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Records with Keyword: Genetic Algorithm
Showing records 1 to 25 of 244. [First] Page: 1 2 3 4 5 Last
Deacidification of Used Cooking Oil: Modeling and Validation of Ethanolic Extraction in a Liquid-Liquid Film Contactor
Sergio A. Rojas, Álvaro Orjuela, Paulo C. Narváez
June 27, 2025 (v1)
Keywords: free fatty acids, Genetic Algorithm, liquid extraction, Liquid-liquid film contactor, mathematical modeling, used cooking oil
Large quantities of used cooking oil (UCO) are produced globally, primarily in densely populated urban centers. Although UCO is highly heterogeneous due to degradation during cooking, it still contains a significant fraction of triacylglycerols (TG) that could be used as raw materials in oleochemical biorefineries. A major challenge in reintegrating this residue into productive cycles is the presence of free fatty acids (FFA), which can affect subsequent catalytic or enzymatic transformations. Conventional processes for FFA removal are energy-intensive, require alkaline feedstocks, and generate problematic residues. To overcome these issues, alcoholic extraction of FFA is considered a promising pretreatment for UCO, enabling the extraction of FFA for subsequent esterification. In this regard, liquid-liquid film contactors (LLFC) have shown potential to intensify FFA extraction because they operate under mild conditions and at laminar flow regime, reducing energy consumption and enhanci... [more]
A Comprehensive study on PHB biosynthesis and biodegradation through kinetic modelling
Ariyan Amirifar, Constantinos Theodoropoulos
June 27, 2025 (v1)
Subject: Biosystems
Keywords: C necator DSM 545, Fermentation, Genetic Algorithm, Modelling, Modelling and Simulations, PHB
Polyhydroxyalkanoates (PHAs) are microbial bioplastics that are fully biodegradable, biocompatible and can be produced by renewable feedstocks through fermentation. These are all desirable attributes for the replacement of current fossil-based plastics. Strong mathematical models describing bioprocesses are invaluable tools that can be used for enhancing bioprocess understanding as well as optimization. In this study, polyhydroxybutyrate (PHB), by Cupriavidus necator DSM 545 was produced using glycerol and ammonium sulphate (AS) as the sole carbon and nitrogen sources, respectively. In addition, a kinetic bioprocess model was developed. The kinetic parameters of the model were calibrated with five fermentation experiments with different initial conditions (e.g. variable glycerol and AS concentrations) in order to properly establish the inhibition regions and provide a generalized model as much as possible. The model was successfully validated by three independent experiments, two with... [more]
Computer-Aided Molecular Design for Bio-Based Solvent Selection from Citrus and Coffee Wastes for Furfural Extraction
Giovana C. A. Netto, Moisés Teles dos Santos, Vincent Gerbaud
June 27, 2025 (v1)
Keywords: Agricultural Wastes, Biomass, CAMD, Furfural, Genetic Algorithm, Molecular Design, Solvent
The global reliance on fossil-based solvents has driven the search for sustainable alternatives. This study employs the IBSS® CAMD tool to evaluate building blocks derived, directly or indirectly, from agricultural residues - specifically orange and coffee wastes-, to replace toluene in furfural extraction. A three-stage methodology was implemented: (1) identification of potential building blocks from residues, (2) multi-objective optimization using genetic algorithms and group contribution models for properties calculation, and (3) analysis of the resulting candidates based on performance indicators. A total of 13 families were evaluated, generating millions of candidates. Target properties included minimization of Hansen Solubility Parameters (HSP) distance, boiling point above 250°C, melting point below 10°C, flash point above 61°C, and octanol-water partition coefficient (log(kow)) below 3. The most promising candidates were derivatives of glycerol (performance: 0.9986), limonene (... [more]
The Smart HPLC Robot: Fully Autonomous Method Development Guided by A Mechanistic Model Framework
Dian Ning Chia, Fanyi Duanmu, Luca Mazzei, Eva Sorensen, Maximilian O. Besenhard
June 27, 2025 (v1)
Keywords: Autonomous, Batch Process, Chromatography, Digital Twin, Genetic Algorithm, Industry 40, Mechanistic Model, Modelling and Simulations, Optimization, Self-driving
Developing ultra- or high-performance liquid chromatography (HPLC) methods for analysis or purification requires significant amounts of material and manpower, and typically involves time-consuming iterative lab-based workflows. This work demonstrates in two case studies that an autonomous HPLC platform coupled with a mechanistic model that self-corrects itself by performing parameter estimation can efficiently develop an optimized HPLC method with minimal experiments (i.e., reduced experimental costs and burden) and manual intervention (i.e., reduced manpower). At the same time, this HPLC platform, referred to as Smart HPLC Robot, can deliver a calibrated mechanistic model that provides valuable insights into method robustness.
Selection of Fitness Criteria for Learning Interpretable PDE Solutions via Symbolic Regression
Benjamin G. Cohen, Burcu Beykal, George M. Bollas
June 27, 2025 (v1)
Physics-Informed Symbolic Regression (PISR) offers a pathway to discover human-interpretable solutions to partial differential equations (PDEs). This work investigates three fitness metrics within a PISR framework: PDE fitness, Bayesian Information Criterion (BIC), and a fitness metric proportional to the probability of a model given the data. Through experiments with Laplace’s equation, Burgers’ equation, and a nonlinear wave equation, we demonstrate that incorporating information theoretic criteria like BIC can yield higher fidelity models while maintaining interpretability. Our results show that BIC-based PISR achieved the best performance, identifying an exact solution to Laplace’s equation and finding solutions with R2-values of 0.998 for Burgers’ equation and 0.957 for the nonlinear wave equation. The inclusion of the Bayes D-optimality criterion in estimating model probability strongly constrained solution complexity, limiting models to 3-4 parameters and reducing accuracy. Thes... [more]
AI-Driven Automatic Mechanistic Model Transfer Learning for Accelerating Process Development
Alexander W. Rogers, Amanda Lane, Philip Martin, Dongda Zhang
June 27, 2025 (v1)
Keywords: Artificial Intelligence, Biosystems, Dynamic Modelling, Genetic Algorithm, Interpretable Machine Learning, Knowledge Discovery, Model-Based Design of Experiments
Accurate mechanistic models provide valuable physical insight and are crucial for efficient process scale-up and optimisation, but their identification requires lengthy experimental data collection, model construction, validation and discrimination. Traditional black-box machine learning transfer methods leverage prior knowledge but lack interpretability and physical insights. To address this, we propose a novel approach using artificial neural network feature attribution to automatically locate corrections and symbolic regression to make structural modifications to an inaccurate or low-fidelity mechanistic model. In a comprehensive in-silico case study, the framework adapted a kinetic model from one biochemical system to a different but related one, enhancing predictive accuracy. Integrated within an iterative model-based design of experiments routine, it minimised the number of new experiments required. The study also discusses the impact of the inductive bias trade-off and alternati... [more]
Comparison of optimization methods for studying the energy mix of infrastructures. Application to an infrastructure in Oise, France
Julien JEAN VICTOR, Zakaria A. SOULEYMANE, Augustin MPANDA, Philippe TRUBERT, Laurent FONTANELLI, Sébastien POTEL, Arnaud DUJANY
June 27, 2025 (v1)
Subject: Optimization
Keywords: Branch-and-Cut, Energy Mix, Energy Systems, Genetic Algorithm, Goal Programming, Optimization, Stochastic Optimization
In the last decades, the growing awareness of climate change and the high political sensitivity of critical resources such as energy have emphasized a need for local, renewable and optimized energy mixes at various scales. Several studies have therefore aimed to optimize renewable energy technologies and plant locations to develop more renewable and efficient Energy Mixes. Following this trend, this paper applies and compares Goal Programming, Branch-and-Cut and NSGA-II to a multi-objective combinatorial optimization problem focused on the energy mix of Oise, France. Results show more optimality for Goal Programming and Branch-and-Cut, accompanied by a high sensitivity to constraints, while NSGA-II provides more technological diversity in the computed solutions.
Pipeline Network Growth Optimisation for CCUS: A Case Study on the North Sea Port Cluster
Victoria Brown, Joseph Hammond, Diarmid Roberts, Solomon Brown
June 27, 2025 (v1)
Keywords: Carbon Capture, Carbon Dioxide Capture, Energy, Genetic Algorithm, Modelling and Simulations
By 2050 around 12% of cumulative emissions reductions will come from Carbon Capture, Utilisation and Storage (CCUS) making it an essential component in the path towards net zero [1]. Focus will initially be on the retrofitting of fossil fuel power plants, which will shift to hard-to-decarbonise industries such as iron, steel, and concrete [1]. Such industries are often grouped together in industrial clusters. Comprising both large and small point sources concentrated over a defined geographical area, industrial clusters offer an opportunity to maximise the impact of CCUS whilst also improving economic feasibility [2]. The North Sea Port (NSP) cluster an example of this. Within the NSP cluster an initial set of five emitters are to join a capture, conditioning, and transport network by 2030. From there other emitters within the area will be able to join incrementally to 2050 [3]. However, the emitters who join and the timing of their connection will have a significant effect on the evo... [more]
A Transparent Techno-Enviro-Economic Assessment of a Coal-Fired Power Plant: Integrating Biomass Co-Firing and CO2 Sequestration Technology in a Carbon-Priced Environment
N. F. E. Nor Fadzil, N. Abdul Manaf, N. Shah
June 27, 2025 (v1)
Subject: Environment
The integration of carbon capture and storage (CCS) into coal and biomass co-firing systems (CBCCS) offers a promising solution for reducing carbon emissions in electricity generation. This study evaluates hypothetical scenarios in Malaysia and Indonesia, focusing on techno-economic-environmental transparency. The analysis shows a negligible change in plant net efficiency (~1%) across biomass co-firing ratios of 5-20% in both countries. The capture penalty increases at higher biomass ratios, particularly at 20% co-firing, due to higher auxiliary power demands and steam extraction. As biomass share increases, net CO2 emissions decrease by an average of 43% in Malaysia and 34% in Indonesia. Economic evaluations show a positive revenue increase for Malaysia at a 20% co-firing ratio, while Indonesia faces a revenue deficit (0.6%) under the same condition, mainly due to an unattractive carbon price and feed-in tariff from 2027 onward. Malaysia faces a higher risk of stranded assets due to e... [more]
Prediction of Short-Term Winter Photovoltaic Power Generation Output of Henan Province Using Genetic Algorithm−Backpropagation Neural Network
Dawei Xia, Ling Li, Buting Zhang, Min Li, Can Wang, Zhijie Gong, Abdulmajid Abdullahi Shagali, Long Jiang, Song Hu
August 23, 2024 (v1)
Keywords: back propagation, Genetic Algorithm, photovoltaic power generation, prediction accuracy, rain and snow weather
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]
Optimum Cutting Parameters for Carbon-Fiber-Reinforced Polymer Composites: A Synergistic Approach with Simulated Annealing and Genetic Algorithms in Drilling Processes
Birhan Isik, Mehmet Sah Gultekin, Ismail Fidan, Martin Byung-Guk Jun
August 23, 2024 (v1)
Subject: Materials
Keywords: CFRP, drilling, Genetic Algorithm, simulated annealing, surface roughness
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]
Optimization of Ternary Activator for Enhancing Mechanical Properties of Carbonized Cementitious Material Based on Circulating Fluidized Bed Fly Ash
Nuo Xu, Suxia Ma, Nana Wang, Yuchuan Feng, Yunqi Liu, Ke Ren, Shanshui Bai
June 7, 2024 (v1)
Subject: Materials
Keywords: artificial neural network, Box–Behnken design, Genetic Algorithm, response surface methodology, ternary activator
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]
Determining Optimal Assembly Condition for Lens Module Production by Combining Genetic Algorithm and C-BLSTM
Hyegeun Min, Yeonbin Son, Yerim Choi
June 6, 2024 (v1)
Subject: Optimization
Keywords: convolutional–bidirectional long short-term memory, Genetic Algorithm, lens module, lens module production, optimal assembly condition, part lens assembly
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]
The Inversion Method of Shale Gas Effective Fracture Network Volume Based on Flow Back Data—A Case Study of Southern Sichuan Basin Shale
Dengji Tang, Jianfa Wu, Jinzhou Zhao, Bo Zeng, Yi Song, Cheng Shen, Lan Ren, Yongzhi Huang, Zhenhua Wang
June 5, 2024 (v1)
Keywords: effective fracture network volume (EFNV), flow back data, fracture network fracturing, Genetic Algorithm, shale gas
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]
Discrete Meta-Simulation of Silage Based on RSM and GA-BP-GA Optimization Parameter Calibration
Gonghao Li, Juan Ma, Xiang Tian, Chao Zhao, Shiguan An, Rui Guo, Bin Feng, Jie Zhang
February 10, 2024 (v1)
Keywords: BP neural network, discrete element method, Genetic Algorithm, parameter calibration, response surface method, silage
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]
Research on Optimization Algorithm of AGV Scheduling for Intelligent Manufacturing Company: Taking the Machining Shop as an Example
Chao Wu, Yongmao Xiao, Xiaoyong Zhu
November 30, 2023 (v1)
Keywords: automatic guided vehicle, Genetic Algorithm, intelligent manufacturing shop, machining shop, scheduling optimization algorithm
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]
Optimization of Cost−Carbon Reduction−Technology Solution for Existing Office Parks Based on Genetic Algorithm
Zhenlan Dou, Lu Jin, Yinhui Chen, Zishuo Huang
September 21, 2023 (v1)
Subject: Optimization
Keywords: carbon reduction, cost benefits, existing office parks, Genetic Algorithm, retrofit, whole life cycle
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]
Intelligent Optimization Design of Distillation Columns Using Surrogate Models Based on GA-BP
Lixiao Ye, Nan Zhang, Guanghui Li, Dungang Gu, Jiaqi Lu, Yuhang Lou
September 21, 2023 (v1)
Keywords: BP neural network, distillation column, Genetic Algorithm, intelligent design, life cycle assessment, surrogate modeling
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]
Observer-Based Control of Inductive Wireless Power Transfer System Using Genetic Algorithm
Mahmoud Abdelrahim, Dhafer Almakhles
July 13, 2023 (v1)
Keywords: eigenstructure assignment, Genetic Algorithm, linear quadratic regulator, wireless power transfer
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]
Matrix Non-Structural Model and Its Application in Heat Exchanger Network without Stream Split
Dinghao Li, Jingde Wang, Wei Sun, Nan Zhang
July 13, 2023 (v1)
Subject: Optimization
Keywords: Genetic Algorithm, heat exchanger network synthesis, matrix real-coded, non-structural model, Optimization
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]
A Novel Hybrid Approach for Modeling and Optimisation of Phosphoric Acid Production through the Integration of AspenTech, SciLab Unit Operation, Artificial Neural Networks and Genetic Algorithm
Marko Pavlović, Jelena Lubura, Lato Pezo, Milada Pezo, Oskar Bera, Predrag Kojić
July 7, 2023 (v1)
Keywords: artificial neural network, Aspen, Genetic Algorithm, multi-objective optimization, phosphoric acid, UCEGO filter
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]
Design Optimization of Counter-Flow Double-Pipe Heat Exchanger Using Hybrid Optimization Algorithm
B. Venkatesh, Mudassir Khan, Bayan Alabduallah, Ajmeera Kiran, J. Chinna Babu, B. Bhargavi, Fatimah Alhayan
July 4, 2023 (v1)
Subject: Optimization
Keywords: double-pipe heat exchanger, Genetic Algorithm, gray
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]
Study on the Skeleton Mechanism of Second-Generation Biofuels Derived from Platform Molecules
Weiwei Fan, Aichun Du, Gang Liu, Qing Liu, Yuan Gao
July 4, 2023 (v1)
Keywords: biomass fuel, Genetic Algorithm, skeleton mechanism
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.
Study on the Optimal Double-Layer Electrode for a Non-Aqueous Vanadium-Iron Redox Flow Battery Using a Machine Learning Model Coupled with Genetic Algorithm
Qiang Ma, Wenxuan Fu, Jinhua Xu, Zhiqiang Wang, Qian Xu
June 9, 2023 (v1)
Keywords: 3D finite-element numerical simulation, artificial neural network, DES electrolyte, Genetic Algorithm, gradient porous electrode, Machine Learning, operational performance, redox flow battery, vanadium-iron
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]
Method of Site Selection and Capacity Setting for Battery Energy Storage System in Distribution Networks with Renewable Energy Sources
Simin Peng, Liyang Zhu, Zhenlan Dou, Dandan Liu, Ruixin Yang, Michael Pecht
May 24, 2023 (v1)
Keywords: battery energy storage system, Genetic Algorithm, simulated annealing algorithm, site selection and capacity setting
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]
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