Forecasting and Optimization of Pressure Loss Using Soft Computing Methods in a Submerged Multi-nozzle Electrolyte Flow System
摘要
The effective design and operation of submerged multi-jet electrolyte flow systems is necessary for a variety of industrial applications, including electrochemical machining and electroplating. The optimization and prediction of pressure drop in such systems utilizing Response Surface Methodology (RSM) in conjunction along with artificial neural network is thoroughly examined in this study. A prototype submerged multi-jet electrolyte flow system is used to gather experimental data under various operating conditions. Then, using critical input factors, ANN were employed to build prediction models for pressure drops. ANN was used to forecast the pressure drop at several heights between 0.01 and 0.25 m, the optimum pressure drops, 1476.2 N/m2, was recorded at 0.25 m. Additionally, the Response Surface Methodology is used to create empirical models that shows the process parameters and pressure drop are related. The recommended methodology offers a systematic and efficient approach to predict and optimize pressure drop in submerged multi-jet electrolyte flow systems. In industrial applications, this enables enhanced process performance and efficiency.