scholarly journals Multi-objective optimization of microfiltration of baker’s yeast using genetic algorithm

2017 ◽  
pp. 211-220 ◽  
Author(s):  
Natasa Lukic ◽  
Marija Bozin-Dakic ◽  
Jovana Grahovac ◽  
Jelena Dodic ◽  
Aleksandar Jokic
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Energy Consumption ◽  
Flow Rate ◽  
Transmembrane Pressure ◽  
Feed Flow Rate ◽  
Multi Objective Optimization ◽  
Permeate Flux ◽  
Multi Objective ◽  
Reduction Of Energy Consumption

This paper presents a multi-objective optimization model by applying genetic algorithm in order to search for optimal operating parameters of microfiltration of baker?s yeast in the presence of static mixer as a turbulence promoter. The operating variables were the suspension concentration, transmembrane pressure, and feed flow rate. Two conflicting objective functions, maximizing the permeate flux and maximizing the reduction of energy consumption, were considered. This multi-objective optimization problem was solved by using the elitist non-dominated sorting genetic algorithm in the Matlab R2015b software. The Pareto fronts along with the process decision variables correspondding to the optimal solutions were obtained. It was found that lower suspension concentrations (2-4.5 g/L), feed flow rate in the range 109-127 L/h, and transmembrane pressure of 1 bar were the optimal process parameters which yielded maximum permeate flux (177-191 L/(m2h)) and maximum reduction of energy consumption (44-50%). Finally, the results were compared with the previously published results obtained by applying desirability function approach. Given that genetic algorithms have generated multiple solutions in a single optimization run, the study proved that genetic algorithms are preferable to classical optimization methods.

Investigation on the modeling and dynamic characteristics of a fast-response and large-flow water hydraulic proportional cartridge valve

2020 ◽  
Vol 234 (22) ◽  
pp. 4415-4432
Author(s):  
Mingxing Han ◽  
Yinshui Liu ◽  
Kan Zheng ◽  
Youchun Ding ◽  
Defa Wu
Keyword(s):  
Genetic Algorithm ◽  
Flow Rate ◽  
Dynamic Characteristics ◽  
Fast Response ◽  
Dynamics Simulation ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Flow Capacity ◽  
Water Hydraulic ◽  
Large Flow

In large-power and high-pressure hydraulic systems, the maximum instantaneous flow rate is often several thousand liters per minute. Normal proportional valves are often difficult to meet their requirements for large flow rate and fast response at the same time. And the leakage of hydraulic oil will seriously pollute the environment. Therefore, a novel water hydraulic proportional valve with fast response and high flow capacity is presented for the large transient power hydraulic system in this paper. The valve utilizes a two-stage structure with two 2/2-way water hydraulic proportional valves as the pilot stage and a cartridge poppet valve as the main stage to achieve fast-response and large-flow capacity simultaneously. A detailed and precise nonlinear mathematical model of the valve considering both structural parameters and flow force is developed. A comprehensive performance optimization has been carried out, which can be mainly divided into computational fluid dynamics simulation optimization based on reducing flow force and multi-objective optimization based on genetic algorithm. The effects of double U-grooves' parameters on the flow force (flow-induced loads) have been studied in detail by numerical simulation. Through the grooves geometry optimization, the maximum flow force can be reduced by 10%. Then, the influences of structure parameters on the performance of step response have been studied, and the optimal parameters of the valve have been obtained by multi-objective optimization based on genetic algorithm. The maximum overshoot has been reduced from 15% to 6% (about 60%) and the adjusting time has been reduced from 58 ms to 48 ms. The dynamic characteristics of the valve have been improved effectively. Finally, a test apparatus which has the ability to provide transient large flow is built. The accuracy of simulation model and optimization design method is verified by test results.

Multi-objective optimization of all-wheel drive electric formula vehicle for performance and energy efficiency using evolutionary algorithms

2019 ◽  
Vol 234 (5) ◽  
pp. 1472-1479 ◽  
Author(s):  
Tey Jing Yuen ◽  
Rahizar Ramli
Keyword(s):  
Genetic Algorithm ◽  
Energy Consumption ◽  
Evolutionary Algorithms ◽  
Differential Evolution ◽  
Evolutionary Algorithm ◽  
Time Frame ◽  
Motor Power ◽  
Multi Objective Optimization ◽  
Pareto Solution ◽  
Multi Objective

A new method based on constraint multi-objective optimization using evolutionary algorithms is proposed to optimize the powertrain design of a battery electric formula vehicle with an all-wheel independent motor drive. The electric formula vehicle has a maximum combined motor power of 80 kW, which is a constraint for delivering maximum vehicle performance with minimal energy consumption. The performance of the vehicle will be simulated and measured against different driving events, that is, acceleration event, autocross event, and endurance event. Each event demands a different aspect of performance to be delivered by the motor. The respective event lap time or energy rating will be measured for performance assessment. In this study, a non-dominated sorting genetic algorithm II and constrained multi-objective evolutionary algorithm based on decomposition by using differential evolution are employed to optimize the motor transmission ratio, motor torque scaling, and downforce scale of both front and rear wheels against the acceleration event to minimize energy consumption and event lap time while constraining the combined motor power of all wheels to not exceed 80 kW. The optimization will be performed through software-in-the-loop between MATLAB and VI-Grade, where the high-fidelity vehicle will be modeled in VI-Grade and optimization algorithms will be implemented on the host in MATLAB. Results show that the non-dominated sorting genetic algorithm II outperforms the constrained multi-objective evolutionary algorithm based on decomposition by using differential evolution in obtaining a wider distributed Pareto solution and converges at a relatively shorter time frame. The optimized results show a promising increase in the performance of the electric formula vehicle in completing those events with the highest combined performance scoring, that is, the lap time of acceleration events improves by 9.18%, that of autocross event improves by 6.1%, and that of endurance event improves by 4.97%, with minimum decrease in energy rating of 32.54%.

scholarly journals Multi-Objective Optimization of Integrated Process Planning and Scheduling Considering Energy Savings

Energies ◽  
2020 ◽  
Vol 13 (23) ◽  
pp. 6181
Author(s):  
Xu Zhang ◽  
Hua Zhang ◽  
Jin Yao
Keyword(s):  
Genetic Algorithm ◽  
Energy Consumption ◽  
Process Planning ◽  
Energy Savings ◽  
Degradation Mechanism ◽  
Integrated Process ◽  
Multi Objective Optimization ◽  
Planning And Scheduling ◽  
Multi Objective

With the emergence of the concept of green manufacturing, more manufacturers have attached importance to energy consumption indicators. The process planning and shop scheduling procedures involved in manufacturing processes can both independently achieve energy savings, however independent optimization approaches limit the optimization space. In order to achieve a better optimization effect, the optimization of energy savings for integrated process planning and scheduling (IPPS) was studied in this paper. A mathematical model for multi-objective optimization of IPPS was established to minimize the total energy consumption, makespan, and peak power of the job shop. A hierarchical multi-strategy genetic algorithm based on non-dominated sorting (NSHMSGA) was proposed to solve the problem. This algorithm was based on the non-dominated sorting genetic algorithm Ⅱ (NSGA-Ⅱ) framework, in which an improved hierarchical coding method is used, containing a variety of genetic operators with different strategies, and in which a population degradation mechanism based on crowding distance is adopted. The results from the case study in this paper showed that the proposed method reduced the energy consumption by approximately 15% for two different scheduling schemes with the same makespan. The computational results for NSHMSGA and NSGA-Ⅱ approaches were evaluated quantitatively in the case study. The C-metric values for NSHMSGA and NSGA-Ⅱ were 0.78 and 0, the spacing metric values were 0.4724 and 0.5775, and the maximum spread values were 1.6404 and 1.3351, respectively. The evaluation indexes showed that the NSHMSGA approach could obtain a better non-dominated solution set than the NSGA-Ⅱ approach in order to solve the multi-objective IPPS problem proposed in this paper.

Improved Memetic NSGA-II Using a Deep Neighborhood Search

2021 ◽  
Vol 12 (4) ◽  
pp. 138-154
Author(s):  
Samir Mahdi ◽  
Brahim Nini
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Pareto Front ◽  
Neighborhood Search ◽  
Pareto Optimal ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Pareto Optimal Front ◽  
Slow Convergence ◽  
Multi Objective

Elitist non-sorted genetic algorithms as part of Pareto-based multi-objective evolutionary algorithms seems to be one of the most efficient algorithms for multi-objective optimization. However, it has some shortcomings, such as low convergence accuracy, uneven Pareto front distribution, and slow convergence. A number of review papers using memetic technique to improve NSGA-II have been published. Hence, it is imperative to improve memetic NSGA-II by increasing its solving accuracy. In this paper, an improved memetic NSGA-II, called deep memetic non-sorted genetic algorithm (DM-NSGA-II), is proposed, aiming to obtain more non-dominated solutions uniformly distributed and better converged near the true Pareto-optimal front. The proposed algorithm combines the advantages of both exact and heuristic approaches. The effectiveness of DM-NSGA-II is validated using well-known instances taken from the standard literature on multi-objective knapsack problem. As will be shown, the performance of the proposed algorithm is demonstrated by comparing it with M-NSGA-II using hypervolume metric.

scholarly journals Multi-objective Genetic Algorithms: Problem Difficulties and Construction of Test Problems

1999 ◽  
Vol 7 (3) ◽  
pp. 205-230 ◽  
Author(s):  
Kalyanmoy Deb
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Optimization Problems ◽  
Test Problems ◽  
Pareto Optimal ◽  
Multi Objective Optimization ◽  
Pareto Optimal Front ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Single Objective

In this paper, we study the problem features that may cause a multi-objective genetic algorithm (GA) difficulty in converging to the true Pareto-optimal front. Identification of such features helps us develop difficult test problems for multi-objective optimization. Multi-objective test problems are constructed from single-objective optimization problems, thereby allowing known difficult features of single-objective problems (such as multi-modality, isolation, or deception) to be directly transferred to the corresponding multi-objective problem. In addition, test problems having features specific to multi-objective optimization are also constructed. More importantly, these difficult test problems will enable researchers to test their algorithms for specific aspects of multi-objective optimization.

scholarly journals Analysis and Multi-Objective Optimization for Reducing Energy Consumption and Improving Surface Quality during Dry Machining of 304 Stainless Steel

Materials ◽  
2020 ◽  
Vol 13 (21) ◽  
pp. 4693
Author(s):  
Feilong Du ◽  
Lin He ◽  
Haisong Huang ◽  
Tao Zhou ◽  
Jinxing Wu
Keyword(s):  
Genetic Algorithm ◽  
Surface Roughness ◽  
Stainless Steel ◽  
Energy Consumption ◽  
Surface Quality ◽  
Feed Rate ◽  
Cutting Parameters ◽  
304 Stainless Steel ◽  
Multi Objective Optimization ◽  
Multi Objective

Cutting quality and production cleanliness are main aspects to be considered in the machining process, and determining the optimal cutting parameters is a significant measure to reduce energy consumption and optimize surface quality. In this paper, 304 stainless steel is adopted as the research objective. The regression models of the specific cutting energy, surface roughness, and microhardness are constructed and the inherent influence mechanism between cutting parameters and output responses are analyzed by analysis of variance (ANOVA). The desirability analysis method is introduced to perform the multi-objective optimization for low energy consumption (LEC) mode and low surface roughness (LSR) mode. Optimal combination of process parameters with composite desirability of 0.925 and 0.899 are obtained in such two modes respectively. As indicated by the results of multi-objective genetic algorithm (MOGA), genetic algorithm (GA) combined with weighted-sum-type objective function and experiment, the relative deviation values are within 10%. Moreover, the results also reveal that the feed rate is the most significant factor affecting the three responses, while the correlation of cutting depth is less noticeable. The effect of low feed rate on microhardness is primarily related to the mechanical load caused by extrusion, and the influence at high feed rate is determined by plastic deformation.

scholarly journals THE EFFECT OF OPERATING CONDITIONS ON CRITICAL FLUX AND REVERSIBLE FOULING IN MEMBRANE FILTRATION USING GAS SPARGING TECHNIQUE

2016 ◽  
Vol 1 (2) ◽  
Author(s):  
Harunsyah Harunsyah ◽  
Nik Meriam Sulaiman
Keyword(s):  
Flow Rate ◽  
Membrane Filtration ◽  
Membrane System ◽  
Operating Conditions ◽  
Transmembrane Pressure ◽  
Feed Flow Rate ◽  
Permeate Flux ◽  
Critical Flux ◽  
Gas Sparging ◽  
Cake Layer

Gas sparging method utilizing injection of nitrogen gas was employed during the ultrafiltration of the natural rubber effluent (latex serum). The objective of this research was to investigate the effect of gas sparging on the critical flux and the observed reversible cake layer resistance during the ultrafiltration of skim latex serum. Experiments were conducted using a 100 kDa MWCO tubular membrane (PCI Membrane System) mounted vertically. The effect of operating parameters, such as feed flow rate, concentration and transmembrane pressure were investigated. The results showed that when the feed flow rate was increased, the permeate was correspondingly increased and the reversible cake resistance decreased. In this research a feed flow rate of 1400 ml/min and transmembrane pressure of 13.00 psig resulted in the maximum total permeate flux of 70.80 L/m2h. Results from this study obtained so far showed that the use of gas sparging has been able to increase total permeate flux between 8.3% and 145.3% compared to non-gas sparged condition. Critical flux occurrence was increased to 82.63% above the value obtained for non-sparged condition and applied transmembrane pressure can be reduced to 2.4% of the non-gas sparged condition.Keyword: skim latex serum, reversible fouling, gas sparged, critical flux

Experimental and Modelling of Aqueous Radioactive Waste Treatment by Ultrafiltration

2018 ◽  
Vol 69 (5) ◽  
pp. 1149-1151
Author(s):  
Laura Ruxandra Zicman ◽  
Elena Neacsu ◽  
Felicia Nicoleta Dragolici ◽  
Catalin Ciobanu ◽  
Gheorghe Dogaru ◽  
...  
Keyword(s):  
Flow Rate ◽  
Suspended Solids ◽  
Waste Treatment ◽  
Operating Pressure ◽  
Feed Flow Rate ◽  
Permeate Flux ◽  
Independent Variables ◽  
Opposite Trend ◽  
Process Factors ◽  
Volumetric Flux

Ultrafiltration of untreated and pretreated aqueous radioactive wastes was conducted using a spiral-wound polysulphonamide membrane. The influence of process factors on its performances was experimental studied and predicted. Permeate volumetric flux and permeate total suspended solids (TSS) were measured at different values of feed flow rate (7 and 10 m3/h), operating pressure (0.1-0.4 MPa), and feed TSS (15 and 60 mg/L). Permeate flux (42-200 L/(m2�h)) increased with feed flow rate and operating pressure as well as it decreased with an increase in feed TSS, whereas permeate TSS (0.1-33.2 mg/L) exhibited an opposite trend. A 23 factorial plan was used to establish correlations between dependent and independent variables of ultrafiltration process.

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