Abstract
A novel trigeneration district energy system (TDES) is designed and evaluated from energy, exergy, exergoeconomic, and exergoenvironmental points-of-view. By recovering the wasted heat of a regenerative gas turbine cycle, a heat exchanger is utilized for heating applications, a Kalina cycle is run for generating some additional power, and an ejector refrigeration cycle is used for producing some cold. The problem is firstly modeled through developing a precise code in Engineering Equation Solver program and then optimal conditions are sought by coupling the outputs of modeling procedure with Artificial Neural Network, and Non-dominated Sorting Genetic Algorithm II approaches. Three new functions of integrated weighted efficiency, exergoeconomic criterion, and exergoenvironmental criterion are defined as the system's evaluation criteria. From a robust parametric study, it is demonstrated that the system's evaluation criteria have the highest and lowest sensitivity on the variation of pressure ratio of compressor and pinch-point temperature difference of heat exchanger 2, respectively. From the optimisation procedure, the optimum values of the system's primary energy ratio, exergetic efficiency, exergoeconomic criterion, and exergoenvironmental criterion are 76.9%, 30.8%, 58.4 $/GJ, and 42.7 kg/GJ, respectively. At these conditions, the capacity of the TDES is 1025.9 kW, 1642.3 kW, and 304.9 kW with the associated cost of production of 149.6 $/GJ, 7.8 $/GJ, and 60.1 $/GJ for power, heat, and cold, respectively.
Original language | English |
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Article number | 113581 |
Journal | Energy Conversion and Management |
Volume | 227 |
ISSN | 0196-8904 |
DOIs | |
Publication status | Published - 1 Jan 2021 |
Bibliographical note
Funding Information:The Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah, Saudi Arabia funded this project, under grant no. (FP-42-42).
Funding Information:
The Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah, Saudi Arabia funded this project, under grant no. (FP-42-42).
Publisher Copyright:
© 2020 Elsevier Ltd
Keywords
- Artificial neural network
- Exergoeconomic analysis
- Exergoenvironmental analysis
- NSGA-II optimisation
- Trigeneration district energy system