Multiple-solution heat exchanger network synthesis for enabling the best industrial implementation

• Published in: Energy (Oxford) Vol. 208; p. 118330
• Main Authors: Orosz, Ákos, Friedler, Ferenc
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.10.2020; Elsevier BV
• Subjects: Combinatorial analysis; energy; Heat; Heat exchanger network (HEN); Heat exchangers; Heat recovery; Heat recovery systems; HEN synthesis; Multiple solutions; Network synthesis; Optimization; P-graph algorithm; Superstructures; Multiple solutions; HEN synthesis; P-graph algorithm; Heat exchanger network (HEN)
• ISSN: 0360-5442; 1873-6785; 1873-6785
• DOI: 10.1016/j.energy.2020.118330

The synthesis of heat recovery networks traditionally results in an optimal or suboptimal solution for the supplied set of streams and simplifying assumptions. In the current work, the assumption of a single optimal solution is replaced by the goal of generating an ordered set of optimal or quasi-optimal networks. This enables industrial engineers to further select the solution most suitable for detailed design and practical implementation. The problem is formulated for and solved by an extension of the P-graph framework for combinatorial process network optimization. The presented method for HEN synthesis generates a list of solutions ranked by the Total Annualized Cost. In addition to the feasibility, all list elements also feature a degree of heat recovery ranging from the thermodynamic maximum, down to a specified margin allowing accounting for the energy-capital trade-off. The current method is illustrated with three case studies. The obtained results demonstrate optimal solutions that cannot be generated by the Pinch-based methods or the stage-wise superstructure approaches. The proposed parameters, an upper limit on the number of heat exchangers per process stream and a maximum relaxation of utility demand compared to the Pinch targets, allow performing parametric evaluations of the resulting solutions. •A P-graph method extension; generates a ranked list of the best-performing HENs.•Obtains solutions that are never generated by the other methods.•New design parameter: maximum number of matches per stream.•New design parameter: maximum relaxation of minimum utility demands.•Sensitivity analyses can be performed to provide confidence in the results.