Simultaneous Target of HEN and Columns with Variable Feed Temperatures for a Toluene Disproportionation Plant

• Published in: Industrial & engineering chemistry research Vol. 53; no. 25; pp. 10429 - 10438
• Main Authors: Zhang, B. J, Luo, X. L, Liu, K, Chen, Q. L, Li, Wenkai
• Format: Journal Article
• Language: English
• Published: American Chemical Society 25.06.2014
• Subjects: Columns (process); Disproportionation; Mathematical analysis; Mathematical models; Mixed integer; Nonlinear programming; Streams; Toluene; Utilities
• ISSN: 0888-5885; 1520-5045
• DOI: 10.1021/ie500111z

Though toluene disproportionation is an important process for producing para-xylene, it is heavily energy intensive because of its high reaction temperature and the need to separate close boiling-point components. Pinch analysis is often used to target utility requirements for process systems. Nevertheless, the supply and final temperatures of process streams are all predetermined according to the sequential method indicated in the onion model. Therefore, the sequential method ignores the influences of outer level facilities on inner level facilities, which leads to suboptimal solutions. To tackle this problem, variable temperatures of process streams are taken into account in this study to simultaneously target the utility requirements of columns and heat exchanger networks in a toluene disproportionation plant. To this end, relevant equations representing the relationships between feed temperatures and heat duties of columns are first obtained based on simulation data. Second, the equations are integrated into a transshipment model. Meanwhile, variable temperatures are introduced into temperature intervals. As a result, a mixed integer nonlinear programming problem is formulated to minimize the utility requirement in the whole toluene disproportionation plant. Third, the solution results are discussed, providing insights into the optimal results and the sensitivity of utility requirement caused by process streams and separation columns.