Nonlinear Autoregressive with Exogenous Inputs Based Model Predictive Control for Batch Citronellyl Laurate Esterification Reactor

Abstract

Esterification is a widely employed reaction in organic process industry. Organic esters are most frequently used as plasticizers, solvents, perfumery, as flavor chemicals and also as precursors in pharmaceutical products. One of the important ester is Citronellyl laurate, a versatile component in flavors and fragrances, which are widely used in the food, beverage, cosmetic and pharmaceutical industries. In industry, the most common ester productions are carried out in batch reactors because this type of reactor is quite flexible and can be adapted to accommodate small production volumes (Barbosa-Povoa, 2007). The mode of operation for a batch esterification reactor is similar to other batch reactor processes where there is no inflow or outflow of reactants or products while the reaction is being carried out. In the batch esterification system, there are various parameters affecting the ester rate of reaction such as different catalysts, solvents, speed of agitation, catalyst loading, temperature, mole ratio, molecular sieve and water activity (Yadav and Lathi, 2005). Control of this reactor is very important in achieving high yields, rates and to reduce side products. Due to its simple structure and easy implementation, 95% of control loops in chemical industries are still using linear controllers such as the conventional Proportional, Integral & Derivative (PID) controllers. However, linear controllers yield satisfactory performance only if the process is operated close to a nominal steady-state or if the process is fairly linear (Liu & Macchietto, 1995). Conversely, batch processes are characterized by limited reaction duration and by nonstationary operating conditions, then nonlinearities may have an important impact on the control problem (Hua et al., 2004). Moreover, the control system must cope with the process variables, as well as facing changing operation conditions, in the presence of unmeasured disturbances. Due to these difficulties, studies of advanced control strategy have received great interests during the past decade. Among the advanced control strategies available, the Model Predictive Control (MPC) has proved to be a good control for batch reactor processes (Foss et al., 1995; Dowd et al., 2001; Costa et al., 2002; Bouhenchir et al., 2006). MPC has influenced process control practices since late 1970s. Eaton and Rawlings (1992) defined MPC as a control scheme in which the control algorithm optimizes the manipulated variable profile over a finite future time horizon in order to maximize an objective function subjected to plant models and