Design and application of multidimensional decision system for aluminum electrolysis
Aluminum electrolysis is a complex process, the outputs of the controller are hard to decide. This paper described a system for the decision of the setting voltage, AlF3 addition and aluminum tapping amount in aluminum electrolysis process according to the equivalent superheat which is regarded as the main parameter. The system has four sub-systems, which are the fault diagnosis, equivalent superheat calculation, control extent decision, decision quantity correction. The cell state diagnosis was implemented based on the frequency characteristics of cell resistance, which helps to select the decision strategy. The superheat soft measurement model was given by the analysis and calculation of electrolyte composition after the aluminum fluoride volatilization rate model was built, and it was on-line corrected by the actual measured value. The outputs of the controller were got by the equivalent differential diagram. At last, the decision content was corrected by the prediction of temperature. This system have been applied in a 300kA aluminum production control system, and the results showed that the temperature can be controlled between 945 degree celsius and 960 degree celsius, and the superheat can be controlled within 12 degree celsius, the current efficiency increased and power consumption reduced.
Diagnosis System for Alumina Reduction Based on BP Neural Network
The diagnosis system for the alumina reduction was developed on the basis of BP neural network with optimization by genetic algorithm. The neural network used the characteristic vectors composed of the frequency energy calculated from cell resistance as 10 inputs and three cell statuses as 3 outputs. The neural network was certified by industrially sampling data. The results showed the accuracy ratio was larger than 80%, which can meet the requirements in the aluminum production. The diagnosis software was designed and applied in an aluminum smelter.
Traceability strategy for gear-pitch-measuring instruments: development and calibration of a multiball artifact
There is a strong demand to ensure the traceability of gear-measuring instruments (GMIs). We propose a multiball artifact (MBA) for the evaluation of pitch-measuring accuracy. The aim of the MBA is to transfer the minimum uncertainty from a calibrated value at the National Metrology Institute of Japan (NMIJ) to a measured value of GMIs at a shop floor. The MBA is composed of equally spaced high-accuracy balls around an axis. The pitch-measuring accuracy of GMIs is evaluated by measuring the angular pitch deviation of the balls instead of the angular pitch deviation of a gear. We calibrated the angular pitch deviation using a coordinate measuring instrument (CMM) at the NMIJ and adapting a multiple-orientation technique. We proposed a calibration strategy of the angular pitch deviation. The calibration value of the pitch deviation for the MBA at the NMIJ was obtained with a measurement uncertainty (U95) of 0.2 µm. We evaluated the pitch-measuring accuracy of a GMI using the calibrated MBA. Each value of U95 for the cumulative and single pitch deviations for the left and right flanks was less than or equal to 0.5 µm. The small uncertainty was transferred from the NMIJ to the GMI.