In the development of soft sensors for chemical processes, outliers of input variables and the time-varying nature of the process are difficult to address, thereby often resulting in unavailable prediction. Motivated by these issues, new just-in-time (JIT) learning is derived to track the normal changes of processes regardless of abrupt noises. Such an approach adapts a proposed robust nearest correlation (RNC) algorithm with multimodel ensemble learning to enhance conventional JIT learning. Furthermore, to gauge the quality of the given prediction, we integrate such JIT learning with the inductive confidence predictor (ICP) to yield a new soft sensor called the "interval soft sensor" which generates not only prediction values but also associated confidence values that represent the credibility of a soft sensor's output. These ideas were applied to a wastewater treatment process. The proposed interval soft sensor was seen to be effective for prediction in the absence and presence of outliers in the process and for validating the online analyzer because of its modeling method independent of output data.