An intelligent industrial control method for leachate treatment based on multivariate data method

Abstract

The present invention proposes an intelligent industrial control method for leachate treatment based on a multivariate data method, which includes the following steps: S1, obtaining multivariate monitoring data through online monitoring equipment, respectively calculating the multivariate monitoring data, and forming the output value of the monitoring data; S2, obtain the monitoring data output value to evaluate the leachate treatment efficiency, and define the evaluation result through the optimal control method; S3, optimize the leachate treatment efficiency through the leachate treatment method after definition, so as to complete the intelligent treatment control of the leachate .

Description

technical field [0001] The invention relates to the field of automatic control, in particular to an intelligent industrial control method for leachate treatment based on a multivariate data method. Background technique [0002] With the development of Industry 4.0, the traditional industrial control based on human participation in the control can no longer meet the needs of industrial development. direction of exploration. In the current intelligent control and energy-saving applications of leachate treatment, intelligent control mainly realizes intelligent control by judging the normal state of the parameters according to various parameters of the process, or realizes intelligent control through machine learning through historical data; and cost reduction Efficiency enhancement is mainly achieved by adjusting the process route, optimizing the process method or adopting some energy-saving equipment. In the field of leachate treatment, the efficiency of leachate treatment i...

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Problems solved by technology

[0003] 1) The current leachate treatment control system is mainly designed and adjusted according to the process flow and on-site production. The operator controls the site through the system at the operation station. This technology relies more on the experience of on-site personnel. Control, but the leachate treatment data is changing in real time, and the process needs to be controlled according to the changes. Excessive human intervention will have a certain lag, resulting in waste of production resources or some emergencies due to human carelessness

[0004] 2) The energy-saving devices for leachate treatment are mostly adjusted according to the on-site process, and the cycle from design to result verification is relatively long, and it may also involve changing the process, which is difficult to implement

[0006] 4) The machine learning control system needs to use a large amount of historical data to complete machine learning, which requires a long historical accumulation in the cycle and data processing, and according to the adjustment of the process flow, the generality of the method model is poor. In addition, the current machine learning data It is basically online monitoring process data, which has certain practicability, but for leachate treatment, a field that is highly dependent on the process environment, machine learning industrial control from process data is theoretically feasible, but it needs to be realized. a complex environment, relatively difficult

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