Enhancing produce safety: State estimation-based robust adaptive control of a produce wash system. (February 2020)

• Record Type: Journal Article
• Authors: Azimi, Vahid; Munther, Daniel; Sharifi, Mojtaba; Vela, Patricio A.
• Is Part Of: Journal of process control. Volume 86(2020)
• Page Start: 1
• Page End: 15
• Publication Date: 2020-02
• Subjects: Produce wash system -- Robust adaptive sliding mode control -- Pathogen levels -- Hybrid extended Kalman filter; COD chemical oxygen demand -- FC free chlorine -- FCIR FC injection rate -- E. coli Escherichia coli -- MPN most probable number -- RASM robust adaptive sliding mode -- HEKF hybrid extended Kalman filter -- NI noise identification -- PSO particle swarm optimization -- RMSE root mean square error -- UUB uniformly ultimately boundedness; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2019.12.006
• Languages: English
• ISSNs: 0959-1524

Abstract

Highlights: The rapid introduction of fresh-cut produce into a produce wash system decreases free chlorine (FC) concentration level. A state estimation-based robust control strategy is developed to stabilize FC concentration level during produce washing. A sufficient FC injection rate is provided to compensate for the fall in FC level and to minimize pathogen levels. The FC tracking, stability, and robustness are guaranteed using a Lyapunov framework. Cross-contamination management is examined in terms of the prevalence and mean pathogen levels of incoming pre-wash lettuce. Abstract: The rapid introduction of fresh-cut produce into a produce wash system can dramatically decrease the free chlorine (FC) concentration level in the wash water, resulting in potential widespread cross-contamination throughout the entire wash system. To minimize such contamination, a sufficient level of FC must be maintained in the wash water. This paper presents a state estimation-based robust adaptive sliding mode (RASM) control strategy for the wash system to stabilize the FC concentration level during fresh-cut iceberg lettuce washing. This feedback control law for FC dosing is suggested to provide a sufficient FC injection rate (FCIR) to the wash system in order to compensate for the fall in the FC level and in turn to minimize the Escherichia coli (E. coli) O157:H7 levels on washed lettuce and in the wash water. The proposed controller uses the estimated chemical oxygen demand (COD) and FC concentration as feedback signals while system states are estimated by a hybrid extended Kalman filter (HEKF) and the unknown noise statistics are identified by a noise identification (NI) algorithm. Uniformly ultimately boundedness (UUB) of the FC concentration tracking error in the presence of unmodelled dynamics is proven using the Lyapunov framework and Barbalat's lemma. The E. coli O157:H7 contamination levels are predicted from the joint estimator and controller properties. Simulation results show that the proposed NI-based HEKF/RASM control methodology achieves FC tracking while the pathogens converge to their predicted levels. The E. coli O157:H7 levels decrease as FC concentration increases and in particular, no E. coli O157:H7 is detected when FC concentration is regulated at 15 mg/L. Two robustness tests are performed to show the performance of the proposed controller in the presence of chlorine actuator failure and system parameter uncertainties. Finally, cross-contamination management is examined in terms of the prevalence and mean pathogen levels of incoming pre-wash lettuce in the context of FC regulation at 15 mg/L.