Complex chemical process modeling method of DNA genetic algorithm based on swarm behavior

Abstract

The invention discloses a complex chemical process modeling method of a DNA genetic algorithm based on a swarm behavior. The method includes the following steps of firstly, obtaining input sampling data and output sampling data in the chemical process through experiments, and using the sum of an error absolute value of estimated output of a model and an error absolute value of practical sampling output in the chemical process as a fitness function aiming at the input sampling data in the same chemical process; secondly, setting control parameters of the algorithm; thirdly, conducting estimation on unknown parameters in a chemical process model by running the algorithm, obtaining estimated values of the unknown parameters in the model through a minimum objective function value, putting the estimated values of the unknown parameters in the model into the chemical process model, and obtaining an optimal chemical process model. According to the complex chemical process modeling method of the DNA genetic algorithm based on the swarm behavior, by the adopting of the DNA genetic algorithm based on a swarm honey gathering behavior and a swarm breeding behavior, the established chemical process model is made to have high fitting precision, and has the advantages of being high in convergence rate and rich in population diversity.

Description

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AI Technical Summary

Benefits of technology

This technology uses mathematical techniques called DNA (Natural Language) algorithms that are used for studying how different types of bacteria reproduce themselves within one type or another species' colonies. These methods allow researchers to accurately predict what kind of microorganism can grow into their own cells without being limited by factors like temperature or nutrients availability. They provide faster and more diverse ways to study these processes than previously possible due to its ability to capture complex patterns from data collected during previous experiments.

Problems solved by technology

Technics: Chemistry Process Control (CPC) involves controlling various processes such as heat generation or cracking reactions during petroleum refining operations. To improve efficiency it requires accurate knowledge about these processes' characteristics beforehand. Traditional methods like linear regression analysis have limitations with regards to optimizing certain parameters while also being limited due to its lack of flexibility towards different types of data. Therefore, there needs a new approach called Beehive Colon Biology Based On Bacterial Computational Quantification (BCQ).

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