Refinery Optimization Research Articles

Two-stage chance-constrained programming based on Gaussian mixture model and piecewise linear decision rule for refinery optimization

The two-stage chance-constrained program (CCP) is studied for a refinery optimization problem. In stage-I, the refinery decision-makers determine the type and quantity of crude oil procurement under operational uncertainties to maximize the expected profit under all possibilities. In stage-II, process unit flowrates are adjusted based on the realized uncertainties and available crude oil, while introducing probabilistic constraints to manage the off-spec risk. To solve such a two-stage optimization problem, we propose a novel approach using Gaussian mixture model (GMM) to characterize uncertainties, and piecewise linear decision rule for stage-II operations. Comparing to the conventional scenario-based mixed-integer linear program (MILP), our new approach offers three advantages. First, it leverages a well-developed global optimization scheme for joint CCP to avoid scenario generation and potential bias. Second, the data-driven GMM enables CCP to handle uncertainties with general distributions. Third, the stage-II variables are parameterized via Gaussian component induced piecewise linear decision rule to strike an excellent trade-off between optimality and computational time. A simplified refinery plant, consisting of distillation, cracker, reformer, isomerization, and desulfurization units, is used as a test bed to demonstrate the superiority of the proposed optimization method in solution time, probabilistic feasibility, and optimality over the large-scale scenario-based MILP.

Economic impact and risk analysis of integrating sustainable aviation fuels into refineries

The growth of the aviation industry coupled with its dependence on energy dense, liquid fuels has brought sustainable aviation fuel (SAF) research to the forefront of the biofuels community. Petroleum refineries will need to decide how to satisfy the projected increase in jet fuel demand with either capital investments to debottleneck current operations or by integrating bio-blendstocks. This work seeks to compare jet production strategies on a risk-adjusted, economic performance basis using Monte-Carlo simulation and refinery optimization models. Additionally, incentive structures aiming to de-risk initial SAF production from the refiner’s perspective are explored. Results show that market sensitive incentives can reduce the financial risks associated with producing SAFs and deliver marginal abatement costs ranging between 136-182 $/Ton-CO2e.

Multiperiod refinery optimization for mitigating the impact of process unit shutdowns

Process unit shutdowns, whether planned or unplanned, disrupt the normal operation of process plants. The negative impact of such disruptions can often be mitigated by the strategic use of material inventories, coupled with redistribution of material flows. In this paper, an optimization framework is proposed for a multiperiod refinery planning model in which a system of inventory tanks is used to mitigate the impact of process unit shutdowns. An economic objective is optimized over a planning horizon, subject to operating, design, and product quality constraints. Two formulation paradigms are considered - an operating problem in which optimal material flow trajectories are determined in response to a shutdown scenario, and a retrofit design problem in which the optimal location and sizing of inventory tanks are determined, the latter formulated as a two-stage stochastic programming problem. The utility of the formulation to shutdown planning is shown through case studies of the operational and design problems applied to a simple refinery scheme.

Real-time refinery optimization with reduced-order fluidized catalytic cracker model and surrogate-based trust region filter method

Since its initial development in the 1980′s, Real-Time Optimization (RTO) has been widely appreciated as an efficient way to optimize process decision variables and improve economic performance of refineries. RTOs consist of nonlinear optimization models with hundreds of thousands of equations, which are built within equation-oriented (EO) modeling platforms. With increasing size and complexity of RTO applications, there is increased demand for improved optimization strategies. To address this demand, surrogate models for complex refinery units have been embedded within the general EO framework for RTO. Moreover, the recent trust region filter (TRF) optimization strategy allows great flexibility in the choice of surrogates, while ensuring convergence to the optimum of the rigorous RTO model. This study considers this approach for a real-world refinery. The Petrobras S.A. RECAP unit in Mauá, Brazil runs an RTO refinery model with an Aspen RTO optimizer to maximize the profit within two hour cycles. To reduce the computational burden, we embed a reduced model (RM) to replace the detailed (truth) model for the residue fluid catalytic cracking (RFCC) unit, and implement a TRF optimization strategy. The TRF driver is written in Python and integrates with the RFCC truth model, the Aspen-EO RECAP model, and the Aspen RTO optimizer. The approach is illustrated on three real-world scenarios in order to demonstrate the effectiveness and efficiency of this RM-based optimization strategy.

Реализация проектов «Прорыв» по комплексному повышению эффективности перерабатывающих заводов

This article describes the necessary methods and tips for success in the implementation of wide refinery optimization Projects. It is achieved by combining three factors: properly selected professional team, methodology and wide range of improvement tools.

Product tri‐section based crude distillation unit model for refinery production planning and refinery optimization

AbstractAccuracy of a crude distillation unit (CDU) model has a significant impact on refinery production planning. High accuracy is typically accomplished via nonlinear models which causes convergence difficulties when the entire refinery model is optimized. CDU model presented in this work is a mixed‐integer linear model with a modest number of binary variables; its accuracy is on par with rigorous tray to tray CDU models. The model relies on the observation12 that a line through the middle of the product true boiling point (TBP) curve depends on the crude feed properties and the yields of the adjacent products. Novelty of the product tri‐section CDU model is that it does not require models of individual distillation towers comprising the CDU, thereby leading to a much simpler model structure. Significant reduction in the computational effort required for the optimization of nonlinear refinery models is illustrated by comparison with previous work.

Scenario Based Two-Stage Stochastic Programming Approach for the Midterm Production Planning of Oil Refinery

Recently, oil refining industry is facing with lower profit margin due to uncertainty. This causes oil refinery to include stochastic optimization in making a decision to maximize the profit. In the past, deterministic linear programming approach is widely used in oil refinery optimization problems. However, due to volatility and unpredictability of oil prices in the past ten years, deterministic model might not be able to predict the reality of the situation as it does not take into account the uncertainties thus, leads to non-optimal solution. Therefore, this study will develop two-stage stochastic linear programming for the midterm production planning of oil refinery to handle oil price volatility. Geometric Brownian motion (GBM) is used to describe uncertainties in crude oil price, petroleum product prices, and demand for petroleum products. This model generates the future realization of the price and demands with scenario tree based on the statistical specification of GBM using method of moment as input to the stochastic programming. The model developed in this paper was tested for Malaysia oil refinery data. The result of stochastic approach indicates that the model gives better prediction of profit margin.

An optimization based strategy for crude selection in a refinery with lube hydro-processing

An optimization-based strategy is developed for crude selection in a refinery with lube base oil (LBO) producing capability. Crude oil is classified into five types based on the viscosity index (VI) and the compositional aspect of vacuum gas oil (VGO). A prediction model for the VI, the most important quality variable in LBO processing, is developed for VGO based on its bulk properties. For each crude type, prediction models for the yield and the VI change vs. the conversion rate during lube hydro-processing are developed. The lube hydro-processing models are then incorporated into the overall refinery optimization to maximize the overall margin while satisfying all the specifications of the lube and fuel products simultaneously. A case study involving several price scenarios illustrates the benefits from using the model-based optimization method for deciding crude procurement, the grade of the LBO to be produced, and the conversion rate in the lube process.

Integrated crude selection and refinery optimization under uncertainty

Crude oil selection and procurement is the most important step in the refining process and impacts the profit margin of the refinery significantly. Due to uncertain quality of the crudes, conventional deterministic modeling and optimization methods are not suitable for refinery profitability enhancement. Therefore, a novel optimization scheme for crude oil procurement integrated with refinery operations in the face of uncertainties is presented. The decision process comprises two stages and is solved using a scenario‐based stochastic programming formulation. In Stage I, the optimal crude selections and purchase amounts are determined by maximizing the expected profit across all scenarios. In Stage II, the uncertainties are realized and optimal operations for the refinery are determined according to this realization. The resulting large‐scale mixed‐integer nonlinear programming formulation incorporates integer variables for crude selection and continuous variables for refinery operations, as well as bilinear terms for pooling processes. Nonconvex generalized Benders decomposition is used to solve this problem to obtain an global optimum efficiently. © 2015 American Institute of Chemical Engineers AIChE J, 62: 1038–1053, 2016

Refinery Optimization Integrated with a Nonlinear Crude Distillation Unit Model

In this paper, the problem of optimal crude oil procurement combined with refinery operations is addressed to obtain an ɛ–global optimal solution. Rather than the traditional planning and scheduling methodologies relying on linear programming (LP), a nonlinear model using Geddes fractional index (FI) is employed to describe the behavior of the crude distillation unit (CDU) and integrated with the entire plant-wide model. Although this representation provides more accurate prediction of the real production of the refinery than the conventional fixed yield approach, its global optimization becomes more difficult owing to the existence of many nonlinear, non-convex terms. To overcome this challenge, advanced interval reduction techniques are developed and combined with state-of-the-art global optimization software to obtain an e–global optimal solution more efficiently. The optimization and comparison are conducted to show the effectiveness of the proposed approach.

Integrated Product Blending Optimization for Oil Refinery Operations

Refinery is a multiproduct manufacturing plant. Crude oil is processed to get intermediate products which are blended to meet quantity, quality and schedule specification of the final products. Refinery optimization is a complex problem therefore it is broken down into sub-problems which are solved independently. The solution obtained using this approach can be improved by integrating different sub-problems for real-time optimization. Refinery works on very small Gross Refinery Margin (GRM); off specification product blends results in considerable cost overhead because product blending is the last operation in the refinery process. Therefore we propose a method for real time integration of product blending with secondary process units to significantly improve the GRM. This method is designed and implemented in the present work. A case study is included to demonstrate the benefits of the proposed method.

Disaggregation–aggregation based model reduction for refinery-wide optimization

In this paper, reduced nonlinear refinery models are developed by generating and using input–output data from a process simulator. In particular, rigorous process models of continuous catalytic reformer (CCR) and naphtha splitter units are used for generating the data. To deal with complexity associated with large amounts of data, that is usually available in the refineries, a disaggregation–aggregation based approach is presented. The data is split (disaggregation) into smaller subsets and reduced artificial neural network (ANN) models are obtained for each of the subset. These ANN models are then combined (aggregation) to obtain an ANN model which represents all the data originally generated. The disaggregation step can be carried out within a parallel computing platform. Refinery optimization studies are carried out to demonstrate the applicability and the usefulness of the proposed model reduction approach.

Uncertainty Analysis for Refinery Production Planning

Nowadays, oil refineries are facing a challenging task to optimize their production levels and increase their incomes. The objective of refinery optimization is to determine the best production lev...

MOLECULAR MODELING FOR PLANT-WIDE OPTIMIZATION

In this article, molecular modeling of process streams and processes is combined with overall refinery optimization to select feedstocks and products, optimize the operation of different processes, and determine the most economic modifications. The molecular compositions of the hydrocarbon mixture are described using a MTHS (Molecular Type Homologous Series) matrix. To model reaction processes, structure-reactivity correlations are developed by regressing a wide range of pilot plant and commercial data. Molecular level kinetics is combined with current reactor modeling techniques. To integrate molecular modeling with plant-wide optimization, the interface between molecular compositions and bulk properties is also developed. The plant-wide optimization consists of a two-level optimization procedure, namely the site level and process level. The site level optimization addresses the overall trade-offs among the selections of feedstocks, products, processes, and the use of utilities. The process level optimization optimizes the individual processes by adjusting the detailed operating parameters. With the detailed molecular information available from the molecular model, it is possible to distinguish the contributions from different feedstocks and optimize the operation conditions to produce the most desirable products. A feedback mechanism is built for the coordination between these two levels. By optimizing the overall plant using molecular models, overall profit will be maximized by maximizing desired molecules to form valuable products, minimizing undesired ones, and routing all the molecules to their most appropriate destinations.

Combine Molecular Modeling with Optimization to Stretch Refinery Operation

A systematic methodology is presented for refinery modeling and optimization on the basis of much more detailed molecular information than is used in conventional lumped methods. The novelty of this work is that it incorporates models based on microscopic understanding into optimization to achieve macroscopic improvements. A molecular matrix based on a homologous series of hydrocarbon compounds is used to characterize refining streams. A transformation method is developed for obtaining molecular compositions from stream bulk properties. Molecular models are then built for several processes. Molecular modeling and optimization are finally integrated into an overall refinery optimization framework. Results show that molecular information can provide much better understanding and new insights into refining operation. Consequently optimization based on molecular modeling can greatly improve total profit while making quality products that satisfy environmental regulations.

A Simultaneous Optimization Strategy for Overall Integration in Refinery Planning

The refining industry is under immense pressure to produce cleaner products but faces low economic margins because of stricter environmental regulations and depressed market demand. In this situation, refinery planning becomes very important as it can exploit all potential opportunities to push the economic margin to the maximum limit. This paper presents a method for overall refinery optimization through integration of the hydrogen network and the utility system with the material processing system. The problem of optimizing each of these three systems is very complex in its own right. To make the problem of overall optimization solvable, the current practice adopts a decomposition approach, in which material processing is optimized first using linear programming (LP) techniques to maximize the overall profit. Then, supporting systems, including the hydrogen network and the utility system, are optimized to reduce operating costs for the fixed process conditions determined from the LP optimization. Essentially these three systems are dealt with separately, which usually leads to nonoptimal solutions for refinery operations. A new optimization method is proposed that is developed on the basis of a sound understanding of interactions between the three systems and the proper use of mathematical modeling. This method considers the optimization of refinery liquid flows, hydrogen flows, and steam and power flows simultaneously. As a result, this method furnishes new insights into the problem of refinery optimization and can provide significant benefits to the refining industry in exploiting the true potential of the processes and obtaining truly optimal operation.

Planning logistics operations in the oil industry

In this paper we apply stochastic programming modelling and solution techniques to planning problems for a consortium of oil companies. A multiperiod supply, transformation and distribution scheduling problem—the Depot and Refinery Optimization Problem (DROP)—is formulated for strategic or tactical level planning of the consortium's activities. This deterministic model is used as a basis for implementing a stochastic programming formulation with uncertainty in the product demands and spot supply costs (DROPS), whose solution process utilizes the deterministic equivalent linear programming problem. We employ our STOCHGEN general purpose stochastic problem generator to ‘recreate’ the decision (scenario) tree for the unfolding future as this deterministic equivalent. To project random demands for oil products at different spatial locations into the future and to generate random fluctuations in their future prices/costs a stochastic input data simulator is developed and calibrated to historical industry data. The models are written in the modelling language XPRESS-MP and solved by the XPRESS suite of linear programming solvers. From the viewpoint of implementation of large-scale stochastic programming models this study involves decisions in both space and time and careful revision of the original deterministic formulation. The first part of the paper treats the specification, generation and solution of the deterministic DROP model. The stochastic version of the model (DROPS) and its implementation are studied in detail in the second part and a number of related research questions and implications discussed.

A bilinear approach to the pooling problem†

In this paper we present an algorithm for the pooling problem in refinery optimization based on a bilinear programming approach. The pooling problem occurs frequently in process optimization problems, especially refinery planning models. The main difficulty is that pooling causes an inherent nonlinearity in the otherwise linear models. We shall define the problem by formulating an aggregate mathematical model of a refinery, comment on solution methods for pooling problems that have been presented in the literature, and develop a new method based on convex approximations of the bilinear terms. The method is illustrated on numerical examples