Actual Curve Research Articles

Simulation and optimisation of vacuum (pressure) swing adsorption with simultaneous consideration of real vacuum pump data and bed fluidisation

Pressure swing adsorption (PSA) is a promising technology for gas separation and purification, receiving considerable attention in the past decades. Existing studies on simulating PSA involving a vacuum pump, the vacuum (pressure) swing adsorption [V(P)SA] process, typically estimate vacuum pump energy consumption using an approximate constant energy efficiency or an empirical energy efficiency correlation, leading to inaccurate representation of realistic vacuum pump performance. In this work, we propose an enhanced computational approach for simulation and optimisation of V(P)SA processes through simultaneous incorporation of realistic vacuum pump performance prediction models and adsorption bed fluidisation limits. The vacuum pump performance prediction models are developed using data-driven modelling techniques with realistic vacuum pump performance data. The enhanced computational approach more accurately accounts for the V(P)SA performance, without relying on an estimated vacuum pump efficiency and assumed pressure/flow rate boundary conditions at the vacuum pump end of the adsorption bed. Furthermore, the impact of bed fluidisation on optimal PSA design is addressed. The computational results show that the developed prediction models accurately represent the actual performance curves of the vacuum pump. It is also demonstrated that incorporating the vacuum pump performance models and fluidisation constraints in PSA optimisation leads to significantly different optimal solutions compared to when these factors are not considered.

Upper limit assessment of GIS equipment failure based on time series model and deep learning algorithm

If a sudden fault of gas-insulated switchgear is not handled in time, it will delay production and cause certain economic losses to the enterprise. Therefore, this study proposes an upper limit assessment algorithm for equipment faults based on time series model and deep learning. The irregular fluctuation components in the time series model are stabilized by using empirical mode decomposition, and the long short-term memory network in the deep learning algorithm is introduced to combine the two. The equipment fault data is obtained by the joint algorithm. After empirical mode decomposition, the intrinsic mode function component of the data signal is obtained, and the sensitive component is extracted from it. The relationship value and mutual information between it and the original data signal are analyzed to complete the fault data feature extraction. In the process of equipment fault upper limit assessment, the parameter optimization and convergence are controlled, and the fault deep learning model is constructed. After the refraction coefficient and reflection coefficient are determined, the upper limit assessment of equipment faults is realized. The proposed method is experimentally tested. The results show that the proposed algorithm has an ideal upper limit assessment result of faults, and the obtained curve has a high degree of fit with the actual result curve. It can be proved that the proposed algorithm can scientifically evaluate the upper limit of equipment faults and has certain application value.

Impact of solvent and water activity on lipase selectivity and acyl migration of ARA-rich 2-monoacylglycerols in catalytic systems: Kinetic study by particle swarm optimization

The 2-arachidonoylglycerol enzymatic alcoholysis reaction model was established and kinetic parameters were calculated to explore the effects of solvent and water activity (aw) on the lipase positional selectivity and 2-monoacylglycerol acyl migration. Six rate constants (k1-k6) with the lowest mean square error were obtained using the particle swarm optimization algorithm, and the calculated molar concentration-time curves were well-fitted to the actual curves. As an indicator to characterize the positional selectivity of lipase, k5/k3 was significantly associated with log P of solvent, which first increased and then decreased with the increase of aw. The highest sn-1,3 selectivity of Lipozyme TL IM was found at the aw of 0.53. The changes of acyl migration with the solvent and aw in the enzymatic and non-catalytic systems showed a consistent law. This study provides theoretical support for the targeted synthesis of structural lipids and enzymatic production of diverse structural lipid products.

Adaptive fuzzy coordinated control design for wind turbine using gray wolf optimization algorithm

Due to the randomness and intermittency of wind speed, the actual output power curve of a wind turbine (WT) deviates greatly from the theoretical power curve, thereby reducing the power generation capacity of the WT. An adaptive fuzzy coordinated control (AFCC) of WT is presented in this study to improve the power generation of WT. Firstly, a multi-objective optimization model (MOOM) for WT output power, generator speed and pitch angle is established, and its optimal solution set is used as the input eigenvector of a novel effective wind speed soft sensor (NEWSSS) model, which is modeled with kernel extreme learning machine (KELM). Secondly, a novel improved gray wolf optimization (NIGWO) algorithm is presented by improving the convergence factor and adaptive weights, which is used to solve MOOM and optimize the parameters of KELM. A variable pitch control (VPC) is designed by estimating the effective wind speed. Finally, an adaptive fuzzy control (AFC) is presented for WT. Based on the AFC and VPC, an AFCC for pitch angle and generator torque is designed for WT. The high measuring precision of NEWSSS and the good robustness and dynamic performance of AFCC are demonstrated by the simulation results.

Analysis of the Variation in Salaries of Employees of Government Ministries in Iraq by Using of The Lorenz Curve and The Gini Coefficient*H.

The disparity in the distribution of income is one of the main reasons for the failure to achieve social justice among members of society, which creates an unhealthy environment for workers in government sectors and institutions and also works to reduce the quality of work and lower the level of productivity. Conversely, reducing disparity leads to increased productivity and creates a suitable environment. It contributes to increasing productivity rates and improving the quality of institutional performance. The research discussed an important topic related to the extent of the discrepancy between the salaries of employees in Iraqi government institutions using the Lorenz curve and the Gini coefficient through a presentation of the research problem, which is the extent of the discrepancy in the salaries of state employees according to the approved salary scale, and what impact this has on social justice in the distribution of income, assuming that the adoption of A salary scale set by the authorities is based on reducing financial disparities between state employees with equal qualifications, such as educational attainment and work tasks, and reduces disparities of discrimination between workers in various government sectors and achieves social justice in income distribution. The research concluded that the actual distribution curve was very close to the symmetry line curve, in addition to the presence of a very small gap between the two lines, which indicates fairness in the distribution of monthly salaries among workers in government ministries who are in the fourth degree/first stage of the approved salary scale. The value of the Gini coefficient complementary to the work of the Lorenz curve was about (0.4), which means that the value of the Gini coefficient is close to zero. This indicates that the distribution of salaries of employees of government ministries is close to equitable distribution.

Generalized system restoration models of ductility bridges for seismic resilience analysis

System restoration models are usually adopted for seismic resilience analysis of bridges. However, no analytical process can be found in the existing literature to develop the restoration models for bridges. This paper proposed a new Monte Carlo-based method to derive the generalized system restoration models for ductility highway bridges. In the proposed method, a large number of random samples for ductility bridges were generated by considering uncertainty of structural parameters. The IDA was adopted for producing the damaged bridge samples at different damage states. The repair time of each bridge sample was estimated to generate the actual restoration curve. The Monte Carlo simulations were then adopted to estimate the expected mean functionality curves, which were used to derive the generalized system restoration models by using mathematical functions. Finally, seismic resilience analysis based on the derived generalized system restoration models was conducted and compared with the traditional method to illustrate the effectiveness of the proposed method. It is concluded that the proposed Monte Carlo-based method is an efficient and reliable method for developing the restoration models for ductility highway bridges.

Multivariate approaches for assessing dissolution behaviour of spironolactone. Dissolution-driven determination of polymorphic purity limits

Spironolactone (SPN) is an aldosterone antagonist effective for preventing and controlling cardiovascular diseases. Form I and Form II exhibit different behaviour in their dissolution properties, which can compromise the quality of the pharmaceutical formulation. Hence, in the present work, we propose the combination of two multivariate approaches to estimate the polymorphic purity of SPN in commercial tablets and predict the dissolution behaviour of these formulations considering their polymorphic composition. Multiple linear regression (MLR) approach was applied to model the dissolution behaviour of formulations depending on their polymorphic composition, showing excellent agreement among actual vs. predicted dissolution curves. This model was applied to establish a design space were the risks of dissolution failure due to the polymorphic purity was avoided. Next, to evaluate the purity of Form II of SPN in formulated products, two quantitative partial least squares (PLS) models were developed based on near (NIR) and mid (MIR) infrared spectroscopies. Training (n = 14) and validation (n = 8) sets were prepared by mixing both polymorphs and the excipient matrix to simulate commercial tablets. Methodologies showed recoveries non-statistical different from 100 %, but MIR-PLS model showed a higher dispersion (100.1 ± 1.82 % and 99.5 ± 6.94 % for NIR and MIR, respectively). Similar values of Form II content (1.02 ± 0.01 w/w with NIR and 1.03 ± 0.03 w/w with MIR) were obtained during the analysis of commercial and spiked SPN tablets. Finally, NIR-PLS was coupled to MLR model for the prediction of dissolution in entire tablets based on polymorphic composition to act as Quality by Design (QbD) tool. The full coincidence among actual and NIR-MLR predicted curves validated the final purpose of this approach.

Research on design and control methods of a lightweight upper limb joint isokinetic rehabilitation training equipment.

Isokinetic exercise can improve joint muscle strength and stability, making it suitable for early rehabilitation of stroke patients. However, traditional isokinetic equipment is bulky and costly, and cannot effectively avoid external environmental interference. This paper designed a lightweight upper limb joint isokinetic rehabilitation training equipment, with a control system that includes a speed planning strategy and speed control with disturbance rejection. Based on the established human-machine kinematic closed-loop model between the equipment and the user, a dynamic evaluation method of torque at the joint level was proposed. To validate the effectiveness of the equipment, experiments were conducted by manually applying random disturbances to the equipment operated at an isokinetic speed. The results showed that the root mean square error between the observed torque curve of the second-order linear extended state observer used in this paper and the actual disturbance curve was 0.52, and the maximum speed tracking error of the speed control algorithm was 1.27%. In fast and slow sinusoidal speed curve tracking experiments, the root mean square errors of the speed tracking results for this algorithm were 9.65 and 5.27, respectively, while the tracking errors for the PID speed control algorithm under the same environment were 19.94 and 12.11. The research results indicate that compared with traditional PID control method, the proposed control strategy demonstrates superior performance in achieving isokinetic control and suppressing external disturbances, thereby exhibiting significant potential in promoting upper limb rehabilitation among patients.

Ocena skuteczności oczyszczania strefy przyodwiertowej

The article discusses issues related to methods of treatment of the bottomhole zone of wells and assessment of its effectiveness. Analyses of a large volume of field material have shown that the most effective treatments for oil fields at a late stage of operation include injection of solvents, the use of acid-containing micro-emulsions, acid treatment, and the use of foam systems. The study proposes a method for identifying the factors that have the greatest impact on the efficiency of geological and technical activities. This approach allows for the correct selection of wells for treatment and the evaluation of the effectiveness of the measures implemented, aiding in determining their potential for further use. For production wells selected for near-wellbore treatment, it is crucial to accurately assess the economic efficiency post-treatment. This requires evaluating changes in the permeability of the near-wellbore zone following treatment. Therefore, in these wells, the “pressure build-up curve” (PBC) is recorded both before and after treatment. The increase in current production due to acid treatment of the near-well zone is determined by comparing production levels before and after treatment. The overall increase in oil production is determined by comparing the well’s production curve prior to development with the actual curve following development. When treating the bottomhole zone, it is essential to consider the well as part of a larger system of interacting objects, where improving the characteristics of one object does not necessarily enhance the operation of the entire system. The effectiveness of treating the bottomhole zone with solvents is significantly influenced by factors such as the percentage of resins in the product, the specific gravity of oil before treatment, the specific gravity of the solvent, and the viscosity of the oil.

Predictive utility of emotional regulation abilities for assessing cognitive improvement in depression

ObjectiveTo construct a predictive model for the improvement of cognitive function in patients with depressive disorder treated with SNRIs, based on emotional regulation abilities, and to provide personalized treatment for depressed patients. MethodsClinical data from 170 patients with depressive disorder treated with SNRIs at Tongji Hospital, Shanghai, from December 2017 to May 2023 were collected. Based on whether the MoCA-B total score at 3–6 months post-treatment was at least 2 points higher than at baseline, patients were divided into the cognitive function improved group (n = 80) and the cognitive function not improved group (n = 90). Stepwise logistic regression and LASSO regression were used to select predictive factors, and logistic regression analysis was applied to construct predictive models solely based on emotional regulation abilities, combined with executive functions and HAMD scores. The models were further validated through Bootstrap internal validation, calibration curve plotting, and C-index calculation, and a comparison between the two models was performed. ResultsAn ER model with an area under the ROC curve of 0.817was established using four emotional regulation ability indicators: the valence of reappraised images, the arousal of negative images, the arousal of neutral images, and the success of reappraisal (arousal). Internal validation using Bootstrap showed a C index of 0.817, and clinical decision curves indicated that this model has a significant net benefit with a probability of improved cognitive function ranging from about 20 to 85%. Additionally, an EREH model including emotional regulation ability, executive function, and HAMD score as predictors was constructed using Lasso and logistic regression methods. This model reached an area under the ROC curve of 0.859and clinical decision curves showed high net benefits with probabilities of improved cognitive function ranging from 10 to 100%. The calibration curves of both models coincided well with the actual curves, with the latter having a higher AUC and significant statistical differences between the two models. ConclusionThis study suggests that emotional regulation ability may serve as a predictor for the improvement of cognitive functions in patients with depression depressive disorder treated with SNRIs. However, it is important to note that there may be other factors not covered or included in this study.The predictive model that includes executive functions and HAMD scores offers better differentiation and consistency and is more feasible in clinical practice.

Assessment of power curve performance of wind turbines in Adama-II Wind Farm

The power curve of a manufacturer must be compared with the actual power curve after commissioning because various factors lead to deviations. The main purpose of this study was to assess and compare the performance of the N72, N73, and N74 wind turbines of the Adama-II wind farm against the manufacturer's guaranteed power curve. The methods employed were high-correlation-based nacelle anemometry and Artificial Neural Network (ANN). The high-correlation-based nacelle anemometry method used here differs from the conventional IEC61400–12–2 in that it was based on a strong correlation between the mast and nacelle speed of a turbine from which the nacelle transfer function (NTF) was computed. The NTF was obtained by power interpolation from 10-minute wind mast data and the nacelle speed of N72. The measured power curve was then obtained by applying NTF to the nacelle speeds. The turbines were modeled using ANN, illustrating manufacturer's power curve was higher than the modeled values above 9 m/s. According to the analysis, the measured power curves of N73 and N72 were within the manufacturer's AEP uncertainty range at a mean wind speed of 4–9 m/s but showed a deviation of 10–18 % at higher wind speeds. The power production of the N74 turbine was within the manufacturer’s uncertainty limit only at a mean wind speed of 4–7 m/s, whereas at a wind speed above 7 m/s, it deviated by 22–40 %. Therefore, performances of N72 and N73 in terms of the AEP were better, except at wind speeds of 10 and 11 m/s. However, the performance of the N74 turbine was significantly lower than the manufacturer. ANN also gave similar results except at higher wind speeds where high-correlation-based nacelle anemometry performed better. Overall, the proposed methods demonstrated the performance of the turbines compared with the guaranteed power curve.

Optical intraday variability analysis for the BL Lacertae object 1ES 1426+42.8

ABSTRACT The observation data of blazar 1ES 1426+42.8 were obtained using the 1.02 m optical telescope of Yunnan Observatories during 2021 to 2023. Intraday variability (IDV) is detected on seven nights. We use the turbulent model to investigate the mechanism of IDV in 1ES 1426+42.8. The fitting light curves match the actual IDV curves well. Using this model, we obtain the parameters such as the size of turbulent cells and the width of pulses in the jet. A possible short-lived quasi-periodic oscillation (QPO) of $58.55 \pm 8.09$ min was detected on 2022 April 26 whose light curve exhibits eight cycles at $\gt 3\sigma$ global significance and confirmed by several different techniques. Through a more detailed analysis of the light curve of this night, we find that the period is shortened from 54.23 min ($4\sigma$) to 29.71 min ($3\sigma$). The possible QPO and period shortening phenomenon are best explained by the processes of magnetic reconnections.

Short-Term Load Forecasting Method Based on Bidirectional Long Short-Term Memory Model with Stochastic Weight Averaging Algorithm

To accommodate the rapid development of the distribution network of China, it is essential to research load forecasting methods with higher accuracy and stronger generalization capabilities in order to optimize distribution system control strategies, ensure the efficient and reliable operation of the power system, and provide a stable power supply to users. In this paper, a short-term load forecasting method is proposed for low-voltage distribution substations based on the bidirectional long short-term memory (BiLSTM) model. First, principal component analysis (PCA) and the fuzzy C-means method based on a genetic algorithm (GA-FCM) are used to extract the main influencing factors and classify different types of user electricity consumption behaviors. Then, the BiLSTM forecasting model utilizing the stochastic weight averaging (SWA) algorithm to enhance generalization capability is constructed. Finally, the load data from a low-voltage distribution substation in China over recent years are selected as a case study. Compared with conventional LSTM and BiLSTM prediction models, the annual electricity load curves for various user types forecasted by the PCA-BiLSTM model are more closely aligned with actual data curves. The proposed BiLSTM forecasting model exhibits higher accuracy and can forecast user electricity consumption data that more accurately reflect real-life usage.

Screening significance of systemic immune-inflammation index (SII) and systemic inflammation response index (SIRI) in coronary heart disease of symptomatic youth.

The incidence of coronary heart disease (CHD) in youth is rapidly increasing but difficultly recognized in the early stage. In this retrospective study, 194 CHD patients under the age of 45 who previously experienced chest pain symptoms and 170 non-CHD patients were included and demographic data were collected. Systemic inflammation index (SII) and systemic inflammation response index (SIRI) were increased in young CHD patients (p < 001). Spearman's correlation analysis showed that both SII and SIRI were negatively correlated with HDL and positively correlated with hypertension, Gensini score, and hsTnI. Logistic regression analysis indicated that SII and SIRI were independently associated with the presence of CHD in youth with chest pain symptoms. The area under the ROC curve (AUC) of the SII model for young CHD patients was 0.805 (0.728-0.869), and the sensitivity and specificity were 0.65 and 0.823, respectively. Meanwhile, the AUC for the SIRI model was 0.812 (0.739-0.872), and the sensitivity and specificity were 0.673 and 0.8022. The calibration curves of both SII and SIRI models are in good agreement with the actual curves. And the decision curves of both models indicated their clinical practicality. SII and SIRI are independent risk factors for CHD in young adults, which can quickly and effectively identify CHD patients among young adults who have previously experienced chest pain symptoms.

Stress wave effects and their mechanisms on stress-strain curves in the elastic phase of SHPB tests

In Split Hopkinson Pressure Bar (SHPB) tests, the stress wave effects during the elastic phase of the stress-strain curve and their influence mechanisms are crucial. Due to the extremely short duration of the elastic deformation phase, the stress wave effects on the specimen during this stage cannot be ignored. This leads to significant errors in the obtained elastic stress-strain curves. However, the dynamic compressive elastic stress-strain relationship forms the basis for studying the viscoelastic behavior of materials. Accurate determination of elastic yield stress and yield strain is also essential for deriving accurate plastic stress-strain relationships. Quantitative research on the stress wave effects during the elastic compression phase of SHPB tests is fundamental for decoupling and obtaining accurate material elastic curves. This paper conducts a quantitative theoretical analysis of the structural effects caused by stress wave evolution during the elastic compression phase, based on the assumption of plane waves. It studies the deviation characteristics and main factors of the phenomenological engineering stress-strain curves of the specimen compared to the actual material stress-strain curves under different conditions, revealing the influence rules and mechanisms of this deviation. The maximum stress deviation value and its corresponding dimensionless time, as well as the variation trend of the maximum stress deviation value within different fluctuation intervals, are calculated. Additionally, the study investigates the cases where the incident wave is arc-shaped or a combination of arc and linear waves. The findings provide theoretical references for the precise design and accurate data processing of SHPB tests.

Monitoring and analysis of settlement and deformation status of high-rise buildings based on nonlinear regression

In order to solve the problems of low reliability and poor prediction accuracy in traditional building structure settlement monitoring, the author proposes a monitoring and analysis of high-rise building settlement deformation status based on nonlinear regression. The author collected and wirelessly transmitted building settlement information through various hardware devices such as sensors and GPRS communication modules. The monitoring data collected by sensors were compared and analyzed to determine the settlement situation of the building. An RBF neural network prediction model was constructed for possible settlement points. Then, the leapfrog algorithm is used to optimize the structural parameters of the RBF neural network. The experimental results show that this method can accurately evaluate the possible settlement of building structures in actual environments, and the prediction error is small, with a maximum relative error of 4.83 %, indicating good warning ability. This method achieved the best actual value fitting curve results, verifying its feasibility in settlement prediction. Subsequently, a more widely applicable settlement detection and prediction system for building complex structures will be established based on the proposed method, in order to promote its large-scale application.

SOH estimation of lithium-ion batteries based on multi-feature deep fusion and XGBoost

State of Health (SOH) is a crucial metric for battery management systems, and accurate estimation of battery SOH is essential for the underlying management and maintenance of batteries. Traditional data-driven approaches lack in-depth analysis of health features and fail to exploit the deep-level health information among these features. Therefore, this paper extracts six health features from both measurement and calculation perspectives based on actual measurable charging curve data, and conducts aging analysis of health features from a mechanistic perspective and Spearman correlation analysis from a quantitative perspective. Additionally, a strategy combining deep learning with ensemble learning is proposed. This strategy utilizes one-dimensional convolutional neural networks to deeply integrate multiple original features, extracting fusion features with deep-level health information. Subsequently, XGBoost is employed to learn the fusion features for estimating battery SOH. Finally, the effectiveness and superiority of the proposed method are verified on battery aging datasets from the University of Maryland and our laboratory. Experimental results demonstrate that the RMSE and MAE of the proposed method are both below 1 % and outperform other methods.

The two-cavity method for characterizing acoustical materials-an interlaboratory study

The study focuses on understanding the sound absorption and transmission characteristics of porous acoustical materials, which are determined by two key parameters independent of material thickness: characteristic impedance and propagation constant. These parameters can be characterized by testing a porous sample concept in a normal incidence impedance tube using either the two-thickness or the two-cavity method. In the two-thickness method, two samples of varying thicknesses are required. In contrast, the two-cavity method requires testing one sample at two air cavity depths. This method is particularly advantageous for materials that are costly or challenging to fabricate. This interlaboratory study evaluates the variability of characteristic properties determined using the two-cavity method. Porous acoustical materials were additively manufactured and tested in the Liner Technology Facility at NASA Langley Research Center and the Mechanics, Acoustics and Dynamics Lab (MADLab) at Michigan Technological University. The characteristic properties, derived from various cavity depth combinations, were used to predict the sound absorption curves of the sample with a rigid backing. These predictions were then compared with actual experimental absorption curves. The findings indicate that both the combinations of cavity depths and the sample's static airflow resistivity significantly influence the accuracy of the deduced properties.

Послідовне встановлення двох двигунів внутрішнього згоряння на автомобілі як метод зниження нерівномірності крутного моменту

An analysis of studies on the possibility of reducing fuel consumption of cars by replacing their power plants with two internal combustion engines installed in series is carried out. It has been determined that it is possible to reduce fuel consumption by reducing the unevenness of torque at the transmission input shaft, which in turn depends on the relative angular orientation of the crankshafts of the installed internal combustion engines. The study has determined that when installing two consecutive internal combustion engines, each of which generates an indicator torque that obeys the harmonic law, it makes it possible to shift the curves of the indicator torque and the crankshaft angle so that both engines operate in opposite phases. It is proposed to use a method of increasing the energy efficiency of cars by using two consecutive internal combustion engines, the crankshafts of which are installed relative to their initial angular position with the difference in the angular coordinates (angles of rotation) of the crankshafts of both engines relative to the initial position, which allows to reduce unproductive energy losses. Approximating the actual curves of change of the indicator torque by the harmonic law, taking into account the angle of rotation of the crankshaft, graphs of dependence of the indicator torque on the angle of rotation of the crankshaft of four-stroke internal combustion engines with different numbers of cylinders are constructed. It has been determined that with an increase in the number of cylinders of an internal combustion engine and the coefficient of the share of torque on the drive wheels, there is a decrease in the coefficient of unproductive energy consumption, which takes into account the influence of the unevenness of the indicator torque. The problem of ensuring the operation of the second engine in the counterphase of the first engine is solved, which should be fulfilled by the ratio of the angles of rotation of the crankshafts of the first and second engines relative to the initial (zero) position with the difference in the angular coordinates (angles of rotation) of the crankshafts of both engines relative to the initial position. The results of calculating the angle of the crankshafts' relative position when two internal combustion engines are installed sequentially in the opposite phase indicate a decrease in this angle with an increase in the number of cylinders.

Theoretical study on the thermoluminescence kinetics by glow curve analysis based on the genetic algorithm

The analysis of thermoluminescence (TL) glow curve is an important way to investigate the trap structure of the materials as well as the kinetics of the charge carrier transfer. Most currently used methods rely on the existence of an explicit expression of the theoretical glow curve. However, since the set of kinetic equations describing the TL process does not have analytical solutions, the derivation of an explicit expression always involves some sort of assumptions and approximations, which can compromise the universality and accuracy of the method. This paper proposes a new algorithm that focuses on the original kinetic equations and does not necessitate an explicit expression to analyze the TL glow curve. This algorithm establishes an objective function to evaluate the fitness of a selected combination of kinetic parameters, and then using the genetic algorithm (GA) to search for the optimal parameters. Various occasions were numerically simulated, demonstrating the algorithm's capability to handle kinetics of different order, multiple interactive peaks, deep trap, non-linear heating, and temperature dependent parameter cases. An actual glow curve of the LiF:Mg,Cu,P chip was also analyzed using this algorithm, and the corresponding kinetic parameters were identified. This algorithm provides a more universal and valid method for TL glow curve analysis.