Abstract
An inferential sensor is a computer program used for inferring the process variables, which are very hard to measure from the available measurement data. Measurement noises can affect the quality of the data which can be improved by wavelet denoising method. The objective of this paper is to design an inferential sensor for estimation of Benzene concentration in a typical distillation column. Selection of the most relevant input variables for estimation can improve the performance of inferential sensor which is done by Principal Component Analysis (PCA) technique. In this paper an inferential sensor is proposed based on a novel modification of the nearest neighbor distance-based clustering for developing a Takagi-Sugeno-Kang (TSK) fuzzy model optimized by the Particle Swarm Optimization (PSO) algorithm. The proposed technique was compared against the conventional nearest neighbor distance-based clustering approach optimized by PSO. The simulation results confirm that the designed inferential sensor based on the proposed method is more accurate even for a noisy data set.
Highlights
Inferential sensors [1,2,3] are the estimation models, which employ measurable variables to predict the variables that can be hardly measured, because of the technological constraints, long measurement periods, and considerable expenses
Data-driven inferential sensors have been broadly employed in the process industry, considering the fact that typical models are based on data measured in process plants and make the model of real processes [6]
The most common techniques used in designing the inferential data-driven sensor are the Artificial Neural Network (ANN) [8], Fuzzy systems including clustering method [9], Partial Least Square (PLS) [10], Neuro-Fuzzy systems (NFS) [11], Principle Component Analysis (PCA) [12] and Support Vector Regression (SVR) [13, 14]
Summary
Inferential sensors [1,2,3] are the estimation models, which employ measurable variables (like temperature and flow) to predict the variables that can be hardly measured, because of the technological constraints, long measurement periods, and considerable expenses. They can detect fault and validate the measurements of physical sensors as a backup sensor [4]. Data-driven inferential sensors have been broadly employed in the process industry, considering the fact that typical models are based on data measured in process plants and make the model of real processes [6]. The data-driven inferential sensor can be designed using hybrid techniques [15]
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