A refinery has to satisfy product deliveries contracted with its customers over a given time period. Several products such as fuel or diesel can be shipped through various shipment types like trucks or pipelines. As a consequence, a set of operations is planned to meet the product delivery requirements.
The main operation type consists of hydrocarbon blends for which components are blended to generate the final product to be delivered. The calculation engine based on Artelys Knitro optimizes the blend recipes as well as the planning of these mixtures in order to minimize the costs of the operations carried out as well as the delays of the customer deliveries while meeting the product quality requirements.
The model complexity comes from the quality specification constraints of the blends and deliveries which involve complex nonlinear equations. The project leverages the advanced black box capabilities of Artelys Knitro to efficiently tackle instances with dozens of product qualities, specifications and planned operations over several weeks.
Start with a tutorial!
You’re not familiar with nonlinear optimization? This tutorial will present some examples of nonlinear problems for various applications. You will discover nonlinear programming methods using the Artelys Knitro solver in a Python notebook, through different examples.
Free trial
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Artelys Knitro has unmatched performance
Best Nonlinear Solver
Artelys Knitro has been ranked every year by public benchmarks consistently showing Artelys Knitro finds both feasible and proven optimal solutions faster than competing solvers.
Technical support
The Artelys technical support team comprises Artelys’consultants (PhD-level) who are used to solving the most difficult problems and deploying enterprise-wide optimization solutions. They can advise on algorithmic or software features that may result in enhanced performance in your usage of Artelys Knitro.
Updates and new features
The development team works continuously to provide two releases of Artelys Knitro every year. Based on feedback, we always improve our solver to meet users’ requirements and need to solve larger models faster.
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