Optimization of reaction parameters based on rSQP and hybrid automatic differentiation algorithm

To optimize the reaction parameters in the dynamical process of methanol-to-hydrocarbons, a method based on reduced sequential quadratic programming (rSQP) and hybrid automatic differentiation technology was presented. The dynamical optimization problem was firstly discredited as a nonlinear programming problem denoted by algebraic equations. With the characteristics of sparseness, relatively low degrees of freedom and equality constraints utilized, the nonlinear programming problem was solved by improved rSQP solver. In the solving process, hybrid automatic differentiation technology was used to obtain the sparse structure and gradient information. Computational results show that the efficiency of the proposed method is more than 100 times as that of the standard sequential quadratic programming (SQP) with differences, and is more than 10 times as that of standard SQP with hybrid automatic differentiation. The accuracy is also improved with the proposed method.