Department of Chemical Engineering

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    Adaptive non-linear soft sensor for quality monitoring in refineries using Just-in-Time Learning—Generalized regression neural network approach
    (Elsevier, 2022-04) Mohanta, Hare Krishna; Pani, Ajaya Kumar
    Real time estimation of target quality variables using soft sensor relevant to time varying process conditions will be a significant step forward in effective implementation of Industry 4.0. Generalized Regression neural network (GRNN) has been used as a steady state quality monitoring soft sensor with reasonable estimation accuracy. However, the accurate prediction capability of GRNN has rarely been explored in a time varying environment. This article reports design of adaptive soft sensor using GRNN as a local model in Just-in-Time learning (JITL-GRNN) framework. The JITL-GRNN adaptive soft sensing technique is further investigated in various dimensions such as, the effect of different similarity index criteria and relevant dataset size on model prediction accuracy and model computation time. Performance of the proposed JITL-GRNN soft sensor is investigated by assessing its prediction accuracy on two benchmark industrial datasets. In addition, dynamic Non-linear autoregressive with exogenous inputs (NARX) neural network model is also developed and the performance of NARX model was compared with the proposed JITL-GRNN model. Results show that the JITL-GRNN adaptive soft sensor has at par or better prediction capability than the NARX model and many other models reported in literature.
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    Support vector regression modeling in recursive just-in-time learning framework for adaptive soft sensing of naphtha boiling point in crude distillation unit
    (Elsiever, 2021-08-15) Pani, Ajaya Kumar; Mohanta, Hare Krishna
    Prediction of primary quality variables in real time with adaptation capability for varying process conditions is a critical task in process industries. This article focuses on the development of non-linear adaptive soft sensors for prediction of naphtha initial boiling point (IBP) and end boiling point (EBP) in crude distillation unit. In this work, adaptive inferential sensors with linear and non-linear local models are reported based on recursive just in time learning (JITL) approach. The different types of local models designed are locally weighted regression (LWR), multiple linear regression (MLR), partial least squares regression (PLS) and support vector regression (SVR). In addition to model development, the effect of relevant dataset size on model prediction accuracy and model computation time is also investigated. Results show that the JITL model based on support vector regression with iterative single data algorithm optimization (ISDA) local model (JITL-SVR:ISDA) yielded best prediction accuracy in reasonable computation time.