Intelligent monitoring and control method for coagulation process based on multisource information fusion technology
A multi-source information fusion and intelligent monitoring technology, which is applied in separation methods, chemical instruments and methods, flocculation/sedimentation water/sewage treatment, etc., can solve problems that cannot fully reflect raw water
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specific Embodiment approach 1
[0005] Embodiment 1: This embodiment is composed of the following steps: 1. Utilize the turbidity sensor 1, the pH sensor 2, the conductivity sensor 3, the temperature sensor 4 and the flow sensor 5 arranged in the raw water to obtain the corresponding water quality parameters respectively. Signals I1, I2, I3, I4 and I5; step 2, input the corresponding signals I1, I2, I3, I4 and I5 representing water quality parameters into the space-time fusion system 6 using the fuzzy neural network algorithm, and the output value α is input to the comparator The positive input terminal of 10 is used as the setting value of the single-factor coagulation intelligent control system; 3. The corresponding calculation is performed in the controller 7 of the single-factor coagulation intelligent control system, and the dosage of the coagulant is output to the mixer. The coagulant dosing pump 9, and the single-factor detector 8 set in the coagulation reaction tank feeds back the detected feedback va...
specific Embodiment approach 2
[0006] Embodiment 2: The present embodiment will be described in detail below with reference to FIG. 2 . The difference between this embodiment and the first embodiment is that the processing steps of the data in the second step in the first embodiment in the spatiotemporal fusion system 6 using the fuzzy neural network algorithm are composed of the following steps: 201. Separate the input signal I1 , I2, I3, I4 and I5 perform data-level fusion, that is, obtain the detection value of each sensor and obtain the rate of change of the detection value of each sensor, and analyze whether the continuous output data of each sensor has abnormal changes to determine whether the data is Reliable, so as to determine whether the instrument has problems such as failure, noise interference, signal loss, etc.; 202. Perform feature-level fusion on the data that has undergone data-level fusion. The fuzzy neural network with a 5-layer network structure converts the information of different meas...
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