Performance analysis using economic model predictive control for hydrolysis of tallow fat

Abstract

AbstractTallow is a resource for glycerine and oleochemicals produced by subsequent hydrolysis, saponification, or transesterification. Since steam hydrolysis, an industrial practice, uses continuous high pressure and temperature, is energy intensive; this has been replaced by use of pressurized hot water from top and bottom of hydrolyzing tower. The fat and water form continuous phase in the heat exchange part where hydrolysis takes place in approximately 3 h. The degree of hydrolysis depends on vapor–liquid equilibrium of water which is a function of temperature and pressure. It has been found that degree of hydrolysis is feasible at 256°C. Continuous countercurrent flow is maintained by the density difference between the two phases in the heat exchange sections. There is a need for improving degree of hydrolysis along the safe operating region in the temperature–pressure domain of energy‐utility spectrum by using model predictive control (MPC). A nonlinear mathematical model, representing glycerine concentration and temperature in fat and aqueous phases, for the fat hydrolysis system is used for system identification and synthesizing model‐based tuning rules for the said process. Auto‐tuning algorithms (multiloop with IMC‐PID–Laurent tuning) are developed for PID tuning. The conventional control algorithms (IMC‐PI and MPC) focus on error minimization and performance optimization; hence, economic model predictive control (EMPC) is adopted (incorporating reaction kinetics into objective function) to optimize the process utility via improved energy efficiency. A closed‐loop comparison (PI, MPC, and economic MPC) of performance indices reveals that economic MPC gives satisfactory results.