A Two-Level Optimization Framework for Cyclic Scheduling of Ethylene Cracking Furnace System

摘要

An ethylene plant typically consists of multiple cracking furnaces in parallel to process various feeds. For tackling this problem, it is convenient to formulate it as a cyclic scheduling problem that can be modeled as a large-scale mixed integer optimization nonlinear programming (MINLP). However, due to the existence of mixed variables and many constraints, the problem is hard to be solved efficiently by conventional deterministic algorithms or stochastic algorithms. To solve this problem, we propose a novel two-level optimization framework based on real-coded genetic algorithms (GA) and sequential quadratic programming (SQP). Our approach is based on reformulating the MINLP as a nested optimization with two loops. In the outer layer, to avoid wasting computation time, the GA is used first to filter out infeasible integer solution candidates and pass the feasible ones to the inner loop for fitness evaluation. In the inner loop, by fixing feasible integer solutions, the problem is simplified to a nonlinear programming problem (NLP), which is then solved by the SQP algorithm. A real-world case study demonstrates the efficacy of the developed methodology compared with existing MINLP solvers.