Optimization of a refinery scheduling process with column generation and a quantum annealer

This study focuses on the optimization of a refinery scheduling process with the help of an adiabatic quantum computer, and more concretely one of the quantum annealers developed by D-Wave Systems. We present an algorithm for finding a global optimal solution of a MILP that leans on a solver for QUBO problems, and apply it to various possible cases of refinery scheduling optimization. We analyze the inconveniences found during the whole process, whether due to the heuristic nature of D-Wave or the implications of reducing a MILP to QUBO, and present some experimental resultsS

Peningkatan Peluang Bisnis Strategis pada Proyek Supply Avtur ke Bandara Soekarno Hatta dariRefineryUnit VIBalongan

Increasedbusiness opportunitiesfrom an economic aspect that includes the financial performance of the RefineryUnit VI is very important to manage. Good financial performance will support the company's business continuity, and also have a positive impact on stakeholders. RefineryUnit VI's commitment to improving financial performance is very high, and strives to achieve predetermined key performance indicators (KPI). RefineryUnit VI measures the achievement of KPI regularly every quarter as part of the evaluation.Targets related to current economic performance are the achievement of KPIs on RefineryGross Margin and Net Margin. The person in charge for recording and reporting financial performance is under the Financial Function. RefineryUnit VI uses Internal Control over Financial Reporting (ICoFR) to control financial reports, where each function that has the authorization uploads it on the ICoFR web system. In 2017, RefineryUnit VI succeeded in 100% compliance with the IcoFR.The realization of the RefineryUnit VI financial performance from 2015 was always above the target despite fluctuations in product prices. This is a good thing, supported by the achievement of the increasing Gross RefineryMargin and Net Margin from 2015–2017. RefineryUnit VI has continuously succeeded in improving financial performance and achieving the highest Gross RefineryMargin among all RefineryUnits owned by PT Pertamina (Persero).The installation of SPM and subsea pipelines is the target of increasing the RefineryUnit VI development program. Keywords: Strategic Business Opportunities, SPM and Subse Pipeline.RefineryUnit V

Advanced and novel modeling techniques for simulation, optimization and monitoring chemical engineering tasks with refinery and petrochemical unit applications

Engineers predict, optimize, and monitor processes to improve safety and profitability. Models automate these tasks and determine precise solutions. This research studies and applies advanced and novel modeling techniques to automate and aid engineering decision-making. Advancements in computational ability have improved modeling software’s ability to mimic industrial problems. Simulations are increasingly used to explore new operating regimes and design new processes. In this work, we present a methodology for creating structured mathematical models, useful tips to simplify models, and a novel repair method to improve convergence by populating quality initial conditions for the simulation’s solver. A crude oil refinery application is presented including simulation, simplification tips, and the repair strategy implementation. A crude oil scheduling problem is also presented which can be integrated with production unit models. Recently, stochastic global optimization (SGO) has shown to have success of finding global optima to complex nonlinear processes. When performing SGO on simulations, model convergence can become an issue. The computational load can be decreased by 1) simplifying the model and 2) finding a synergy between the model solver repair strategy and optimization routine by using the initial conditions formulated as points to perturb the neighborhood being searched. Here, a simplifying technique to merging the crude oil scheduling problem and the vertically integrated online refinery production optimization is demonstrated. To optimize the refinery production a stochastic global optimization technique is employed. Process monitoring has been vastly enhanced through a data-driven modeling technique Principle Component Analysis. As opposed to first-principle models, which make assumptions about the structure of the model describing the process, data-driven techniques make no assumptions about the underlying relationships. Data-driven techniques search for a projection that displays data into a space easier to analyze. Feature extraction techniques, commonly dimensionality reduction techniques, have been explored fervidly to better capture nonlinear relationships. These techniques can extend data-driven modeling’s process-monitoring use to nonlinear processes. Here, we employ a novel nonlinear process-monitoring scheme, which utilizes Self-Organizing Maps. The novel techniques and implementation methodology are applied and implemented to a publically studied Tennessee Eastman Process and an industrial polymerization unit

Modeling the Crude Oil Scheduling Problem with Integration with Lower Level Production Optimization and Uncertainty

This research is focused on the modeling and optimization of the crude oil scheduling problem in order to generate the most appropriate schedule for the unloading, charging, blending, and movement of crude oil in a refinery, which means obtaining the schedule that generates the lowest costs. Uncertainty, which is often present in these types of optimization problems, is also analyzed and taken into account for the resolution of crude oil scheduling problem. A comprehensive novel model is proposed to describe the upper level crude oil scheduling problem, generate an optimal solution for the mentioned problem, and allow integration with the lower level production optimization problem of a refinery. This integration is possible due to the consideration of total flows of the different types of crude oil instead of flows of a particular key component in the crude oil to linearize the upper level problem and generate a less complex model. The proposed approach incorporates all the logistical costs including the sea waiting, unloading and inventory costs together with the costs associated with the transfer of crude oil from one to another entity. Moreover, this model also offers the possibility of considering multiple tank types including storage and blending tanks throughout the supply chain and the incorporation of the capability of storing more than one crude oil type in the storage tanks during the schedule horizon. A comparative analysis is performed against other models proposed and preliminary results of integration with a lower operational level are provided. In order to take into account the possibility of uncertainty or fuzziness in the scheduling problem, for the first time an approach is proposed to face the resolution of this problem in order to obtain a more realistic scheduling of the allocations of crude oil. Fuzzy linear programming theory is used here to represent this uncertainty in order to find an optimal solution that takes into account the lack of precise information on the part of the decision maker without losing the linearity of the original system. Uncertainty in the minimum demand to be satisfied in the distillation unit according to the necessities of the market and the lack of precise information about certain costs involved in the operations throughout the supply chain are separately considered. Among the different approaches utilized in fuzzy linear programming, the flexible programming or Zimmermann method and its extension to fuzziness in objective functions are implemented. A comparison between the two cases studied and the crisp model is performed with the aim of determining the effect of these uncertainties in the schedule of the crude oils movements between the different entities in the supply chain and the total cost generated

Otimização do scheduling de nafta petroquímica utilizando algoritmos genéticos

Atualmente, as indústrias petroquímicas enfrentam um aumento nos preços da nafta, matéria-prima para a primeira geração, o que torna necessária a busca por novos fornecedores e, muitas vezes, a compra de lotes que apresentam preços mais baixos em função da presença de contaminantes. O gerenciamento otimizado dos lotes recebidos através de operações de blending viabiliza o recebimento dos lotes que apresentam contaminantes e o enquadramento destes nos limites de processamento das unidades. Desta forma, técnicas de otimização aplicadas ao blending e ao scheduling dos recebimentos podem fornecer ferramentas que ajudem a flexibilizar a compra de matérias-primas, diminuindo os gastos com este insumo e aumentando o lucro da empresa. O objetivo principal deste estudo é a solução do problema de recebimento de matéria-prima de uma indústria petroquímica de primeira geração via otimização matemática, visando auxiliar no processo de tomada de decisões. Como resultado, tem-se a definição das quantidades das matérias-primas disponíveis que irão compor a mistura final que será entregue para processamento nas unidades. O modelo leva em consideração os estoques de nafta disponíveis e a suas respectivas composições no instante inicial da otimização, a disponibilidade de navios para descarregamento, as demandas de consumo das unidades, as restrições operacionais de bombeamento e armazenagem e as restrições de qualidade. Estas últimas englobam os limites de processamento de contaminantes e o percentual mínimo de parafinicidade, principal parâmetro de rendimento da nafta, que serve como parâmetro para definir a mistura ideal dos componentes de modo a maximizar o seu rendimento em produtos finais desejados para cada cenário de produção. O modelo de otimização foi desenvolvido baseado em programação mista inteira não-linear (MINLP), com representação discreta do tempo. As variáveis de decisão envolvem a alocação de descarga de navios em tanques de armazenagem, bem como operações de transferência entre tanques de diferentes parques de tancagem através de oleodutos. Sendo assim, para fins de modelagem, as variáveis de decisão do problema foram descritas como o status de abertura e fechamento das válvulas de entrada e saída de cada tanque do sistema, as quais totalizam 34 válvulas, para cada um dos instantes da simulação, os quais totalizam 56, obtendo-se assim um total de 1.904 variáveis de decisão. Foram consideradas restrições operacionais relacionadas a volumes de produto nos tanques, status de abertura e fechamento das válvulas dos tanques e trocas excessivas de tanques de recebimento/expedição, assim como restrições de qualidade relacionadas aos limites de processamento de contaminantes das unidades. Para a resolução do problema de otimização, foi empregado um algoritmo genético e adotado um horizonte de predição de tamanho igual a 56. O modelo proposto foi aplicado ao sistema de recebimento de matéria-prima de uma indústria petroquímica real e os resultados mostram o desempenho do modelo quando aplicado a cenários distintos, envolvendo diferentes graus de dificuldade. A partir dos resultados obtidos e do seu comparativo com uma programação realizada por um especialista ad hoc através da Tabela 2, evidenciou-se que o algoritmo foi capaz de resolver cada um dos cenários avaliados, sempre mostrando aderência à estratégia de blending adotada pela indústria

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 optimization with piecewise linear approximation

textGlobal optimization deals with the development of solution methodologies for nonlinear nonconvex optimization problems. These problems, which could arise in diverse situations ranging from optimizing hydro-power generation schedules to estimating coefficients of non-linear regression models, are difficult for traditional nonlinear solvers that iteratively search the neighborhood around a starting point. The Piecewise Linear Approximation (PLA) method that we study in this dissertation seeks to generate ‘good’ starting points, hopefully ones that lie in the basin of attraction of the globally optimal solution. In this approach, we approximate the non-linear functions in the optimization problem by piecewise linear functions defined over the vertices of a grid that partitions the domain of each nonlinear function into cells. Based on this approximation, we convert the original nonlinear program into a mixed integer program (MIP) and use the solution to this MIP as a starting point for a local nonlinear solver. In this dissertation, we validate the effectiveness of the PLA approach as a global optimization approach by applying it to a diverse set of continuous and discrete nonlinear optimization problems. Further, we develop various modeling and algorithmic strategies for enhancing the basic approach. Our computational results demonstrate that the PLA approach works well on non-convex problems and can, in some cases, provide better solutions than those provided by existing nonlinear solvers.Information, Risk, and Operations Management (IROM