(570z) Distillation Curve Optimization Using Monotonic Interpolation

Oil-refining involves a series of complex manufacturing processes in which final products such as fuels, lubricants and petrochemical feedstocks are produced from crude-oil feedstocks by separation and conversion unit-operations in coordination with tanks, blenders and transportation vessels. To manage the processing of the hydrocarbon streams, well-known distillation curves or assays of both the crude-oil and its derivatives are decomposed or characterized into several temperature “cuts” based on what are known as the True Boiling Point (TBP) temperature distribution or distillation curves.^1,2 These curves are relatively simple and one-dimensional representations of how a complex hydrocarbon material’s yield and quality data such as density, sulfur and pour-point are distributed or profiled over its TBP temperatures where each cut is also referred to as a component, pseudo-component or hypothetical in process simulation and optimization technology. Throughout the oil-refinery processing the full range of hydrocarbon components is transformed (blended, reacted, separated) into smaller boiling-point temperature ranges resulting in intermediate and final products in which planning and scheduling optimization using TBP curves of the various streams can be used to effectively model these process unit-operations and predict the macro (i.e., cold flow) properties of their outlet streams.³ The entire refining process can be categorized into three distinct areas: crude-oil blending, refinery unit-operation processing and product blending⁴ where our focus is related to the last two areas.

Our proposed new technique is to integrate both the optimization of blending several streams’ distillation curves together with also shifting or adjusting the cutpoints of one or more of the stream’s initial and/or final boiling-points (IBP and FBP) in order to manipulate its TBP curve in an either off- or on-line environment. This shifting or adjusting of the TBP curve’s IBP and FBP (front and back-end respectively) ultimately requires that the upstream unit-operation has sufficient handles or controls to allow this type of cutpoint variation where the solution from this higher-level optimization would provide setpoints or targets to a lower-level advanced process control system which are now common place in oil-refineries. By shifting or adjusting the front- and back-ends of the TBP curve for one or more distillate blending streams, it allows for improved control and optimization of the final product demand quantity and quality, affording better maneuvering closer and around downstream bottlenecks such as tight property specifications and volatile demand flow and timing constrictions.

Specifically, we propose a novel technique using monotonic interpolation to blend and cut distillation temperatures and evaporations for petroleum fuels in an optimization environment is proposed. Blending distillation temperatures is well known in simulation whereby cumulative evaporations at specific temperatures are mixed together then these data points are used in piece-wise cubic spline interpolations to revert back to the distillation temperatures. Our method replaces the splines with monotonic splines to eliminate Runge's phenomenon and to allow the distillation curve itself to be adjusted by optimizing its initial and final boiling points known as cutpoints. By optimizing both the recipes of the blended material and its blending component distillation curves, very significant benefits can be achieved especially given the global push towards ultra low sulfur fuels (ULSF) due to the increase in natural gas plays reducing the demand for other oil distillates.  Two examples are provided to highlight and demonstrate the technique.

(1) Riazi, M. R. Characterization and Properties of Petroleum Fractions. American Society for Testing and Materials, 2005.

(2) Menezes, B.C.; Kelly, J. D.; Grossmann, I. E. Improved Swing-Cut Modeling for Planning and Scheduling of Oil-Refinery Process. Ind. Eng. Chem. Res. 2013, 52, 18324-18333.

(3) Kelly, J. D. Formulating Production Planning Models. Chem. Eng. Prog. 2004, 100, 43-50.

(4) Jia, Z.; Ierapetritou, M.; Kelly, J. D. Refinery Short Term Scheduling Using Continuous Time Formulation: Crude-Oil Operations. Ind. Chem. Eng. Res. 2004, 42, 3087-3097.