Mittermaier, Heinz KarlLe Roux, Johan DerikOlivier, Laurentz EugeneCraig, Ian Keith2024-07-302024-07-302023-11Mittermaier, H.K., Le Roux, J.D., Olivier, L.E. et al. 2023, 'Model-plant mismatch detection for a plant under model predictive control : a grinding mill circuit case study', IFAC-PapersOnLine, vol. 56, no. 2, pp. 11778-11783. DOI: 10.1016/j.ifacol.2023.10.566.2405-896310.1016/j.ifacol.2023.10.566http://hdl.handle.net/2263/97299This articles investigates two different techniques of identifying model-plant mismatch for a grinding mill circuit under model predictive control. A previous attempt at model-plant mismatch detection for a grinding mill, in the form of a partial cross correlation analysis, is used as a benchmark for model-plant mismatch detection and degraded sub-model isolation. This is followed by an investigation of the plant model ratio technique applied to the same system. The plant model ratio technique is able to isolate the sub-model containing a mismatch as well as detect the specific parameter in a first-order-plus-time-delay model responsible for the mismatch. A simulation study is used to quantify and compare the results between the two model-plant mismatch detection methodologies. The results indicate plant model ratio accurately and timeously detects mismatches in sub-models. This allows for system reidentification or controller adaption to ensure optimal process performance. The advantage above partial cross correlation is the parameter diagnosis within the degraded sub-model coupled with the mismatch direction.en© 2023 The Authors. This is an open access article under the CC BY-NC-ND license.Controller performance monitoringGrinding mill circuitModel-plant mismatchProcess performance monitoringModel predictive control (MPC)SDG-09: Industry, innovation and infrastructureArticle