Using a Neural Network Approach to Predict Deposits on the Surfaces of Heat Exchange Equipment

This work proposes a neural network (NN) approach for predicting the following values: the heat transfer coefficient at the point of interest in the operational period of plate heat exchangers (PHEs), and the time-point to reach the lower allowable limit of the heat transfer coefficient. In this approach, neural network models replace complex mathematical modelling that used systems of differential equations and matrices of heuristic coefficients to calculate the flow rate of deposits on PHE plates, which required the involvement of serious computing resources. Training a feed-forward neural network (FFNN) on a small dataset simulated in the vicinity of reference points obtained by industrial measurements showed the proper coefficient of determination R2 = 0.99 (accuracy) of the short-term prediction forecasts and for operational evaluation of the heat transfer coefficient due to the static type of NN

Resource and Energy Saving Neural Network-Based Control Approach for Continuous Carbon Steel Pickling Process

Steel pickling processes are very important for steelmaking production quality. Pickling process is based on chemical reaction of  acidic pickling solution with scale impurities on steel strip surface. In sulfuric acid pickling process together with scale removal. The partial dissolving of steel surface takes place because of sulfuric acid attack takes place. Continuous sulfuric acid carbon steel pickling in existing plants is very energy and water consumptive. An innovative approach is proposed for modernization of continuous sulfuric acid pickling process performance. The proposed neural network model may be used to optimize consumption of sulfuric acid, decrease energy consumption, reduce steel losses and, respectively, reduce harmful wastes and emissions from continuous steel pickling lines. This is possible because of quick adaptation of neural network model to changing environment through fast training algorithms. The developed model identifies the temperature necessary to provide the set process rate at the current variable values of the parameters: concentration of sulfuric acid and concentration of ferrous sulfate multi-hydrates in solution and transmits the temperature value as a current task to regulator in each discrete moment of the process. The results of application of the developed neural network, included as a part of the presented process supervisor, prove its efficiency in use for pickling process operational control: steam consumption for pickling process was decreased by 8%, acid consumption for pickling process was decreased by 26%, while the process efficiency and quality remain unaffected