Non-sorting Genetic Algorithm Research Articles

Dual-objective optimization and energy efficiency enhancement of natural decantation assisted extractive distillation process for n-butanol/isobutanol/water separation

Isobutanol (i-BuOH) and n-butanol (n-BuOH) are widely used as solvents and organic raw materials in fine chemical and pharmaceutical fields. Hence the recovery of high-purity products from chemical industrial effluent containing i-BuOH and n-BuOH is of great significance to improve economic and environmental benefits. In this article, two extractive distillation processes with or without natural decantation are developed with 1,4-butanediol as entrainer, namely CED and NDED. In order to obtain the optimal process parameters under the trade-off between economic and environmental benefits, the two processes are optimized with total annual cost (TAC) and CO2 emissions as objective functions by a non-sorting genetic algorithm. Further, a heat integrated extractive distillation process with natural decantation and vapor recompressed heat pump is designed and investigated, namely HI-NDED-VRHP. Finally, a fair multi-criteria evaluation is conducted on all three optimal processes in terms of TAC, CO2 emissions, and total energy consumption (TEC). The results show that the HI-NDED-VRHP process is highly eco-efficient, which can reduce TAC by 18.9%, CO2 emissions 44.2% and TEC 44.7% than the CED process and decrease TAC by 12.4%, CO2 emissions 36.7% and TEC 37.1% than the NDED process.

Optimal techno-economic energy coordination of solar PV water pumping irrigation systems

In this study, a water pumping photovoltaic system (WPPVS) provided with a water storage tank is introduced to supply freshwater for drip irrigation in Farafra oasis, Egypt. A multi-objective K-means clustering based on non-sorting genetic algorithm is utilized to minimize both the loss of water supply probability and the total annual costs including capital and running costs. Due to the variability of the water flow rate, a techno-economic energy coordination is developed with particular attention to avoid vibration of water pumps. The economic feasibility is based on the tomato crop yield against the total cost of the drip irrigation system. Sensitivity analysis for dynamic head, crop price, and interest and inflation rates is conducted. At specific dynamic head of 6 m, the results revealed that the optimal design of the WPPVS pumps 10220 cubic meter of water for irrigation through the developed energy coordination against 18575 cubic meter with pump vibration which saves 45 % of the pumped water. This reflects its effect on a decrease of the pumped water for all dynamic head values. Furthermore, the vibration avoidance strategy allows the pump to operate at lower dynamic heads above the water threshold level in the well, which is not possible with the presence of vibrations.

A novel optimization framework for minimizing the surface roughness while increasing the material processing rate in the SLM process of 316L stainless steel

Purpose316 L stainless steel alloy is potentially the most used material in the selective laser melting (SLM) process because of its versatility and broad fields of applications (e.g. medical devices, tooling, automotive, etc.). That is why producing fully functional parts through optimal printing configuration is still a key issue to be addressed. This paper aims to present an entirely new framework for simultaneously reducing surface roughness (SR) while increasing the material processing rate in the SLM process of 316L stainless steel, keeping fundamental mechanical properties within their allowable range.Design/methodology/approachConsidering the nonlinear relationship between the printing parameters and features analyzed in the entire experimental space, machine learning and statistical modeling methods were defined to describe the behavior of the selected variables in the as-built conditions. First, the Box–Behnken design was adopted and corresponding experimental planning was conducted to measure the required variables. Second, the relationship between the laser power, scanning speed, hatch distance, layer thickness and selected responses was modeled using empirical methods. Subsequently, three heuristic algorithms (nonsorting genetic algorithm, multi-objective particle swarm optimization and cross-entropy method) were used and compared to search for the Pareto solutions of the formulated multi-objective problem.FindingsA minimum SR value of approximately 12.83 μm and a maximum material processing rate of 2.35 mm3/s were achieved. Finally, some verification experiments recommended by the decision-making system implemented strongly confirmed the reliability of the proposed optimization methodology by providing the ultimate part qualities and their mechanical properties nearly identical to those defined in the literature, with only approximately 10% of error at the maximum.Originality/valueTo the best of the authors’ knowledge, this is the first study dealing with an entirely different and more comprehensive approach for optimizing the 316 L SLM process, embedding it in a unique framework of mechanical and surface properties and material processing rate.

Two-stage multi-step energy model calibration of the cooling systems of a large-space commercial building

Buildings play a major role in energy expenditure, representing 40% of Europe’s total energy consumption. It is estimated that heating, ventilation, and air conditioning systems consume between 50–60% of the total energy spent inside the building, thus corresponding to 20% of global worldwide energy consumption. Hence, there is a need to improve the accuracy of building thermal simulation and energy models that are essential in regulatory compliance calculations. In the present study, the authors empirically validate an optimization-based calibration methodology based on its application to a fully operational commercial building located in Pamplona, Navarre. The methodology used a white-box two-stage model in EnergyPlus, which combines a load profile object and a district cooling component to distribute the cooling load inside the building’s thermal zones. The study optimized the parameters and performance curves of different cooling system components using a second-generation non-sorting genetic algorithm in jEPlus software and 985 h of ten-minute time-step data. Finally, a multi-level benchmark is executed, which evaluates the electric energy consumption of the building’s heat pumps and the interior temperature of the different thermal zones for summer 2020 conditions. The assessment of the thermal and energy performance of the simulation model was conducted according to the requirements of the American Society of Heating, Refrigerating and Air-Conditioning Engineers, Guideline 14-2002, and the Chartered Institution of Building Services Engineers, Operation Performance Technical Memoranda 63.

Multi-Objective Electromagnetic Design Optimization of a Power Transformer Using 3D Finite Element Analysis, Response Surface Methodology, and the Third Generation Non-Sorting Genetic Algorithm

This paper presents a multi-objective design optimization of a power transformer to find the optimal geometry of its core and the low- and high-voltage windings, representing the minimum power losses and the minimum core and copper weights. The optimal design is important because it allows manufacturers to build more efficient and economical transformers. The approach employs a manufacturer’s design methodology, which is based on the usage of the laws of physics and leads to an analytical transformer model with the advantage of requiring a low amount of computing time. Afterward, the multi-objective design optimization is defined along with its constraints, and they are solved using the Non-Sorting Genetic Algorithm III (NSGA-III), which finds a set of optimal solutions. Once an optimal solution is selected from the Pareto front, it is necessary to fine-tune it with the 3D Finite Element Analysis (FEA). To avoid the large computing times needed to carry out the 3D Finite Element (FE) model simulations used in multi-objective design optimization, Response Surface Methodology (RSM) polynomial models are developed using 3D FE model transformer simulations. Finally, a second multi-objective design optimization is carried out using the developed RSM empirical models that represent the cost functions and is solved using the NSGA-III. The numerical results of the optimal core and windings geometries demonstrate the validity of the proposed design methodology based on the NSGA-III. The used global optimizer has the feature of solving optimization problems with many cost functions, but it has not been applied to the design of transformers. The results obtained in this paper demonstrate better performance and accuracy with respect to the commonly used NSGA-II.

Insight on the sustainable design and multi-objective optimization for separating the ternary azeotropic mixture of toluene/n-butanol/water by natural decanting coupled with pressure swing distillation

The effective recovery of n-butanol (BuOH) and toluene from wastewater via sustainable separation process is critical to promote environmental protection and resource utilization. In this work, we propose a comprehensive methodology to develop a natural decanting coupled with pressure-swing distillation (NDPSD) for the azeotropic separation of BuOH/toluene/water system. Firstly, the thermodynamic topologic insights are employed to analyze the feasibility of the NDPSD. Secondly, conceptual design involving the understanding of mass balance lines, the liquid–liquid envelope, and distillation boundaries were applied to develop initial design of the NDPSD with two different separation sequences (i.e., configurations). Then, both initial designs are optimized to obtain the optimum column configurations via an improved non-sorting genetic algorithm, with the total capital and operating cost as dual objective function. Finally, both optimized schemes are heat-integrated for the purpose of energy conservation. The performance of all the developed configurations were examined using three indicators, i.e., economic, environmental, and energy efficiency, and our results revealed that the heat-integrated configuration B with separation sequence of water → BuOH → toluene provides the best economic, environmental, and thermodynamic efficiency.

A 2E, energy and environment performance of an optimized vernacular house for passive cooling - Case of North-East India

Energy consumption in buildings has been increasing in recent years due to global warming, resulting in the increased use of HVACs, and causing environmental pollution. To fulfill the target of net-zero energy building and less building-generated pollution, vernacular houses can be constructed for their climate-adaptive architectural design, natural ventilation, and low cost. However, such houses suffer from excessive heat gain in summer due to large window openings, infiltration, and occupancy. There is also a lack of understanding of the energy and environment (2E) performance of a vernacular house in passive cooling compared to modern buildings with active cooling features. This paper aims to find optimal energy-efficient and environment-friendly passive cooling features that can be integrated with a 1BHK vernacular house for summer cooling as a case for North-East India. This study employs a simulation-based multi-objective optimization method that combines the architectural modeling tool EnergyPlus with a non-sorting genetic algorithm (NSGA-II) to find the best possible combinations of different passive cooling features. The findings show that conventional vernacular houses can be more energy efficient and eco-friendly by incorporating passive cooling strategies. The most optimal design is obtained with passive cooling elements, such as an external wall bonded with paraffin, cemented ground floor, false roof, double blue 6 mm air glazing, 1.5 m overhang, and the window-to-wall ratio of 32%, and site orientation of 167°. The optimal passive house has resulted in nearly 50.45% of energy savings and 29.40% less operational and embodied carbon emissions than conventional buildings. Thus, this study contributes to the debate over the applicability of different passive cooling strategies in vernacular houses and encourages further exploration.

Electromagnetic Optimal Design of a PMSG Considering Three Objectives and Using NSGA-III

This article presents the optimal design of a permanent magnet synchronous generator (PMSG). A finite element (FE) model is used to construct a metamodel, which afterward is utilized to define the objective function that models the PMSG. Kriging modeling is employed along with the design of experiments based on Latin hypercube sampling. The utilization of a surrogate model allows to speed up the optimization process while keeping the accuracy since they are developed from the FE analysis. On the other hand, it has been reported that the non-sorting genetic algorithm (NSGA) III is better than NSGA-II because it can solve multi- and many-objective optimization problems. This article demonstrates by numerical experiments that NSGA-III can be successfully used in the optimal design of PMSG with three objectives.

Prism-based solar system optimization adopting stochastic light demands

Solar energy is considered as one of the most propitious alternatives among various renewable resources to preserve the environment and attain sustainable energy. However, Stochastic light demand in a building and sunlight variations increases the difficulties of installing a solar system and the light transmission. This paper proposes the optimal allocation model for a prism-based solar concentrator system to collect sunlight and satisfy lighting. A bi-layer stochastic optimization model is presented where the upper layer will maximize the profit of the energy provider by coordinating the prism allocation design and sunlight assignment. The lower layer can facilitate an affordable budget for the light user to minimize the expenditure. The model is solved using a non-sorting genetic algorithm to simultaneously determine the optimal prism installation and light transmission route. The contribution and novelty of this study pivots on proposing the joint optimal system design for the sunlight prism installation and sunlight delivery plans for a prism-based solar concentrator system. The present model resolved the difficulties of installing a solar concentrator system and scheduling the light transmission against stochastic light demand in a building.

Modularity-based quality assessment of a disruptive reconfigurable manufacturing system-A hybrid meta-heuristic approach

This study considers quality aspects in the process planning of a reconfigurable manufacturing system. The goal is to analyze how the variation in quality impacts the process planning, i.e., cost-based design and modular features. Besides this, the analysis helps in identifying the number of conforming and failed products delivered by a process plan. First, a multi-objective mixed integer non-linear programming model is proposed that contains the novel objectives of cost, quality decay, and modular efforts. Secondly, the model is implemented on an industrial case study by using an exact solution approach and a novel hybrid version of two popular meta-heuristics, namely non-sorting genetic algorithm and multi-objective particle swarm optimization. The hybrid heuristic helps strengthening the application of approaches by creating a balance in searching the solution space. The performance of different approaches is assessed by using two metrics and two termination criteria. The findings will help the decision-makers in assessing how quality-related issues impact the choice of a process plan and in understanding the trade-off among cost, quality, and modularity. Finally, conclusion and future research avenues are provided.

Optimum Network Ageing and Battery Sizing for Improved Wind Penetration and Reliability

A multiobjective framework that optimises the uprating of the line’s real-time thermal rating and capacity of battery storage against wind curtailment, network ageing and reliability is proposed. The two enhancements are limited to the accumulated expected amount and duration of wind power losses of each wind farm bus. In the framework, actual conductor properties, line failures due to thermal effects, weather data, battery operation policy and wind farm model are considered. The trade-off of the optimisation criteria, the Pareto front, is solved using the non-sorting genetic algorithm and fuzzy decision-making method. Results show that the conductor maximum allowable temperature affects all the three optimisation parameters, but battery efficiency only affects wind curtailment level.

Experimental investigation and optimization of FDM process parameters for material and mechanical strength

The aim of this work is an experimental investigation and multi-objective optimization of FDM process parameters to produce strong and lightweight parts for intelligent structural a♦pplication. The study also analyzed the potentials of bio-inspired infill patterns for additive manufacturing. All infill patterns are selected inspired by nature. Two important objectives namely material consumption and compressive strength with respect to selected parameters namely, layer thickness, build orientation, infill patterns with varying densities have been investigated. The FDM machine is used for sample fabrication which are further tested to study the compressive strength and feedstock material consumption. The Taguchi approach is used for experimental design and the empirical models have been developed in Minitab software. Resulted objective functions have been optimized by using non-sorting genetic algorithm (NSGA)-II approach. The Pareto optimal solutions compared with experimental data and discussed. The results show that the sample with 80% infill gyroid pattern at 0.2 mm layer thickness and 90° build orientation provides the compressive strength close to solid part of acrylic butadiene styrene (ABS) material. It also saves a significant amount of material. The findings of present study can help designers of intelligent structure to obtain high strength to weight ratio in mechanical components.

Demand Response and Dynamic Line Ratings for Optimum Power Network Reliability and Ageing

This study proposes a methodology to optimise the use of average demand loss of each load bus to enhance line ratings and modify load curves, by minimising demand loss and network ageing due to elevated conductor temperatures. The considered lines are connected to load buses, operated with dynamic line rating technology and have actual conductor physical properties. The simulation of line failures considers line loadings, whose values are based on utilizations of the average demand loss of load buses where the lines are connected, and the remaining service life of the conductor. Demand response in the form of peak-shaving and valley-filling is used to modify load demand curves, with the allowable peak load reduced based on utilizations of the remaining average demand loss. The average demand loss values are determined in the preliminary screening module of the proposed method. Various trade-offs between ageing and reliability of the network are solved based on the two-objective non-sorting genetic algorithm and fuzzy decision-making method in the execution module of the proposed method. Results have shown that the proposed method is cost-effective in that it strategically increase line ageing slightly to enhance system reliability, by as much as 71.9%, based on the equal emphasis of network ageing and reliability, when compared with the scenario that only prioritizes the protection of network ageing. Line ageing is also 68.2% lower on average across the entire spectrum of rating exceedance (1% to 25%) compared to the scenario that only prioritizes enhancement of network reliability.

Sustainability-Focused Multi-objective Optimization of a Turning process

Using optimized cutting parameters can represent a key contribution for obtaining sustainable machining processes. This study presents the multi-objective optimization of the multi-passes cylindrical turning, where conflicting goals are simultaneously considered: economic, environmental and social sustainability. The first costs. The environmental impact was taking into account through the carbon dioxide emission. Finally, the key issue in the social sustainability was the operational safety. Also, the constraints resulting from the technical aspects of the turning process were also considered. From the decision-making point of view, an a posteriori approach was used, where the optimization process, which gives a Pareto front, is followed by the selection of the most convenient solution, depending on the specific workshop conditions. The non-sorting genetic algorithm (NSGA-II) was used as optimization heuristic. The main contribution of the paper is the use of a tridimensional Pareto front for selecting the best cutting conditions, by considering the three pillars of the sustainability.

A multi-objective approach to analyse the effect of fuel consumption on ship routing and scheduling problem

This paper investigates the impact of fuel consumption on ship routing and scheduling problem. In current trends, the anthropogenic emission due to excess fuel consumption is a topic of intense debate in the global world ship trading society. We have modelled a problem as a mixed integer nonlinear programming (MINLP), which encounters the issues related to routing, fuel consumption, and customer demand. A meta-heuristic approach controlled elitist non-sorting genetic algorithm (CENSGA) has been proposed to solve the bi-objective problem. Finally, the utility of the model is demonstrated by a case study.

Experimental study on electrical discharge machining of Inconel using RSM and NSGA optimization technique

This work presents the optimization while machining of Inconel superalloy as base material and copper as the electrode using ZNC electrical discharge machine with positive polarity. Formation of complex shapes by this material alloy with reasonable speed and surface finish is not an easy task with the traditional machining. Moreover, the edge profile (which is circular in this work) formed by the energy released by the spark does not result with expected profile. After machining, it is observed that the edge of the profile gets disintegrated because of which the material will lose its fatigue strength. Central composite design of response surface methodology (RSM) is used with six blocks for the analysis. For experimentation, peak voltage (Vin), peak current (Iin) and pulse on-time (Ton) are taken as key input parameters based on the literature support. MRR: metal removal rate, SR: surface roughness, TWR: tool wear rate and edge disintegration (overcut OC) have been taken as performance measures. Then, the RSM is employed over the input parameters to get the suitable mathematical model, and the outcome reveals that the suggested model helps to predict the factor values within the limit of investigation. At 95% CI, the results show that the input current value has the greatest influence on the disintegration factor, followed by the input voltage and then spark-on on-time the MRR and OC of measurements. It is observed that almost near optimal is observed at voltage level of 1 mV, current value of 17 mA and on-time of 850 μs. The desirability is 0.489 for MRR, 0.703 for TWR, 0.649 for SR and 0.649 for OC at the optimal setting of the parameters. Finally, non-sorting genetic algorithm is utilized for optimization of multiple responses.

Whole building optimization of a residential home with PV and battery storage in The Bahamas

Abstract In this paper a whole building optimization approach is used to assess the building performance and design of residential homes in The Bahamas with the goal of providing objective data for policy makers to achieve the sustainability goals in the country by minimizing carbon emissions and life cycle costs. This study accounts for the effects of building envelope improvements as well as a renewable energy system in the form of PV and battery electricity storage simultaneously in achieving the optimization objectives. EnergyPlus and jEPlus + EA provide the platform for this study, which implements the non-sorting genetic algorithm (NSGA-II) to find optimal solutions to building envelope design and renewable energy integration. Optimal design solutions are compared to a standard building model developed from audited data to provide an understanding of the interactions between the design objectives and optimal configurations. The results show that improvements to the thermal envelope and both the use of PV and battery storage are feasible and potentially advantageous to current building designs. Additionally, the results show a reduction in the NPV of up to 40%, with a net positive and carbon negative status, as well as a reduction in the yearly building energy consumption of up to 30%

Robust inverse identification of the effective three-dimensional elastic behaviour of a piezoceramic patch bonded to a multilayer unidirectional fibre composite

The effective three-dimensional elastic behaviour of a rectangular piezoceramic patch centrally surface-bonded to a free multilayer unidirectional carbon-fibre reinforced plastic composite plate is inversely identified from three potential ones: orthotropic, quasi transversely isotropic (QTI) or transversely isotropic. This is reached through minimizing the finite element-experimental frequency relative deviations of the first eight short-circuit modes of the smart composite structure using a robust multi-objective evolutionary optimization procedure. The latter combines full factorial design of experiments (DoE)-based surface response meta-modelling of the frequency dependence on the patch’s elastic engineering constants and a non-sorting genetic algorithm of second generation. The inverse identification robustness comes from considering large uncertainties (±20%) of the design parameters nominal (initial) values. DoE-based sensitivities of the considered frequencies to the investigated piezoceramic patch’s elastic behaviour engineering constants are analyzed in order to identify the most influent design parameters. The latter are used for reducing the DoE plans and corresponding finite element computations. It is found that the bonded patch’s effective elastic behaviour is QTI (or orthotropic) when all (or the most influent) engineering constants are optimized.

Decision Support System for Discrete Robust Berth Allocation

This paper aims to develop a decision support system for bulk material handling ports in relation to ship scheduling and discrete berth allocation. Ship scheduling according to available discrete berths and to customer priority is a complex problem. A multiobjective formulation is then proposed to model the problem in minimizing ship waiting times and deviation of customer priority. An modified Non-sorting Genetic Algorithm (Mod-NSGA II) is proposed to solve the problem in large-scale realistic environments. Utility of the developed decision support system in achieving good utilization of the available berths and resources is demonstrated using illustrative scenarios inspired from a real port management case.

A systematic approach for energy efficient building design factors optimization

We developed a systematic methodology to minimize the building heating and cooling loads using experimental design and non-sorting genetic algorithm to select optimal sets of building design factors. The analysis of experimental design provided a ranked list of important through less important factors design factors affecting the building heating and cooling loads. The factors related to window performance were found to be most significant ones. The non-sorting genetic algorithm offered piecewise linear pareto front lines where the optimum building design factor sets for minimum heating and cooling loads lie. The results of experimental design analysis were statistically verified against TRNSYS simulation results. It was found that the ratio of the efficiencies of heating and cooling systems affected the optimum passive building design, hence the active and passive parts of a building should be considered simultaneously in a coupled manner for the optimum design of net zero energy buildings.