprojeto de redes de hidrogênio flexíveis e economicamente eficientes integradas ao planejamento de produção

O hidrogênio é utilizado nas refinarias de petróleo como insumo no hidrotratamento dos combustíveis. Através da reforma catalítica, o hidrogênio é produzido nas refinarias nas chamadas unidades de geração de hidrogênio (UGH), e juntamente com unidades de purificação e unidade de hidrotratamento (consideradas unidades consumidoras), se formam as redes de hidrogênio. Com o aumento das restrições no teor de enxofre nas frações de petróleo, como o diesel, o gerenciamento das redes de hidrogênio começou a ganhar destaque devido a sua importância econômica e ambiental. Ou seja, há interesse no uso de forma mais eficiente do hidrogênio. Através de programação matemática é possível realizar a modelagem e otimização da rede de hidrogênio, visando a sua produção ótima e uma melhor distribuição entre as unidades. Formulações MILP (Mixed Integer Linear Programming) e MINLP (Mixed Integer Nonlinear Programming) foram desenvolvidas em GAMS para representar a rede de hidrogênio. O modelo pode ser utilizado para o caso de retrofit ou de novos projetos, prevendo a instalação de novos compressores, unidades de purificação e linhas. Devido às limitações do modelo MILP, foi proposta uma técnica para diminuir a instalação de novos compressores, permitindo a mistura entre correntes, mas mantendo a linearidade do processo. Com o objetivo de facilitar a resolução do modelo não linear, foi proposta uma técnica de inicialização baseada no ótimo obtido através da formulação linear. Como uma extensão das formulações MILP e MINLP nominais e com o objetivo de incluir as incertezas do processo de refino de petróleo, que surgem principalmente devido aos diferentes petróleos e seus teores de enxofre, a otimização multicenário também é abordado neste trabalho. É importante que a rede de hidrogênio seja flexível, ou seja, seja capaz de atender as variações no consumo de hidrogênio nas unidades de hidrotratamento. O planejamento de produção é responsável por conectar os diferentes petróleos disponíveis com a demanda de produtos e assim, consegue-se estimar a quantidade de hidrogênio necessária num horizonte de tempo, normalmente mensal. Nesse sentido, este trabalho une o desenvolvimento de um planejamento de produção para uma refinaria, com o conceito de avaliação de flexibilidade da rede e otimização multicenário, a fim de obter o maior lucro possível, com uma rede mais flexível possível e capaz de atender os cenários estabelecidos, com o menor custo operacional, podendo incluir o redesign da rede. As otimizações foram validadas através de estudos de caso da literatura e de dados reais de uma refinaria brasileira. Como resultados, concluiu-se que a formulação não linear combinada com a inicialização proveniente da formulação MILP e a técnica de rearranjo de compressores é a mais adequada para redesign de redes de hidrogênio, fornecendo economias significativas de custo operacional. Além disso, através do planejamento de produção, foi possível avaliar economicamente a rede de hidrogênio, unindo o maior lucro possível, com o menor custo operacional da rede capaz de atender a demanda.Hydrogen is used in oil refineries as a raw material in fuel hydrotreatment. Through catalytic reform, hydrogen is produced in refineries in so-called hydrogen generation units (UGH), and together with purification units and hydrotreatment units (considered consuming units), hydrogen networks are formed. With the increase in restrictions on sulfur content in oil fractions, such as diesel, the management of hydrogen networks has begun to gain prominence due to its economic and environmental importance. That is, there is interest in the more efficient use of hydrogen. Through mathematical programming, it is possible to perform the modeling and optimization of the hydrogen network, aiming its optimal production and a better distribution between the units. MILP (Mixed Integer Linear Programming) and MINLP (Mixed Integer Nonlinear Programming) formulations were developed in GAMS to represent the hydrogen network. The model can be used for retrofit or new projects to install new compressors, purification units, and lines. Due to the limitations of the MILP model, a technique was proposed to reduce the installation of new compressors, allowing the mixing between flowrates but maintaining the linearity of the process. In order to facilitate the resolution of the nonlinear model, an initialization technique based on the optimal obtained through the linear formulation was proposed. Multiscenario optimization is also addressed as an extension of nominal MILP and MINLP formulations. It includes the uncertainties of the oil refining process, which arise mainly due to the different oils and their sulfur contents. The hydrogen network must be flexible; that is, it should comply with the variations in hydrogen consumption in hydrotreatment units. Production planning is responsible for connecting the different available oils with the demand for products and thus can estimate the amount of hydrogen needed in a time horizon, usually monthly. In this sense, this work unites the development of production planning for a refinery, evaluating network flexibility and multi-scenario optimization. It is done to obtain the highest possible profit, with a flexible network to secure the established scenarios, with the lowest operational cost. It may also include the redesign of the network. The optimizations were validated through case studies of the literature and actual data of a Brazilian refinery. As a result, it was concluded that the nonlinear formulation combined with the initialization from the MILP formulation and the compressor rearrangement technique is the most appropriate for the redesign of hydrogen networks, providing significant savings in operating costs. In addition, through production planning, it was possible to economically evaluate the hydrogen network, uniting the highest possible profit, with the lowest operational cost of the network capable of achieving the demand