Multiobjective optimization of a geothermal power plant

Khanmohammadi, Shoaib; Kızılkan, Önder; Musharavati, Farayi

doi:10.1016/b978-0-12-821037-6.00011-1

Cited by 5 publications

(2 citation statements)

References 31 publications

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“…In this case, if one set of solutions in the diagram is presented in the form of design variations, then there are 70 sets of MCC design solutions that do not dominate each other. The meaning of not dominating each other is certainly related to the compromise value that is in accordance with the optimization objectives without reducing the ability of one of the output variables [55], [56]. Furthermore, the 70 sets of solutions were analyzed in greater depth through equal weighting.…”

Section: Ann-moga Optimization Resultsmentioning

confidence: 99%

Design of Metallic Catalytic Converter using Pareto Optimization to Improve Engine Performance and Exhaust Emissions

Ariyanto

Suprayitno

Wulandari

2023

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In this paper, Metallic Catalytic Converter (MCC) is installed in motorcycle exhausts to produce the minimum CO as well as to produce the optimum engine power. The results from previous research were collected and then used to predict the best MCC design using the Artificial Neural Network Multi-Objective Genetic Algorithm (ANN-MOGA). In addition, the ANN parameter tuning process was also carried out using the Taguchi method to find the initial weighting and bias that is able to provide the best and the most stable performance to predict the best MCC design. The best two sets of design solutions out of 70 sets of Pareto solutions were obtained by ANN-MOGA. Those two MCC designs are the optimum emission design and the optimum multi-objective design. The verification results show that the optimum multi-objective design tends to be superior in terms of CO emissions and engine power. In terms of CO emissions, the optimum multi-objective design gets a larger S/N ratio of -10.98, while the optimum emission design only gets an S/N ratio of -11.21. Meanwhile, in terms of engine power, the optimum multi-objective design gets a larger S/N ratio of 16.13, while the optimum emission design only gets an S/N ratio of 15.86 S/N. It is in line with the ANOVA test results which show that the optimum multi-objective design is proven to be better than the optimum emission design.

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Section: Ann-moga Optimization Resultsmentioning

confidence: 99%

Design of Metallic Catalytic Converter using Pareto Optimization to Improve Engine Performance and Exhaust Emissions

Ariyanto

Suprayitno

Wulandari

2023

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“…The set of Pareto optimal solutions obtained is referred to as Pareto set, and its image is referred to as the Pareto front [40,41].…”

Section: Pareto Front Multiobjective Optimizationmentioning

confidence: 99%

Multiobjective Optimization of the Energy Efficiency and the Steam Flow in a Bagasse Boiler

Molina,

Vidal Medina,

Sagastume Gutiérrez

et al. 2023

Sustainability

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Renewable energy and energy efficiency are essential for a transition to cleaner and sustainable energy. Photovoltaic and wind turbine systems introduce operation, control, protection, and planning issues, particularly affecting frequency stability in the grid. In contrast to more widespread wind turbines and photovoltaic systems, biomass based electricity systems are more stable with no negative impacts on the grid stability. The efficiency of bagasse boilers is essential to guaranteeing adequate economic profit and environmental performance in sugar plants. To realize universal access to affordable, reliable, and modern energy services by 2030 (SDG 7), the use of renewable energy sources in energy mixing and energy efficiency must increase globally. Sugar plants include cogeneration systems to provide heat and electricity to the process and frequently sell an electricity surplus to the grid, which depends on their energy efficiency. Boilers are an essential component of cogeneration systems in sugar plants, and their efficiency is crucial to guarantee electricity surplus. Therefore, this study assessed a bagasse boiler to optimize its operational efficiency. To this end, the exergy assessment and multiobjective optimization based on a genetic algorithm are used. The results show that the exergy efficiency of the boiler improved by 0.8% with the optimization, reducing bagasse consumption by 23 t/d.

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A novel approach for spam detection using horse herd optimization algorithm

Hosseinalipour

Ghanbarzadeh

2022

Neural Comput & Applic

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Communication via email has expanded dramatically in recent decades due to its cost-effectiveness, convenience, speed, and utility for a variety of contexts, including social, scientific, cultural, political, authentication, and advertising applications. Spam is an email sent to a large number of individuals or organizations without the recipient's desire or request. It is increasingly becoming a harmful part of email traffic and can negatively affect the usability of email systems. Such emails consume network bandwidth as well as storage space, causing email systems to slow down, wasting time and effort scanning and eliminating enormous amounts of useless information. Spam is also used for distributing offensive and harmful content on the Internet. The objective of the current study was to develop a new method for email spam detection with high accuracy and a low error rate. There are several methods to recognize, detect, filter, categorize, and delete spam emails, and almost the majority of the proposed methods have some extent of error rate. None of the spam detection techniques, despite the optimizations performed, have been effective alone. A step in text mining and message classification is feature selection, and one of the best approaches for feature selection is the use of metaheuristic algorithms. This article introduces a new method for detecting spam using the Horse herd metaheuristic Optimization Algorithm (HOA). First, the continuous HOA was transformed into a discrete algorithm. The inputs of the resulting algorithm then became opposition-based and then converted to multiobjective. Finally, it was used for spam detection, which is a discrete and multiobjective problem. The evaluation results indicate that the proposed method performs better compared to other methods such as K-nearest neighbours-grey wolf optimisation, K-nearest neighbours, multilayer perceptron, support vector machine, and Naive Bayesian. The results show that the new multiobjective opposition-based binary horse herd optimizer, running on the UCI data set, has been more successful in the average selection size and classification accuracy compared with other standard metaheuristic methods. According to the findings, the proposed algorithm is substantially more accurate in detecting spam emails in the data set in comparison with other similar algorithms, and it shows lower computational complexity.

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Multiobjective optimization of a geothermal power plant

Cited by 5 publications

References 31 publications

Design of Metallic Catalytic Converter using Pareto Optimization to Improve Engine Performance and Exhaust Emissions

Design of Metallic Catalytic Converter using Pareto Optimization to Improve Engine Performance and Exhaust Emissions

Multiobjective Optimization of the Energy Efficiency and the Steam Flow in a Bagasse Boiler

A novel approach for spam detection using horse herd optimization algorithm

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