On-line detection model of total alkalinity of water based on improved genetic algorithm

Abstract

In order to quantitatively analyze the total alkalinity of water in eco-industrial park, conduct on-line detection of total alkalinity of water in eco-industrial park, and improve the monitoring and dredging ability of total alkalinity of water and related pollutants in eco-industrial park, an on-line detection model of total alkalinity of water in eco-industrial park based on improved genetic algorithm was proposed. The statistical information mining model of total alkalinity and concentration characteristic distribution of related pollutants in eco-industrial park is constructed, the hydrogeological conditions are identified, the characteristic structural parameters of total alkalinity in eco-industrial park are determined, the improved genetic evolutionary algorithm method is adopted to manage the distribution characteristic of total alkalinity and concentration of related pollutants in eco-industrial park, and the aquifer parameters in the evaluation area are simulated. A transfer conduction control model of total alkalinity and related pollutants concentration characteristic distribution in eco-industrial park was established. The diversion rate and throughput of total alkalinity and related pollutants concentration characteristic distribution in eco-industrial park were taken as constraint indicators, and the transfer control of total alkalinity and related pollutants concentration characteristic distribution in eco-industrial park was carried out, so as to realize numerical quantitative detection and analysis of total alkalinity in eco-industrial park. The simulation results show that this method has good performance in online detection of total alkalinity of water quality in eco-industrial park, and has strong dredging ability, which improves the treatment level of total alkalinity of water quality and related pollutants in eco-industrial park.