A Decomposition Approach for the Solution of Scheduling Including Process Dynamics of Continuous Processes

Abstract

Scheduling is often obtained without the consideration of process dynamics that affect the transition between steady states where production takes place. In this work we first formulate the scheduling optimization problem including process dynamics and then propose a decomposition approach that results in the efficient solution of the integrated problem. Optimality Analysis is utilized to prove that the production sequence and transition times are independent of products’ demands. The proof leads to the decomposition of the integrated problem into two subproblems that can be solved separately without the need for iterations. Results of case studies verify the feasibility and effectiveness of the proposed approach in reducing the computational complexity of the integrated problem but also obtaining the optimal solution.

Combined Noncyclic Scheduling and Advanced Control for Continuous Chemical Processes

“…To accomplish this objective, the problem is decomposed into an MILP programming problem and multiple separable NLP dynamic optimization, or optimal control, problems without the need for alternating iterations. This formulation builds on previous work that demonstrates the separability of the integrated scheduling and control problem into subproblems without the need for iterations [20] and builds on previous work which demonstrates the separation into MILP and dynamic optimization problems [10,46,47]. This formulation also builds on previous work that demonstrates benefits from shifting separable computational burden into offline portions of the integrated problem [11,19,22,55].…”

“…A simplified piece-wise affine (PWA) model to represent process dynamics for the scheduling level, rather than a first-principles process model, and fast model predictive control (fast MPC) at the control level are implemented, resulting in a significant reduction of computational burden. Additionally, a decomposition approach is presented based on optimality analysis showing that production sequence and transition times are independent of product demands [20]. The transition stages, a smaller-scale mixed-integer nonlinear programming (MINLP) problem, are solved separately from the production stages, a smaller-scale NLP problem, as opposed to solving an integrated large-scale MINLP problem.…”

“…The majority of integrated scheduling and control formulations have used cyclic schedules [7][8][9][10][11][15][16][17][18][19][20][21][22]. However, it has been suggested that a dynamic cyclic schedule may improve process economics [23].…”

Economic Benefit from Progressive Integration of Scheduling and Control for Continuous Chemical Processes

“…Zhuge and Ierapetritou also present methodology to reduce the computational burden of ISC to enable closed-loop online operation for batch and continuous processes. They propose using multi-parametric model predictive control for online batch scheduling and control [48], fast model predictive control coupled with reduced order (piece-wise affine) models in scheduling and control for continuous processes [49], and decomposition into separate problems for continuous processes [50]. Chu and You demonstrate the economic benefit of closed-loop moving horizon scheduling with consideration of process dynamics in batch scheduling [29].…”

Sustainability and process control: A survey and perspective

“…formulate the scheduling problem using detailed large-scale process models along with the corresponding control actions, possibly allowing for closedloop implementation [455][456][457][458][459]. The resulting problem, typically a mixed integer dynamic optimization problem, is computationally intensive, requiring specialized solution approaches [460,461]. An approach which seeks approximate low-order process models relevant to scheduling decisions and incorporates them in a combined scheduling/supervisory control problem has been recently introduced [462,463].…”