Abstract
This paper presents the development of soft sensor empirical models using support<br /> vector machine (SVM) for the continual assessment of 2,3-dimethylbutane and 2-methylpentane mole percentage as important product quality indicators in the refinery isomerisation process. During the model development, critical steps were taken, including selection and pre-processing of the industrial process data, which are broadly discussed in this paper. The SVM model results were compared with dynamic linear output error model and nonlinear Hammerstein-Wiener model. Evaluation of the developed models on independent data sets showed their reliability in the assessment of the component contents. The soft sensors are to be embedded into the process control system, and serve primarily as a replacement during the process analysersb failure and service periods.
Highlights
Process analysers, used for measurement of key process variables, are often weak links in refinery plants
This paper presents data-driven soft sensors which have common steps in the development procedure: selection of real process data from plant history database, data pre-processing, determination of a model structure and regressors, model estimation and validation[2]
A particular part is dedicated to the description of the refinery isomerisation process, while soft sensor model development is explained in detail
Summary
Process analysers, used for measurement of key process variables, are often weak links in refinery plants. Their long analysis time, tendency of failure, and high price usually make them impractical and unprofitable. Soft sensors that enable real-time prediction of key product properties occur as an alternative to process analysers. This paper presents data-driven soft sensors which have common steps in the development procedure: selection of real process data from plant history database, data pre-processing, determination of a model structure and regressors, model estimation and validation[2]. The support vector machine is a popular method for soft sensor model development presented by Vapnik[3] as part of a general learning theory. The method has attractive features, such as the ability to learn well with only a very small number of free
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