Energy, economic and environmental (3E) analysis and multi-objective optimization of a spray-assisted low-temperature desalination system

In this paper, a combined heat and power (CHP) system is proposed that comprises a recuperative gas turbine (GT) cycle, a heat recovery steam generator (HRSG), and a recuperative-regenerative organic Rankine cycle (RR-ORC) for the cogeneration of process heat and power. The low-grade heat from the GT exhaust is utilized to operate the HRSG and the RR-ORC. The CHP system is modelled based on energy, exergy, economic and environmental (4E) analyses. The results showed that at the base condition, the GT cycle and RR-ORC provide a net power of 30 MW and 671.40 kW, respectively, while HRSG recovers 40.74 MW of thermal energy from the GT exhaust gas to produce 8.43 tonnes/h of saturated steam for process heat application. The thermal and exergy efficiency of the overall system are 86.81% and 53.38%, respectively, whereas the total product cost rate and the specific CO2 emission are 1569.6 $/h and 234.13 kg/MWh, respectively. Further, a Pareto optimal envelope-based selection algorithm-II (PESA-II) is applied for the tri-objective optimization of the CHP system considering the overall exergy efficiency, total product cost rate, and specific CO2 emission as the objective functions with five decision variables. The intent of this study is to maximize the first objective function and minimize the remaining two. Lastly, the multi-criteria decision analyses is performed by applying the technique for order preference by similarity to ideal solution (TOPSIS) to select the best optimal solution that gives an improvement of 11.12%, 5.73%, and 9.88%, respectively, over the base case condition.

Energy, exergy and exergoeconomic (3E) analysis and multi-objective optimization of a closed-circuit desalination system with side-conduit

The evaluation of hydrogen production via a geothermal-based multigeneration system with 3E analysis and multi-objective optimization

Comparative assessment and multi-objective optimization for the gray and blue ammonia synthesis processes: Energy, Economic and Environmental (3E) analysis

4E analysis and multi objective optimization of a micro gas turbine and solid oxide fuel cell hybrid combined heat and power system

A novel multi-objective spiral optimization algorithm for an innovative solar/biomass-based multi-generation energy system: 3E analyses, and optimization algorithms comparison

4E analysis and multi-objective optimization of a CCHP cycle based on gas turbine and ejector refrigeration

4E analysis and multi-objective optimization of a micro poly-generation system based on SOFC/MGT/MED and organic steam ejector refrigerator

Targeting zero energy increment for an onboard CO2 capture system: 4E analyses and multi-objective optimization

4E analysis and multi-objective optimization of compression/ejection transcritical CO2 heat pump with latent thermal heat storage

Multi-objective optimization of cooling water package based on 3E analysis: A case study

A new collaborative optimization method for a distributed energy system combining hybrid energy storage

Reasonable distribution of cooling load between chiller and ice tank is the key to realize the economical and energy-saving operation of ice-storage air-conditioning (ISAC) system. A multi-objective optimization model based on improved firefly algorithm (IFA) was established in this study to fully exploit the energy-saving potential and economic benefit of the ISAC system. The proposed model took the partial load rate of each chiller and the cooling ratio of the ice tank as optimization variables, and the lowest energy consumption loss rate and the lowest operating cost of the ISAC system were calculated. Chaotic logic self-mapping was used to initialize population to avoid falling into local optimum, and Cauchy mutation was used to increase the population’s diversity to improve the algorithm’s global search ability. The experimental results show that compared with the operation strategy based on constant proportion, particle swarm optimization (PSO) algorithm, and firefly algorithm (FA), the optimal operation strategy based on IFA can achieve more significant energy-saving and economic benefits. Meanwhile, the convergence accuracy and stability of the algorithm are significantly improved. Practical application: The optimized operation strategy of the ice-storage air-conditioning system can reduce energy loss and operating costs. The traditional operation strategies have the problems of low optimization precision and poor optimization effect. Therefore, this study presents an optimal operation strategy based on IFA. The convergence accuracy and stability of the algorithm are increased after the algorithm is improved. The operation strategy can get the maximum energy-saving effect and economic benefit of the ISAC system.

Enhancing solar still performance through innovative modeling, integration with reflectors, and semi-transparent solar cells: A 3E analysis and multi-objective optimization

Energy consumption and energy saving potential in clothing industry