Decrease Butane Consumption In Course Of Aromatics Extraction Through The Method Of Optimizing Operating Parametric

In this paper, in view of the problem of how to decrease butane consumption in course of aromatics extraction, solution of optimizing operating parametric is presented. Optimization problem model is described by artificial neural network and optimization method applies genetic algorithm. The process of selection of self-variation, collection of data, artificial neural network modeling and the procedure of genetic algorithm optimization are introduced, and with MATLAB program, compiling relevant program and optimized result is obtained.Aromatic extraction process is the procedure that, with dimethyl sulfoxide under room temperature and ordinary pressure, aromatic can be extracted, then, with the use of butane, to extract aromatic dimethyl sulfoxide. In practical production, single consumption of butane is always over standard, the design value, three times over at most sometimes. Since aromatic extraction is a complex process, higher quality, more self-variations that influence the consumption, so, the effect of one variation is often not among its operational unit, and even not in the whole unit, therefore, the isolated variation method can not be used to find out the relation between variations. Artificial neural network, with collective computation ability and self-suit learning ability, offers a means for the building of the model, with its coherent parallel, stronger robustness and high searching rates, making it possible that with genetic algorithm, searching for optimization to solve the less limited target variation .The newest point in this paper is that using MATLAB program, train result of artificial neural network is combined effectively with genetic algorithm to realize the optimization control in chemical process.