Abstract In process design, flexibility analysis is an important technique for evaluating the operability of a chemical process. The cylindrical algebraic decomposition (CAD) method has been proposed for flexibility analysis to derive analytical expressions of a feasible region. Due to the heavy computational burden caused by symbolic computation, this method can only handle small-scale problems currently. To overcome this limitation, a novel method is proposed for high-dimensional systems in this work. As the inequality constraints for flexibility analysis are usually limited in most cases, a surrogate model is first built to correlate the inequality constraints based on an initial sample set. Then, the flexibility region is obtained with explicit expressions via the CAD method. Next, the sampling validation is conducted on the boundary. For any violation, a refinement will be activated by taking an iterative process of data sampling, surrogate modelling, and region deriving, until the correctness condition is satisfied. The case study shows the proposed method can effectively describe the flexibility region for high-dimensional systems.
[1]
Fei Zhao,et al.
Analytical and triangular solutions to operational flexibility analysis using quantifier elimination
,
2018,
AIChE Journal.
[2]
M. Ierapetritou,et al.
A novel feasibility analysis method for black‐box processes using a radial basis function adaptive sampling approach
,
2017
.
[3]
Efstratios N. Pistikopoulos,et al.
Flexibility analysis and design using a parametric programming framework
,
2002
.
[4]
Marianthi G. Ierapetritou,et al.
Feasibility Evaluation of Nonconvex Systems Using Shape Reconstruction Techniques
,
2005
.
[5]
George E. Collins,et al.
Quantifier elimination for real closed fields by cylindrical algebraic decomposition
,
1975
.
[6]
Marianthi G. Ierapetritou,et al.
Framework for evaluating the feasibility/operability of nonconvex processes
,
2003
.