277282 Performing Solution of Market-Driven Optimization for Large-Scale Corporates
Performing Solution of Market-driven Optimization for Large-scale Corporates
Matteo D'Isanto1, Flavio Manenti1, Maria Grazia Grottoli1, Sauro Pierucci1, Guido Buzzi-Ferraris1, Marcello Altavilla2, Ornella Martinelli3, Davide Jurissevich4, Roberto Di Marco3
1Politecnico di Milano, Dipartimento di Chimica, Materiali e Ingegneria Chimica “Giulio Natta”, Piazza Leonardo da Vinci 32, 20133 Milano, Italy
2Linde Gas Italia, Terni Plant, Viale Benedetto Brin 214, 05100, Terni, Italy
3Linde Gas Italia, Headquarter, Via Guido Rossa 3, 20010, Arluno, Italy
4Linde Gas Italia,Trieste Plant, Via Di Servola 1, 34145, Trieste, Italy
Many projects to rationalize and optimize the supply chain of complex corporates are ongoing in United States and Europe. The leitmotif is the high potential margins that may come from a high level optimization of decentralized productions and distribution networks, but also the need to raise the decision-making level from plantwide's purpose to the enterprise wide level. Actually, the global crisis of these last years is strongly pushing towards global optimization since the single production sites, although optimized, present relevant economic losses and the net operating margins are very small with respect to several years ago, especially in chemical and process industries. Thus, the corporate optimum, which almost never corresponds to the optimum of the single production sites, is a promising way to significantly improve the profits as well as to debottleneck inefficiencies highlighting and exploiting hidden potentialities.
The main problem is that such an approach unavoidably means to optimize in an integrated way all the operations of the corporate and, hence, to account simultaneously for billions of variables and to elaborate a solution in real-time for the optimal decision support.
Science fiction for the large industries, at least until computers will not be enough powerful, the parallel computing enough exploited, and the algorithms enough robust and efficient at the same time.
The present paper discusses (and applies by the field – Linde's air separation units) certain novel methodologies to solve separately several problems behind the real-time market-driven corporate optimization, looking forwards to their integration for a performing solution of the overall problem. Specifically, the following topics will be discussed:
· The analysis of very large data sets. New methods for the analysis of very large (industrial) data sets have been developed and validated. Such methods are able to promptly detect the outliers that could affect the data acquired by the field and the DCS.
· Prevision of the future market demand. A combination of weighted averages based on the historian and linear short-term extrapolations can be successful to foresee the incoming market demand for each final customer.
· Allocation of available commodities to match the demand. A Gaussian-based approach can be useful and very performing to assign the right source of commodity to the final customer so as to satisfy it by accounting for the distance, the availability of cryogenic trucks, and the availability of product.
· KPIs for final customers supply. Each society monitors its production and distribution by means of specific key performance indicators (KPIs). Some of the most important ones will be introduced and the item of the cryogenic truck is optimized basing on them and clustering the customers.
· Just in time production and minimum inventory. Being one of the most relevant cost items, the liquid levels of the cryogenic tanks must be minimized. To do so, it is necessary to foresee the market demand with a certain reliability and to change the operational sets of the production plants accordingly. If the former bullets are mainly related to mixed-integer linear programming, this point is strongly related to nonlinear programming.
· Optimal production. Basing on nonlinear models for air separation units, the production and the energy consumptions are both optimized in a predictive way.
What is in common among all these topics is that the solvers are all based on the novel Attic method and the robust optimizer (exploiting parallel computing for shared memory machines) belonging to BzzMath library. The application to Linde Gas Italia Corporate is also provided as validation case.
See more of this Group/Topical: Computing and Systems Technology Division