Industrial process modeling forecasting method oriented at flow object

Abstract

The invention discloses an industrial process modeling forecasting method oriented at a flow object. The method comprises the following steps: building FNT models, extracting an industrial flow object original data set S from a data warehouse which has already been generated by the flow object, creating an initial species group of the FNT models, and customizing the individual numbers of the species group as required, wherein each individual represents an FNT model; utilizing the PIPE algorithm for optimizing FNT model structures, and adopting mean square errors or root-mean-square errors for fitness functions; utilizing the particle swarm optimization (PSO) algorithm for optimizing FNT model parameters; utilizing the FNT models for conducting modeling forecast for a flow object production process. According to the method, based on the flexible neural tree, an equation of variation tendency among measuring point data of the flow object is obtained, the industrial production process is simulated, based on relevant parameters of a current production state, production states in a period of time in the future are forecast, so that an enterprise is assisted and instructed for adjusting the production process parameters, and the production is guided for drawing on advantages and avoiding disadvantages in a microcosmic sense.

Description

technical field [0001] The invention relates to the field of industrial process production, in particular to a process object-oriented industrial process modeling and prediction method. Background technique [0002] With the development of production technology, the control of process links is becoming more and more strict, so the influence relationship between production parameters becomes more and more important; and with the expansion of production data scale and long-term accumulation, massive production data This will inevitably further increase the complexity of process parameters and the correlation between parameters, and also bring greater difficulties to the establishment of process object models. The selection of correct and effective parameters is the premise of effective process modeling, but the selection of process parameters of process objects depends to a large extent on the long-term accumulated experience of workers, which lacks theoretical basis and scien...

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Problems solved by technology

[0002] With the development of production technology, the control of process links is becoming more and more strict, so the influence relationship between production parameters becomes more and more important; and with the expansion of production data scale and long-term accumulation, massive production data This will inevitably further increase the complexity of process parameters and the correlation between parameters, and also bring greater difficulties to the model establishment of process objects

The selection of correct and effective parameters is the premise of effective process modeling, but the selection of process parameters of process objects depends to a large extent on the long-term accumulated experience of workers, lacking theoretical basis and scientific

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