This paper presents a successful application of soft sensor technologies in process industry. The hybrid modeling technique, online prediction update, and robust implementation procedure are presented in detail. Once-through steam generators (OTSGs) have wide applications in In Situ oil sands industry for producing high pressure steam. Real-time control of steam qualities is essential to ensure optimal performance of the OTSGs and ultimately to reduce the production cost and emissions. However, neither existing online measurement nor off-line lab analysis of steam quality can meet this control purpose due to their own limitations. To resolve this problem, soft sensors for steam quality measurements of OTSGs are designed in this work based on a hybrid modeling technique, where online bias update with optimized weighting factor is incorporated to compensate the model error. Furthermore, online outlier detection is considered to ensure the robustness and reliability of the developed soft sensors. The successful applications to an industrial OTSG demonstrate the effectiveness and advantages of the developed soft sensors. (c) 2013 Elsevier Ltd. All rights reserved.