Parameter Optimization Technique Research Articles

Enhancing DC Motor Speed Control Performance Using Heuristic Optimization and Comparative Analysis of Control Methods

Direct Current (DC) motors are an important component that converts electrical energy into mechanical energy, used in a wide range of applications from industrial applications to home appliances. DC motor speed control has an important role in industrial processes to increase efficiency, realize precise movements and optimize energy consumption. In this study, various control methods and parameter optimization techniques for speed control of DC motors, which have a wide range of applications, have been systematically analyzed. The aim of the study is to develop an effective control strategy to ensure that DC motors reach the determined target speed by monitoring them in real time at different speeds and to minimize fluctuations caused by variable loads or external factors. In our study, Proportional-Integral-Derivative (PID), Proportional-Integral (PI), and Proportional-Derivative (PD) control methods were used. The parameters of these controllers were tuned using Matlab Tuned, The Cheetah Optimizer (CO) Algorithm, a new generation heuristic optimization method, and Particle Swarm Optimization (PSO), a widely accepted optimization method. The performances of the controllers were determined using criteria such as Integral of Absolute Error (IAE), Integral Squared Error (ISE), and Integral of Time multiplied by Absolute Error (ITAE). According to the results obtained, it was found that the PID, PI and PD control parameters determined using the CO Algorithm performed better than the controllers created using Matlab Tuned and PSO methods. New optimization methods, such as the CO Algorithm, have been found to have significant potential to improve the performance of control systems. Thanks to this study, it offers a practical approach for optimizing DC motor speed control in industrial processes. As a result, it has been found that the control parameters determined by the CO Algorithm have significant potential in improving the performance of DC motor speed control and control systems compared to other optimization methods.

Deep Learning Empowered Water Quality Assessment: Leveraging IoT Sensor Data with LSTM Models and Interpretability Techniques

Addressing the imperative demand for accurate water quality assessment, this paper delves into the application of deep learning techniques, specifically leveraging IoT sensor datasets for the classification and prediction of water quality parameters. The utilization of LSTM (Long Short-Term Memory) models navigates the intricacies inherent in environmental data, emphasizing the balance between model accuracy and interpretability. This equilibrium is achieved through the deployment of interpretability methods such as LIME, SHAP, Anchor, and LORE. Additionally, the incorporation of advanced parameter optimization techniques focuses on fine-tuning essential parameters like learning rates, batch sizes, and epochs to optimize model performance. This comprehensive approach ensures not only precise predictions but also enhances the transparency and interpretability of the model, addressing the critical need for actionable information in water quality management. The research significantly contributes to the convergence of deep learning, IoT, and environmental science, offering valuable tools for informed decision-making while highlighting the importance of fine-tuning parameters for optimal model performance

Review on Engineering Application Status of Gob-Side Entry Retaining Technology in China

Gob-side entry retaining (GSER) technology has been widely used in underground coal mines. However, the applicable conditions of GSER technology in practical engineering still need further clarification to prevent the safety hazards and significant economic losses caused by its failure. In this study, 587 application cases of GSER in China from 2000 to 2024 were collected, and the relevant data, such as geological conditions and key technical parameters of GSER projects and their impact on engineering practices, were systematically analyzed. Considering the technical characteristics and the developing status of GSER technology, the current status of the application of GSER engineering in the mining areas of China was obtained, and then the applicable geological conditions and optimal technical parameters for the effective implementation of GSER technology were identified. Additionally, the existing technical challenges and further prospects of GSER technology were illustrated. This study provides a reference for reasonable applications and further research of GSER technology.

Optimizing hierarchical classifiers with parameter tuning and confidence scoring

Hierarchical classifiers play a crucial role in addressing complex classification tasks by breaking them down into smaller, more manageable sub-tasks. This paper continues a series of works, focused on the technical Ukrainian texts hierarchical classification, specifically the classification of repair works and spare parts used in automobile maintenance and servicing. We tackle the challenges posed by multilingual data inputs – specifically Ukrainian, Russian, and their hybrid – and the lack of standard data cleaning models for the Ukrainian language. We developed a novel classification algorithm, which employs TF-IDF victimization with unigrams and bigrams, keyword selection, and cosine similarity for classification. This paper describes a method for training and evaluating a hierarchical classification model using parameter tuning for each node in a tree structure. The training process involves initializing weights for tokens in the class tree nodes and input strings, followed by iterative parameter tuning to optimize classification accuracy. Initial weights are assigned based on predefined rules, and the iterative process adjusts these weights to achieve optimal performance. The paper also addresses the challenge of interpreting multiple confidence scores from the classification process, proposing a machine learning approach using Scikit-learn's GradientBoostingClassifier to calculate a unified confidence score. This score helps assess the classification reliability, particularly for unlabeled data, by transforming input values, generating polynomial parameters, and using logarithmic transformations and scaling. The classifier is fine-tuned using hyper parameter optimization techniques, and the final model provides a robust confidence score for classification tasks, enabling the verification and classification results optimization across large datasets. Our experimental results demonstrate significant improvements in classification performance. Overall classification accuracy nearly doubled after training, reaching 92.38 %. This research not only advances the theoretical framework of hierarchical classifiers but also provides practical solutions for processing large-scale, unlabeled datasets in the automotive industry. The developed methodology can enhance various applications, including automated customer support systems, predictive maintenance, and decision-making processes for stakeholders like insurance companies and service centers. Future work will extend this approach to more complex tasks, such as extracting and classifying information from extensive text sources like telephone call transcriptions.

Active Vibration Control and Parameter Optimization of Genetic Algorithm for Partially Damped Composites Beams.

The paper partially covered Active Constrained Layer Damping (ACLD) cantilever beams' dynamic modeling, active vibration control, and parameter optimization techniques as the main topic of this research. The dynamic model of the viscoelastic sandwich beam is created by merging the finite element approach with the Golla Hughes McTavish (GHM) model. The governing equation is constructed based on Hamilton's principle. After the joint reduction of physical space and state space, the model is modified to comply with the demands of active control. The control parameters are optimized based on the Kalman filter and genetic algorithm. The effect of various ACLD coverage architectures and excitation signals on the system's vibration is investigated. According to the research, the genetic algorithm's optimization iteration can quickly find the best solution while achieving accurate model tracking, increasing the effectiveness and precision of active control. The Kalman filter can effectively suppress the impact of vibration and noise exposure to random excitation on the system.

Application of Response Surface Methodology for concentration of fluorosilicic acid by extraction technique with benzyl alcohol as extractant

As a kind of alternative of natural fluorinated minerals and raw material for producing anhydrous hydrogen fluoride, fluorosilicic acid has attracted more and more attention. In order to raise its utilization rate, dilute fluorosilicic acid solution coming from chemical enterprises needs to be concentrated. The extraction technology with benzyl alcohol as extraction agent was used to concentrating fluorosilicic acid aqueous solution. In order to determine the optimizing operation conditions of the extracting process, single - factor experiments were carried out to confirm the significant affecting factors and their operating range. Then the Response Surface Methodology (RSM) was conducted to accomplish regression of a nonlinear quadratic polynomial model to the experimental data and statistical analysis. Based on these studies, the optimal technical parameters of extracting process are acquired by optimization of the model using the Design-Expert 13 software. The statistical analysis for the fitting response surface quadratic equation model indicated that the model was reliable and accurate with very low p-values (<0.0001), the predicted values with the model equation had remarkable consistency in experimental data. By a great number of optimizations carried out with the Design Expert 13 software based on the fitting model equation, the optimal extractive processing conditions for concentrating fluorosilicic acid aqueous solution were obtained: the phase ratio 7, the extraction time 120.11 min and the extraction temperature 39.70 °C. The optimal concentration of fluorosilicic acid is 26.68 %. In order to increase the concentration of fluorosilicic acid, three-stage cross-current extraction for dilute fluorosilicic acid solution was carried out at the optimizing extracting conditions, 17.43 wt% fluorosilicic acid solution can be concentrated up to 37.46 wt%. The extractant-benzyl alcohol can be recovered with vacuum distillation and recovery rate reached up to 98 %.

Tailoring properties of PET-derived Sn-MOFs through efficiency structure defects using trifluoroacetic acid (TFA) with water-based facile and green synthesis route

BackgroundThe synthesis of Metal-Organic Frameworks (MOFs) is increasingly focused on achieving green and cost-efficient methods while producing high-quality products with abundant active sites. This approach is attracting significant attention from researchers. One promising method, modulated synthesis, stands out for its ability to induce structural defects in MOFs and enhance their active sites. However, the challenges in identifying the optimal conditions for critical factors, particularly the quantitative correlation between the modulator and crucial independent variables influencing MOFs performance, underscore the importance of research work in this field. MethodsThis study synthesized tin-based MOFs (Sn-MOFs) utilizing a linker derived from recycled polyethylene terephthalate (PET) waste. A hydrothermal approach was employed, utilizing water-like solvents and trifluoroacetic acid (TFA) as a modulator to effectively induce structural defects. Response Surface Methodology (RSM) was applied to evaluate the effects and interactions of temperature, reaction time, and TFA concentration on optimizing yield and crystalline index (CI) while simultaneously reducing the residual percentage of 1,4-benzene dicarboxylate (H2BDC) in the Sn-MOFs (DI). Significant findingsThe research revealed that temperature, reaction time, and TFA concentration significantly influenced the performance of Sn-MOFs, highlighting the considerable potential of TFA in creating active sites and enhancing the surface area and pore volume of Sn-MOFs through defect engineering. Optimal synthesis conditions for Sn-MOFs included a temperature of 148℃, a reaction time of 24 h, and a molar ratio of H2BDC/TFA of 1.7, yielding 98.51 ± 1.47 % for yield and 80.21 ± 1.32 % for CI, with no detectable residual H2BDC. The resulting Sn-MOF-150 exhibited characteristics such as high thermal and chemical stability, abundant function groups, and a unique hierarchical nanostructure composed of spherical nanoparticles. These findings further emphasize the efficacy of the synthesis approach for Sn-MOF through critical parameter optimization and defect engineering techniques.

Parametric optimization for estimating beta detection efficiency in thin plastic scintillation detector

A condenser air removal system plays a crucial role in radiation monitoring within nuclear power plants, specifically for detecting potential leaks of radioactive material from primary-to-secondary systems. This system assesses the leakage rate (LR) by measuring the emission of β particles from noble gases using a thin plastic scintillation (TPS) detector, which is specifically designed for counting purposes. Among the factors used in LR calculation, the detection efficiency (k) must be experimentally determined in advance. However, this is challenging because of the limitations posed by radiation safety regulations. Moreover, simulation-based calculations are difficult to achieve because only a portion of the β-ray energy is transferred in the TPS detectors, resulting in a lack of prior energy information. To overcome these challenges, this study employed a multi-objective parameter optimization technique to simultaneously calculate the parameters required for the β-ray detection response function and energy calibration equation. Subsequently, k values were calculated using the optimization results. The optimization performance was demonstrated by comparing the measured spectra of the TPS detector with the spectra obtained from the Monte Carlo N-particle simulation. Furthermore, we confirmed that the calculated k values agreed with the reference values.

Efficient conversion process and mechanism of bromine in bromine-rich saline wastewater

Bromine resources, widely utilized in basic chemical engineering, are considered non-renewable. A substantial amount of Bromine-Rich Saline Wastewater (BRSW), a hazardous waste, is generated during the smelting process of waste printed circuit boards (WPCBs). The efficient conversion of bromine resources is important in BRSW, which contain high concentrations of bromide ions. This study developed a synergistic treatment method utilizing electrolytic manganese anode sludge (EMAS) achieved efficient conversion of Br− to Br2 in BRSW. Optimal technical parameters are determined through single-factor experiments, and the oxidative acid leaching process was optimized using response surface methodology (RSM). The significant influencing factors for Br− conversion were ranked as acidity > anode mud addition amount > temperature > time, resulting in a bromine conversion efficiency of 96.55%. Kinetic studies of bromine conversion in BRSW during the oxidation-acid leaching process indicated a two-stage process: Stage I being a chemical reaction control model and Stage II being a diffusion control model. The reaction mechanism was elucidated, where H+ initially breaks the Mn-O bond in MnO2, exposing O vacancies and forming Mn4+, leading to a redox reaction between free Br− and Mn4+. This research embodies the concept of synergistic solid waste disposal, achieving cost-effectiveness and streamlined preparation of bromine, and providing technical and theoretical support for bromine resource recovery in BRSW.

Parameter Tuning of Agent-Based Models: Metaheuristic Algorithms

When it comes to modelling complex systems using an agent-based approach, there is a problem of choosing the appropriate parameter optimisation technique. This problem is further aggravated by the fact that the parameter space in complex agent-based systems can have a large dimension, and the time required to perform numerical experiments can be large. An alternative approach to traditional optimisation methods are the so-called metaheuristic algorithms, which provide an approximate solution in an acceptable time. The purpose of this study is to compare various metaheuristic algorithms for parameter tuning and to analyse their effectiveness applied to two agent-based models with different complexities. In this study, we considered commonly used metaheuristic algorithms for agent-based model optimisation: the Markov chain Monte Carlo method, the surrogate modelling approach, the particle swarm optimisation algorithm, and the genetic algorithm, as well as the more novel chaos game optimisation algorithm. The proposed algorithms were tested on two agent-based models, one of which was a simple toy model of the spread of contagious disease, and the other was a more complex model of the circulation of respiratory viruses in a city with 10 million agents and 26 calibrated parameters.

Mix design determination procedure for geopolymer concrete based on target strength method

This study presents the development and validation of a mix design determination procedure for geopolymer concrete to achieve the desired compressive strength. The procedure integrates artificial neural network (ANN) model developed based on a comprehensive data base from literature, data clustering, and parameter optimization techniques to enhance accuracy and reliability. Experimental validation is undertaken to demonstrate the mix design determination procedure’s capability to accurately predict mix designs for geopolymer concrete based on the target compressive strength, validating its efficacy for mix proportion determination. The integration of chemical oxide content in fly ash, curing time, curing temperature, and activator properties results in a 15.9% improvement in prediction accuracy for the training dataset and a 68.3% enhancement for the testing dataset, compared to the base ANN model that includes only the weight of fly ash and activator properties. Employing data clustering techniques enables the identification of prior estimates for the mix design parameters related to specific fly ash types and target compressive strength, streamlining the mix design process by analyzing pertinent data subsets. Parameter optimization ensures refined mix proportions, achieving the desired target strength economically while minimizing material waste and cost. The development of a user interface facilitates easy manipulation of mix designs, catering to users of varying expertise levels. Additional options for deeper insights into geopolymer concrete characteristics can be integrated into the mix design determination procedure. To assess the mix design determination procedure's ability to generalize effectively, a variety of fly ash samples with distinct chemical compositions were utilized, differing from those already present in the database. This approach allows for a thorough evaluation of the mix design determination procedure's performance when presented with fly ash compositions it has not encountered before. By doing so, this provides insights into the adaptability of the mix design determination procedure beyond the limitations of the training and testing datasets.

Thermodynamic simulation study on compression ignition engine operating with oil obtained via catalytic pyrolysis of waste polythene covers

In recent years, waste-to-energy systems are receiving more attention due to their dual benefit of reduction of pollution caused by waste materials and reducing the overconsumption of fossil fuels. Since plastic waste is readily available and has a high heating value, it is a viable source of energy.; plastic garbage makes an excellent source of energy. Today, a feedstock made up of several types of plastic is used to quickly pyrolyze and produce plastic pyrolysis oil (PPO) or waste plastic oil (WPO). The oil could be utilized in internal combustion engines to produce heat and power. In the current work, waste polythene covers were transformed into value added products by utilizing fly ash catalyst and oil obtained is examined for suitability to run in the diesel engine. In the primary phase simulation study is performed on a four-stroke single cylinder DI Compression ignition engine using Diesel-RK engine simulator, results indicated that, waste plastic oil was found to emit higher oxides of nitrogen compared to diesel therefore in the secondary phase the same engine simulated by implementing exhaust gas recirculation at 3.5 %,7 %,10.5 % and 14 % rates. In the end engine parameters get optimized using 3D parametric optimization technique.Simulation Results indicate that, performance parameters such as brake power, brake thermal efficiency and brake specific fuel consumption were found to be decreased slightly under EGR conditions. Furthermore, combustion parameters such as cylinder peak pressure and peak temperature decrease up on implementing the exhaust gas recirculation, similarly the emission characteristics such as CO2, PM and smoke found to be increase with increase in EGR ratio while oxides of nitrogen emissions significantly decreased under EGR conditions. In order to reduce the emissions further, a 3D parametric optimization technique was adopted and optimal results obtained when engine runs at compression ratio of 16.5, injection timing of 19.38 and EGR percentage of 4.8 %.

Modeling and Calibration of Gaia, Hipparcos, and Tycho-2 Astrometric Data for the Detection of Dark Companions

Hidden within the Gaia satellite’s multiple data releases lies a valuable cache of dark companions. To facilitate the efficient and reliable detection of these companions via combined analyses involving the Gaia, Hipparcos, and Tycho-2 catalogs, we introduce an astrometric modeling framework. This method incorporates analytical least-square minimization and nonlinear parameter optimization techniques to a set of common calibration sources across the different space-based astrometric catalogs. This enables us to discern the error inflation, astrometric jitter, differential parallax zero-points, and frame rotation of various catalogs relative to Gaia Data Release 3 (DR3). Our findings yield the most precise Gaia DR2 calibration parameters to date, revealing notable dependencies on magnitude and color. Intriguingly, we identify submilliarcsecond frame rotation between Gaia DR1 and DR3, along with an estimated astrometric jitter of 2.16 mas for the revised Hipparcos catalog. In a thorough comparative analysis with previous studies, we offer recommendations on calibrating and utilizing different catalogs for companion detection. Furthermore, we provide a user-friendly pipeline (https://github.com/ruiyicheng/Download_HIP_Gaia_GOST) for catalog download and bias correction, enhancing accessibility and usability within the scientific community.

Study of the relationships between the technical and economic characteristics of river cargo ships during operational and economic feasibility studies

One of the problems of operational and economic feasibility is determining the economic efficiency of a new ships. The complexity of the problem increases for shipis od river and sea-river class due to the high variability of navigation conditions, which significantly affect efficiency indicators. The purpose of the article is to study the mutual influence of the technical indicators of sea-river cargo ship and the economic indicators of its operation, taking into account the navigation conditions on specific sections of waterways. Various technical indicators are considered and it is shown that the most comprehensive indicator is the specific fuel consumption per ton-kilometer. The influence of the error of forecast technical indicators on economic indicators was assessed. It is shown that the optimality of economic indicators depends on the characteristics of the waterway so that a vessel that is optimal on one waterway may not be optimal on another. It has also been established that optimal technical parameters do not always agree with optimal economic ones.

IMPROVEMENT OF THE METHODOLOGY FOR OPTIMIZING THE PARAMETERS OF COMPLEX SYSTEMS UNDER THE INFLUENCE OF THERMAL LOAD SOURCES

The article investigates some aspects of solving the problem of constructing mathematical models for optimizing the parameters of local laser action on multilayer microbiological systems. The constraints on the desired laser parameters and on the temperature field in the material subject to laser fission are set. Verification of compliance with the restrictions on not exceeding the maximum value of the temperature field requires multiple calculations of the corresponding temperature field in the microbiological material. Control over the temperature field limits ensures the viability of its parts during the biotechnological process of laser fission. To construct adequate optimization mathematical models, the author substantiates the adequacy of the computational mathematical models describing the state of a microbiological system under the action of laser radiation sources. A multi-point boundary value problem with a system of inhomogeneous differential heat conduction equations for a multilayer microbiological medium is investigated and the correctness of this problem is substantiated for minor perturbations of the right-hand side of the differential equation. The results obtained in the article guarantee the adequacy of applied optimization mathematical models for finding rational values of technical parameters of laser emitters. In order to improve the accuracy of optimization of the technical parameters of laser emitters, the article presents a mathematical modeling of the preparatory stage of embryo defrosting. The presented mathematical models and methods of their implementation are necessary to improve the quality of embryo defrosting. Improvement of the methodology for solving applied biotechnological problems will certainly lead to the complication of mathematical models, but it will increase the accuracy of calculation and optimization of technical parameters of the biotechnological process of laser embryo division.

Small-Signal Stability Analysis and Improvement in Multi-Converter Grid-Tied System Based on Gerschgorin Disc Theorem

The integration of a large number of voltage source converters (VSCs) into the power grid decreases the small-signal stability of the power system. When several VSCs with different control parameters are simultaneously connected to the power grid to form a multi-converter grid-tied system, the potential destabilizing factors increase. Thus, parameter optimization for stability-weakest parameters that have the greatest impact on the system stability becomes more significant in addressing small-signal stability issues. This paper first proposes a stability evaluation function based on the Gerschgorin disc theorem, which can assess the stability of the multi-converter grid-tied system. Then a parameter sensitivity method is proposed to identify the stability-weakest parameters. Finally, an iterative calculation-based parameter optimization method is developed to regulate the identified stability-weakest parameters. Hence, the parameter optimization technique in this research can improve the system stability without requiring eigenvalue solutions and has the merit of low computational complexity. Simulation results based on both the MATLAB/Simulink (2023a) and the RT-LAB (OPAL-RT 5700) platform of a multi-converter grid-tied system validate the correctness of the theoretical analysis and the effectiveness of the parameter optimization method.

Predicting the Power Requirement of Agricultural Machinery Using ANN and Regression Models and the Optimization of Parameters Using an ANN–PSO Technique

Optimizing the design and operational parameters for tillage tools is crucial for improved performance. Recently, artificial intelligence approaches, like ANN with learning capabilities, have gained attention for cost-effective and timely problem solving. Soil-bin experiments were conducted and data were used to develop ANN and regression models using gang angle, velocity ratio, soil CI, and depth as input parameters, while tractor equivalent PTO (PTOeq) power was used as an output. Both models were trained with a randomly selected 90% of the data, reserving 10% for testing purposes. In regression, models were iteratively fitted using nonlinear least-squares optimization. The ANN model utilized a multilayer feed-forward network with a backpropagation algorithm. The comparative performance of both models was evaluated in terms of R2 and mean square error (MSE). The ANN model outperformed the regression model in the training, testing, and validation phases. A well-trained ANN model was integrated with the particle-swarm optimization (PSO) technique for optimization of the operational parameters. The optimized configuration featured a 36.6° gang angle, 0.50 MPa CI, 100 mm depth, and 3.90 velocity ratio for a predicted tractor PTOeq power of 3.36 kW against an actual value of 3.45 kW. ANN–PSO predicted the optimal parameters with a variation between the predicted and the actual tractor PTOeq power within ±6.85%.

Optimizing seismic fragility assessments for high-speed railway track bridges: a novel multi-parameter methodology and intensity measure selection

This research innovatively presents a multi-parameter seismic fragility analysis methodology anchored on Blending ensemble learning tailored for the high-speed railway track-bridge (HSRTB) system. Leveraging ensemble machine learning models, a seismic demand model encapsulating multiple earthquake intensity measures (IMs) is formulated. The research progresses to deduce the mean seismic fragility representations for single, dual, and composite earthquake parameters. Key findings of this investigation underscore that the advanced methodology aligns well with the Monte Carlo (MC) fragility, signifying its robustness. Crucially, earthquake IM parameters, Sa(0.5) and Sa(0.3)_ASI, emerged as the optimal choices for singular and dual-parameter seismic fragility delineations. Further enriching this research, a composite parameter optimization technique rooted in multi-CPU genetic algorithms is proposed. This avant-garde method remarkably minimizes the fragility estimation error, emphasizing its unparalleled efficacy in enhancing the precision of fragility evaluations.

Optimization of structural and electrical properties of graphene-based TiO2 thin film device using Bayesian machine-learning approach

The optimization of laser-induced graphene (LIG) patterning is crucial for achieving desirable electronic properties in flexible electronic devices. In this study, we applied state-of-the-art automated parameter optimization techniques, specifically Bayesian optimization, to enhance the electrical resistance of LIG patterns. By iteratively optimizing the laser power, irradiation time, pressure, and gas type, we achieved minimum LIG resistance within eight batch configurations suggested by the Bayesian optimization (BO) approach. Our method eliminates the reliance on skilled operators, as the initial surrogate models were trained with random parameter evaluations. Notably, our system enables the optimization of material properties even when characterizations are only available outside the experiment loop. Furthermore, the surrogate model provided insights into the underlying mechanisms of LIG growth on quartz substrates. Through partial dependence analysis, we identified relevant physical domains for further investigation, leading to the discovery of negative capacitance and a correlation between structural and electrical properties in LIG. These findings were supported by XPS, Raman, and optical characterizations. Our approach streamlines the experimental design, reducing time and cost while accelerating materials research, and offers human-interpretable conclusions for a deeper understanding of LIG patterning processes.

Fabrication of electrospun nanofiber from a blend of PVC and PHB

Abstract In this work, the fabrication process of electrospun nanofibers from a blend of polyhydroxy butyrate (PHB) and polyvinyl chloride (PVC) has been investigated. PVC/PHB nanofibers have been fabricated from solutions using different PVC and PHB ratios. The influence of technical parameters of the electrospinning process on the fabrication, morphology, and diameter of nanofibers has been evaluated. The chemical structure and thermal properties of PVC/PHB have been studied. The results show that the diameter of PVC/PHB nanofibers increases as the PHB content increases. In addition, the optimal technical parameters of the electrospinning process for each PVC and PHB ratio are different. Infrared spectroscopy analysis revealed an enhancement of the crystalline phase of the polymer composite with increasing PHB content. The thermal properties of PVC/PHB nanofibers were evaluated through differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA). The change in PHB ratios leads to a change in the glass transition temperature of PVC/PHB nanofibers. The thermal degradation process of PVC/PHB includes two steps. Increasing the PHB content leads to an enhancement in the mechanical strength of PVC/PHB nanofiber mats; however, it also results in a reduction in tensile elongation. Based on the results of structural, morphological, interaction analysis, and mechanical properties of PVC/PHB nanofibers, this study contributes to the optimization of the fabrication of nanofibers from PVC and PHB. PVC/PHB nanofibers have the potential to be used for air filtration applications.