Predicting The Regularity Of The Fouling Characteristics Of Heat Exchanger Equipments

The formation of heat exchanger fouling is a multiphase, multi-component flow process affected by many factors, momentum, energy, and mass transfer, even the existence of biological activities. Its theoretical basis involves not only heat and mass transfer, but chemical kinetics, fluid mechanics, colloid chemistry, thermodynamics and statistical physics, microbiology, nonlinear science and interface science, etc. It is a typical multi-disciplinary highly complex problem. Fouling prediction, as the foundation of fouling study since the1980s and the three main directions, is aiming at establishing a common, accurate, and easily applied prediction models though theoretical analysis and experimental study of the formation of fouling, to provide guidance for the design and operation of the heat exchanger. The traditional prediction methods have made some encouraging progress, however, because of the impact of many factors on fouling formation process and difficulties brought by multidisciplinary, research progress is still slow, even far away from the target. Based on the experimental system built and fouling data accumulated during the PhD, this paper attempts to model construction and prediction of the heat exchanger fouling characteristics, by using intelligent forecasting theory and methods, such as support vector machines, partial least squares algorithm, fuzzy mathematics, etc. The specific contents are as follows:As a tool for model building, support vector machine (SVR) was introduced into modeling of the heat exchanger fouling characteristics. The affection of the parameters on the SVM model was studied under the condition that RBF function was selected as kernel function. To solve the problem encountered in the optimization process of penalty coefficient and nuclear factor, the "microscope" theory was put forward for the first time. It was proved by practice tests that the method could improve the speed and accuracy of the optimization and became the basis of subsequent works coupled with effective co-ordination of the simulated annealing algorithm.Fouling characteristic of plate heat exchanger was studied through the experimental system, with the Songhua River water as working fluid. Several water quality parameters: pH value, conductivity, dissolved oxygen, turbidity, hardness, alkalinity, chloride ion, iron ion concentration, chemical oxygen demand, total bacterial count, which had great influence on the formation of fouling, as well as running condition, fouling resistance and other parameters were measured through the experimental system built. A group of fouling data of the typical water quality was obtained. Two prediction models of fouling characteristics of the plate heat exchanger were built based on partial least squares algorithm (PLS) and support vector regression machine (SVR) with water quality parameters as independent variables and fouling resistance as dependent variable, and the impact of water quality parameter on predicting accuracy was analyzed. Research results showed that:the prediction accuracy of two methods could be controlled within10%and meet the requirements of the project, which proved that it was feasible to predict heat exchanger fouling characteristics by water quality of circulating cooling water, and put forward an effective new method to forecast fouling characteristic under the condition of known water quality parameters in the process of designing the cooling water system. Through the comparison of the prediction results, it was proved that the SVR method was better than the method of PLS, and it was recommended modeling and predicting of the fouling characteristic of plate heat exchanger based on SVR. The impact of the water quality parameters on prediction model was discussed by the means of removing the water quality parameters one by one. The results showed that deletion of part of water quality parameters could both improve the prediction accuracy of the model to some extent, but also reduce the cost of measurement.Fouling characteristic of plain tube was studied through the experimental system with the man-made hardness water as cooling medium to simulate the crystallization fouling. The parameters of temperature, fouling resistance et al. were measured and a group of fouling data of the same test tube was obtained, which was across the two operation cycle. Two predicting models of fouling characteristics of the tube were built based on PLS and SVR with outlet temperature, inlet temperature, wall temperature et al. as independent variables and fouling resistance as dependent variable. Research results showed that:the prediction accuracy of two methods could meet the requirements of the project and be used to predict the crystallization fouling characteristic of plain tube. Relatively speaking, environment temperature et al. gets easier, and saves manpower and material resources, so, it could realize on-line monitoring of fouling resistance of heat exchangers to predict the fouling resistance by temperature et al. At the same time, fouling characteristic of arc tube was studied through the experimental system, in which there were the same two stainless steel test tube, with the MgO particle being added into working liquid to simulate particle solution. The SVR model was selected to predict the fouling characteristics of arc tube. By comparison, the results showed that:the predicting model should be modified to improve the precision of the model, when the flow velocity or other main parameters were no longer constant, changing along with time.The theory of class centroid vector was introduced into predicting the slagging characteristics of coal-fired. The method could not only accurately predict the slagging tendency of mixed coal, but also effectively solve the problem of mixing ratio of the coals. It was based on fuzzy theory that the fuzzy correlation coefficient was put forward, fuzzy relative weight was constructed, and the pattern recognition algorithm of slagging characteristics of heat exchanger was proposed. The predicting results showed that the method proposed was feasible and effective. It provided a new research method of predicting the slagging characteristics of heat exchanger, which was a development and perfection of traditional pattern recognition theory. Vague sets theory was introduced into the prediction of the slagging characteristics of coal-fired boilers, and at the same time, a new formula of similarity degree, which was based on the sense of distance, was proposed to calculate the similarity between vague sets. The results showed that not only this method was feasible, but also the operators could easily predict the slagging state of the coal-fired boilers based on this method, and thus eliminate the influence of interference factors. The prediction model of slagging state of coal-fired boilers was built based on RBF network. The prediction results showed that, the RBF model was higher in prediction accuracy than that of the conventional BP network, and avoided the local minima problem. The slagging characteristics of coal-fired boilers were predicted effectively based on nonlinear support vector machine for regression. The method was not only high in prediction accuracy, but also had the most prominent advantage of small samples learning, which solved the problem of pattern recognition in multi-dimensional vector space. In order to compare the predicting methods above, the same known samples were used for training and testing, the comparing results showed that:RBF method, SVR method, FRW method and Vague sets method had the highest predicting accuracy rate, followed by PLS method, and the lowest was the method of class centroid vector.The prediction model of softening temperature of coal ash was built based on the chemical analysis component and Elman network. The model could not only be able to accurately predict the softening temperature of coal ash from the thermal power plan, but also was higher in prediction accuracy than that of traditional BP network. Through analysis of the model, the eight kinds of ash composition were found out, which played the major role in softening temperature of coal ash.