Techno-economic optimization of a district heat condenser in a small cogeneration plant with a novel greedy cuckoo search. (15th January 2022) - Saari, Jussi; Martinez, Clara Mendoza; Kaikko, Juha; Sermyagina, Ekaterina; Mankonen, Aleksi; Vakkilainen, Esa
Abstract: The goal of the study was to develop an optimization methodology combining a cost model, a 2-D heat transfer model, and load variation, executable in a feasible time in a personal computer. The objective function to maximize is the annual net cash flow. For this purpose, the cogeneration plant performance is determined at different load points as a function of the condenser performance using IPSEpro process simulation software. This data is then used to implement the calculation process to obtain the objective function value. Due to the computationally heavy heat transfer model, and each objective function evaluation requiring multiple runs, one per load point, improving optimizer performance was given particular attention. A novel hybridization of cuckoo search and a greedy differential evolution strategy was created for this; the new algorithm is shown to perform better than either of its parent algorithms or other benchmarks, finding optimal solution reliably, and within approximately half the number of function evaluations as required by the parent algorithms. Highlights: Differental mutation methods e.g. cuckoo search do well in engineering optimization. Heat exchanger optimization needs a cost model; area-based correlations inadequate. Multiple load point power plant condenser optimization feasible in desktop PC. Differential mutation can benefit from methods aimed at global exploration.