Metaheuristic optimization of double flash based combined heat and power system by using process simulator as black-box function generator

Abstract

The energy crisis in Europe has increased the importance of energy alternatives to hydrocarbons. For example, geothermal resources have long been proven to be very efficient heat sources for conventional power cycles. To get the maximum benefit from such a system, it is essential to carefully optimize the system parameters. On the other hand, the topology and nonlinear nature of the system prevent it from being expressed as an analytical function and being differentiable. Thus, derivative-based deterministic optimization methods are difficult to apply. In this study, it is proposed to model a geothermal-based dual-flash combined heat and power system in a process simulator and use it as a black-box function generator to calculate the values of the objective and constraint functions. The system parameters that will provide the maximum combined heat and power efficiency are determined by the Genetic Algorithm. Accordingly, with the optimum system design, the total net turbine power is 132.78 kW. The amount of heat utilized is 15020.10 kW.

Keywords

• Process simulation
• black-box modelling
• metaheuristic optimization
• Rankine cycle

Kaynakça

1. [1] EU gas price rockets higher after Russia halts Nord Stream flows Reuters. https://www.reuters.com/business/energy/no-stream-eu-gas-markets-brace-price-surge-after-latest-russia-gas-cut-2022-09-04/ (accessed Sep. 06, 2022).
2. [2] Linnhoff B, Hindmarsh E. The pinch design method for heat exchanger networks. Chemical Engineering Science 1982; 38(5): 745–763.
3. [3] Tester JW, Anderson BJ, Batchelor AS, Blackwell DD, DiPippo R, Drake EM, Garnish J, Livesay B, Moore MC, Nichols K, Petty S, Nafi Toksoz M, Veatch RW, Baria R, Augustine C, Murphy E, Negraru P, Richards M. Impact of enhanced geothermal systems on US energy supply in the twenty-first century. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 2007; 365(1853): 1057–1094.
4. [4] DiPippo R. Geothermal power plants, Butterworth-Heinemann, Oxford, UK, 2012.
5. [5] Medeiros D. DWSIM 2022; http://dwsim.org.
6. [6] Ponce-Ortega JM., Hernández-Pérez LG. Optimization of process flowsheets through metaheuristic techniques, Springer International Publishing, Cham, USA, 2019.
7. [7] Whitley DA. Genetic algorithm tutorial. Statistics and Computing 1994: 4(2); 65–85.
8. [8] Benítez-Hidalgo A, Nebro AJ, García-Nieto J, Oregi I, Ser JD. jMetalPy: A Python framework for multi-objective optimization with metaheuristics. Swarm and Evolutionary Computation 2019; 51(2019): 100598.

9. [9] Hamdan MA. Dynamic polynomial mutation for evolutionary multi-objective optimization algorithms. International Journal on Artificial Intelligence Tools 2011; 20(01): 209–219.
10. [10] Deb K, Sindhya K, Okabe T. Self-adaptive simulated binary crossover for real-parameter optimization. Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation - GECCO ’07 London, UK, 2007.
11. [11] Mirjalili S. Genetic algorithm in evolutionary algorithms and neural networks. Springer International Publishing, Cham, USA, 2019.