Abstract
The problem of efficient computational models design for control and scheduling problems in terms of oil and gas refinery column distillation units is discussed in the paper. Such efficient computational models can be constructed in the form of fast static regression models supplemented with dynamic models of measurement and input channels. The effectiveness of methods for constructing fast static regression models is examined in the paper. The input parameters for such regression models are determined. It is proposed to use parametric optimization methods for such models. A preliminary study showed the possibility of using an evolutionary genetic algorithm. Numerical studies were performed using data from column distillation units. The efficiency of using the methods of additional parametric optimization is shown.
Highlights
Improving the efficiency of oil refineries at the present stage can be achieved through the introduction of highly efficient scheduling and control systems
Modeling of column distillation units (CDUs) based on complex physicochemical models is investigated in sufficient detail [1 - 3]
As can be seen from the results presented in table 1, the use of the additional parametric optimization procedure allowed us to significantly reduce the modeling error by more than two times: from 15.6% to 7.5%
Summary
Improving the efficiency of oil refineries at the present stage can be achieved through the introduction of highly efficient scheduling and control systems. This is a consequence of such factors and problems as scheduling of high dimensions, decision support under uncertainty, optimization problems with a large number of constraints and complexity of modeling technological processes separately and as a dynamic system In this regard, the actual scientific problem is development and research of methods and algorithms, integration of which within the framework of distributed information and analytical systems will contribute to the effective solution of relevant production problems. It is necessary to design models that meet the criteria of simplicity, accuracy and simplification of input formation Such models are to efficiently calculate values of CDU’s parameters for technological processes control tasks within the production systems of oil refining.
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