In the low-carbon era, photovoltaic power generation has emerged as a pivotal focal point. The inherent volatility of photovoltaic power generation poses a substantial challenge to the stability of the power grid, making accurate prediction imperative. Based on the integration of a backpropagation (BP) neural network and a genetic algorithm (GA), a prediction model was developed that contained two sub-models: no-rain and no-snow scenarios, and rain and snow scenarios. Through correlation analysis, the primary meteorological factors were identified which were subsequently utilized as inputs alongside historical power generation data. In the sub-model dedicated to rain and snow scenarios, variables such as rainfall and snowfall amounts were incorporated as additional input parameters. The hourly photovoltaic power generation output was served as the model’s output. The results indicated that the proposed model effectively ensured accurate forecasts. During no-rain and no-snow weather con... [more]

This paper presents a unique approach to generate a number of cutting knowledge blocks for the surface roughness analysis of the drilling process for carbon-fiber-reinforced polymer composite (CFRP) materials. The influence of drilling on the surface quality of woven CFRP materials was investigated experimentally. The CFRP material (0/90° fiber orientation) was drilled at different cutting parameters and the surface roughness of the hole was measured. A set of tests was carried out using carbide drills of 8 mm in diameter at 50, 70, and 90 m/min cutting speeds, 2, 3, and 4 flute numbers, and 0.2, 0.3, and 0.4 mm/rev feed rates. The Simulated Annealing (SA) and Genetic Algorithm (GA) methods were used for optimization. Based on the experimental findings and optimization techniques applied, optimal cutting parameters were derived, which were subsequently adjusted to enhance surface quality. Overall, the cutting parameters are carefully optimized to achieve good surface roughness quality... [more]

In this study, circulating fluidized bed fly ash (CFBFA) non-sintered ceramsite was innovatively developed. The CFBFA was addressed by adding ternary activator (including cement, hydrated lime, and gypsum) to prepare ceramsite. In the curing process, the use of power plant flue gas for curing not only captured greenhouse gas CO2, but also enhanced the compressive strength of the ceramsite. The compressive strength of the composite gravels prepared by the CFBFA was modeled using a novel approach that employed the response surface methodology (RSM) and artificial neural network (ANN) coupled with genetic algorithm (GA). Box−Behnken design (BBD)-RSM method was used for the independent variables of cement content, hydrated lime content, and gypsum content. The resulting quadratic polynomial model had an R2 value of 0.9820 and RMSE of 0.21. The BP-ANN with a structure of 3-10-1 performed the best and showed better prediction of the response than the BBD-RSM model, with an R2 value of 0.9932... [more]

Mobile camera modules are manufactured by aligning and assembling multiple differently shaped part lenses. Therefore, selecting the part lenses to assemble from candidates (called cavities) and determining the directional angle of each part lens for assembly have been important issues to maximize production yield. Currently, this process is manually conducted by experts at the manufacturing site, and the manual assembly condition optimization carries the risk of reduced production yield and increased failure cost as it largely depends on one’s expertise. Herein, we propose an AI framework that determines the optimal assembly condition including the combination of part lens cavities and the directional angles of part lenses. To achieve this, we combine the genetic algorithm with convolutional bidirectional long-term short-term memory (C-BLSTM). To the best of our knowledge, this is the first study on lens module production finding the optimal combination of part lens cavities and direct... [more]

Fracture network fracturing is pivotal for achieving the economical and efficient development of shale gas, with the connectivity among fracture networks playing a crucial role in reservoir stimulation effectiveness. However, flow back data that reflect fracture network connectivity information are often ignored, resulting in an inaccurate prediction of the effective fracture network volume (EFNV). The accurate calculation of the EFNV has become a key and difficult issue in the field of shale fracturing. For this reason, the accurate shale gas effective fracture network volume inversion method needs to be improved. Based on the flow back characteristics of fracturing fluids, a tree-shaped fractal fracture flow back mathematical model for inversion of EFNV was established and combined with fractal theory. A genetic algorithm workflow suitable for EFNV inversion of shale gas was constructed based on the flow back data after fracturing, and the fracture wells in southern Sichuan were used... [more]

The EDEM software (Altair EDEM 2022.0 professional version 8.0.0) was used to create a discrete element model of silage to address the lack of silage evidence parameters and contact parameters between silage and conveying equipment when using the discrete element method to simulate and analyze crucial aspects of silage conveying and feeding. Physical tests and simulations were used to calibrate the significant parameters, and the silage stacking angle obtained from simulation and tests was then validated. The response value of the stacking angle (38.65°) obtained from the physical examination was used as the response value. The response surface (RSM) finding and the GA finding based on the genetic algorithm (GA) artificial neural network (BP) model were used to compare the significance parameters. The PB and steepest climb tests were used to screen the significant factors. Results indicate that the static friction coefficient between silage and silage, the rolling friction coefficient... [more]

Intelligent manufacturing workshop uses automatic guided vehicles as an important logistics and transportation carrier, and most of the existing research adopts the intelligent manufacturing workshop layout and Automated Guided Vehicle (AGV) path step-by-step optimization, which leads to problems such as low AGV operation efficiency and inability to achieve the optimal layout. For this reason, a smart manufacturing assembly line layout optimization model considering AGV path planning with the objective of minimizing the amount of material flow and the shortest AGV path is designed for the machining shop of a discrete manufacturing enterprise of a smart manufacturing company. Firstly, the information of the current node, the next node and the target node is added to the heuristic information, and the dynamic adjustment factor is added to make the heuristic information guiding in the early stage and the pheromone guiding in the later stage of iteration; secondly, the Laplace distribution... [more]

With limited investment costs, how to fully utilize the carbon-reduction capacity of a campus in terms of buildings, equipment, and energy is an important issue when realizing the low-carbon retrofit of office parks. To this end, this paper establishes a mathematical optimization model for the decarbonization-based retrofit of existing office parks, based on the genetic algorithm, taking into account the relationship between cost, energy-consumption, and carbon-emissions, and taking the maximum carbon reduction of the park over its whole life as the optimization goal. The validity of the model was verified in conjunction with a case study of an office park in Nanchang, China. The case study shows that, compared with current typical parks, the carbon reduction through an office park’s decarbonization retrofit has a non-linear correlation with the investment cost, and when the total investment cost of the park is above CNY 60 million, the increase in carbon reduction with the increase in... [more]

The design of distillation columns significantly impacts the economy, energy consumption, and environment of chemical processes. However, optimizing the design of distillation columns is a very challenging problem. In order to develop an intelligent technique to obtain the best design solution, improve design efficiency, and minimize reliance on experience in the design process, a design methodology based on the GA-BP model is proposed in this paper. Firstly, a distillation column surrogate model is established using the back propagation neural network technique based on the training data from the rigorous simulation, which covers all possible changes in feed conditions, operating conditions, and design parameters. The essence of this step is to turn the distillation design process from model-driven to data-driven. Secondly, the model takes the minimum TAC as the objective function and performs the optimization search using a Genetic Algorithm to obtain the design solution with the min... [more]

In this paper, we studied the feedback stabilization of an inductive power transfer system based on available output measurement. The proposed controller relies on a full-order state observer in order to estimate the unmeasured state. The control design problem is challenging due to the large dimension of the closed-loop system, which requires too many tuning parameters to be determined when conventional control methods are employed. To solve this issue, we propose an LQR methodology based on a genetic algorithm such that the weighing coefficients of the cost function matrices can be automatically computed in an optimized manner. The proposed approach combines the method of eigenstructure assignment and the LQR technique in order to design both the controller and the observer gain matrices. The design methodology provides a systematic way to compute the parameters of the LQR technique for a wireless power transfer system in an optimized manner, which can be a useful design tool for man... [more]

Heat integration by a heat exchanger network (HEN) is an important topic in chemical process system synthesis. From the perspective of optimization, the simultaneous synthesis of HEN belongs to a mixed-integer and nonlinear programming problem. Both the stage-wise superstructure (SWS) model and the chessboard model are the most widely adopted and belong to structural models, in which a framework is assumed for stream matching, and the global optimal solution outside its feasible domain may be defined by the framework. A node-wise non-structural model (NW-NSM) is proposed to find more universal stream matching options, but it requires a mass of structural variables and extra multiple correction strategies. The aim of this paper is to develop a novel matrix non-structural model (M-NSM) for HEN without stream splits from the perspectives of global optimization methods and superstructure models. In the proposed M-NSM, the heat exchanger position order is quantized by matrix elements at eac... [more]

The purpose of the study was to identify and predict the optimized parameters for phosphoric acid production. This involved modeling the crystal reactor, UCEGO filter (as a detailed model of the filter is not available in Aspen Plus or other simulation software), and acid separator using Sci-Lab to develop Cape-Open models. The simulation was conducted using Aspen Plus and involved analyzing 10 different phosphates with varying qualities and fractions of P2O5 and other minerals. After a successful simulation, a sensitivity analysis was conducted by varying parameters such as capacity, filter speed, vacuum, particle size, water temperature for washing the filtration cake, flow of recycled acid and strong acid from the separator below the filter, flow of slurry to reactor 1, temperature in reactors, and flow of H2SO4, resulting in nearly one million combinations. To create an algorithm for predicting process parameters and the maximal extent of recovering H3PO4 from slurry, ANN models we... [more]

Double-pipe counter-flow heat exchangers are considered more suitable for heat recovery in the heat transfer industry. Numerous studies have been conducted to develop static tools for optimizing operating parameters of heat exchangers. Using this study, an improved heat exchanger system will be developed. This is frequently used to solve optimization problems and find optimal solutions. The Taguchi method determines the critical factor affecting a specific performance parameter of the heat exchanger by identifying the significant level of the factor affecting that parameter. Gray relational analysis was adopted to determine the gray relational grade to represent the multi-factor optimization model, and the heat exchanger gray relation coefficient target values that were predicted have been achieved using ANN with a back propagation model with the Levenberg−Marquardt drive algorithm. The genetic algorithm improved the accuracy of the gray relational grade by assigning gray relational co... [more]

This paper focuses on the combustion mechanism of furan-based fuels synthesized from lignocellulose. The fuel is a binary alternative fuel consisting of 2-methylfuran and 2,5-dimethylfuran derived from furfural. The key reactions affecting the combustion mechanism of this fuel were identified via path analysis, and the initial reaction kinetic mechanism was constructed using a decoupling methodology. Then, a genetic algorithm was used to optimize the initial mechanism. The final skeleton mechanism consisted of 67 species and 228 reactions. By comparing experimental data on ignition delay, component concentration, and laminar flame velocity under a wide range of conditions over various fundamental reactors, it was shown that the mechanism has the ability to predict the combustion process of this fuel well.

To boost the operational performance of a non-aqueous DES electrolyte-based vanadium-iron redox flow battery (RFB), our previous work proposed a double-layer porous electrode spliced by carbon paper and graphite felt. However, this electrode’s architecture still needs to be further optimized under different operational conditions. Hence, this paper proposes a multi-layer artificial neural network (ANN) model to predict the relationship between vanadium-iron RFB’s performance and double-layer electrode structural characteristics. A training dataset of ANN is generated by three-dimensional finite-element numerical simulations of the galvanostatic discharging process. In addition, a genetic algorithm (GA) is coupled to an ANN regression training process for optimizing the model parameters to elevate the accuracy of ANN prediction. The novelty of this work lies in this modified optimal method of a double-layer electrode for non-aqueous RFB driven by a machine learning (ML) model coupled wi... [more]

The reasonable allocation of the battery energy storage system (BESS) in the distribution networks is an effective method that contributes to the renewable energy sources (RESs) connected to the power grid. However, the site and capacity of BESS optimized by the traditional genetic algorithm is usually inaccurate. In this paper, a power grid node load, which includes the daily load of wind power and solar energy, was studied. Aiming to minimize the average daily distribution networks loss with the power grid node load connected with RESs, a site selection and capacity setting model of BESS was built. To solve this model, a modified simulated annealing genetic algorithm was developed. In the developed method, the crossover probability and the mutation probability were modified by a double-threshold mutation probability control, which helped this genetic method to avoid trapping in local optima. Moreover, the cooling mechanism of simulated annealing method was presented to accelerate the... [more]

Additive manufacturing (AM) or 3D printing has opened up new opportunities for researchers in the field of electrical machines, as it allows for more flexibility in design and faster prototyping, which can lead to more efficient and cost-effective production. An overview of the primary AM techniques utilized for designing electrical machines is presented in this paper. AM enables the creation of complex and intricate designs that are difficult or impossible to achieve using traditional methods. Topology Optimization (TO) can be used to optimize the design of parts for various purposes such as weight, thermal, material usage and structural performance. This paper primarily concentrates on the most recent studies of the AM and TO of the reluctance machines. The integration of AM with TO can enhance the design and fabrication process of magnetic components in electrical machines by overcoming current manufacturing limitations and enabling the exploration of new design possibilities. The t... [more]

In order to accurately calculate the geometric characteristics of the twin-screw compressor and obtain the optimal profile parameters, a calculation method for the geometric characteristics of twin-screw compressors was proposed to simplify the profile parameter design in this paper. In this method, the database of geometric characteristics is established by back-propagation (BP) neural network, and the genetic algorithm is used to find the optimal profile design parameters. The effects of training methods and hidden layers on the calculation accuracy of neural network are discussed. The effects of profile parameters, including inner radius of the male rotor, protection angle, radius of the elliptic arc, outer radius of the female rotor on the comprehensive evaluation value composed of length of the contact line, blow hole area and area utilization rate, are analyzed. The results show that the time consumed for the database established by BP neural network is 92.8% shorter than that of... [more]

Water conservancy project scheduling is an extension to the classic resource-constrained project scheduling problem (RCPSP). It is limited by special time constraints called “forbidden time windows” during which certain activities cannot be executed. To address this issue, a specific RCPSP model is proposed, and an approach is designated for it which incorporates both a priority rule-based heuristic algorithm to obtain an acceptable solution, and a hybrid genetic algorithm to further improve the quality of the solution. In the genetic algorithm, we introduce a new crossover operator for the forbidden time window and adopt double justification and elitism strategies. Finally, we conduct simulated experiments on a project scheduling problem library to compare the proposed algorithm with other priority-rule based heuristics, and the results demonstrate the superiority of our algorithm.

Due to the complexity of risk factors in constructing immersed tube tunnels, it is impossible to accurately identify risks. To solve this problem, and the uncertainty and fuzziness of risk factors, a risk assessment method for immersed tube tunnel construction was proposed based on WBS-RBS (Work Breakdown Structure-Risk Breakdown Structure), improved AHP (analytic hierarchy process), and cloud model theory. WBS-RBS was used to analyze the risk factors of immersed tube tunnel construction from the aspects of the construction process and 4M1E, and built a more comprehensive and accurate construction risk index system. The weight of each index was calculated by the improved AHP of a genetic algorithm. The cloud model theory was used to build the cloud map of risk assessment for immersed tunnel construction and evaluate construction risk. Taking the Dalian Bay subsea tunnel project as an example, the risk assessment method of immersed tunnel construction was verified. The results showed th... [more]

Airborne wind energy systems benefit from high-lift airfoils to increase power output. This paper proposes an optimisation approach for a multi-element airfoil of a fixed-wing system operated in pumping cycles to drive a drum-generator module on the ground. The approach accounts for the different design objectives of the tethered kite’s alternating production and return phases. The airfoil shape is first optimised for the production phase and then adapted for the requirements of the return phase by modifying the flap setting. The optimisation uses the multi-objective genetic algorithm NSGA-II in combination with the fast aerodynamic solver MSES. Once the optimal shape is determined, the aerodynamic performance is verified through CFD RANS simulations with OpenFOAM. The resulting airfoil achieves satisfactory performance for the production and return phases of the pumping cycles, and the CFD verification shows a fairly good agreement in terms of the lift coefficient. However, MSES signi... [more]

Pre-reforming of propane was studied over an industrial nickel-chromium catalyst under pressures of 1 and 5 bar, at a low steam to carbon molar ratio of 1, in the temperature range of 220−380 °C and at flow rates of 4000 and 12,000 h−1. It was shown that propane conversion proceeded more efficiently at low pressure (1 atm) and temperatures above 350 °C. A genetic algorithm was applied to search for kinetic parameters better fitting experimental results in such a wide range of experimental conditions. Power law and Langmuir−Hinshelwood kinetics were considered. It was shown that only Langmuir−Hinshelwood type kinetics correctly described the experimental data and could be used to simulate the process of propane pre-reforming and predict propane conversion under the given reaction conditions. The significance of Langmuir−Hinshelwood kinetics increases under high pressure and temperatures below 350 °C.

The performance of a wind turbine is affected by wind conditions and blade shape. This study aimed to optimize the performance of a 20 kW horizontal-axis wind turbine (HAWT) under local wind conditions at Deniliquin, New South Wales, Australia. Ansys Fluent (version 18.2, Canonsburg, PA, USA) was used to investigate the aerodynamic performance of the HAWT. The effects of four Reynolds-averaged Navier−Stokes turbulence models on predicting the flows under separation condition were examined. The transition SST model had the best agreement with the NREL CER data. Then, the aerodynamic shape of the rotor was optimized to maximize the annual energy production (AEP) in the Deniliquin region. Statistical wind analysis was applied to define the Weibull function and scale parameters which were 2.096 and 5.042 m/s, respectively. The HARP_Opt (National Renewable Energy Laboratory, Golden, CO, USA) was enhanced with design variables concerning the shape of the blade, rated rotational speed, and pi... [more]

Electricity consumption forecasting plays an important role in investment planning of electricity infrastructure, and in electricity production/generation and distribution. Accurate electricity consumption prediction over the mid/long term is of great interest to both practitioners and academics. Considering that monthly electricity consumption series usually show an obvious seasonal variation due to their inherent nature subject to temperature during the year, in this paper, seasonal exponential smoothing (SES) models were employed as the modeling technique, and the particle swarm optimization (PSO) algorithm was applied to find a set of near-optimal smoothing parameters. Quantitative and comprehensive assessments were performed with two real-world electricity consumption datasets on the basis of prediction accuracy and computational cost. The experimental results indicated that (1) whether the accuracy measure or the elapsed time was considered, the PSO performed better than grid sea... [more]

To design a residential or commercial building with high energy performance that would be economical at the same time, an analysis was performed that relates these two aspects of the problem. The first aspect is focused on evaluation of the thermal performance of a multi-layered wall in order to achieve the lowest energy consumption for heating and cooling. The second aspect of the analysis covered the choice of materials (type, thickness and price) so that the building has the lowest possible construction costs, but the best achieved thermal comfort. The three types of external walls with the same structure were analyzed in this paper. The lowest and highest values of the layer thickness offered by the manufacturer were chosen and their dynamic characteristics for the heat transfer were calculated. The following step was to perform optimization of the objective function, which was defined by the unit price of the material per mass of the material, that is, the economical aspect was pr... [more]