Multi-objective Optimization Analysis Research Articles

A multi-objective optimization analysis to assess the potential economic and environmental benefits of distributed storage in district heating networks: a case study

Conventionally, district heating networks have been developed with a centralized logic, with large generation units designed to provide space heating to distributed users. Some networks have already evolved to a stage in which multiple generation units are distributed throughout the network and are supplying heat from different sources and with different schedules. ICT technologies can be the basis for a live optimization of the network, which can be implemented by minimizing energy supply cost for the users or minimizing greenhouse gases emissions. This paper presents an analysis of the potential effects of including distributed heat storage units to an existing DH network, as the first step toward opening the interaction with users. The results show the limited effect of the demand profile variation in comparison with the potential benefits of optimization strategies against the current operation of the case study under analysis, the main reason being the good flexibility of the available heat generation units. Thus, the installation of distributed storage units should be preferred in DH networks characterized by a large share of non-flexible generation options, such as solar energy or waste heat from industries, or where the energy prices show large variations over the day.

Multi-objective optimization, design and performance analysis of an advanced trigenerative micro compressed air energy storage system

This paper proposes an advanced trigenerative micro compressed air energy storage (CAES) system, which acts as combined cooling, heating and power system by recovering cooling, heating and power energy during or after expansion. The proposed CAES system can be integrated with grid and placed close to users’ side. Based on the users’ demand, the proposed CAES system can produce different kinds of energy by adjusting the turbine inlet temperature in different seasons. The mathematical model of the proposed system including energy analysis and economic analysis has been set up. A multi-objective optimization by the aid of Non-dominated sorting Genetic Algorithm-Ⅱ(NSGA-Ⅱ) is employed to determine the design parameters of the proposed system considering the round trip efficiency and the annual total cost saving. A case application reveals the effectiveness of the above method. At the final optimal point, the round trip efficiency and the annual total cost saving are 0.762 and 147,820.0 $/year, respectively. Then, the design parameters of the proposed system are as follow: the capacity of each compressor is 256.0 kW, the capacity of turbine is 830.0 kW, and the volume of compressed air reservoir is 502.0 m3. In addition, a sensitivity analysis is performed mainly to assess the influence of key parameters variation on the proposed CAES system performance. This research will provide a guiding principle for design and practical application of trigenerative CAES system to mitigate the power shortage and improve economy for users.

Approach to robust multi-objective optimization and probabilistic analysis: the ROPAR algorithm

AbstractThis paper considers the problem of robust optimization, and presents the technique called Robust Optimization and Probabilistic Analysis of Robustness (ROPAR). It has been developed for finding robust optimum solutions of a particular class in model-based multi-objective optimization (MOO) problems (i.e. when the objective function is not known analytically), where some of the parameters or inputs to this model are assumed to be uncertain. A Monte Carlo simulation framework is used. It can be straightforwardly implemented in a distributed computing environment which allows the results to be obtained relatively fast. The technique is exemplified in the two case studies: (a) a benchmark problem commonly used to test MOO algorithms (a version of the ZDT1 function); and (b) a design problem of a simple storm drainage system, where the uncertainty is associated with design rainfall events. It is shown that the design found by ROPAR can adequately cope with these uncertainties. The approach can be useful for assisting in a wide range of risk-based decisions.

Incorporating energy storage and user experience in isolated microgrid dispatch using a multi‐objective model

In order to coordinate multiple different scheduling objectives from the perspectives of economy, environment and users, a practical multi-objective dynamic optimal dispatch model incorporating energy storage and user experience is proposed for isolated microgrids. In this model, besides Microturbine units, energy storage is employed to provide spinning reserve services for microgirds; and furthermore, from the perspective of demand side management, a consumer satisfaction indicator is developed to measure the quality of user experience. A two-step solution methodology incorporating multi-objective optimization (MOO) and decision analysis is put forward to address this model. First, a powerful heuristic optimization algorithm, called the {\theta}-dominance based evolutionary algorithm, is used to find a well-distributed set of Pareto-optimal solutions of the problem. And thereby, the best compromise solutions (BCSs) are identified from the entire solutions with the use of decision analysis by integrating fuzzy C-means clustering and grey relation projection. The simulation results on the modified Oak Ridge National Laboratory Distributed Energy Control and Communication lab microgrid test system demonstrate the effectiveness of the proposed approach.

Review on Single and Multi-objective Optimization Process Parameters of EDM Using Taguchi Method and Grey Relational Analysis

In recent years use of the nonconventional type of machines is increasing due to some limitations of the conventional type of machines. Electric discharge machine is one of the nonconventional types of the machine generally used for cutting an intricate shape from the hard work piece. Variation in process parameters of electric discharge machining affects its response variable like material removal rate, surface finish, recast layer, wear ratio etc. Because, of that for getting good results of response variables its necessary that proper selection of response of variable. Many researchers tried to optimize process parameters of electric discharge machining with the help of Taguchi method and Grey relational analysis for getting acceptable results of the response variable. Researchers use the Taguchi method for single objective optimization and grey relational analysis for multi-objective optimization. The main focus is on optimization of process parameters of EDM and its modified versions like WEDM, Micro-EDM and PMEDM using Taguchi method and Grey relational analysis and hence, only such research works are included in this review. Where the Taguchi method and Grey relational analysis method is used to optimizing process parameters of EDM; that were published in the last 15 years since 2003. The review work on such large extent was not tried earlier by considering this optimization process for EDM and its modified versions and hence this review work may become ready information at on a place and help researchers to decide their direction of research.

Research on multidisciplinary fatigue optimization design method in structural design of high speed train

Aiming at the shortcomings of single-discipline fatigue design and life prediction optimization design method for high-speed trains, considering the characteristics of structural design integrity, a durability analysis method for key structural components based on multi-disciplinary fatigue optimization design is proposed. According to the characteristics of vibration fatigue and structural failure of key structural components of high-speed trains, the multi-disciplinary fatigue design of carbody components is carried out from the perspectives of materials, loads and structural design. In the research process, qualitative and quantitative analysis and comparison of multi-objective optimization analysis (MOO) and multi-disciplinary optimization (MDO) processes were carried out. At the same time, considering the actual example, considering the vibration characteristics of the vehicle structure, the typical load spectrum and the complex material characteristics, the failure characteristics of the structural components are analyzed in detail. The research results are shown that there are certain limitations in single-disciplinary fatigue design and analysis. In order to effectively solve the complex engineering structure failure problem, it is necessary to fully consider the vibration characteristics of the entire vehicle structure. Through the multi-disciplinary fatigue design and optimization method of high-speed train structural components, the safety and structural integrity of key structural components of rail transit can be better guaranteed.

Multi-objective optimization and comparative performance analysis of hybrid biomass-based solid oxide fuel cell/solid oxide electrolyzer cell/gas turbine using different gasification agents

In this study, a novel configuration consisting of a biomass-based anode/cathode recycling solid oxide fuel cell integrated with a gas turbine and solid oxide electrolyzer cell is proposed for power and hydrogen production. The new configuration is modeled using Air, O2-enriched air, O2 and CO2 as the gasification agents. Accordingly, the waste heat of the SOFC is exploited in the gas turbine and subsequently the generated power of the gas turbine is transferred to a solid oxide electrolyzer cell unit for hydrogen production. Consequently, the proposed system is analyzed and compared from energy, exergy and exergoeconomic viewpoints using different gasification agents through the parametric study. Subsequently, the system in which pure CO2 is considered as the gasification agent is optimized by multi-objective optimization method based on genetic algorithm. Accordingly, the optimal solution points are gathered as four Pareto frontiers considering exergy efficiency, total product cost, levelized emissions and rate of hydrogen production as the objective functions. The results of parametric study show that the highest exergy efficiency/power production at the same time the lowest total product cost are expected for the system using O2 as the gasification agent. On the other hand, using CO2 as the gasification agent (by recycling 20% of the emitted CO2 into the gasifier) leads to the highest hydrogen production rate and the lowest levelized emissions in a wide range of the effective parameters. Considering exergy efficiency and total product cost as the objective functions, the results of the multi-objective optimization indicate that, exergy efficiency and total product cost would be 45.25% and 16.21 $/GJ, respectively at the optimum operating condition. Furthermore, results of multi-objective optimization from hydrogen production rate and levelized emissions viewpoints show that the corresponding values may be 8.561 kg/h and 7.745 t/MWh, respectively.

Traffic lights synchronization for Bus Rapid Transit using a parallel evolutionary algorithm

This article presents a parallel evolutionary algorithm for public transport optimization by synchronizing traffic lights in the context of Bus Rapid Transit systems. The related optimization problem is NP-hard, so exact computational methods are not useful to solve real-world instances. Our research introduces a parallel evolutionary algorithm to efficiently configure and synchronize traffic lights and improve the average speed of buses and other vehicles. The Bus Rapid Transit on Garzon Avenue (Montevideo, Uruguay) is used as a case study. This is an interesting complex urban scenario due to the number of crossings, streets, and traffic lights in the zone. The experimental analysis compares the numerical results computed by the parallel evolutionary algorithm with a scenario that models the current reality. The results show that the proposed evolutionary algorithm achieves better quality of service when compared with the current reality, improving up to 15.3% the average bus speed and 24.8% the average speed of other vehicles. A multiobjective optimization analysis also demonstrates that additional improvements can be achieved by assigning different priorities to buses and other vehicles. In addition, further improvements can be achieved on a modified scenario simply by deleting a few bus stops and changing some traffic lights rules. The benefits of using a parallel solver are also highlighted, as the parallel version is able to accelerate the execution times up to 26.9x when compared with the sequential version.

Multi-objective storage location allocation optimization and simulation analysis of automated warehouse based on multi-population genetic algorithm

Aiming at the requirement of working efficiency and security of automated warehouse and taking the operation time of outbound–inbound, the equivalent center of gravity of overall shelf and the degree of relative accumulation of related products as the multi-objective functions, the mathematical model is constructed for multi-objective storage location allocation optimization. According to the simple weighted genetic algorithm, it is easily prone to the problem of immature convergence when solving multi-objective programming problems. So, the multi-population genetic algorithm is proposed to solve the mathematical model of storage location allocation optimization. Combining with the experiment data of toy car assembly and automated warehouse, the results of the automated warehouse storage location allocation are obtained. FlexSim dynamic simulation model is established based on the storage location allocation solution, the physical parameters of automated warehouse and the experimental requirements plan of vehicle model assembly. The operation effect of the model and the utilization rate of the equipment are analyzed. The result of multi-population genetic algorithm is more reasonable and effective. It is proved that the result of multi-population genetic algorithm is superior to the result of simple weighted genetic algorithm, which provides an effective method for storage location allocation optimization and outbound–inbound dynamic simulation.

Design Optimization Approach of a Large-Scale Moving Framework for a Large 5-Axis Machining Center

The traditional machine tool design method with metal materials makes large-scale moving structures very heavy, which seriously impacts dynamic performance and results in significant energy consumption. Using sandwich structures of composite materials to replace metal materials is an important strategy for lightweight large-scale moving structures. However, this kind of substitution is generally believed to be difficult because foam-filled sandwich structures usually show nonlinear characteristics and must balance the moving mass, material costs, and structural stiffness. In the present study, we proposed a design optimization approach for a large-scale moving framework in a large 5-axis machining center (L5AMC) considering large dimensions in the x, y, and z work space and high machining speed with the aim of minimizing the displacements of the milling head. An improved approach, named the 3-step design optimization, was executed to obtain the optimum framework structures to solve the contradiction between the moving mass, material costs, and structural stiffness. This approach was based on multi-objective optimization and finite element analysis. The structural stiffness of the framework after optimization increased by 89% compared with before optimization although the mass increased by 6% and the material costs increased by 9%. A finite element simulation under four given operational loads showed that the displacements of the milling head were all less than the design requirement of 0.25 mm. The results indicated that the proposed 3-step design optimization approach for the optimal design of a large-scale moving framework was feasible and successful. A 40 m × 6 m × 4 m L5AMC prototype was manufactured, and the actual verification results indicated that the large-scale moving framework fully met the design requirements of the L5AMC and reduced energy consumption.

Multi-objective optimization and exergetic analysis of a low-grade waste heat recovery ORC application on a Brazilian FPSO

This paper presents an analysis of the application of an Organic Rankine cycle (ORC) for power generation of a Brazilian FPSO (Floating Product Storage Offload). The peculiarity of this analysis is the investigation of power generation using low-temperature waste heat sources. Low-grade sources represent a significant amount of heat rejected on the platform. The primary production in the platform processes was evaluated using ASPEN-HYSYS® software v.8.6. The main sources of low-temperature residual heat were preliminarily identified, and the highest potential for energy recovery was: the heat rejected in the intercoolers and aftercoolers of the compression processes in the Main Compression Unit and the CO2 Compression Unit. For the development of this study, a computational tool was elaborated in MATLAB® to evaluate the thermodynamic performance and to predict the design of the heat exchanger of the ORC. A multi-objective optimization was conducted to verify the ORC application at the established sources. The higher net power was obtained at Main Compression Unit heat recovery, operating with R245CB2 as working fluid. This application allows to generate up to 2063 kW with a heat transfer area of 2997 m2, providing a 23.6% increase in exergy efficiency of the system. The results of this study suggest that the application of ORC cycles on FPSO platforms for heat recovery from low-temperature sources allows an essential increase in the energy and exergetic efficiency of the production processes of the platform. Although the ORC doesn’t give a substantial increase in the supply of electricity, they contribute to less gas consumption in gas turbines. In this way, contributing to a significant reduction of GHG emissions.

SUSCAPE: A framework for the optimal design of SUStainable ChemicAl ProcEsses incorporating data envelopment analysis

Developing computer aided tools for process design is of paramount importance in the transition toward a more sustainable chemical industry. In this work, we present a framework to incorporate sustainability principles in the design of chemical processes that combines a palette of tools, including life cycle assessment, surrogate modeling, objective reduction, multi-objective optimization and data envelopment analysis (DEA). The latter methodology facilitates the post-optimal analysis of the Pareto front by narrowing down the number of designs and ranking them without the need to define weights in an explicit manner. DEA provides in turn improvement targets for the suboptimal alternatives that if attained would make them optimal, thereby guiding retrofit efforts toward the most effective actions based on benchmarking them against the best technologies available. The capabilities of the framework are demonstrated in a case study based on the production of methanol from CO2 and hydrogen.

The properties of chopped basalt fibre reinforced self-compacting concrete

The use of basalt fibre in the production of self-compacting concrete (SCC) has been studied to identify how the fresh and hardened properties of SCC are affected by the addition of fibre. The basalt fibres of 3, 6, 12 and 24 mm in length are incorporated into the SCC mixtures as 0%, 0.1%, 0.3% and 0.5% of concrete volume. After the progression of flow diameter, T500 flow time and V-funnel time tests on the fresh SCC, the tests of compressive strength, flexural strength, splitting tensile strength, rapid chloride permeability and water penetration have been conducted to identify the hardened properties of SCC produced with basalt fibre. The response surface method is employed in the conducted multi objective optimization analysis considering the maximisation of the compressive, flexural and splitting tensile strength parameters while minimising the response criterion of rapid chloride permeability and water penetration depth. The results reveal that the use of basalt fibre decreases the workability but improves the mechanical properties of SCC. The highest flexural and splitting tensile strength results are obtained from the concrete mixtures incorporated with the content of 0.5% fibre having the length of 24 mm. Whereas, the highest compressive strength result is obtained from the mixtures containing the fibre content of 0.1% for the utilised fibre lengths of 12 mm and 24 mm. The optimum volume fraction and length of basalt fibre are determined as 0.49% and 21.12 mm considering the optimized strength and permeability based durability properties of SCC.

Multi-objective optimization and exergoeconomic analysis of waste heat recovery from Tehran's waste-to-energy plant integrated with an ORC unit

Recovery of waste heat in large industrial plants is nowadays an important topic of thermal optimization. In the present study, energy, exergy, and exergoeconomic analysis of an integrated system, Tehran's waste-to-energy power plant coupled with an organic Rankine cycle (ORC), is analyzed. Parametric study of essential parameters (moisture content, pinch point temperature differences of the HROG, steam generator's superheat temperature difference, and steam turbine inlet pressure) is performed thermodynamically. The best system performance achieved using R123 as the working fluid of ORC. After implementation of the waste-heat-recovery system into the WtE plant, with R123 the energy and exergy efficiencies increase from 17.27% to 19.51% and 14.49%–16.36%, respectively. Exergy analysis reveals that the gasifier and steam generator are the main source of exergy destruction in the overall system. Additionally, the results of single-objective optimization based on maximum exergy efficiency and minimum total product unit cost were calculated. Furthermore, multi-objective optimization based on genetic algorithm using MATLAB software is implemented to find the optimum point with respect to exergy efficiency and total product unit cost as the objective functions. The exergy efficiency and total product unit cost at the optimum point, considering multi-objective optimization, are 19.61% and 24.65 $/GJ, respectively.

Multi-objective optimization and flexibility analysis of a cogeneration system using thermorisk and thermoeconomic analyses

In the present study, an optimal power and freshwater cogeneration system is proposed to meet the global requirements sustainably. A Rankine cycle (RC), an organic Rankine cycle (ORC) and a reverse osmosis (RO) module are integrated to form the proposed system. The performance of the system is investigated using thermo-mathematical models allocating seven organic fluids in the bottoming ORC. A novel evolutionary algorithm-based multi-objective optimization approach is applied using thermorisk and thermoeconomic analyses. Thus, an optimal configuration is determined at both global and local scales. Finally, a flexibility analysis is performed to the optimal configuration considering probable uncertainties in the market. The optimization results showed that the total accidental risk impact and the total product cost rate improved by 2.49–48.73% and 5.67–62.41%, respectively, depending on the employed organic fluid. The highest exergetic efficiency and the minimum specific power consumption were obtained as 52.74% and 4.111 kWh/m3, allocating R245fa in the optimal system. The system enjoying R123 had the widest flexibility range without any increases in the optimum total product costs.

Multi-objective optimization and sensitivity analysis of an organic Rankine cycle coupled with a one-dimensional radial-inflow turbine efficiency prediction model

The organic Rankine cycle (ORC) has been demonstrated to be a viable approach to recover low-grade waste heat and has been widely investigated in recent years. In the current research focused on the multi-objective optimization problem of ORC systems, few scholars consider the variation in turbine efficiency with the cycle parameters. This paper focused on the comparison of multi-objective optimization with variable turbine efficiency and that with constant turbine efficiency. The results obtained for the two types of turbine efficiency were compared, and the differences were analyzed. Flue gas at 523.15 K was used as the heat source, and pentane, hexane, heptane, cyclohexane, benzene and toluene were selected as working fluid candidates. The one-dimensional radial-inflow turbine efficiency prediction model was applied to replace constant turbine efficiency. The multi-objective model in conjunction with the turbine efficiency model was constructed by defining the net power output and system total cost per unit net power output as the objective functions. The non-dominated sorting genetic algorithm-II (NSGA-II) was used to optimize the evaporation temperature and condensation temperature as the decision variables. With the aid of the ideal point, the optimal solution of each working fluid was selected from the Pareto frontier. The results showed that the turbine efficiency varies with changes in evaporation temperature and condensation temperature. In the multi-optimization with constant turbine efficiency, toluene and cyclohexane are the optimal working fluids, whereas with variable turbine efficiency, benzene is the optimal working fluid. In the sensitivity analysis, the optimal exergy efficiency shows opposite trends for the multi-objective optimization with constant and variable turbine efficiency.

Multiobjective Optimization in Pavement Management System Using NSGA-II Method

AbstractThe performance of multiobjective optimization analysis with respect to the multidimensional nature of pavement management systems can be improved. In this paper, a deterministic multiobjec...

An efficient combination of multi-objective evolutionary optimization and reliability analysis for reliability-based design optimization of truss structures

Over the past decades, the reliability-based design optimization of truss structures has been still a major challenge for engineering designers and even for researchers because of its complexities and high computational cost. In this paper, a new approach based on a novel combination of multi-objective evolutionary optimization and reliability analysis is proposed to deal with such kinds of problems. The proposed method consists of two separate steps. First, a multi-objective design optimization problem is formulated and solved by a multi-objective evolutionary optimization algorithm. Secondly, reliability analysis problems are formed by taking into account the uncertainty of input data of the problem, and a reliability analysis method is used to evaluate the reliability of all solutions obtained at the first step. Based on the obtained reliability, the suitable optimal solutions with a certain level of reliability can be readily identified. The proposed approach has some advantages such as: 1) it can give a set of many optimal solutions with different levels of reliability by only one run; 2) it can easily handle the design optimization problems of truss structures with continuous, discrete or mixed continuous-discrete design variables; 3) it is quite simple for engineering designers to understand and implement. To demonstrate the efficiency and applicability of the proposed method, three examples of truss structures are carried out, and the obtained results are compared to those available in the literature. The acquired results reveal that the proposed approach is reliable and more competitive compared to other methods.

Multi-objective Optimized Design for Intermediate-Frequency Noise Reduction in Aircraft Cabins

A multi-objective optimized noise reduction design strategy for aircraft cabins was formulated and a mathematical model of multi-objective structural optimization was established based on the dynamic response and acoustic radiation equations of the structure. Later, the finite element and statistical energy analysis of the simplified hybrid simulation model of the aircraft cabin section was conducted to identify noise sources in aircraft cabin under different excitation conditions. A multistage optimization task was set up using the collaborative optimization algorithm, and an overall optimization process was designed for multi-objective optimization calculation and analysis. Results were compared with non-optimized results to verify the validity of the proposed optimization algorithm. Calculated results demonstrated that the proposed multi-objective optimized design can reduce sound pressure in aircraft cabin effectively, with an average reduction of 3–4 dB in the intermediate-frequency band.

Multi-objective optimization and scenario-based robustness analysis of the MoneyMaker Hip Pump

Water-lifting technologies for irrigation have significant potential to increase agricultural yields and stimulate economic growth in rural areas of the developing world. Human-powered water pumps have been used with great success in this rapidly developing market. KickStart's MoneyMaker Hip Pump is a human-powered water pump with additional design features that make it lightweight and portable for use in remote fields in several countries throughout Africa. This work first applied numerical optimization techniques to the design of the MoneyMaker Hip Pump and offers further improvements to its design. Deterministic multi-objective optimization methods were employed to maximize the flow rate of the pump, maximize the pumping height of water, and minimize cost. Following optimization, the robustness of the optimized pump design was analyzed under several modified scenarios, including fouling of the hoses and a decrease in operator power due to fatigue or aging. The set of cost-optimized pump designs was then compared to a second set of optimal designs that was found using material volume as a simplified proxy for the cost objective function. Findings indicate that several technical improvements can be made to the current MoneyMaker Hip Pump design to reduce cost by up to 37% without affecting water flow rate, or increase water flow rate by up to 88% without increasing cost. The numerical model was validated through physical experimentation of the MoneyMaker Hip Pump, and design alterations to reduce cost were experimentally shown to maintain pump performance.