Integration of scheduling and control for batch process based on generalized Benders decomposition approach with genetic algorithm

A Benders decomposition framework for the optimization of disjunctive superstructures with ordered discrete decisions

“…Some applications of Bendersbased algorithms for MINLP problems include the optimization of distillation sequences 29 ; the solution of two-stage and multi-stage stochastic MINLP problems, [26][27][28] and the optimal integration of decision layers, for example, simultaneous scheduling and control, process design and operation under stochastic uncertainty, and integrated planning, scheduling and dynamics. 17,32,33 While these methods build upon GBD, the proposed LD-BD strategy incorporates concepts of discrete convex analysis within the LBBD theory to introduce a new type of Benders-based method. Note that the proposed LD-BD method does not incorporate features considered by other Benders methods for MINLPs, for example, including convex relaxations of nonconvex functions within a nonconvex-GBD strategy that guarantees global optimality 26,33 ; adding multiple cuts per iteration to increase convergence speed 17 ; or hybridizing GBD with other methods (e.g., branch and bound) to make the algorithm more efficient.…”

“…Similar to the proposed LD‐BD method, other works in the field have focused on developing Benders‐based methods that exploit special structures within specific problems. Some applications of Benders‐based algorithms for MINLP problems include the optimization of distillation sequences 29 ; the solution of two‐stage and multi‐stage stochastic MINLP problems, 26–28 and the optimal integration of decision layers, for example, simultaneous scheduling and control, process design and operation under stochastic uncertainty, and integrated planning, scheduling and dynamics 17,32,33 . While these methods build upon GBD, the proposed LD‐BD strategy incorporates concepts of discrete convex analysis within the LBBD theory to introduce a new type of Benders‐based method.…”

“…Note that the proposed LD-BD method does not incorporate features considered by other Benders methods for MINLPs, for example, including convex relaxations of nonconvex functions within a nonconvex-GBD strategy that guarantees global optimality 26,33 ; adding multiple cuts per iteration to increase convergence speed 17 ; or hybridizing GBD with other methods (e.g., branch and bound) to make the algorithm more efficient. 27,32 The LD-BD algorithm overcomes some of the practical limitations of the current LD-SDA. For instance, the LD-SDA requires the user to provide an initial feasible guess for external variables; otherwise, the algorithm will not converge to a solution.…”

“…While these methods build upon GBD, the proposed LD‐BD strategy incorporates concepts of discrete convex analysis within the LBBD theory to introduce a new type of Benders‐based method. Note that the proposed LD‐BD method does not incorporate features considered by other Benders methods for MINLPs, for example, including convex relaxations of nonconvex functions within a nonconvex‐GBD strategy that guarantees global optimality 26,33 ; adding multiple cuts per iteration to increase convergence speed 17 ; or hybridizing GBD with other methods (e.g., branch and bound) to make the algorithm more efficient 27,32 …”

Integrated operation optimization strategy for batch process based on process transfer model under disturbance

“…[10] However, since the MVT is optimized only once offline, such a scheme may lead to suboptimal operation because of plant-model mismatch. [11,12] A self-tuning batch-to-batch optimization method is proposed by Camacho et al to optimize the feeding profile of the Saccharomyces cerevisiae cultivation, which is an extremum optimization controller based on the unfold-partial least squares (PLS) model. [13] However, the approach of adapting the local data-driven model iteratively to the current operation point may lead to a slow convergence rate.…”