Abstract
Updating industrial facilities to increase the level of automation and digitalization to match Industry 4.0 paradigms has become essential for many companies. Following such a trend, this paper presents a real-time optimization algorithm that plays a central role in a larger project framework devoted to highly interconnecting different network components of an Italian chemical industrial site. The proposed methodology aims at best managing the production rates of various products to fulfill a sales plan organized to satisfy numerous client requests. The considered model takes into account both batch and continuous processes as well as salable and non-storable products. The algorithm structure relies on the use of a non-linear optimization scheme and on the concepts of batch scheduling. Different features of the proposed methodology have been tested on real plant data, showing how the predicted forecast always improved the initial operation plan by considering both aspects of feasibility and economic nature. The use of the proposed algorithm assures the basis for fully integrating the control systems and the selling department of the facility in a more interactive and responsive manner.
Highlights
Within Industry 4.0 paradigms, both process simulation and simulation-based optimization have acquired a relevant role in the definition of the so-called virtual twin of the physical process[2]
A real-time optimization algorithm to best manage production rates based on the sales plan has been presented
This work is part of a larger project involving the integrated digitalization of an Italian industrial site according to Industry 4.0 paradigms
Summary
Within Industry 4.0 paradigms, both process simulation and simulation-based optimization have acquired a relevant role in the definition of the so-called virtual twin of the physical process[2] In this context, mathematical modeling is not anymore dedicated to describe an industrial process, and any product or a service on top of which specific analyses and/or suitable strategies have to be performed[3]. The RTO methods exploit process measurements to run an optimization framework that often, but non mandatorily, relies on a (possibly inaccurate) process model and data extrapolated from measurements Due to their versatility, process industry applications of RTO strategies nowadays are multiple and can be found in different fields, as managing energy consumption efficiently[7] or optimizing batch and continuous operations[8]
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