Chemical production on-line fault detection and diagnosis technique based on physical-large data hybrid model

Abstract

The invention discloses a chemical production on-line fault detection and diagnosis technique based on a physical-large data hybrid model. The chemical production on-line fault detection and diagnosis technique is characterized by comprising the following steps: after selecting a target operation unit, scanning all historical data of the unit, and after parameters are verified, establishing an accident knowledge base and a parameter model; in later on-line detection process, directly loading on-line data into the parameter model, scanning so as to obtain fault data in the on-line data, making an alarm, and comparing the fault data with data in the accident knowledge base, thereby obtaining fault reasons. The method disclosed by the invention is applied to confirmed single chemical unit operation, a reliable physical model can be established, a chemometrical method is used in the whole production process, a big-data processing technique is introduced to process real-time data of years, operators focus on faults of performance indexes within a controllable variable range, and the influence of uncontrollable production process variable to fault detection is eliminated.

Description

technical field [0001] The invention relates to the field of safety detection and control in the chemical production process, in particular to an online fault detection and diagnosis technology for chemical production based on a physical-big data hybrid model. Background technique [0002] The chemical production process is extremely complex, accompanied by many unexplored physical and chemical reactions. For the continuous process industry, maintaining a stable working condition can not only reduce accidents and secondary disasters, but also stabilize product quality and operate at optimal cost under optimized working conditions to obtain maximum economic benefits. Therefore, the faults referred to here include not only abnormal conditions affecting device safety (collectively referred to as safety faults), but also deviations in product quality and optimal operating conditions (collectively referred to as performance faults). [0003] For complex chemical production proce...

AI Technical Summary

Problems solved by technology

But for chemical production, many unit operations, especially physical processes (such as heat exchange, compression, rectification, etc.), have a definite mass and energy balance relationship, and there are often physical isolation between the front and rear operation units of the process (such as being separated by the middle The storage tank isolates two separation units), there is no physical correlation between physical parameters, and the big data analysis technology is directly used, because the noise effect between the measurement data, or is affected by the utility (such as power steam temperature, fuel Influenced by atmospheric pressure composition), these physically unrelated data often have a high degree of correlation in the model established by big data, which makes big data analysts mistakenly believe that there is a correlation. This correlation that violates the laws of physics makes the fault diagnosis The increase in misjudgment rate has led to distrust of production technicians in fault detection, which has brought certain resistance to the promotion of Industry 4.0 and the construction of smart factories

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