Thermal Analysis and Optimizing Exergy and Energy of a Modified Incinerator Plant with an Orc

Vanaei, P.; Jalili, Bahram; Hosseinzadeh, Morteza; Jalili, P.

doi:10.2139/ssrn.4173686

Cited by 2 publications

(2 citation statements)

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“…In this way, many optimization techniques have been presented to solve various real-world problems by researchers in the recent years (5)(6)(7). Among them, evolutionary algorithms have attracted more considerations for linear/non-linear, convex/non-convex and constrained/non-constrained problems (8)(9)(10). In which, the Genetic Algorithm (GA) (11) and Particle Swarm Optimization (PSO) (12) as the most prominent and wellknown approaches have been broadly implemented in the scientific research studies (13)(14)(15).…”

Section: Introductionmentioning

confidence: 99%

A New Optimization Algorithm Based on Particle Swarm Optimization Genetic Algorithm and Sliding Surfaces

Mahmoodabadi,

Nemati,

Danesh

2024

IJE

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To enhance the performance of meta-heuristic algorithms, the development of new operators and the efficient combination of various optimization techniques are valuable strategies for discovering global optimal solutions. In this research endeavor, we introduce a novel optimization algorithm called PGS (Particle Swarm Optimization-GA-Sliding Surface). PGS combines the strengths of particle swarm optimization (PSO), genetic algorithm (GA), and sliding surface (SS) to tackle both mathematical test functions and real-world optimization problems. To achieve this, we adaptively tune the weighting function and learning coefficients of the PSO algorithm using the sliding mode control's SS relation. The global best particle discovered through the PSO method serves as one of the parents in the GA's crossover operation. This new crossover operator is then probabilistically integrated with an improved particle swarm optimization algorithm, enhancing convergence speed and facilitating escape from local optima. We evaluate the proposed algorithm's performance on both uni-modal and multi-modal mathematical test functions, considering un-rotated and rotated cases, thereby testing its effectiveness and efficiency against other prominent optimization techniques. Furthermore, we successfully implement the PGS algorithm in optimizing the state feedback controller for a nonlinear quadcopter system and determining the cross-section for an inelastic compression member.

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Section: Introductionmentioning

confidence: 99%

A New Optimization Algorithm Based on Particle Swarm Optimization Genetic Algorithm and Sliding Surfaces

Mahmoodabadi,

Nemati,

Danesh

2024

IJE

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“…They conducted specific and rigorous experiments. Vanaei et al (17) conducted an evaluation and refinement of an altered incineration facility through the Organic Rankine Cycle.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Operating Parameters of a Scroll Expander Based on Response Surface and NSGA II

Shi,

Peng

2024

IJE

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Adjusting the operating parameters to optimize the performance of the scroll expander has been a hot research topic among scholars. This paper innovatively combines the response surface method and NSGA2 algorithm for parameter optimization. This novel method can accurately predict the optimal operating parameters of the scroll expander and improve the overall efficiency of the scroll expander. Initially, a three-dimensional transient simulation model of the scroll expander was established, and the effects of three key operating parameters (suction pressure, exhaust pressure, and rotational speed) on the output power and isentropic efficiency of the scroll expander were analyzed through numerical simulation. On this basis, the response surface model between the input parameters and the objective function was established by using the response surface methodology. Consequently, three different optimization algorithms were compared, and it was found that NSGA-II had a better performance both in terms of convergence and solution performance,. Threfore, the NSGA-II algorithm was used for the multi-objective optimization. Under the premise of considering the maximum output power and isentropic efficiency, based on the established response surface model, the Pareto optimal solution was used to determine the optimal combination of its operating parameters: suction pressure of 1.62 MPa, exhaust pressure of 0.45 MPa, and rotational speed of 2,099.58 rpm.Finally, the numerical model is verified by the laboratory-built test bed of the Organic Rankine cycle low-temperature waste heat oilfree power generation system. The experimental results match well with the numerical simulation results and verify the model accuracy. The results from this pioneering and thorough thr study will provide a solid benchmark for the development and refinement of upcoming scroll machines.

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Thermal Analysis and Optimizing Exergy and Energy of a Modified Incinerator Plant with an Orc

Cited by 2 publications

References 0 publications

A New Optimization Algorithm Based on Particle Swarm Optimization Genetic Algorithm and Sliding Surfaces

A New Optimization Algorithm Based on Particle Swarm Optimization Genetic Algorithm and Sliding Surfaces

Optimization of Operating Parameters of a Scroll Expander Based on Response Surface and NSGA II

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