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Static estimators are commonly used as "soft sensors" in the process industry. The performance of the estimators is dependent on whether it is used for monitoring (open-loop) or for closed-loop control applications. In this work, we propose to design estimators that are specialized for each case. The approach is to minimize the estimation error for expected disturbances and measurement noise. The main extension, compared to previous work, is to include measurement noise and to provide explicit formulas for computing the optimal static estimator. We also compare the results with standard existing estimators (e.g., partial least-squares (PLS)). The approach is applied to estimation of product composition in a distillation column from a combination of temperature measurements.

Thermal cracking of hydrocarbons converts them into valuable materials in the petrochemical industries. Multiplicity of the reaction routes and complexity of the mathematical approach has led us use a kind of black-box modelingartificial neural networks. Reactor feed type plays an essential role on the product qualities. Feed type is a qualitative character. In this paper, a method is presented to introduce a range of petroleum fractions to the neural network. To introduce petroleum cuts with final boiling points of 865 °F maximum to the neural network, a real component substitute mixture is made from the original mixture. Such substitute mixture is fully defined, it has a chemical character, and physical properties can be simply retrieved from databases. The mixture compositions are defined with the aid of an optimization algorithm−interval method. The obtained TBP curves of substitute mixture are in good agreement with the experimentally obtained curves. Nine single carbon structural increments will be the representative of 93 real component compositions in order to make the topology of the neural network smaller and hence to have a less complex model.