The invention discloses a
fouling experimental device, provides a particulate
fouling resistance prediction method for an arc-tube
heat exchanger, and further discloses a corresponding
prediction system by utilizing the object-oriented high-level language Delphi. The prediction method comprises establishment of the
fouling experimental device, determination of experimental tubular products, determination of geometric dimensions, installation of a measurement and
control unit, measurement and
processing of parameters, establishment of prediction models, optimization of important parameters, and establishment and application of judgment models and the
prediction system. According to the particulate fouling experimental device, the prediction method and the
prediction system for the arc-tube
heat exchanger, the defect of a local minimum of a neural network and other conventional methods is overcome, the phenomena of under-learning and over-learning are effectively restrained, the problem of generalization in the
machine learning theory is solved, the calculated amount is small, the model optimization speed is high, and online monitoring of fouling resistance of a heat-exchange device can be achieved. The most prominent
advantage is that small samples can be used for training models. Due to the fact that the fouling characteristics of the
heat exchanger are predicted by the models in terms of temperature, flow speed and other parameters which are easy to measure, a lot of manpower and
material resources are saved, and a new method is provided for designing a cooling water
system under a known
water quality condition afterwards and predicting the fouling characteristics.