The study of techniques for qualitative trend analysis (QTA) has been a popular approach to address challenges in fault diagnosis of engineered processes. Such challenges include the lack of reliable extrapolation of available models and lack of representative data describing previously unseen circumstances. Many of these challenges appear in biological systems even when normal operation can be assumed. It is for this reason that QTA techniques have also been proposed for the purpose of fault detection, automation, and dynamic modeling. In this work, we adopt a shape-constrained spline function method for the purpose of unknown input estimation. Thanks to data collected at laboratory-scale in a biological reactor for urine nitrification, this novel approach has been demonstrated successfully for the first time. (C) 2017 Elsevier Ltd. All rights reserved.