Currently, solar thermochemical energy conversion technology has attracted more and more attention because of the increasing global need for clean fuel and chemical energy demand. The thermochemical hydrogen productio...
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Currently, solar thermochemical energy conversion technology has attracted more and more attention because of the increasing global need for clean fuel and chemical energy demand. The thermochemical hydrogen production technology uses solar radiation with concentrated high-density as a high-temperature heat source to produce hydrogen by decomposing water through two-step cycle redox reactions. It is an environment-friendly technology based on artificial photosynthesis utilizing water vapor as the reaction gas. However, limited by the separation efficiency, current gas-liquid separation devices cannot output stable high-purity water vapor and sophisticate the experimental processes. In this research, intensive numerical investigations were carried out on the widely-used corrugated plate steam separators. The structural parameters are designed by the multi-objective optimization method combining the artificial neural network and the genetic algorithm. Nine optimized structures are designed by adding hydrophobic hooks, streamlined, and reducing the plate distance optimizationmethods. The results give engineering application conditions of different optimized separators. The streamlined single-hook corrugated plate separator with rounded corners exhibited the best separation efficiency under the heat state condition, which is 97.27%. It can also completely separate droplets over 28.5 mu m. The applicability of the cyclone steam separators indicated that it is more suitable for pretreatment. The benchmark experimental system was built for the measurement of the separation efficiency resulting in only a 0.73% error. The designed steam separator was successfully applied to the solar thermochemical hydrogen production experiment, which ensures its practicability and pertinence in the process of solar fuel synthesis.
With the development of high-throughput techniques, systems biology has been pushing researchers to focus on how to optimize the steering of biomolecular networks from their actual state to a desired state. This pheno...
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With the development of high-throughput techniques, systems biology has been pushing researchers to focus on how to optimize the steering of biomolecular networks from their actual state to a desired state. This phenomenon known as the ”transittability” means that complex biomolecular networks can be steered from an unexpected state to a desired state. This paper investigates the optimization of the transittability of complex biomolecular networks taking into account different objective functions. To solve this problem, we propose a multi-objectiveoptimization approach which consists of two steps, the search and decision making step. The search step is based on a powerful multi-objective genetic algorithm, the non-dominated sorting genetic algrorithm (NSGA-II), to solve our problem and obtain a Pareto-optimal set. As regards the decision making step is based on the use of a multi-criteria decision making method, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), for providing the best compromise solution according to the user preferences. The proposed approach was tested and applied to solve the steering of the p53 Signaling network. Experimental results illustrate the effectiveness of this approach.
The major obstacle in designing of the wind turbine/photovoltaic/battery storage system lies in the task of choosing the most optimum solution while simultaneously considering techno-economic objectives. Consequently,...
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The major obstacle in designing of the wind turbine/photovoltaic/battery storage system lies in the task of choosing the most optimum solution while simultaneously considering techno-economic objectives. Consequently, this study provides a distinctive amalgamation of multi-objectivemulti-perspective and group multicriteria decision-making methodologies to ascertain collections of Pareto optimum configurations and to weight, rank, and pick up the most desirable optimum solutions that represent best trade -off between conflicting objectives. In this paper, the multi-objective Dragon fly algorithm is developed based on mutation scheme and crossover tactic algorithm is advanced to handle a lack of diversity and poor convergence, while non-dominated sorting and crowding distance strategy is addressed to store the best found optimum solutions in the achieve. Loss of load probability, excess energy, and life cycle cost are conflicting techno-economic objectives solved by multi-objectivemethod. The optimum Pareto front solutions are ranked based on hybrid multi -criteria decisionmaking method for determining the most favorable solution for the WT/PV/BS system. The experimental outcomes indicated that the developed approach has a distinct performance in constructing a collection of Pareto front solutions in terms of diversity, converge, and convergence. Moreover, it demonstrates outstanding results when compared to well-organized multi-objective optimization methods. The future direction can be accomplished by applying the proposed hybrid sizing methodology for solving gird-connected system along with electric vehicle based on techno-economic scenarios.
In this paper, voltage and reactive power control (VQC) of a system with load changes using a multi-objective optimization method is proposed. The objective is to minimize the transmission loss, voltage deviation, and...
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ISBN:
(纸本)9781467327275
In this paper, voltage and reactive power control (VQC) of a system with load changes using a multi-objective optimization method is proposed. The objective is to minimize the transmission loss, voltage deviation, and manipulated variable using a multiple objectiveoptimization Genetic Algorithm (GA). In this research, the simulation was carried out by using an excellent multi-objectiveoptimization GA known as Elitist Non-Dominated Sorting Genetic Algorithm (NSGA-II).
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