A large-scale continuous halogenation process parameter closed-loop production control method and system
By constructing a multi-source data processing link and coordinated linkage adjustment, the problem of determining the input load, brine decay state and product maturation stage in large-scale continuous braising was solved. The generation of parameter target sets and synchronous adjustment of control commands were realized, forming a traceable closed-loop optimization archive. The problems of inconsistent data and disturbance judgment were solved, ensuring the stability and continuity of production.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- HUNAN TIEDAN FOOD CO LTD
- Filing Date
- 2026-04-10
- Publication Date
- 2026-07-17
AI Technical Summary
Existing technologies have several drawbacks in large-scale continuous brining processes. These include difficulty in identifying changes in the input load, determining the state of brine decay, and coordinating the determination of product maturation stages. Consequently, it is difficult to generate a parameter target set, implement control command adjustments and correction strategies, and the online detection results are disconnected from the parameter target set, resulting in a discontinuous link between control results and abnormal record write-back.
By constructing a data processing link from multi-source data to a continuous halogenation input set, and then to a process state set, time alignment, state verification, and unified formatting are performed. Combined with parallel recognition and state fusion, a parameter target set is generated, and coordinated linkage adjustment and adaptive execution of working conditions are carried out. Online detection result comparison, disturbance discrimination, and correction strategy generation are completed, and finally, write-back archiving and version tidying are performed.
It enables precise determination of input load, brine decay status and product maturation stage, ensuring that control actions are synchronized with changes in operating conditions, forming a traceable and verifiable closed-loop optimization archive, solving the problems of inconsistent data and difficulty in determining disturbances, and achieving a stable process state set and control effect.
Smart Images
Figure CN122411299A_ABST