On the eve of another “low cycle” in the petrochemical market, because of projects with large capacities and high technology, the result is a higher offer than demand and a reduction in the profitability of the companies.
Competitiveness is the key factor for the companies that are preparing themselves for these challenges, among other actions, through an efficient choice of its raw materials, the best use of its assets and the “exploration” of the best markets.
The volatility of the prices, sometimes favoring the olefins, sometimes the aromatics products and the complexity and diversity of Braskem's processes which include Reforming and Cracking units, strengthen the necessity of searching the best point between raw materials, market and operation.
To solve this highly complex problem, the acquisition of an advanced optimization application, gives these companies a competitive advantage. One year and half ago Braskem acquired Optience SCMart Suite as the optimization application platform to help their planning and scheduling process.
The main model, developed in SCMart, is a multiperiod nonlinear planning model which includes all plant constraints and uses a low order nonlinear polynomial correlation yield model based on SPYRO results for the cracking furnaces. It also includes a second order polynomial to estimate the reformer yields as well as a complex model for a naphtha fractionator. The planning model is supported by a scheduling model to achieve optimal results at the daily level. The system also uses a feedstock database library that tracks the different available feedstock qualities and properties.
In this work, we discuss the planning module that is used to support the technical and economical decisions at Braskem. The nonlinear planning model consists of about 5,000 variables per period including flows, inventories, properties and other process variables. It covers two ethylene plants, one reformer plant, plus other C4s, C5s and aromatics plants, in which the main products are ethylene, propylene, butadiene, isoprene, benzene, para-xylene and other aromatics. These process variables include COTs, reforming and cut temperatures, possible co-cracks, furnace and reformer utilization, ethane recycling flows and loads to other downstream units.
We present different case studies and scenarios which have been run during these last months that demonstrate how the tool has been helping us in the decisions. The study of these scenarios has been helping us to decide the amount and quality of the Naphtha to be bought and processed in each unit. It has also helped us to identify the ideal markets and in the exploration of trade offs and ideal yields, as well with the optimal use of the assets, through concepts of shadow price and profit variability.