Multi-objective Optimization Differential Evolution Algorithm Research Articles

Numerical simulation and multi-objective optimization design of conjugate heat transfer in novel double shell-passes multi-layer helically coiled tubes heat exchangers

A novel double shell-passes multi-layer helically coiled tubes heat exchanger (DSMHCTHE) is proposed to improve the flow and heat transfer performance of the shell side. It was studied by using conjugate heat transfer numerical model. By changing coil diameter and pitch, the influence of coil torsion on the performance of the heat exchangers was investigated in the range of 0.0637–0.3183, and compared with the conventional multi-layer helically coiled tubes heat exchangers (MHCTHE). Two kinds of heat exchangers were optimized by using multi-objective optimization differential evolution algorithm. The results indicate that the coil pitch has a greater effect on the overall heat transfer coefficient, and the effects of coil pitch variations in the inner and outer helically coiled tubes follow different patterns. Compared with MHCTHE, the heat transfer rate of DSMHCTHE are increased by 13.9 %–19.1 %, the comprehensive performance is increased by 13.0 %–18.0 %, and the heat transfer entropy generation number and friction entropy generation number are lower. It shows that DSMHCTHE have better application potential as heat exchangers.

Economic-effectiveness analysis of micro-fins helically coiled tube heat exchanger and optimization based on multi-objective differential evolution algorithm

In this study, the heat transfer characteristics and hydrodynamics of a new type of micro-fin helically coiled tube (MF-HCT) are modelled and compared with those of typical smooth helically coiled tubes (HCTs). Calculation models are developed and compared for both helically coiled tube heat exchangers (HCTHEXs) and micro-fin helically coiled tube heat exchangers (MF-HCTHEXs). The total cost and effectiveness are selected as the objective functions, and five independent structural parameters are selected for each type of HCTHEX. Subsequently, an adaptive multi-objective optimization differential evolution algorithm is implemented for both of them, and the results are compared. The results indicate that, for Reynolds numbers smaller than 22,000, the MF-HCT performs better than the HCT, and the total cost of the MF-HCTHEX is lower than that of the HCTHEX under the same effectiveness. The difference between their total cost increases with their effectiveness and can be up to 79,000 €. For both the HCTHEX and MF-HCTHEX, when the effectiveness is less than 0.9, it is more appropriate to select a larger winding angle, layer spacing, and tube spacing.

Robust Multi-Objective Singular Optimal Control Ofpenicillin Fermentation Process

The determination of optimal feeding profile of fed-batch fermentation requires the solution of a singular optimal control problem. The complexity in obtaining the solution to this singular problem is due to the nonlinear dynamics of the system model, the presence of control variables in linear form and the existence of constraints in both the state and control variables. Traditionally, during the optimization process, uncertainties associated with design variables, control parameters and mathematical model are not considered. In this contribution, a systematic methodology to evaluate uncertainties during the resolution of a singular optimal control problem is proposed. This approach consists of the Multi-objective Optimization Differential Evolution algorithm associated with Effective Mean Concept. The proposed methodology is applied to determine the feed substrate concentration in fed-batch penicillin fermentation process. The robust multi- objective singular optimal control problem consists of maximizing the productivity and minimizing the operation total time.

Wind turbine blade geometry design based on multi-objective optimization using metaheuristics

The application of evolutionary algorithms to wind turbine blade design can be interesting, by reducing the number of aerodynamic-to-structural design loops in the conventional design process, hence reducing the design time and cost. Recent developments showed satisfactory results with this approach, mostly combining genetic algorithms with the blade element momentum theory. The general objective of the present work is to define and evaluate a design methodology for the rotor blade geometry in order to maximize the energy production of wind turbines and minimize the mass of the blade itself, using for that purpose stochastic multi-objective optimization methods. An optimization benchmark problem was proposed, which represents the wind conditions and present wind turbine concepts found in Brazil. A variable speed pitch-controlled 2.5 MW direct-drive synchronous generator turbine with a rotor diameter of 120 m was chosen as concept. Four different multi-objective evolutionary algorithms were selected for evaluation in solving this benchmark problem: Non-dominated Sorting Genetic Algorithm version II (NSGA-II), Quantum-inspired Multi-objective Evolutionary Algorithm (QMEA), Multi-objective Evolutionary Algorithm Based on Decomposition (MOEA/D), and Multi-objective Optimization Differential Evolution Algorithm (MODE). Detailed analysis of the best compromise blade design showed that the output of the design methodology is feasible for manufacturing.

Robust Multiobjective Optimization Applied to Optimal Control Problems Using Differential Evolution

AbstractRobust optimization is an approach for modeling optimization problems under uncertainty where the modeler aims at finding decisions that are optimal for the worst‐case realization of the uncertainties within a given set of values. Typically, the original uncertain optimization problem is converted into an equivalent deterministic form, called the robust counterpart, using strong duality arguments and then solved by standard optimization algorithms. A methodology is proposed for the treatment of optimal control problems applying the multiobjective optimization differential evolution algorithm associated with the concept of mean effective for the insertion of robustness. The results obtained with applications in chemical systems demonstrate that the method conveyed is configured as an interesting approach for the solution of robust optimization problems.