As an important chemical
intermediate, 2-mercaptobenzothiazole
(MBT) is widely used in various processes, especially in the rubber
industry. However, there is no first-principles model that describes
the synthetic process of MBT. This paper focuses on the formulation
of a reliable mathematical model represented by a series of differential
and algebraic equations for the industrial batch MBT production process.
It is difficult to estimate all of the unknown parameters in the model
because of the lack of sufficient industrial/experimental data. Thus,
a reduced estimable parameter set is derived by performing estimability
analysis on the original estimation problem. A multiple-starting-point
strategy is then applied to numerically solve the non-convex parameter
estimation problem with the weighted least-squares approach. Subsequently,
a cross-validation technique is employed to evaluate the generalizability
of the proposed model. Finally, it is confirmed that the proposed
model produces encouraging prediction performance with regard to independent
test data.
We formulate an integrated framework for the robust dynamic optimization of nonlinear chemical processes under measurable and unmeasurable uncertainties. An affine decision rule is proposed to approximate the causal dependence of the wait‐and‐see decision variables on the gradually revealed measurable uncertainties. To overcome the computational intractability of the proposed model, a linearization technique based on the first‐order Taylor expansion is introduced around the nominal values of uncertainties to derive the robust dynamic counterpart, which can be discretized to a large‐scale nonlinear programming (NLP) formulation. Effects of first discretizing the dynamic models or introducing the affine decision rule are investigated. The proposed framework is also compared with the state‐of‐the‐art re‐optimization and traditional robust optimization approaches. An illustrative example and an industrial semi‐batch 2‐mercaptobenzothiazole production case are involved to demonstrate the advantages and applicability of the proposed framework.
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