A METHODOLOGY TO OBTAIN ANALYTICAL MODELS THAT REDUCE THE COMPUTATIONAL COMPLEXITY FACED IN REAL TIME IMPLEMENTATION OF NMPC CONTROLLERS

摘要

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.