The Research Of Corrosion Prediction Of The Circulating Cooling Water System

Industrial circulating cooling water is one of the largest unit water consumption of petrochemical industry.In industrial circulating cooling water system of constant and circulatory use,causing water quality deterioration.After the water quality variation will lead to corrosion,scaling and microbial growth,etc.Especially scaling can reduce heat exchange equipment capacity,when scaling problems,will lead to pipeline jam,resulting in production.If can predict scaling tendency,take corresponding measures in time,can reduce the accident.Petrochemical field commonly used recycled water scale common test method:1.The fouling resistance tester is to test the water quality,but in the process of the fouling resistance tester is the use of equipment testing probe depletion,belong to the perishable goods,and equipment are in need of regular maintenance and maintenance cost is higher;2.Rely on the experience of the field staff to circulating water scaling trend judgment but lack of scientific theoretical basis;3.To test the water quality by applying the method of bolt has a lot of lag.Therefore predict the trend of scale formation of circulating cooling water is not only save the running cost for the enterprise,but also can improve the economic benefit.Due to the circulating cooling water system fouling mechanism is very complex,there is no a unified recognized forecast model,so need to find a suitable for petrochemical industry circulating cooling water fouling prediction model.Select the parameters of current scaling prediction model is dependent on the actual experience in the operation of the system is selected,all failed to forecast the relationship between parameter and the adhesion rate reflected,so the precision and accuracy of scaling prediction results there will be some deviation,is not conducive to cleaning work,so the use of grey relational analysis method to select parameters research work.After a comprehensive comparison of several methods,and finally use a weighted grey correlation model(II)to select parameters,and ultimately determine the impact of scaling associated with 6 parameters is larger as input variables of the prediction model for water quality,the production work has been focused on detection,improve the validity of data.The generalization ability of the least squares support vector machine regression is used as the foundation of the prediction model,and the two key parameters are based on the LSSVM:Regularization parameter and kernel width are optimized,The particle swarm optimization algorithm(PSO),which has a strong ability of global search,and its improved algorithm AMPSO and QPSO are adopted to optimize the three optimization algorithms.Relatively accurate prediction results are obtained,which improves the prediction accuracy and model performance.Accurate prediction results will make production equipment life improved,timely dosing makes repeated use of industrial circulating cooling water rate is greatly improved,reducing the operation cost of enterprises,and has a guiding significance for the detection of water quality.