Application of Model Predictive Control to Renewable Energy Development via Reactive Distillation Process

Abstract

Abstract.Reactive distillation is a process that combines chemical reaction and separation in a single piece of equipment (distillation column). The process has a lot of benefits especially for those reactions occurring at conditions suitable for the distillation of the process components, and these result in significant economic advantages. However, owing to the complexities resulting from the integration of reaction and separation, its control is still a challenge to process engineers because it requires a control method that is robust enough to handle its complexities. Therefore, in this work, model predictive control (MPC) has been applied to a reactive distillation process used for developing a renewable energy known as biodiesel. The control algorithm of the MPC was formulated with the aid of MPC toolbox of MATLAB/Simulink in which the closed-loop models of the process were developed and simulated. The analysis of the results obtained from the simulations carried out for the optimization of the tuning parameters revealed that, among the tuning parameters considered, integral absolute error of the control system was less affected by the control horizon because its p-value was greater than 0.05 based on 95% confidence level. Furthermore, the simulation of the closed-loop system of the process using model predictive control tuned with control horizon of 11, prediction horizon of 18, weight on manipulated variable rate of 0.05 and weight on output variable of 2.17, which were the optimum parameters obtained using Excel Solver, showed that the system was well handled by the controller under servo control because it was able to get settled at desired mole fractions within 60 min. However, the settling time recorded in the case of regulatory control system of the process with the same controller was found not to be encouraging. Therefore, it is recommended that further work should be carried out on this subject matter in an attempt to obtain tuning parameters that will make the settling time of the closed-loop system of the process under regulatory control simulation very reasonable. Nomenclature ANOVA

Optimization and Sliding Mode Control of Dividing-Wall Column

“…In brief, formula 4 can correctly and efficiently establish a mathematical model for DWC. Furthermore, according to the literature, 20,43 it is feasible to estimate a DWC by formula 4. Therefore, the dynamic model of the DWC is modeled as a transfer function with a simple first-order pure lag.…”

“…In brief, formula can correctly and efficiently establish a mathematical model for DWC. Furthermore, according to the literature, , it is feasible to estimate a DWC by formula .…”

“…By optimizing K p , K i , and K d of the PID controller and c , k 1 , and k 2 of the SMC controller, the control system can provide excellent performance with quick response, small overshoot, and small steady-state deviation within a certain limit. The performance index functions ISE (integral square error) and IAE (integral absolute error) were used as the objective functions Figures and show the Simulink model of PID control and SMC control with ISE and IAE, respectively.…”

“…The performance index functions ISE (integral square error) and IAE (integral absolute error) were used as the objective functions. 43 When the limits of each selected parameter are input into Design-Expert, the running data of each simulation is generated and then is used for simulation in MATLAB. For each simulation performed, the integrated absolute error (IAE) given at the output is recorded and then input to Design-Expert to obtain the optimum tuning parameters used in the process control.…”

Experimental performances of nonlinear GPC and discrete–time PID in multivariate process: NARIMAX and ARX models in temperature control of reactive distillation column in synthesis of biodiesel from waste cooking oil

“…Cheng et al [15] designed a time-delayed active disturbance rejection GPC strategy. Giwa et al [16] implemented MATLAB/Simulink's MPC toolbox for renewable energy. Regarding the multivariable theoretical control, Wu [17] proposed an improved PID controller optimized with an extended non-minimal state space model based MPC, Saravanakumar et al [18] used Lagrange-based state transition algorithm to tune the decentralized PID controller, using its numerical stability and performance, Abraham et al [19] developed an optimal GPC using the first principle and linearized 16 th order and reduced fifth order models, Hadian et al [20] used an event-based neural network prediction controller using the Cuckoo Optimization Algorithm for the nonlinear process, Shin et al [21] used HYSYS and examined an MPC integrated neural network model in the nonlinear process, and Cheng et al [22] proposed a dynamic decoupling strategy based on active disturbance rejection control for a first order system with an observer based on a nonlinear wave model.…”