Combined heat and power plant using a multi-objective Henry gas solubility optimization algorithm: A thermodynamic investigation of energy, exergy, and economic (3E) analysis

Abstract

The principal context of this study was a combined heat and power plant (CHPP) system, with the aim of conducting the multi-objective optimization (MOO) of an energy, exergy, and economic (3E) analysis. To meet rising energy demands, optimal operational conditions for CHPPs are required. Enhancements to plant equipment and improvements in plant design are critical. CHPP design has its basis in the first law of thermodynamics; the losses from such systems are therefore most accurately determined via exergy analysis. Energy quality can also be assessed using exergy analysis. Consequently, it is possible for the designers of thermodynamic systems to apply the findings to achieve improved efficiencies. The economic aspect of CHPP optimization is also critical because the structure is highly complex. This study therefore makes use of a Henry gas solubility optimization (HGSO) algorithm in a CHPP base case situation to achieve MOO. In this particular CHPP system, the respective enthalpy and exergy efficiencies were increased in the case of the boiler (7.22% and 7.21%), the turbogenerator (4.52% and 6.84%), and the condenser (3.06% and 31.37%). In this study, four scenarios are proposed, whereby the design of a heat exchanger network (HEN) aims to optimize energy savings and economic performance through analysis of the profits generated through electricity and steam production. A payback period of around two to three years was reported, where the cost increase under optimal conditions was found to be 0.3824%. The results demonstrate clearly that the tested techniques may be appropriate in practical scenarios when enhancing CHPP performance in the context of the base case.

Advances in Henry Gas Solubility Optimization: A Physics-Inspired Metaheuristic Algorithm With Its Variants and Applications

“…Extensive evaluations across test functions and engineering design problems showcase its compelling performance, with superior results compared to established algorithms. Evaluation metrics like IGD and Sp, along with statistical analyses using the Wilcoxon test, confirm MOHGSO's significant outperformance at a confidence level of 99 Sukpancharoen et al [129] applied the HGSO algorithm for multi-objective optimization in a Combined Heat and Power Plant (CHPP) system, focusing on energy, exergy, and economic (3E) analysis. The study aims to enhance enthalpy and exergy efficiencies across CHPP components, particularly the Heat Exchanger Network (HEN).…”

Novel hybrid HGSO optimized supervised machine learning approaches to predict the compressive strength of admixed concrete containing fly ash and micro-silica

“…The SVR model is considered a supervised learning algorithm that converges to the best solution by considering the following equations. (a) Equilibrium at a pressure P 1 in a saturated solution of a gas, (b) increasing of pressure to P 2 Moreover, decreasing the volume of gas [57].…”

“…Figure2. (a) Equilibrium at a pressure P 1 in a saturated solution of a gas, (b) increasing of pressure to P 2 Moreover, decreasing the volume of gas[57].…”

Energy-saving optimization of the parallel chillers system based on a multi-strategy improved sparrow search algorithm

“…In addition, due to the rapid development of meta-heuristic algorithms, many scholars use algorithms to optimize all aspects of energy systems. For example, the optimization of the thermal comfort of the house [ 16 ], the optimization of the stability of the nuclear-renewable energy hybrid system [ 17 ], the optimization of the utilization of renewable energy in the building [ 18 ], the optimization of the equipment capacity configuration of combined heat and power plant [ 19 ], the optimization of the energy cost of the HVAC energy terminal [ 20 ], the optimization of the energy management of the micro-grid [ 21 ], the optimization of the photovoltaic distribution system [ 22 ], carbon emission optimization [ 23 ], thermo-economic and environmental optimization of solar collector [ 24 ], energy subsidy optimization [ 25 ], economic transaction optimization of energy system [ 26 ], Multi-objective optimization of integrated energy systems [ 27 , 28 ], etc. By using the optimization algorithm to optimize the energy system, the energy system is more economical, energy-saving and stable.…”