Global Optimization of Refinery-wide Production Planning with Highly Nonlinear Unit Models

Abstract

Optimization of the refinery production is essential for the economic success of a petroleum refinery. Classical refinery production planning models employ input− output relationships for different refinery units. However, these input−output relationships usually do not include decision making variables related to the units' operation (pressure, temperature, etc.). In addition, they fail to capture the nonlinear nature of the refinery units, and the inaccuracy caused by these models may reduce the overall profitability or compromise product quality. The corresponding nonlinear refinery problem is nonconvex due to the presence of bilinear or quadratic, exponential or logarithmic terms in some of the mass balance, product yield, and quality constraints, and hence the standard methods for solving this refinery-wide optimization problem may fail to converge to a solution or lead to suboptimal solutions. In this work, a planning model for refinery-wide production is proposed which employs two different nonlinear models for the distillation unit and empirical nonlinear models for the remaining refinery units. The effectiveness of the nonlinear models in terms of improving the overall refinery profit margin over a linear input−output model has been investigated. In addition, the global optimal solution of refinery profit substantiates the huge advantage of nonlinear model utilizing distillation correlations based on Geddes fractionation index over the data-based nonlinear and linear input−output models. From a managerial perspective, this study provides an effective tool to make advanced decisions on the amount of the crude oil to purchase and to obtain higher profits by adjusting process severity which results in maximum yield and the desired product property.

Simulation Multi‐Objective Particle Swarm Optimization of a Crude Oil Distillation Unit

“…CDU was optimized by Inamdar et al [5] by selecting the elitist non-dominated sorting genetic algorithm (NSGA-II) to minimize energy cost and maximize profit. In order to generate more realistic operating models, two different nonlinear designs for distillation units were employed in the planning of the refinery [6].…”

Hinging Hyperplanes Crude Oil Mixing Model for Production Planning Optimization

“…Crude oil characterization and mixing are issues in petroleum refining operations such as scheduling of crude oil blending , and planning of petroleum supply chain. , The crude oil mixing process is usually simplified via a linear blending which causes imprecision in the final results. In the past decade, several researchers have sought to improve the crude oil characterization , and the models of the distillation (CDU) process employed in production planning and scheduling. , Unfortunately, the complex nature of the pseudocomponent method and the complexity of distillation models make it difficult to implement highly accurate crude mixing and CDU models while ensuring that the models can be solved rapidly. Recent studies using the swing-cuts model ,, and fractionation index model ,− have only included the distillation products mixing instead of the crude oil mixing in production planning and scheduling optimization.…”