High-Octane Gasoline Production from Catalytic Naphtha Reforming

The global drive for environmental sustainability necessitates continuous adjustment, optimization, and improvement in petroleum refining processes to generate energy and products including automotive fuels such as gasoline. At the same time, refiners need to maximize their asset utilization to maintain competitiveness in the business setting. This chapter presents a process advisory and monitoring application to optimize a catalytic naphtha reforming operation to produce high octane gasoline feedstock. A mathematical model is developed for the process to produce hydrocarbons with high anti-knock ratings. The proposed methodology involves formulating a nonlinear programming optimization model to perform data reconciliation. The model objective minimizes the deviations (or errors) between the measured values and the model-reconciled values to reflect the accuracy and reliability of the measurements. The overall procedure is carried out subject to various real-world operation constraints to ensure sustainable processing of the required products, which include hydrogen gas and aromatics. We present a case study to illustrate an implementation of the resulting model in an online environment to improve process operation at an actual refinery in Canada. The computational results show enhanced product quality of a reformate stream with high octane number and increased yields

A Model-Based Investment Assessment for Heavy Oil Processing in the Petroleum Refining Industry

The need for heavy oil processing has increased in recent years worldwide, backed by higher demands for petroleum products in the face of declining light crude oil resources. The situation encourages refineries to focus more on maximizing the production of high-value outputs from this lower-value heavier feedstock. This study purports to assess heavy oil processing potential in the refining industry through model-based economic evaluation. We formulate a refinery model suitable for preliminary investment decision making, which considers various cost elements for a number of conventional commercial heavy oil processing technologies. The formulated model is applied to a case study on the worldwide potential for heavy oil processing. This chapter demonstrates the application of a model-based approach to perform or assist with investment assessment

A Hybrid of Stochastic Programming Approaches with Economic and Operational Risk Management for Petroleum Refinery Planning under Uncertainty

In view of the current situation of fluctuating high crude oil prices, it is now more important than ever for petroleum refineries to operate at an optimal level in the present dynamic global economy. Acknowledging the shortcomings of deterministic models, this work proposes a hybrid of stochastic programming formulations for an optimal midterm refinery planning that addresses three factors of uncertainties, namely price of crude oil and saleable products, product demand, and production yields. An explicit stochastic programming technique is utilized by employing compensating slack variables to account for violations of constraints in order to increase model tractability. Four approaches are considered to ensure both solution and model robustness: (1) the Markowitz’s mean–variance (MV) model to handle randomness in the objective coefficients of prices by minimizing variance of the expected value of the random coefficients; (2) the two-stage stochastic programming with fixed recourse approach via scenario analysis to model randomness in the right-hand side and left-hand side coefficients by minimizing the expected recourse penalty costs due to constraints’ violations; (3) incorporation of the MV model within the framework developed in Approach 2 to minimize both the expectation and variance of the recourse costs; and (4) reformulation of the model in Approach 3 by adopting mean-absolute deviation (MAD) as the risk metric imposed by the recourse costs for a novel application to the petroleum refining industry. A representative numerical example is illustrated with the resulting outcome of higher net profits and increased robustness in solutions proposed by the stochastic models

Recent Advancements in Commercial Integer Optimization Solvers for Business Intelligence Applications

The chapter focuses on the recent advancements in commercial integer optimization solvers as exemplified by the CPLEX software package particularly but not limited to mixed-integer linear programming (MILP) models applied to business intelligence applications. We provide background on the main underlying algorithmic method of branch-and-cut, which is based on the established optimization solution methods of branch-and-bound and cutting planes. The chapter also covers heuristic-based algorithms, which include preprocessing and probing strategies as well as the more advanced methods of local or neighborhood search for polishing solutions toward enhanced use in practical settings. Emphasis is given to both theory and implementation of the methods available. Other considerations are offered on parallelization, solution pools, and tuning tools, culminating with some concluding remarks on computational performance vis-à-vis business intelligence applications with a view toward perspective for future work in this area

Saybolt color prediction for condensates and light crude oils

Abstract Saybolt color determination is one of the techniques used to evaluate the quality of petroleum products as an indicator of the degree of refinement. As color is a property readily observed by operators, conventional procedures require operators to determine Saybolt color either through direct visual observation or through Saybolt chromometers. These methods are subjective due to the variability in perception of colors across different observers and may be influenced by external factors such as the level of illuminance. Digital oil color analyzers, on the other hand, cost almost four times as much as Saybolt chromometers. An alternative approach to color measurement is to develop a correlation model between Saybolt color with the physical and chemical properties of condensates and light crude oils from Malaysian oil and gas fields. This work applies several multiple linear regression techniques (such as stepwise regression) performed both manually and using the R software (version 3.6.1) to obtain statistically significant results. The step, regsubsets and glmulti functions from R are explored to develop the correlation model which predicts Saybolt color using only identified key properties, overcoming the possible drawbacks associated with conventional laboratory analysis. The models developed through these different techniques are analyzed and compared based on criteria indicated through the coefficient of multiple determination, R 2 and F-tests to infer on suitable regression approaches. Results obtained from these regression methods for models with and without interaction terms report deviations of less than 5% for 75% of the samples used for validation

A model-based approach for biomass-to-bioproducts supply chain network planning optimization

Supply chain network operation for biomass conversion and utilization is one of the major areas with influence on biomass-related technological progress and commercialization activities. This paper contributes towards optimizing a biomass-to-bioproducts supply chain planning operation by considering multiple cost factors including biomass resource acquisition cost, production cost, and transportation cost as well as direct sales to meet market demands. A superstructure-based modeling approach provides alternatives of biomass processing routes towards an objective of maximizing annualized profit. The formulated model entails five echelons and is implemented on a practical supply chain operational planning case study that involves a biomass-based manufacturing company in southwestern Ontario, Canada intent on long-term business expansion and product portfolio improvement. The results obtained indicates that an optimal product mix comprising a number of products from different processing stages (including preprocessing) can be expected to be achieved, with profit mainly derived through the sales of biofiller, bioethanol, and byproducts. Importantly, the developed model demonstrates the applicability of such a model-based approach in offering insights on operational optimization to attain economic decision-making on biomass resource utilization and processing route selection

Computational Experience with Piecewise Linear Relaxations for Petroleum Refinery Planning

Refinery planning optimization is a challenging problem as regards handling the nonconvex bilinearity, mainly due to pooling operations in processes such as crude oil distillation and product blending. This work investigated the performance of several representative piecewise linear (or piecewise affine) relaxation schemes (referred to as McCormick, bm, nf5, and nf6t) and de (which is a new approach proposed based on eigenvector decomposition) that mainly give rise to mixed-integer optimization programs to convexify a bilinear term using predetermined univariate partitioning for instances of uniform and non-uniform partition sizes. The computational results showed that applying these schemes improves the relaxation tightness compared to only applying convex and concave envelopes as estimators. Uniform partition sizes typically perform better in terms of relaxation solution quality and convergence behavior. It was also seen that there is a limit on the number of partitions that contribute to relaxation tightness, which does not necessarily correspond to a larger number of partitions, while a direct relationship between relaxation size and tightness does not always hold for non-uniform partition sizes

Chemical Engineering Programme,

Abstract: In this review, we survey the widespread use of optimisation or mathematical programming approaches in the upstream sector of the petroleum industry, specifically to problems in the area of (1) production systems design and operations, (2) lift gas and production rate allocation and (3) reservoir development, planning, management and optimisation. Early applications have adopted Linear Programming (LP) alongside heuristics-based methods, but the recent ongoing explosion in computing power and advances in optimisation, simulation and computational techniques have enabled the adoption of increasingly complex models. These formulations include non-linear programming and Mixed-Integer Linear (MILP) and Non-Linear (MINLP) programming models. Within these representations, various algorithms and approaches have been employed, for example, metaheuristics such as genetic algorithms to address non-smooth objective functions; techniques for simultaneous decision making in design, planning and scheduling and stochastic programming to handle uncertainty in reservoir information, with the ultimate aim of improving solution quality while reducing computational intensity