A METHODOLOGY TO OBTAIN ANALYTICAL MODELS THAT REDUCE THE COMPUTATIONAL COMPLEXITY FACED IN REAL TIME IMPLEMENTATION OF NMPC CONTROLLERS
Datasets usually provide raw data for analysis. This raw data often comes in spreadsheet form, but can be any collection of data, on which analysis can be performed.
Abstract Model Predictive Control, MPC and NMPC, and real-time optimization, RTO and D-RTO, are known to help plant operability through the mitigation of impacts caused by external disturbances. However, the usage of these tools in industry requires overcoming some challenges, for instance: accurate models of the process, particularly in regard to nonlinearities; suitable computational time for obtaining the solution of large-scale problems and model mismatch between the RTO or D-RTO and NMPC. In this paper, we present a methodology to obtain analytical model predictions based on a Hammerstein structure to represent the process nonlinearities, reducing the computational effort in real-time applications. Unlike most common approaches that transform NMPC internal models, described by differential-algebraic equations (DAE), into an approximate system of nonlinear algebraic (NLA) equations using, for instance, orthogonal collocation, in the proposed approach, the obtained NLA is an exact description of the original DAEs system. The proposed algorithm was applied to a non-isothermal CSTR (continuous stirred tank reactor) integrated with an optimization layer. The results show that the proposed structure presented a significant reduction in computational time without performance loss, when compared with the NMPC using a rigorous model. Moreover, the proposed strategy demonstrated good performance in tracking the targets sent by the optimization layer, without model mismatches between layers.