A New Multi-Heuristic Method to Optimize the Ammonia–Water Power/Cooling Cycle Combined with an HCCI Engine

Abstract

Nowadays, sustainability is one of the key elements which should be considered in energy systems. Such systems are essential in any manufacturing system to supply the energy requirements of those systems. To optimize the energy consumption of any manufacturing system, various applications have been developed in the literature, with a number of pros and cons. In addition, in the majority of such applications, multi-objective optimization (MOO) plays an outstanding role. In related studies, the MOO strategy has been mainly used to maximize the performance and minimize the total cost of a trigeneration system with an HCCI (homogeneous charge compression ignition) engine as a prime mover based on the NSGA-II (non-dominated sorting genetic algorithm-II) algorithm. The current study introduces a novel multi-heuristic system (MHS) that serves as a metaheuristics cooperation platform for selecting the best design parameters. The MHS operates on a proposed strategy and prefers short runs of various metaheuristics to a single long run of a metaheuristic. The proposed MHS consists of four multi-objective metaheuristics collaborating to work on a common population of solutions. The optimization aims to maximize the exergy efficiency and minimize the total system cost. By utilizing four local archives and one global archive, the system optimizes these two objective functions. The idea behind the proposed MHS is that metaheuristics will be able to compensate for each other’s shortcomings in terms of extracting the most promising regions of the search space. Comparing the findings of the developed MHS shows that implementing the suggested strategy decreases the total unit costs of the system products to 25.85 USD/GJ, where the total unit cost of the base system was 28.89 USD/GJ, and the exergy efficiency of the system is increased up to 39.37%, while this efficiency was 22.81% in the base system. The finding illustrates significant improvements in system results and proves the high performance of the proposed method.

Transitioning to sustainable energy: opportunities, challenges, and the potential of blockchain technology

“…The adoption of sustainable energy sources like hydro, wind, and solar can mitigate the adverse environmental effects of traditional energy sources, resulting in a cleaner and healthier environment. Additionally, renewable energy technologies are inexhaustible and do not produce greenhouse gas emissions (Armaroli and Balzani, 2007;Magar et al, 2023;Owusu and Asumadu-Sarkodie, 2016;Bahlouli et al, 2023).…”

Optimizing a Green and Sustainable Off-Grid Energy-System Design: A Real Case

“…Similarly, constraints (8) to (10) limit the number of wind turbines, batteries, and convertors to 15, 350, and 300, respectively. Constraints (11) to (13) show that the stored electricity at time t cannot be less than zero, or more than 40 kWh. The maximum number of PV arrays and batteries was determined in order to keep the project's overall cost from becoming excessively high.…”

“…Different component arrangements, such as solar panels, wind turbines, battery banks, hydrogen storage, etc., have been used to designi hybrid systems, using a variety of modeling techniques, ranging from sophisticated metaheuristics to exact solution methods, according to the literature [12]. Other cutting-edge approaches have been put forth, including the non-dominated sorting GA (NSGA-II) [13,14], Tabu Search (TS), fuzzy logic (FL) [15], Grey Wolf (GW), etc. A brief summary of some of the published studies in this field is listed in Table 1, where FC, HP, ST, Fs, and NPC stand for fuel cell, hydropower, storage, fuels, and net present cost, respectively.…”

A New Hybrid Algorithm Based on Improved MODE and PF Neighborhood Search for Scheduling Task Graphs in Heterogeneous Distributed Systems

“…For instance, SGA and EP methods were developed by authors in [14], as were MOGA and MOEP methods applied in [15]. Moreover, HEFT-NSGA, MFA, weighted sum MOEP, hybrid algorithms, and a multi-agent system were developed in [8,[16][17][18], respectively. It can be seen that in the majority of state-of-the-art literature, a simple metaheuristic or weighted-sum method has been used.…”