The aim of the study was to estimate the optimal parameters of apple drying and the rehydration temperature of the obtained dried apple. Conducting both processes under such conditions is aimed at restoring the rehydrated apple to the raw material properties. The obtained drying parameters allow the drying process to be carried out in a short drying time (DT) and at low energy consumption (EC). The effect of air velocity (vd), drying temperature (Td), characteristic dimension (CD), and rehydration temperature (Tr) on rehydrated apple quality was studied. Quality parameters of the rehydrated apple as: color change (CC), mass gain ratio (MG), solid loss ratio (SL), volume gain ratio (VG) together with DT and EC were taken into consideration. The artificial neural network was used for modeling of rehydrated apple quality parameters, DT, and EC. A multi-objective genetic algorithm was developed in order to optimize parameters of the drying and rehydration processes. The simultaneous minimi... [more]

The friction rotary welding (FRW) of magnesium alloy to aluminum alloy was presented in a paper due to significant interest in the manufacturing industry. A genetic algorithm (GA) method for optimizing FRW process parameters of dissimilar light alloys was presented. After obtaining the welding parameters by GA method, it was possible to determine the best tensile strength of the friction joint. The obtained joints were subjected to tensile strength. The highest tensile strength TS = 178 MPa was found using a genetic algorithm for the following friction welding parameters: friction force FF = 16 kN, friction time FT = 4 s, and upsetting force UF = 44 kN. The optimized values were compared with the experimental results. The application of the genetic algorithm method allowed increasing the tensile strength joint from 88 to 180 MPa. The maximum tensile strength of the friction welded magnesium alloy-aluminum alloy joints was 73% of the base AZ31B metal. The relationship between welding pa... [more]

Globally, wind power plays a leading role in the renewable energy industry. In order to ensure the normal operation of a wind farm, the staff will regularly check the equipment of the wind farm. However, manual inspection has some disadvantages, such as heavy workload, low efficiency and easy misjudgment. In order to realize automation, intelligence and high efficiency of inspection work, inspection robots are introduced into wind farms to replace manual inspections. Path planning is the prerequisite for an intelligent inspection robot to complete inspection tasks. In order to ensure that the robot can take the shortest path in the inspection process and avoid the detected obstacles at the same time, a new path-planning algorithm is proposed. The path-planning algorithm is based on the chaotic neural network and genetic algorithm. First, the chaotic neural network is used for the first step of path planning. The planning results are encoded into chromosomes to replace the individuals w... [more]

Parallel Machine Scheduling (PMS) is a well-known problem in modern manufacturing. It is an optimization problem aiming to schedule n jobs using m machines while fulfilling certain practical requirements, such as total tardiness. Traditional approaches, e.g., mix integer programming and Genetic Algorithm (GA), usually fail, particularly in large-size PMS problems, due to computational time and/or memory burden and the large searching space required, respectively. This work aims to overcome such challenges by proposing a heuristic-based GA (DAS/GA). Specifically, a large-scale PMS problem with n independent jobs and m identical machines with a single server is studied. Individual heuristic algorithms (DAS) and GA are used as benchmarks to verify the performance of the proposed combined DAS/GA on 18 benchmark problems established to cover small, medium, and large PMS problems concerning standard performance metrics from the literature and a new metric proposed in this work (standardized... [more]

There are unfavorable conditions such as constantly changing working conditions and frequent disturbances that affect Mach number control in wind tunnel flow fields. As the proportional, integral and differential (PID) parameters need to be re-tuned for each working conditions of a wind tunnel, the operational costs of wind tunnels are very high. Therefore, to lower these costs, a genetic algorithm was utilized to tune the PID parameters to achieve Mach number control of a multi-mode wind tunnel flow field. In this paper, firstly, models for the multi-mode wind tunnel were established; secondly, a PID control system was designed based on the genetic algorithm and the control effects of the proposed PID control system were verified by simulations and were compared with the effects of a PSO tuning PID control system.

In this article, we describe a prototype photoreactor of which the geometrical configuration was obtained by Genetic Algorithms to maximize the residence time of the reactant gases. A gas reaction mixture of CO2:H2O (1:2 molar ratio) was studied from the fluid dynamic point of view. The two main features of this prototype reactor are the conical shape, which enhances the residence time as compared to a cylindrical shape reference reactor, and the inlet heights and position around the main chamber that enables turbulence and mass transfer control. Turbulence intensity, mixing capability, and residence time attributes for the optimized prototype reactor were calculated with Computational Fluid Dynamics (CFD) software and compared with those from a reference reactor. Turbulence intensity near the envisioned catalytic bed was one percentage point higher in the reference than in the optimized prototype reactor. Finally, the homogeneity of the mixture was guaranteed since both types of react... [more]

In this paper, the Nonlinear Auto-Regressive with exogenous inputs (NARX) model with parameters of interest for design (NARX-M-for-D), where the design parameter of the system is connected to the coefficients of the NARX model by a predefined polynomial function is studied. For the NARX-M-for-D of nonlinear systems, in practice, to predict the output by design parameter values are often difficult due to the uncertain relationship between the design parameter and the coefficients of the NARX model. To solve this issue and conduct the analysis and design, an improved algorithm, defined as the Weighted Extended Forward Orthogonal Regression (WEFOR), is proposed. Firstly, the initial NARX-M-for-D is obtained through the traditional Extended Forward Orthogonal Regression (EFOR) algorithm. Then a weight matrix is introduced to modify the polynomial functions with respect to the design parameter, and then an improved model, which is referred to as the final NARX-M-for-D is established. The ge... [more]

In this paper the emphasis is on the optimization of synthetic inertial response of wind power plants (WPPs) for power systems with high wind power penetration levels, considering different wind speed operating conditions. The synthetic inertial response of wind power plants can play an important role in the resilience of future power systems with low inertia during large frequency disturbances. In order to investigate this role, a generic optimization methodology employing the genetic algorithm is proposed, taking into consideration the frequency nadir, second frequency dip, and time to reach the quasi⁻steady-state frequency. This optimization methodology comprehends the inertial response capability of WPPs and the frequency control dynamics of the power system. Accordingly, offline parameter tuning of synthetic inertial response is performed at the power system level with the proposed methodology. Based on the optimization results, the relevant aspects to be considered by transmissio... [more]

Torrefaction is an effective technology to overcome the defects of biomass which are adverse to its utilization as solid fuels. For assessing the torrefaction process, it is essential to characterize the properties of torrefied biomass. However, the preparation and characterization of torrefied biomass often consume a lot of time, costs, and manpower. Developing a reliable method to predict the fuel properties of torrefied biomass while avoiding various experiments and tests is of great value. In this study, a machine learning (ML) model of back propagation neural network (BPNN) hybridized with genetic algorithm (GA) optimization was developed to predict the important properties of torrefied biomass for the fuel purpose involving fuel ratio (FR), H/C and O/C ratios, high heating value (HHV) and the mass and energy yields (MY and EY) based on the proximate analysis results of raw biomass and torrefaction conditions. R2 and RMSE were examined to evaluate the prediction precision of the m... [more]

: Wind speed and solar radiation are two of the most well-known and widely used renewable energy sources worldwide. Coal, natural gas, and petroleum are examples of fossil fuels that are not replenished and are thus non-renewable energy sources due to their high carbon content and the methods by which they are generated. To predict energy production of renewable sources, researchers use energy forecasting techniques based on the recent advances in machine learning approaches. Numerous prediction methods have significant drawbacks, including high computational complexity and inability to generalize for various types of sources of renewable energy sources. Methodology: In this paper, we proposed a novel approach capable of generalizing the prediction accuracy for both wind speed and solar radiation forecasting data. The proposed approach is based on a new optimization algorithm and a new stacked ensemble model. The new optimization algorithm is a hybrid of Al-Biruni Earth Radius (BER) an... [more]

Metaheuristic optimization techniques have successfully been used to solve the Optimal Power Flow (OPF) problem, addressing the shortcomings of mathematical optimization techniques. Two of the most popular metaheuristics are the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The literature surrounding GA and PSO OPF is vast and not adequately organized. This work filled this gap by reviewing the most prominent works and analyzing the different traits of GA OPF works along seven axes, and of PSO OPF along four axes. Subsequently, cross-comparison between GA and PSO OPF works was undertaken, using the reported results of the reviewed works that use the IEEE 30-bus network to assess the performance and accuracy of each method. Where possible, the practices used in GA and PSO OPF were compared with literature suggestions from other domains. The cross-comparison aimed to act as a first step towards the standardization of GA and PSO OPF, as it can be used to draw preliminary c... [more]

Throughout the past several years, the renewable energy contribution and particularly the contribution of wind energy to electrical grid systems increased significantly, along with the problem of keeping the systems stable. This article presents a new optimization technique entitled the Archimedes optimization algorithm (AOA) that enhances the wind energy conversion system’s stability, integrated with a superconducting magnetic energy storage (SMES) system that uses a proportional integral (PI) controller. The AOA is a modern population technique based on Archimedes’ law of physics. The SMES system has a big impact in integrating wind generators with the electrical grid by regulating the output of wind generators and strengthening the power system’s performance. In this study, the AOA was employed to determine the optimum conditions of the PI controller that regulates the charging and discharging of the SMES system. The simulation outcomes of the AOA, the genetic algorithm (GA), and pa... [more]

The accuracy of doubly fed induction generator (DFIG) models and parameters plays an important role in power system operation. This paper proposes a parameter identification method based on the hybrid genetic algorithm for the control system of DFIG converters. In the improved genetic algorithm, the generation gap value and immune strategy are adopted, and a strategy of “individual identification, elite retention, and overall identification” is proposed. The DFIG operation data information used for parameter identification considers the loss of rotor current, stator current, grid-side voltage, stator voltage, and rotor voltage. The operating data of a wind farm in Zhangjiakou, North China, were used as a test case to verify the effectiveness of the proposed parameter identification method for the Maximum Power Point Tracking (MPPT), constant speed, and constant power operation conditions of the wind turbine.

This paper presents an optimization method for the civil aircraft environmental control system (ECS) mainly involving two airstreams: the ram airstream for cooling and the bleed airstream for supplying the cabin. The minimum total fuel energy consumption rate (FECR), defined as the weighted sum of the shaft power extraction and propulsive power loss, is obtained under the precondition of the constant outputs in the cooling capacity and outlet pressure. A modified genetic algorithm (GA) is proposed to acquire the optimal values of the heat transfer areas, temperature ratio of bleed air, mass flow rate of ram air, and pressure ratios of the turbine, compressor, and fan. The statistical results show that the multipoint crossover and continuity improvement implemented in the modified GA improve convergence and distribution performance. The probability of reaching a satisfactory result using modified GA is 62.4% higher than standard GA. Due to the decrease of inlet parameters of bleed air a... [more]

The technology of coal breaking and punching by a high-pressure water jet can increase the permeability of coal seam and prevent gas explosion accidents. As one of the key components of this technology, the structural parameters of the nozzle have an important effect on the performance of the water jet. At present, the relationship between multiple optimization indexes and structural parameters of the nozzle is mostly studied separately. In fact, the influence of the nozzle structural parameters on different optimization indexes is different. When there are multiple optimization indexes, they should be considered collaboratively to achieve the best water jet performance of the nozzle. Therefore, a multi−objective collaborative optimization method is proposed which takes the maximum velocity in X-axis and effective extension distance in Y-axis as the performance evaluation indexes of the water jet. The numerical simulation of the nozzle jet is carried out by computational fluid dynamics... [more]

Temperature control is widely perceived to be superior to direct composition control for the control of dividing-wall distillation columns (DWDCs) due to its advantages in dynamic characteristics. However, because of the limited estimation accuracy to the controlled product purities, the former cannot eliminate the steady-state errors in the maintained product purities as completely as the latter. In order to reduce the steady-state deviations in the maintained product purities, an effective temperature control method is proposed in the current article by means of a kind of simple but effective product quality estimator (PQE). For the proposed PQE, temperatures of three stages located in the controlled column section (TI1, TI2, and TI3) are employed as inputs, and a linear sum of these three inputted stage temperatures (α × TI1 + β × TI2 + γ × TI3) is given as output. A genetic algorithm with an elitist preservation strategy is used to optimize the locations of the three stage temperat... [more]

The flexible job-shop scheduling problem with indirect energy and time-of-use (ToU) electricity pricing (FJSP-IT) is investigated. Considering the production cost, which includes the indirect energy cost, direct energy cost and time cost, the cost evaluation model under ToU pricing is built. To minimize the total production cost of the FJSP-IT, an approach based on a genetic algorithm and Petri nets (GAPN) is presented. Under this approach, indirect energy and direct energy are modeled with Petri net (PN) nodes, the operation time is evaluated through PN simulation, and resource allocation is fine-tuned through genetic operations. A group of heuristic operation time policies, especially the exhausting subsection policy and two mixed policies, are presented to adapt to the FJSP-IT with vague cost components. Experiments were performed on a data set generated from the banburying shop of a rubber tire plant, and the results show that the proposed GAPN approach has good convergence. Using... [more]

The three-phase separator is one of the most important pieces of equipment in the combined station of the oilfield. The control level of the oil−water interface directly affects the energy consumption of the subsequent production of the combined station and the effect of oil, gas and water separation. In order to avoid these situations, the Siemens PLC control system, configuration software WinCC and MATLAB were used. The OPC technology is used to connect communication between WinCC and MATLAB, and the genetic algorithm in MATLAB is used to obtain the optimal separation height of the oil−water interface under the produced liquid in different periods. Subsequently, through the Siemens PLC system and WinCC configuration software, the automatic control of the three-phase separator is achieved, and finally the water content of crude oil is significantly reduced. The system provides a visual interface function. In the future, it will also provide an effective simulation platform for the the... [more]

Decanter centrifuges are widely used for solid−liquid separation. Although parameter analysis for decanter centrifuges was performed by numerical simulation in previous studies, some structural parameters are rarely mentioned and investigated. At the same time, the results obtained by the single-parameter analysis in previous studies are difficult to truly realize the comprehensive performance optimization of decanter centrifuges. In this paper, the influences of the window structure and bowl−conveyor gap on the separation performance are systematically analyzed with the employment of a numerical computation method. The results show that the increase in the window angle and window height will accelerate the flow of the upper layer, while the increase in the bowl−conveyor gap may make particles flow through it directly and further form a solid retention zone. Both of the structural changes will lead to deterioration of the separation performance. On the basis of numerical simulation ana... [more]

The MDAO framework has become an essential part of almost all fields, apart from mechanical, transportation, and aerospace industries, for efficient energy conversion or otherwise. It enables rapid iterative interaction among several engineering disciplines at various fidelities using automation tools for design improvement. An advanced framework from low to high fidelity is developed for ducted and unducted turbomachinery blade designs. The parametric blade geometry tool is a key feature which converts low-fidelity results into 3D blade shapes and can readily be used in high-fidelity multidisciplinary simulations as part of an optimization cycle. The geometry generator and physics solvers are connected to DAKOTA, an open-source optimizer with parallel computation capability. The entire cycle is automated and new design iterations are generated with input parameter variations controlled by DAKOTA. Single- and multi-objective genetic algorithm and gradient method-based optimization case... [more]

Health monitoring and fault diagnosis of liquid rocket engine (LRE) are the most important concerning issue for the safety of rocket’s flying, especially for the man-carried aerospace engineering. Based on the sensor measurement signals of a certain type of hydrogen-oxygen rocket engine, this paper proposed a real-time fault detection approach using a genetic algorithm-based least squares support vector regression (GA-LSSVR) algorithm for the real-time fault detection of the rocket engine. In order to obtain effective training samples, the data is normalized in this paper. Then, the GA-LSSVR algorithm is derived through comprehensive considerations of the advantages of the Support Vector Regression (SVR) algorithm and Least Square Support Vector Regression (LSSVR). What is more, this paper provided the genetic algorithm to search for the optimal LSSVR parameters. In the end, the computational results of the suggested approach using the rocket practical experimental data are given out.... [more]

In this paper, a new prediction model for accurately recognizing and appropriately evaluating the trends of domestic chemical products and for improving the forecasting accuracy of the chemical products’ prices is proposed. The proposed model uses the minimum forecasting error as the evaluation objective to forecast the settlement price. Active contracts for polyethylene and polypropylene futures on the Dalian Commodity Futures Exchange for the next five days were used, the data were divided into a training set and test set through normalization, and the time window, batch processing size, number of hidden layers, and rejection rate of a long short-term memory (LSTM) network were optimized by an improved genetic algorithm (IGA). In the experiments, with respect to the shortcomings of the genetic algorithm, the crossover location determination and some gene exchange methods in the crossover strategy were improved, and the predicted results of the IGA−LSTM model were compared with those... [more]

Axial flow fans are commonly used for a system or machinery cooling process. It also used for ventilating warehouses, factories, and garages. In the fan manufacturing industry, the demand for varying fan operating points makes design parameters complicated because many design parameters affect the fan performance. This study combines the deep neural network (DNN) with a genetic algorithm (GA) for axial flow design and development. The characteristic fan curve (P-Q Curve) can be generated when the relevant fan parameters are imported into this system. The system parameters can be adjusted to achieve the required characteristic curve. After the wind tunnel test is performed for verification, the data are integrated and corrected to reduce manufacturing costs and design time. This study discusses a small axial flow fan NACA and analyzes fan features, such as the blade root chord length, blade tip chord length, pitch angle, twist angle, fan diameter, and blade number. Afterwards, the wind... [more]

Extractive distillation has great significance for the separation of ethylbenzene from C8 aromatic hydrocarbons. Herein, a distillation process for the separation of ethylbenzene was designed using methyl phenylacetate as an extractant. A genetic algorithm (GA) was used to evaluate the economic and environmental factors of the process, and Aspen Dynamic was used to assess the dynamic performance. The sequential optimization method was used to obtain the initial process parameters. Then, the total annual cost and CO2 emissions were minimized by NSGA-III to increase the economic and environmental benefits. To enhance the search performance of GA, the mutation probability and crossover probability were studied and adjusted. The optimal total annual cost and CO2 emissions were 11.7% and 23.7% lower than those of the initial process. Based on a steady process, two control strategies, which were the flow rate of the recycling solvent controlled by entrainer makeup flow rate (CS1) and the bot... [more]

Low carbon manufacturing has received increasingly more attention in the context of global warming. The flexible job shop scheduling problem (FJSP) widely exists in various manufacturing processes. Researchers have always emphasized manufacturing efficiency and economic benefits while ignoring environmental impacts. In this paper, considering carbon emissions, a multi-objective flexible job shop scheduling problem (MO-FJSP) mathematical model with minimum completion time, carbon emission, and machine load is established. To solve this problem, we study six variants of the non-dominated sorting genetic algorithm-III (NSGA-III). We find that some variants have better search capability in the MO-FJSP decision space. When the solution set is close to the Pareto frontier, the development ability of the NSGA-III variant in the decision space shows a difference. According to the research, we combine Pareto dominance with indicator-based thought. By utilizing three existing crossover operators... [more]