Intelligent adjusting method for edible oil alkali refining process based on data analysis

Abstract

The invention discloses an intelligent adjusting method for an edible oil alkali refining process based on big data analysis. The method mainly comprises the following steps: 1, aligning crude oil quality, acid-base reaction data, centrifuge parameters and quality test data into a splicing table according to time delay; 2, forming a decision table after redundancy and contradiction records are removed; 2, training an Xgboost model based on the decision table to calculate an oil yield; 3, optimizing the optimal oil yield interval of the key parameters by adopting a Kmeans clustering algorithm;4, optimizing the optimal parameter combination in the optimal oil yield interval by adopting an adaptive simulated annealing genetic algorithm and taking the highest oil yield as a target; 5, adopting a rough set algorithm to integrate field expert rules to form adjustment basic rules; and 6, performing basic rule constraint on the optimal parameters given by the genetic algorithm, and performingbasic rule filtering on the optimal parameter combination given by the genetic algorithm. According to the invention, the stable operation of the alkali refining process is ensured, the production cost is reduced, and the benefit maximization is realized.

Description

technical field [0001] The invention relates to the technical field of complex industrial process intelligence of big data mining, in particular to an intelligent adjustment method of edible oil alkali refining process based on historical data analysis. Background technique [0002] The edible oil alkali refining process is a typical complex industrial process with chemical changes and physical changes, and the interference factors are difficult to locate accurately. At present, the neutralization section of the refining production line adopts the method of manual adjustment, and the crude oil to be processed is input into the refining plant in batches. Each batch of crude oil needs to be adjusted by the team leader during the oil change stage through the test machine due to the different oil products, and the setting is suitable for the batch. For the production process parameters of secondary oil, during the oil change stage, the key process parameters such as the amount o...

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Problems solved by technology

The current adjustment method based on manual experience cannot achieve accurate quantitative calculations. There are only basic directional adjustment rules. It is necessary to analyze a large amount of historical production data to find out the quantitative relationship between key process parameters and oil yield. The optimal process parameter setting value can maximize the oil yield under the premise of ensuring the qualified oil product

Moreover, the lack of agitation of each batch of oil may result in stratification of the oil product, resulting in a change in quality within a certain range. To reach the maximum oil production rate, it is necessary to adjust key parameters in a timely manner according to different oil products. This is the manual adjustment of the current production process. not available

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