Intellectual control of the electrolysis process in the production of caustic soda

4Result and Discussion

Values ​​of electrolyzer parameters at different points in time were used as data for modeling. These data are obtained empirically and loaded into the process neuroregulator program. The purpose of this system is to keep the measured parameter at a certain value and reduce the error as much as possible. The operation of the system is as follows. The more the number of trainings, the less the error. Therefore, we took the number of measurements up to 4 times. In addition, we can get a graphical view of the system control based on the results obtained using this program. Below we will consider examples of performing measurements on various parameters, training them, and how the system works in different situations. The article presented a model for the following input parameters: salt solution temperature, pressure, pH level, chlorine gas pressure, temperature, pH level.

Fig.4. Training the neural network to control the parameters of the salt water in the electrolyzer. Here A is the temperature limit of the salt water, B is the pressure value, and C is the pH of the value. Input line 1, 2, 3, 4 values ​​obtained from measurement results. Out1, out2, out3, out4 – output parameters.



Fig.5. Training of the neural network system to control the parameters of chlorine gas.

Fig.6. If the value given during neural network training does not match the threshold value, this program will not work. That is, it works only if the values ​​given to Input line 1, input line 2, input line 3, input line 4 in the program are based on the limit set to A, B, C (the values ​​in this range are taught in the system). In this picture, the value of parameter input line 4 does not correspond to the given values ​​of B, in such a case, it is necessary not to get the result from output parameter out 4 in order to properly control the system.
5Conclusion:
In this article, the control systems of the membrane electrolysis method of caustic soda production with the neural network method were considered. A neural network model of the operating mode of the membrane electrolyzer was built including and found to be effective for practical use. Thus, this model is used to predict the safety system, emergency situations, as well as to calculate the parameters of the electrolyzer during its design and reconstruction. This modeling method can be applied to technological devices of any complexity when experimental data are available.
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