Multi-objective Optimization Study Research Articles

Advanced exergo-economic analysis of an advanced adiabatic compressed air energy storage system with the modified productive structure analysis method and multi-objective optimization study

In this study, conventional and advanced exergy/exergo economic analyses of an advanced adiabatic compressed air energy storage system (AA-CAES) system with a power output of 6 MW were performed. In addition, sensitivity analysis and multi-objective optimization study of the proposed system were also carried out. For the first time in the literature, advanced exergo-economic analysis was applied to the AA-CAES system with the Modified Productive Structure Analysis (MOPSA) method, and a new parameter was introduced as a performance criterion for this method. As a result of the study, the system's exergy efficiencies were found to be 54.51 % and 77 %, respectively, according to the conventional and advanced exergy analyses of the system. According to the conventional exergy analysis, the first three system components that should be given priority are the wind turbine (WT), the regulator (REG), and the heat exchangers-3 (HE-3), respectively. According to the advanced exergy analysis, this order changes as WT, the compressor-1 (C-1), and the compressor-2 (C-2). 40 % of the system's exergy destruction and exergy destruction cost rates are avoidable endogenous. This value shows the improvement potential of the system. As a result of the multi-objective optimization study, the modified exergy efficiency was found to be 94.58 %. The average unit product-exergy destruction cost was 59.51 $/GJ; these values were 22.83 % and 1.6 % improvement, respectively, compared to the base case.

Using Reduced Kinetic Model for the Multi-Objective Optimization of Thermal Section of the Claus Process Leading to a More Cost-Effective and Environmentally Friendly Operation

The Claus process is a sulfur recovery unit wherein hydrogen sulfide is converted into the elemental sulfur. This study aims to model the thermal section of the Claus process, which consists of a reaction furnace and a waste heat boiler, as a configuration of two reactors, and subsequently optimize the entire section. Two different reduced kinetic schemes were provided for both units. Using the validated kinetics, mathematical models were developed. The waste heat boiler was modeled as a plug flow reactor with heat transfer, instead of a heat exchanger. The main objective was to maximize the amount of elemental sulfur at the end of the thermal section. Additionally, maximizing the amount of steam generated in the WHB was considered as a secondary objective, and the multi-objective optimization problem was solved. The sulfur production was improved 14.1% and 30% as a result of single- and multi-objective optimization studies. In addition, as an alternative, the Taguchi method was also used for optimization studies, and optimum values were determined. Using the Taguchi method, we determined that an increase in sulfur production by 24% is possible.

Research on Multi-Objective Optimization of High-Speed Solenoid Valve Drive Strategies under the Synergistic Effect of Dynamic Response and Energy Loss

Under high-frequency operating conditions, the high-speed solenoid valve (HSV) experiences energy loss and heat generation, which significantly impacts its operational lifetime. Reducing the energy loss of an HSV without compromising its opening response characteristics poses a significant challenge. To address this issue, a finite element simulation model of an HSV coupled with a current feedback model is constructed to investigate the synergistic effects of dynamic response and energy loss. Prediction models for the opening response time, HSV driving energy, and Joule energy using a back propagation neural network (BPNN) are established. Furthermore, a multi-objective optimization study on the current driving strategy using a non-dominated sorting genetic algorithm II (NSGA-II) is conducted. After optimization, although there was a 6.24% increase in the opening response time, both HSV drive energy and Joule energy were significantly reduced by 15.67% and 22.49%, respectively. The proposed multi-objective optimization method for an HSV driving strategy holds great significance for improving its working durability.

Biological Activities of the Golden Chantarelle Mushroom Cantharellus cibarius (Agaricomycetes) Extracts Obtained as a Result of Single and Multi-Objective Optimization Studies

In our study, the biological activities of the wild edible mushroom <i>Cantharellus cibarius</i> were determined. First of all, 64 different experiments were performed in the Soxhlet device at 40-70°C extraction temperatures, 4, 6, 8, and 10 h of extraction time and 0.25, 0.50, 1, and 2 mg/mL extract concentrations, and total antioxidant (TAS) and total oxidant values (TOS) were determined, and both single-purpose and multi-objective optimization studies were performed. The extraction conditions were optimized so that the objective function of the single-purpose optimization would be the maximum TAS value, and the objective function of the multi-objective optimization would be the maximum TAS and minimum TOS values. The data obtained from the experimental study were modeled with artificial neural networks from artificial intelligence methods, and optimization was performed with a genetic algorithm. After the single-objective optimization process, the optimum extraction conditions were found as 46.622°C, 5.110 h and 1.973 mg/mL and the multi-objective optimum extraction conditions were found as 50.927°C, 6.094 h and 1.439 mg/mL. The TAS values of the extracts obtained as a result of single-objective and multi-objective optimization were measured as 5.511 and 5.359, respectively, and the TOS values were measured as 7.289 and 6.061. The anti-AChE value of the extracts was calculated as 16.12, 20.31 and anti-BChE value as 36.92, 40.28. In addition, it was determined that the single-objective optimization extract had stronger antiproliferative activity than the multi-objective extract. As a result, it was determined that the biological activities of the extracts of <i>C. cibarius</i> obtained under suitable conditions were high.

An integrated assessment of the financial and environmental impacts of exterior building insulation application

In the literature, optimum thermal insulation thickness on building's exterior was examined by many researchers. Optimum insulation thickness was generally studied with the aim of minimizing the total cost, and suitable values were determined within this scope. In this study, it has also considered the amount of CO2 released into the atmosphere due to the fuels used in building heating and cooling processes, as well as insulation materials, in addition to the total cost. This study has been focused on optimization of the determination of the ideal insulation thickness of buildings by simultaneously minimizing the total cost and CO2 emission, using the knee point approach. In this context, a multi-objective optimization study has been conducted for thermal insulation applied to buildings located in different climatic zones considering the solar radiation. A comprehensive analysis has been carried out to determine the impact of the heating source type (natural gas, electricity, fuel oil), insulation material (expanded polystyrene, extruded polystyrene, mineral wool, polyurethane foam), climatic zone (I, II, III, IV) and the purpose of the building's use (summer house, winter house or all-season house) on the optimum insulation thickness. The degree-day approach has been used to calculate annual energy consumption, and accordingly the life-cycle cost (LCC) method has been used in economic analysis. The optimum insulation thicknesses have been calculated with two separate objectives: one to minimize only the total cost and the other to minimize only the CO2 emissions. It has been found that when the primary goal of insulation application is to minimize CO2 emissions, the calculated optimum insulation thicknesses are consistently higher compared to situations where the main goal is minimizing total cost. Furthermore, for various climatic zones and different purpose of the building's use, an ideal insulation thicknesses that simultaneously minimizes both the cost and CO2 emissions have been identified. In this research, the equations providing environmentally optimum insulation thicknesses to minimize CO2 emissions have been derived and presented to researchers. Finally, the conclusion is drawn that the knee point approach can be applied in insulation scenarios where two objective functions need simultaneous optimization.

Enhancing Cutting Efficiency and Minimizing Forces for Nomex Honeycomb Core Using Grey Relational Analysis and Desirability Function Analysis.

Nomex Honeycomb core is the foundational building block for manufacturing aerospace composite components. Its usage requires machining honeycomb in complex aerodynamic profiles where the quality of the core is governed by accuracy and precision of cut profiles. The assessment of accuracy and precision is directly related to forces induced in the cutting tool and cutting efficiency. These two parameters form the basis of a multi-objective function that this paper aims to optimize for the milling operation. The parameter of depth of cut considered in this paper has not been analyzed in a multi-objective optimization study of the Nomex Honeycomb core previously. A Taguchi-based array of Design of Experiments followed by Analysis of Variance and correlation analysis is utilised. The results indicate that the most significant factor is the feed rate, with a percentage contribution of 72% for the cutting forces and depth of cut, with a percentage contribution of 85% in the case of cutting efficiency. The two parameters are optimized using Desirability Function Analysis and Grey Relational Analysis. The results are validated through experimental runs with an error within 5% of the statistical predictions, with the percentage improvement in cutting forces for optimum runs as compared to the worst experimental run at 47.8%. The percentage improvement in cutting efficiency likewise is 11%.

Multidisciplinary Optimization of Thermodynamic Cycles for Large-Scale Heat Pumps With Simultaneous Component Design

Abstract The performance of high temperature heat pumps (HTHPs) is highly dependent on the efficiency of its main components, which need to be optimally matched especially in closed cycles. The design process is therefore a challenging task as many disciplines and varying modeling depths need to be considered. Consequently, this is usually a sequential procedure beginning with cycle definition and raising the fidelity for component design. Fundamental design decisions are made based on assumptions for component performance. Mistakes in the phase of cycle definition are hard to reverse in later design stages. Therefore, this work introduces holistic approaches to the multidisciplinary design of closed Brayton cycles. Aerodynamic compressor design with two-dimensional throughflow analysis and geometry-based heat exchanger sizing are simultaneously optimized with thermodynamic cycle parameters. The presented methodologies make use of highly sophisticated design tools drawing on many years of experience in gas turbine design. The results demonstrate that holistic heat pump optimization can be successfully performed with reasonable computational effort. The advantages compared to conventional sequential design are elaborated. A comparison of two optimization concepts indicates that splitting up the design vectors of cycle and components shows the tendency to improve robustness. Finally, the tradeoff between system compactness and performance is demonstrated with a multi-objective optimization study.

Multiobjective Optimization Study On the Aerodynamic Performance and Anti-Erosion Characteristics of a Single-Stage Dusty Flue Gas Turbine

Abstract Solid particle erosion of dusty energy recovery turbine blades has a great impact on the operating economics and safety of the unit. To mitigate the erosion of blade and improve the aerodynamic performance of the turbine, a multiobjective optimization method for turbine cascade based on the experimental design method, genetic algorithm and CFD multiphase flow simulation was developed. The optimization results show that the number of stator and rotor blades and the trailing edge angle at 50% blade span are the main parameters affecting the efficiency and blade erosion of the dusty turbine. By reducing the number of stator blades and the circumferential bending angle of the stator trailing edge, the impingement velocity and impingement probability of particles impinging on the stator trailing edge decrease by 7.5%~16.8% and 8.9%~46.2%, respectively. Additionally, compared with the original design, the flow separation loss and secondary flow intensity of the rotor cascade are suppressed by adjusting the load distribution and inlet attack angle of the rotor; thus, the turbine efficiency effectively improves by 2.28%. Meanwhile, the optimized blade reduces the particle impingement velocity and probability on the rotor leading edge, and the erosion condition of the rotor leading edge decreases by 70%.

A multi-objective optimization approach for the production of polyhydroxybutyrate via Chlorogloea fritschii under diurnal light with single-stage cultivation

The present study aims to optimize the nutrients for maximization of cyanobacterial biomass with high content of polyhydroxybutyrate (PHB), a bioplastic, and recovery of biomass by auto-sedimentation under diurnal light mimic to sunlight. The multi-objective optimization with desirability approach was used to improve dry cell weight (DCW), PHB content (% w/w), and auto-sedimentation concentration factor (SCF) of biomass. Initially, NaNO3, K2HPO4, TRACE (micronutrient solution), Na2EDTA, and MgSO4.7H2O were screened as important media compositions. Screening was followed by the application of response surface methodology for the development of a model used in multi-objective optimization. The optimized media selected from many optimal solutions, a set of Pareto solutions generated by multi-objective optimization was validated in a flat panel photobioreactor. Using a single-stage cultivation strategy under diurnal light, Chlorogloea fritschii TISTR 8527 has shown capability to produce DCW of 1.23 g/l with PHB content of 31.78 % and SCF of 93.63 with optimal media. This leads to the enhancement of both PHB content (2.72 fold) and SCF (1.64 fold) were observed when compared to the non-optimal medium. This is the first multi-objective optimization study for media optimization using cyanobacteria reported till now under diurnal light mimic to sunlight for bioplastic production.

Multi-objective optimization study of hydrothermal aging on NOx-assisted soot catalytic combustion and secondary pollution emission in regeneration of CeO2 catalytic diesel particulate filter

The present study aimed to explore the effect of hydrothermal aging on the heterogeneous catalytic combustion process of soot in CeO2 catalytic diesel particular filter and the formation of secondary pollutants (N2O and CO).The present study was based on a fixed-bed experimental bench for NOx-assisted soot catalytic oxidation in CeO2-CDPF with different soot loading and catalyst coating amounts. A non-linear regression surrogate model and a multi-objective optimization decision approach were used to analyze the trade-off relationship between the design variables and the optimization objectives. The results showed that lower levels of soot loading (2.5 g/L), CeO2 catalyst coating amount (2.5 g/L) and higher NOx concentration (1000 ppm) in fresh catalysts were favorable for improving the performance of soot-assisted catalytic combustion and minimizing the formation of N2O and CO. However, it was observed that higher levels of catalyst coating amount (10 g/L) resulted in better soot catalytic combustion performance with lower N2O and CO emissions. Global sensitivity analysis showed a positive correlation between N2O and CO peak concentrations and peak temperatures with soot loading, but a negative correlation with catalyst loading. Over time, hydrothermal aging of CDPF caused the inhibitory effect of CeO2 catalyst on N2O to shift from negative to positive correlation, while also decreasing the synergistic performance of NOx in soot catalytic combustion.

Multi-objective optimization study of CCHP-GSHP-PV-ST system under different climatic zone conditions

In this research, a system with multiple energy sources of combined cooling, heating, and power (CCHP)/ground source heat pump (GSHP)/photovoltaic (PV)/solar thermal collector (ST) (CCHP-GSHP-PV-ST system) is constructed. And this system is combined with the operation strategy of following total electric demand, that is, giving priority to meeting the electrical load of the building, the additional power of the coupling system, and the power of driving the GSHP. Therefore, a model for multi-objective optimization of the CCHP-GSHP-PV-ST system based on energy, environment, and economic indicators is established, and the optimization model is resolved using the genetic algorithm. The load simulation of office buildings in representative cities of five climate zones in China (Harbin (severe cold climate), Beijing (cold climate), Kunming (mild climate), Guangzhou (hot summer and warm winter climate), Changsha (hot summer and cold winter climate)) was selected as the basis for coupling system optimization, and the multi-objective optimization study of CCHP-GSHP-PV-ST system under different climate zone conditions was carried out. The findings indicate: (1) Under various climatic zone conditions, the coupling system’s optimized composition varies a little; (2) The multi-energy coupled system of office buildings in Beijing (cold climate) has primary energy saving rates, carbon dioxide emission reduction rates, annual total cost saving rates, and comprehensive performance that are respectively 46.06%, 60.43%, 44.05%, and 50.18% better than the split-production system when compared to other climate zones.

Empirical study of evolutionary computation-based multi-objective Bayesian optimization for materials discovery

Empirical study of evolutionary computation-based multi-objective Bayesian optimization for materials discovery

Multi-objective optimization and parametric study of a hybrid waste gasification system integrated with reverse osmosis desalination

The increasing population has created two fundamental issues on the islands (in this case, Kish Island): an expansion in waste production and handling and a rise in the need for freshwater for daily consumption. Waste gasification can be used to reduce waste and generate energy. Sea salt water can be converted into drinkable freshwater using energy. This article describes a computational code using a combination of EES (Engineering Equation Solver) and MATLAB software for a hybrid cycle that includes waste gasification and reverse osmosis to generate freshwater and power. Kish Island waste data was carefully collected onsite. An exergy evaluation has been conducted to verify the cycle's irreversibility. Lastly, optimization has been performed to identify the best operation mode. In terms of irreversibility, a gasifier is far more irreversible than any other type of plant equipment. Based on the parametric analysis, salt water and waste flow significantly impact freshwater production. Approximately 20,000 m3/day are consumed on Kish Island each day, so if the entire production power of the steam turbine is used in the reverse osmosis high-pressure pump, 2860 m3/day can be obtained each day (14% of the required amount).

Single and multi-objective dynamic optimization study of an industrial scale fed batch reactor

Abstract The present work focuses on obtaining optimal operational policies of an industrial scale penicillin fed-batch fermentation process using dynamic optimization (DO). The three process objectives considered in this work include, maximization of total penicillin formed (J 1), minimization of total biomass formed (J 2), and minimization of fed-batch operation time (J 3). The control variables for the optimization study include, the feed flowrates of sugar, soyabean oil, and phenylacetic. We perform a single-objective optimization study with J 1 as the objective function and showcased the importance of DO. Consequently, we solve three multi-objective optimization (MOO) problems corresponding to all the pairwise combinations of the aforementioned three objectives. Outcomes of MOO problems are presented in terms of several non-dominated Pareto-optimal solutions. Furthermore, descriptive analysis of three representative Pareto points for all the MOO problems is presented, which highlights the importance of MOO study of the penicillin bio-reactor.

Multi-objective optimization study applied to an ethanol fermentation of cashew apple juice

Multi-objective optimization study applied to an ethanol fermentation of cashew apple juice

Multi-objective optimization study of wave elimination and electricity generation performance of Savonius hydrokinetic turbine based on metamodel

The floating power generation and wave elimination system is getting more and more attention because of its multiple functions. The Savonius hydrokinetic turbine (SHT) can be used for wave elimination and power generation of offshore platforms due to its good self-start performance and simple structure. The wave elimination performance (WEP) and electricity generation performance (EGP) are closely related to the parameters of SHT, and these parameters are coupled with each other. Improving the performance of SHT under wave conditions and quickly analyzing the relationship between performance and parameters is a topic worth studying. In this paper, a multi-parameter multi-objective metamodel is established by CFD and RBFNN to meet the accuracy. And then coupled with NSGA –II algorithm to solve the Pareto front, quickly determining the optimized parameters of the SHT according to the decision criterion. Finally, a physical model is used for experimental verification, and the influence of each parameter on the performance is further analyzed. After optimization, the WEP is 0.3834 and the EGP is 0.2256. 55.7% and 28.6% improvement over the conventional structure, respectively. The metamodel is well representative within the optimization range. The optimized model still shows better performance in other conditions.

Optimizing building retrofit through data analytics: A study of multi-objective optimization and surrogate models derived from energy performance certificates

The building stock is responsible for a large share of global energy consumption and greenhouse gas emissions, therefore, it is critical to promote building retrofit to achieve the proposed carbon and energy neutrality goals. One of the policies implemented in recent years was the Energy Performance Certificate (EPC) policy, which proposes building stock benchmarking to identify buildings that require rehabilitation. However, research shows that these mechanisms fail to engage stakeholders in the retrofit process because it is widely seen as a mandatory and complex bureaucracy. This study makes use of an EPC database to integrate machine learning techniques with multi-objective optimization and develop an interface capable of (1) predicting a building’s, or household’s, energy needs; and (2) providing the user with optimum retrofit solutions, costs, and return on investment. The goal is to provide an open-source, easy-to-use interface that guides the user in the building retrofit process. The energy and EPC prediction models show a coefficient of determination (R2) of 0.84 and 0.79, and the optimization results for one case study EPC with a 2000€ budget limit in Évora, Portugal, show decreases of up to 60% in energy needs and return on investments of up to 7 in 3 years.

Crushing performance and optimization of bio-inspired multi-cell columns

Abstract Multi-cell columns inspired by diatom algae have been examined regarding crashworthiness. Crushing simulations have been conducted for AA2024-T351, B1500HS-O25, and a combination of these materials. The highest specific energy absorption and lowest peak crushing force value were obtained with a homogenous AA2024-T351 column. Single and multi-objective optimization studies have been conducted with this column to find the best design. This design could be used in crash-box-like structures in automotive design.

A comprehensive multi-objective optimization study for the aerodynamic noise mitigation of a small wind turbine

The noise emitted by small wind turbines is one of the main reasons for their poor public perception for use in urban areas. An optimization study was performed to mitigate the aerodynamic noise of a 750 W turbine through altering the geometry of solid and more importantly hollow blades for the first time. A validated in-house code simultaneously minimizes the aerodynamic noise and maximizes the output power. The inertia of the blades is another important design parameter for small turbines as it influences the starting performance. The minimization of starting time was added to the above-mentioned goals and another optimization was done. The outcomes highlight the great significance of two ends of the blade; the tip section has a key role in alleviation of the noise as well as the improvement of the power coefficient while the root part could speed up the starting. Quantitatively, the optimization of solid blades shows that a 1.9 dB reduction in aerodynamic noise is attainable at the expense of a 1.6% loss in the power coefficient. Compared to solid blades, the aerodynamic noise and the starting time were reduced for hollow ones by 1 dB and 30% respectively. The latter is very attractive for use of small turbines in low-wind areas.

PSO vs GA: A Comparative Study of Multi-Objective Reliability Optimization using Fuzzy Nonlinear Programming Functions

Abstract— Multi-objective reliability optimization is a complex problem that involves simultaneously optimizing multiple objectives while ensuring that the system meets certain reliability requirements. In this paper, we present a methodology for solving multi-objective reliability optimization problems using fuzzy nonlinear programming. The methodology involves representing the reliability of each component as a triangular interval number and each objective function as an interval membership function. Conflicts between objectives are resolved using linear and nonlinear membership functions, and exponential and quadratic membership functions are used to obtain definite biases towards the objective. The proposed methodology employs Particle Swarm Optimization (PSO) or Genetic Algorithm (GA) to solve the problem, and the approach is compared with GA for linear and nonlinear membership functions. The results indicate the effectiveness of the methodology in addressing multi-objective reliability optimization problems.