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A new scheme of robust adaptive partial least squares (PLS) method was proposed for the purpose of prediction and monitoring of an industrial wastewater treatment process that has highly complex and time-varying process dynamics. The essential feature of this method is that all incoming process data are preliminarily screened on the basis of a combined monitoring index and each observation identified as an outlier is simply eliminated (hard threshold) or suppressed by using a weight function (soft threshold) prior to model update. To elucidate the feasibility of the proposed scheme, various PLS modeling approaches, including conventional ones, were evaluated and their results were compared with each other. While the conventional approaches clearly revealed their limitations such as the inflexibility of the model to process changes and the misleading model update by high leverage outliers, most robust adaptive PLS approaches based on the proposed scheme exhibited fairly good performances both in the prediction and monitoring aspects. Among the tested methods, the robust adaptive PLS method using Fair weight function showed the best performances, reasonably maintaining the robustness of the PLS model.

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