Process Scheduling Analysis and Dynamic Optimization Maintaining the Operation Margin for the Acetylene Hydrogenation Fixed-Bed Reactor

Abstract

The full-cycle operation optimization of the acetylene hydrogenation reactor should strictly adhere to the operation optimization scheme within the operation cycle, regardless of scheduling changes. However, in actual industrial processes, in order to meet temporary process scheduling requirements, the acetylene hydrogenation reactor needs to adjust its operation strategy temporarily within the remaining operation cycle based on the results of dynamic optimization for a certain period. It brings additional challenges and a research gap to the operational optimization problem. To make up for this research gap, this paper focuses on researching a type of full-cycle dynamic optimization problem where the operation optimization scheme is temporarily adjusted during the operation cycle. The methods employed for changing the operation optimization scheme include modifying the operation cycle, maximizing economic benefits, and altering the optimization goal to maximize the operation cycle. A novelty full-cycle scheduling optimization framework based on surplus margin estimate is proposed to build a platform for these methods. The paper analyzes the impact of process scheduling changes on full-cycle optimization using a dynamic optimization model that maintains the operation margin. It establishes a full-cycle scheduling optimization model and obtains the optimal scheduling strategy by a novelty method NSGBD (non-convex sensitivity-based generalized Benders decomposition). In this process, an adaptive CVP (control vector parameterization) based on a decomposition optimization algorithm is proposed, which tackles the challenge of optimizing complex acetylene hydrogenation reactor models on a large time scale. Scheduling optimization can be realized as an annualized benefit of 1.56 × 106 and 1.57 × 106 ¥ separately within two scheduling optimization constraints, and the computational time required is much less than previous operational optimizations.