Detail Cantuman

Modeling, simulation, and control are important in chemical process industries because simulation helps to increase the transparency of the structures and operating rules within a production system and it allows a quantitative assessment of the efficiency of material and information flows, and it can not be solved manually because its mathematical difficulties to simulate and control the modeled system is using computer programms. Analysis by indentifying the process model of the reactor. For this analysis is done by several methods depend on the purpose. The methodes used including Laguerre function, extended model, model predictive control, finding receding horizon control using DMPC (Discrete-time Model Predictive Control) to find the optimum condition with Hilderth quadratic algorithm. The core technique in designing the discrete-time MPC is based on optimizing the future control trajectory, i.e., the difference of the control signal Δu(k). By assuming a finite control horizon Nc,the difference of control signal Δu(k) for k =0, 1, 2, ..., Nch -1, the difference ΔU while the rest of the Δu(k) for k = Nch, Nch+1, ..., Npr is assumed to be zero. With parametrization of the control signal trajectory using Laguerre functions, we have flexibility to take the location of the future constraints and this could reduce the number constraints within the prediction horizon and it is sufficient to prove that the constraints are imposed in the transient period of the response. This is the reason to consider disctete-time MPC with constraints. Keywords : discrete orthonormal basis; discrete-time MPC; Laguerre function; model predictive control