Process-Shift-Aware Three-State Output Supervision for Recursive Alumina-Concentration Soft Sensing in Aluminium Electrolysis

Dissolved alumina concentration in the electrolyte cannot be measured continuously in aluminium reduction cells. Recursive soft sensing estimates the concentration from online process variables; however, process-condition shifts and the write-back of previous estimates can change the inputs to later predictions before the next laboratory concentration measurement is available. This study develops a dual-timescale three-state output supervisor for a published 730-sample sequence from one 400 kA aluminium reduction cell, using working current, cell voltage, electrolyte level, aluminium level, and electrolyte temperature as online inputs. Unlike monitoring methods that report process or model change, the supervisor assigns a use state to each recursive estimate. Process-variable deviation, adjacent prediction change, predictor-corrector discrepancy, and a cumulative sum (CUSUM) of process deviation are combined by state rules to determine Warning and Unavailable transitions. The observed later interval (samples 601–730) was evaluated by continuous recursive replay, while separate gradual-shift tests were synthetically constructed from the observed variable ranges. The supervisor entered Warning and Unavailable after 6.2 and 19.0 samples, on average. At matched prediction availability, it reduced the out-of-tolerance rate among available predictions by 15.81 percentage points and increased the failure blocking rate, defined as the fraction of all out-of-tolerance predictions assigned Unavailable, by 15.59 percentage points relative to random blocking. The method assigns Normal, Warning, and Unavailable use states to recursive estimates to support reference-measurement requests and output management.