Crude logistics scheduling
Upgraded crude scheduling enabled a refiner to increase its use of opportunity crudes for higher margin.
AURELIO FERRUCCI, Prometheus SRL
MANOJ KUMAR, HPCL - Mittal Energy Limited
Viewed : 433
Generation of a refinery’s operations plan requires the capability to monitor and determine the quality and the composition of crude oil to be processed in the crude unit as well as its availability date. Batches fed to the CDU must fit both quality constraints set by the operations department and simultaneously maximise the content of opportunity crude oils for the best processing margin.
Poor scheduling can erode up to 5% of the theoretical monthly result predicted by linear programming planning models and can result in higher ship demurrage costs. Thus, optimisation of crude oil logistics is fundamental, especially when the available crude inventories are limited and it is difficult to segregate crude quality.
Prometheus has developed a tool, Proraf, which offers an innovative algorithm to concurrently manage crude oil logistic events (reception, transfer, and processing) and predict the hourly evolution of the status of all tanks.
Crude data, simulation, and optimisation techniques are integrated by an algorithm which is also able to work high-level transfer instructions where the sequences of tank loading/unloading operations are not detailed.
The calculation engine manages the service requests associated with material transfers according to specified priorities and selection criteria, to bring each event to completion as fast as possible. This simulation highlights real bottlenecks to manage to prevent operational problems.
The software can solve autonomously the major components of the scheduling problem, requiring the user’s intervention only to manage significant issues requiring appropriate action.
Furthermore, integration of economically driven optimisation methods generates operational plans to maximise the processing of opportunity crudes while meeting the CDU‘s feedstock quality requirements.
Significant time saving enables schedulers to explore a greater number of options, resulting in a more effective and optimised scheduling plan. It is a tool that supports, in the same environment and with the same model, development of both long and short term scheduling tasks:
• Long term scheduling models can calculate and update in a few minutes the evolution of the status of all tanks and pipelines in the logistic network. It is able to define cargo arrival dates and to identify solutions in case of unforeseen operational changes.
• Short term scheduling models enable detailed instruction reports to be issued and store validated operational results (that is, actually executed operations) into a centralised historical database which can provide the information to reproduce details of past activity.
This article describes the technology and reports a case study illustrating its implementation for the scheduling of crude supply operations for an Indian petrochemical refining complex.
The operator’s maritime depot receives a variety of crude oils of variable quality (in terms of sulphur content, acidity, and API). The number of available tanks does not enable proper quality segregation, therefore each tank’s evolving composition must be tracked.
Concurrently with cargo reception, some tanks are unloaded and fed in parallel (three to five pumping channels) to a pipeline. The blend resulting from parallel pumping must respect the quality constraints required for CDU processing at the other end of the pipeline. Batches exiting the pipeline can either be fed directly to the crude unit or go to refinery storage.
Prometheus has developed a set of technologies for crude oil characterisation, plant simulation, and blending calculations and has integrated them into five modules, designed to support the most relevant planning and scheduling tasks.
These modules can work standalone to perform a specific set of scheduling tasks, or share data and processes in case of extended solutions:
• CUTS is a crude assay data elaboration for database building and recutting. This module provides crude oil data for all the other modules.
• Simraf refinery LP optimisation is integrated with process plant simulation models for yield and quality calculations. This module supports ordinary and strategic planning tasks.
• Proraf provides modelling and optimisation of logistics.
• Prolav provides modelling and optimisation of processing operations.
• Ottmix provides LP optimisation of blending operations.
These modules support short term scheduling tasks.
The crude scheduling model described in this article applies the CUTS and Proraf modules. Further integration with other DSS modules to model crude oil processing and finished product blending operations is in the design phase.
Figure 1 summarises the findings of a study carried out to identify the refining operations which mostly impact the difference between the theoretical result expected from the business plan and the actual result.
According to these results and for various reasons, the actual refinery margin can decrease, compared to the planning figure, by up to 25%. One of these reasons is improper crude logistics management (reception facilities, deposits, and pipelines) which can represent, for a mid-sized refinery, an annual $15-20 million loss: this is particularly true when variability of feedstocks forces operators to monitor the quality of their crude refinery tanks or to store different crude types separately to prevent quality contamination.
Typically, the average residence time of crude oil in refinery inventories is too low to enable proper quality segregation, and it is fundamental to improve the intelligence of tools dedicated to the scheduling of logistic assets, to manage the quality of the stocks finally fed to refinery crude units. In such cases, the crude scheduling process becomes critical, and great effort and investment is made to improve performance in this area.
Figure 2 lists the leading causes of economic losses arising from poor scheduling of crude oil logistics. In practice, LP models assume the capability to process optimal crude mixes in refinery crude units for the whole period, while in fact feedstock availability depends on the supply schedule and the logistic constraints involve unforeseen quality contamination.
In this case, the refiner must exploit all degrees of freedom available during the operational planning and scheduling process to maximise the final result. Crude batches fed to the CDU must fit the quality constraints set by the operations department and maximise the content of opportunity crude oils for the best processing margin.
Typically in this framework, crude quality and cargo size is input from planning while scheduling can define the arrival dates of cargoes as well as handling operations throughout the logistics network up to the crude unit.
In the case of definition of cargo arrival dates, the impact of this material intake on crude logistics must be foreseen.
Given the supply programme, it is crucial to plan handling operations to avoid undesirable contamination, especially in the case of different operating modes based on different crude qualities, and to optimise batches by maximising the use of opportunity crudes.
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