Search

The proposed research work analyzes the bio-inspired problem through artificial neural networks with a feed-forward approach utilized to approximate the numerical results for singular nonlinear bio-heat equation (BHE) with boundary conditions based on four different scenarios created on the variation of environmental temperature to illustrate the effects of temperature on the human dermal region. The log-sigmoid function is used to construct the fitness function, while the optimization solvers: pattern search and genetic algorithm, are then hybridized with the active set technique, interior point technique, sequential quadratic programming for accurate and reliable results of the proposed BHE with various scenarios where the convergence of the numerical results is also analyzed. Moreover, a comparison of the proposed technique is expressed through residual error that reveals the nature of the numerical results and their efficiency. Additionally, a comprehensive statistical analysis is presented for the designed technique to better illustrate the accuracy, reliability, and efficiency of the obtained results. [ABSTRACT FROM AUTHOR]

Using the power of numerical computation through Levenberg Marquardt backpropagation ($LMB$LMB), the fluidic issue in the present study has features of heat transfer by convection in the hybrid nanofluid suspension of magnesium oxide and gold nanoparticles. The influence of slip effects on boundary layer flow with viscous dissipation at the nodal/saddle stagnation point is examined using a mathematical model. The current study considers water to be the base fluid, $MgO$MgO and $Au$Au as nanoparticles, and the circular cylinder to be the medium’s surface. Non-linear partial differential equations are converted into ordinary differential equations via similarity transformations. The dataset for Levenberg Marquardt backpropagation is generated in Mathematica software using an explicit RK-4 approach by varying different parameters such as the slip parameter ($\lambda $λ), convective parameter ($Bi$Bi), Eckert number ($E{c^*}$Ec∗) and volume fraction (${\phi _2}$ϕ2), Bi with Ec. Validation, training, and testing are utilized for network modeling through the Levenberg-Marquardt backpropagation approach for various scenarios of the fluidic model. The result shows that the base fluid’s thermal behavior is quickly increased by the $MgO - Au$MgO−Au/water hybrid nanofluid. The reliability of the findings is evaluated using autocorrelation, regression, mean square error, absolute error, and error histogram analyses. [ABSTRACT FROM AUTHOR]

This paper presents a novel neuro-stochastic adaptive processing to investigate the dynamic behavior of the nonlinear SIS epidemic delayed model with the impact of double disease (SIS-EDDM) using the knack of prediction and modeling of artificial neural networks (ANNs) optimized through Levenberg-Marquardt technique (LMT), i.e. ANNs-LMT. The model captures the dynamic progression of individuals in the population, differentiating between those susceptible to infection and affected by two unique viruses. The inclusion of time delays in the differential equations introduces a critical temporal aspect to the model, enhancing its precision in portraying the transmutation process. The reference dataset for ANNs-LMT is produced using the explicit Runge-Kutta method, with variations in the transmission coefficients between susceptible and infective populations, the death rate of infective population caused by diseases, the cure rate of treatment for infected population, vaccination proportional coefficient for the susceptible population, white noise of the environment, and time delay. The designed computing framework of ANNs-LMT is used to determine the numerical solutions of the variants of SIS-EDDM by incorporating the training, testing, and validation samples based adaptive learning processing. The neuro-stochastic adaptive processing of ANNs-LMT is validated by minimal absolute and mean squared errors, coupled with the attainment of nearly optimal regression measures, demonstrating its accuracy and effectiveness. [ABSTRACT FROM AUTHOR]

Process uncertainties have been a big challenge to the stable operation and control of process industries. The current study is based on the use of an artificial intelligence model as a surrogate in the online optimization of process conditions of reactive units of a petroleum refinery under uncertainty. Initially, a steady-state Aspen model was used to perform exergy analysis for quantifying the exergy efficiency, irreversibility, and improvement potential of the plant. The process model was then transformed to a dynamic mode by inserting ±5 % uncertainty in process conditions, i.e., mass flow rate, pressure, and temperature, to generate a dataset of 216 samples. An artificial neural network (ANN) model was developed using the dataset to predict exergy efficiency. The ANN model was used as a surrogate in GA and PSO environments to achieve higher exergy efficiency under uncertainty. The optimized process condition derived through GA and PSO based approaches were fed to Aspen model for cross-validation. The Fourier Amplitude Sensitivity Test (FAST) was performed to identify the most influential process conditions in terms of their effect on process exergy efficiency. The overall plant had an exergy efficiency, irreversibility, and improvement potential of 50.57 %, 34,955.55 kW, and 17,276.98 kW, respectively. The correlation coefficient of ANN model was 0.97432. The use of ANN as a surrogate in the optimization frameworks outperformed the standalone (SA) Aspen model of the process in attaining the highest exergy efficiency. Overall the performance of both the GA and the PSO based approaches were comparable. Based on sensitivity analysis, inlet temperatures of reactors were the most sensitive variables affecting the process exergy efficiency. The current study helps in laying a foundation for the simulation of Refinery 4.0. [Display omitted] [ABSTRACT FROM AUTHOR]

The present research, a numerical approach to examine magnetohydrodynamics (MHD) Casson nanofluid flow in a porous medium along a stretchable surface with different slips using artificial neural networks (ANNs) by taking inverse multiquadric (IMQ) radial basis function (RBF) as an activation function. i.e. ANNs-IMQ-RBF. The hybridization of genetic algorithms (GAs) and sequential quadratic programming (SQP) is used for learning in ANNs-IMQ-RBF. The PDEs representing the fluid flow are converted into a nonlinear system of dimensionless form of ODEs through an appropriate transformation while effects of variation in the values of Casson parameter (β), Brownian motion parameter (Nb), Prandtl number (Pr), stretching parameter (n), porosity parameter (P), Lewis number (Le) along with temperature slip parameter (λ 2) on velocity, temperature and nanofluid concentration are depicted through graphs. The effectiveness, convergence and accuracy of the proposed solver are validated evidently through boxplot analysis, histograms and cumulative distribution function (CDF) plots. [Display omitted] • A new ANNs-IMQ-RBF solver is designed for MHD Casson nanofluid problem in the shape of a system of nonlinear ODEs. • Velocity, temperature and concentration profiles are calculated for sundry cases of physical parameters of the interest. • Results are compared with the reference solutions of Adams numerical technique to analyze the accuracy and convergence. • Statistical operators via box plot, CDF and histograms analyses are used to verify the effectiveness and stability. [ABSTRACT FROM AUTHOR]

Wire coating is a commercial method to insulate wires for mechanical intensity and environmental protection. In this experimental study, the technique of computational intelligence is used for nonlinear wire coating analysis by soaking the wires in Oldroyd 8-constant fluid under a constant pressure gradient with the help of feed forward artificial neural networks (ANNs). The system of partial differential equations generated for the process of wire coating is transformed into a nonlinear dimensionless ordinary differential equation. One of the salient features of ANNs is the mathematical modeling of transformed equations by exploring the unsupervised error. A new scheme based on inverse multiquadric neural networks (IMQNNs) is used with a hybridization process of well-known genetic algorithms (GAs) and sequential quadratic programming (SQP) to obtain expeditious convergence, i.e., IMQNNs-GA-SQP. The applicability of the problem is investigated by altering the values of the dilatant constant, pressure gradient, shear stress, and pseudo-plastic constant, the outcome of which is in the form of varying polymer coating thickness. Comparison of highly accurate results in the shape of error analysis based on absolute errors of obtained results with those of the reference solution, calculated using the Adams numerical technique using MATHEMATICA software, statistical analysis such as root-mean-square error, Theil's coefficient for inequality, E-R2 (error function based on the coefficient of determination), E-VAF (variance accounted for), E-NSE (Nash–Sutcliffe efficiency), mean absolute error, box plot analyses, and the cumulative distributive function through histogram analyses, is also carried out in this research, which guarantees the effectiveness of the used scheme. [ABSTRACT FROM AUTHOR]

In this study, bio-inspired computational techniques have been exploited to get the numerical solution of a nonlinear two-point boundary value problem arising in the modelling of the corneal shape. The computational process of modelling and optimization makes enormously straightforward to obtain accurate approximate solutions of the corneal shape models through artificial neural networks, pattern search (PS), genetic algorithms (GAs), simulated annealing (SA), active-set technique (AST), interior-point technique, sequential quadratic programming and their hybrid forms based on GA–AST, PS–AST and SA–AST. Numerical results show that the designed solvers provide a reasonable precision and efficiency with minimal computational cost. The efficacy of the proposed computing strategies is also investigated through a descriptive statistical analysis by means of histogram illustrations, probability plots and one-way analysis of variance. [ABSTRACT FROM AUTHOR]

The porous media transport theories are thoroughly operative to analyse transferral phenomenon in reducing the bio-convective flow instabilities and biological tissues. The present study is designed to investigate the heat transfer phenomena in nanofluidic system involving Cu − H 2 O over the stretched porous media with the strength of stochastic solver via Levenberg-Marquardt backpropagation networks. The mathematical model of physical phenomena is described in PDEs that are reduced to system of ODEs through scaling group transformations. The datasets are determined through explicit Runge-Kutta numerical method and used as a target parameter for the development of continuous neural networks mapping. The training, testing and validation processes are utilized in learning of neural network models based on backpropagation of Levenberg-Marquardt technique to determines the solution of different scenarios constructed on the various values of porosity parameter along with six different cases based on the stretching ratio values. Validation and verification of neural network model to find the solution of nanfluidic problem is endorsed on the assessment of achieved accuracy through mean squared error, error histograms and regression studies. [ABSTRACT FROM AUTHOR]

This study is based on gray-box modeling for the prediction of optimum mass flow rates of inlet streams of a Plate and Fin Heat Exchanger under uncertainty. A first principle model of the Plate and Fin Heat Exchanger was developed in Aspen Exchanger Design and Rating. Genetic algorithm was integrated with the first principle model to achieve the highest possible exit temperature of the cold process stream under uncertainty. A dataset of uncertain process conditions and their corresponding optimum inlet flow rates derived through the first principle-genetic algorithm integration was used to develop an artificial neural networks model. The artificial neural networks model was then integrated with the first principle model by replacing the genetic algorithm to form a novel gray-box framework. The proposed gray box model, i.e., artificial neural networks and first principle integration, achieved a higher effectiveness and higher outlet temperature than those derived through the straight run first principle model. The performance of the gray box framework was also comparable to the first principle integrated genetic algorithm approach and significantly minimized the computation time needed for estimating the optimum process conditions. First principle integrated genetic algorithm approach enhanced the effectiveness of the straight run model by 3.05%. Performance of the proposed gray-box model was comparable to the integrated framework of the genetic algorithm with the first principle model but was significantly faster. The developed artificial neural networks model was employed as a surrogate in Sobol and FAST sensitivity analysis framework to identify the impact of input variables on output variables. The proposed gray box based method enhanced the capability of the plate and fin heat exchanger to recover energy from the process stream and its robustness to cope with uncertainty. The proposed approach is suitable for real-time application and would contribute to laying a foundation for petroleum refinery 4.0. • A Gray-box (GB) modeling framework was developed for the optimization of process conditions of a plate and fin heat exchanger of a tile industry under uncertainty. • The GB framework was based on the integration of first principle (FP) model and Artificial Neural Networks (ANN) model. • The proposed GB approach attained the optimum conditions in a significantly less period of time than the conventional FP-GA framework. [ABSTRACT FROM AUTHOR]

The present study aims to provide an innovative stochastic numerical solver's application by the use of neural networks with Levenberg-Marquardt backpropagation to examine the dynamics of hydrogen possessions and variable viscosity in the fluidic system of electrically conducting copper and silver nanoparticles with mixed convection. The system of PDEs obtained by mathematical modeling of the physical phenomena are reduced into non-linear ODEs by utilizing suitable transformations. The ODEs dataset is constructed through Adams numerical solver and target parameters for input and output parameter of neural networks. The testing, validation and training processes are exploited in neural network models with learning based on backpropagation of LM method to calculate the solution for different scenarios created on variation of physical parameters of the proposed flow of Reynolds and Vogel models. Validation and verification of neural network model to find the solution of fluid flow problem is endorsed on the assessment of achieved accuracy through mean squared error, error histograms and regression studies. • Novel application of stochastic computing for fluidic system as hydrogen-possessions. • Fluidic model analyzed by exploitation with Levenberg-Marquardt backpropagation. • Dynamics of both Reynold and Vogel models are presented sundry physical parameters. • Validation are endorsed on MSE through histograms and regression analysis. [ABSTRACT FROM AUTHOR]

Soft sensors have been used for estimation of research octane number (RON) of gasoline and to realize stability in process operation. In this study, a novel framework is proposed by incorporating uncertainty analysis in the conventional soft-sensor design for RON prediction. Ensemble learning method, i.e., boosting, and artificial neural networks (ANN) were used to realize highly efficient soft sensor. The individual impact of uncertainty of each process variable on RON was analyzed using Sobol technique and Fourier Amplitude Sensitivity Test (FAST). The accumulative effect of uncertainties on RON was investigated through nonintrusive polynomial chaos expansion (PCE). For realizing reliability in online application of the predictive distribution of RON, control limits were derived using a Just-in-Time method, i.e., k-d tree. The proposed method was validated through process data where the results were found suitable for industrial applications. [ABSTRACT FROM AUTHOR]

In this study, a stochastic numerical technique is used to investigate the numerical solution of heat transfer temperature distribution system using feed forward artificial neural networks. Mathematical model of fin equation is formulated with the help of artificial neural networks. The effect of the heat on a rectangular fin with thermal conductivity and temperature de-pendent internal heat generation is calculated through neural networks optimization with optimizers like active set technique, interior point technique, pattern search, genetic algorithm and a hybrid approach of pattern search - interior point technique, genetic algorithm - active set technique, genetic algorithm - interior point technique, and genetic algorithm - sequential quadratic programming with different selections of weights. The governing fin equation is transformed into an equivalent non-linear second order ODE. For this transformed ODE model we have performed several simulations to provide the justification of better convergence of results. Moreover, the effectiveness of the designed models is validated through a complete statistical analysis. This study reveals the importance of rectangular fins during the heat transformation through the system. [ABSTRACT FROM AUTHOR]

This article presents a methodology to solve a one-dimensional steady-state nonlinear reactive transport model (RTM) that is meant for fluid and solute transport model of soft tissues and microvessels. The methodology integrates the artificial neural network (ANN), genetic algorithms (GAs), and pattern search (PS) aided by active-set technique (AST) and interior-point technique (IPT). The RTM is represented with nonlinear second-order system based on the boundary value problem of ordinary differential equation. The ANN modeling is used for governing expression of RTM to form a fitness function in mean square sense, and optimization solvers based on the GA, PS, GA-AST, GA-IPT, PS-AST, PS-IPT are used for viable learning of weights. Proposed techniques are applied to different nonlinear RTMs based on variation in the characteristic reaction rate and half-saturation concentration. The proposed stochastic numerical solutions are compared with state-of-the-art solvers in order to check the accuracy and convergence based on sufficient large multiple runs of the algorithms. [ABSTRACT FROM AUTHOR]

The present study investigate the numerical solution of nonlinear singular system represented with sixth Painlev́e equation by the strength of artificial intelligence using feed-forward artificial neural networks (ANNs) optimized with genetic algorithms (GAs), interior point technique (IPT), sequential quadratic programming (SQP), and their hybrids. The ANN provided a compatible method for finding nature-inspired mathematical model based on unsupervised error for sixth Painlev́e equation and adaptation of weights of these networks is carried out globally by the competency of GA aided with IPT or SQP algorithms. Moreover, a hybrid approach has been adopted for better proposed numerical results. An extensive statistical analysis has been performed through several independent runs of algorithms to validate the accuracy, convergence, and exactness of the proposed scheme. [ABSTRACT FROM AUTHOR]

In this study, biologically inspired intelligent computing approached based on artificial neural networks (ANN) models optimized with efficient local search methods like sequential quadratic programming (SQP), interior point technique (IPT) and active set technique (AST) is designed to solve the higher order nonlinear boundary value problems arise in studies of induction motor. The mathematical modeling of the problem is formulated in an unsupervised manner with ANNs by using transfer function based on log-sigmoid, and the learning of parameters of ANNs is carried out with SQP, IPT and ASTs. The solutions obtained by proposed methods are compared with the reference state-of-the-art numerical results. Simulation studies show that the proposed methods are useful and effective for solving higher order stiff problem with boundary conditions. The strong motivation of this research work is to find the reliable approximate solution of fifth-order differential equation problems which are validated through strong statistical analysis. [ABSTRACT FROM AUTHOR]

In the present study, stochastic numerical computing approach is developed by applying artificial neural networks (ANNs) to compute the solution of Lane-Emden type boundary value problems arising in thermodynamic studies of the spherical gas cloud model. ANNs are used in an unsupervised manner to construct the energy function of the system model. Strength of efficient local optimization procedures based on active-set (AS), interior-point (IP) and sequential quadratic programming (SQP) algorithms is used to optimize the energy functions. The performance of all three design methodologies ANN-AS, ANN-IP and ANN-SQP is evaluated on different nonlinear singular systems. The effectiveness of the proposed schemes in terms of accuracy and convergence is established from the results of statistical indicators. [ABSTRACT FROM AUTHOR]

Functional magnetic resonance imaging (fMRI) data analysis is developing rapidly in a fast emerging society, because of temporal and spatial resolution and the inoffensive feature of their acquisition in human brains. Spatial resolution governs how 'sharp' the image is in appearance, whereas temporal resolution denotes the precision of a measurement with respect to time. The goal of fMRI technique is to identify the activation pattern and functional connectivity in the brain regions. In our study, artificial neural network technique was used for the identification and classification of decision-making voxels of fMRI data based on Brodmann areas 10 and 47 from the prefrontal cortex of human brain. The total number of voxels of Brodmann areas was 159, and we determined that some particular voxels played dominant role for decision-making process while performing a visual task. We also analyzed true positive and false positive classification rates between two decisions in the context of a well-known receiver operating characteristic curve (ROC). [ABSTRACT FROM AUTHOR]

In this article, the artificial intelligence techniques have been used for the solution of Falkner-Skan (FS) equations based on neural networks optimized with three methods including active set technique, sequential quadratic programming and genetic algorithms (GA) hybridization. Log-sigmoid activation function is used in artificial neural network architecture. The proposed techniques are applied to a number of cases for Falkner-Skan problems, and results were compared with GA hybrid results in all cases and were found accurate. The level of accuracy is examined through statistical analyses based on a sufficiently large number of independent runs. [ABSTRACT FROM AUTHOR]

The current study explores a new direction of research in which the magnetohydrodynamic (MHD) effects on two-dimensional nanofluid boundary layer flow under the influence of radiation are investigated in a porous medium using inverse multiquadric (IMQ) radial basis neural networks (RBNNs) i.e. IMQ-RBNNs. A quite new computational approach is used here in which artificial neural networks (ANNs) acquired IMQ radial basis function as an activation function in an amalgam of globally searching solver named genetic algorithms (GAs) together with an efficient local searching sequential quadratic programming (SQP) solver. The basic equations that represent the fluid flow are first obtained in the form of partial differential equations (PDEs) and then converted into dimensionless ordinary differential equations (ODEs) by applying similarity transformations. The dynamical properties of the nanofluid including velocity, temperature along mass concentration are analyzed by varying physical parameters including radiation parameter, magnetic parameter, constant moving parameter, heat source parameter, Brownian motion parameter and Eckert number. Moreover, the escalation in the value of the constant moving parameter diminishes the fluid velocity but this effect is reversed in the case of magnetic parameter. The increasing values of heat source parameter, Brownian motion parameter and Eckert number inflate the nanofluid temperature but a reversal effect is observed in the case of radiation parameter. It is also observed that larger values of heat Brownian motion parameter and radiation parameter amplify the nanofluid concentration but an opposite effect is obtained in the case of heat source parameter. The numerical results of the proposed model obtained through the IMQ-RBNNs based solver optimized with GASQP algorithm. The efficacy and tendency of rapid convergence of the proposed solver are endorsed through graphical illustrations and tables using well-known statistical operators with various fitness analyses on MATLAB are also part of this research. [ABSTRACT FROM AUTHOR]

This study reports novel hybrid computational methods for the solutions of nonlinear singular Lane-Emden type differential equation arising in astrophysics models by exploiting the strength of unsupervised neural network models and stochastic optimization techniques. In the scheme the neural network, sub-part of large field called soft computing, is exploited for modelling of the equation in an unsupervised manner. The proposed approximated solutions of higher order ordinary differential equation are calculated with the weights of neural networks trained with genetic algorithm, and pattern search hybrid with sequential quadratic programming for rapid local convergence. The results of proposed solvers for solving the nonlinear singular systems are in good agreements with the standard solutions. Accuracy and convergence the design schemes are demonstrated by the results of statistical performance measures based on the sufficient large number of independent runs. [ABSTRACT FROM AUTHOR]

• Development of detailed thermal modeling of a furnace in Aspen HYSYS and Aspen exchanger design and rating (EDR) environment. • Implementation of genetic algorithm (GA) based optimization technique for the maximization of efficiency. • Artificial neural network (ANN) model training and validation on the data generated from interfacing of Aspen HYSYS and MATLAB software. • Exergy analysis and comparison of the exergy efficiency predicted by the straight run and ANN. • Development of a graphical user interface (GUI) based on the proposed ANN model. The current work developed an integrated framework of artificial intelligence and genetic algorithm (GA) for the furnace of a petroleum refinery to predict the optimum amount of excess air and mass flow rates of crude oil and fuel stream in the presence of uncertainty in process conditions. A furnace model was built in Aspen Exchanger Design and Rating (EDR) using industrial data. Then interface between the furnace model and MATLAB was established using the com server. Data points were generated by varying the crude oil composition as well as the inlet temperature and pressure of cold crude oil, fuel, and air stream through ±1%, ±2%, ±3%, ±4%, and ±5% change in their original values. Then GA was used to determine the optimal amount of excess air and mass flow rates of crude oil and fuel for each data point. In total, 360 data points were generated and 70 % of the data set was utilized for training an Artificial Neural Networks (ANN) model while the remaining data points were evenly distributed for its validation and testing. The proposed ANN model achieved high performance with a correlation coefficient of 0.99984. Furthermore, exergy analysis of the standalone furnace model, the GA based optimized model, and the ANN integrated model was performed. The GA based approach outperformed the standalone model in terms of exergetic efficiency. On the hand, the ANN based framework achieved comparable exergetic efficiency to the GA based approach within significantly lesser period of time. [ABSTRACT FROM AUTHOR]

• Process modeling and analysis of the oil shale retorting process. • Hybrid modeling by using MATLAB, Excel, and Aspen Plus software. • Comparison of Artificial Neural Network (ANN), Least Square Boosting (LSB), and Bagging techniques for the prediction model. • Using Polynomial Chaos Expansion (PCE) framework for the uncertainty analysis of oil shale in retorting process. • Sensitivity analysis of oil shale retorting process through Sobol and Fourier Amplitude Sensitivity Test (FAST) techniques. Shale oil reserves exploration is getting huge investments due to the depletion of conventional reserves. Computational methods have been used to realize optimum design and operation of shale oil and gas reserves exploration and processing. The uncertainty associated with the composition of shale reserves and operating conditions during processing put a challenge to the realization of high yield and mitigation of environmental impact. In the current work, machine learning (ML) based models are proposed for the estimation of the yield and environmental impact of the oil shale retorting process under uncertainty. An artificial uncertainty of 1% was inserted in feed composition and process conditions of an Aspen model of the process to generate data for the development of the ML models. Artificial Neural Network (ANN), Least Square Boosting (LSB), and Bagging techniques were compared to find the best ML model. ANN models, with the highest correlation coefficient of 0.995 and 0.999 for the oil yield and CO 2 content respectively, are used as surrogates in a Polynomial Chaos Expansion (PCE) framework for the uncertainty analysis of the process. For 1% uncertainty in feed composition and process conditions, a mean absolute deviation of 0.319 and 0.580 was obtained for the oil yield and carbon dioxide emissions respectively. To find the hierarchy in the process inputs in terms of their effect on the oil yield and carbon dioxide emissions, the ANN model is used as a surrogate in sensitivity analysis through Sobol and Fourier Amplitude Sensitivity Test (FAST) indices. The most sensitive input variables were feed temperature and air molar flow rate. The proposed modeling framework will provide a base for future real-time monitoring and analysis of the oil shale retorting processes. [ABSTRACT FROM AUTHOR]

In this paper, a computational technique is introduced for the solution of the first order multi-pantograph differential equation (MPDE) through some well-known optimization algorithms like sequential quadratic programming (SQP) and Active Set Technique (AST). Furthermore, artificial neural network (ANN) is used for networking of the first order multi-pantograph differential equation in used to provide mathematical model based on unsupervised error for equation. Moreover, mathematical modeling has been performed perfectly through multi-runs for simulation to justify the better convergence of the solutions. Also, two examples are presented to exhibit the aptitude of the method SQP and AST. The comparative study will be made with reported techniques such as variational iteration technique (VIT) [6] and collocation based on Bernstein polynomial method (BCM) [6]. [ABSTRACT FROM AUTHOR]

Conventional fuels for power systems are now plaguing the environment more than ever before. Efforts to curb the environmental impacts of fossil fuels are now driving modern microgrids (MGs) to utilize green energy sources. In this regard, DC microgrids are well known for their versatility. Considering the rising significance of DC-MGs in future, this paper presents an aggregated DC microgrid model comprising of wind resource, photovoltaics, fuel cell, battery, and a supercapacitor. The model also includes DC–DC power converters and a two-level control structure. Firstly, the high-level of the proposed control structure constitutes an artificial neural network for maximum power point tracking and an intelligent energy management powered by a fuzzy logic controller. At this control level, the unified operation of the fuel cell and storage components under various dynamic conditions is ensured. Furthermore, the low-level control structure is based on an adaptive terminal sliding mode non-linear controller that derives the control signal for the converters. Finally, results showed that the energy management system along with the adaptive terminal sliding mode control successfully regulated the DC bus voltage by keeping the sources within their operational constraints. Moreover, the proposed non-linear controller demonstrated model's robustness, successfully converged the sliding surface to zero in finite time, ensured global asymptotic stability and effectively dealt with the model uncertainties. All the simulation leading to these results were performed in MATLAB/Simulink and further evaluation was carried out by hardware in loop experiments using C2000 Delfino microcontroller F28397D launchpad. • An optimum design of hybrid DC microgrid integrated with two renewable energy resources, an auxiliary source and hybrid energy storage system. • Multi-level control based on ANN, fuzzy Logic and adaptive terminal sliding mode non-linear controller for power balance and control. • HIL verification of controller performance using Delfino Microcontroller F28397D launchpad. [ABSTRACT FROM AUTHOR]

• Novel computing method is designed for nonlinear Thomas-Fermi system. • Neural networks, active set algorithm, interior-point and sequential quadratic programming are exploited for the system. • Comparison from exact and numerical results verify the correctness of the scheme. • Accuracy and convergence of the solver is validated from the statistical analysis. • Proposed scheme is an alternate, accurate, and reliable methods for such systems. We propose a computational intelligence technique to solve the nonlinear Thomas-Fermi equation for a finite interval. This technique uses an optimized approximation to reduce the error between the reference solution and optimized numerical solution with trigonometric and hyperbolic functions. Computational intelligence technique is based on optimization tools like Active Set Technique (AST), Interior Point Technique (IPT) with Sequential Quadratic Programming (SQP) through artificial neural networks (ANN). Furthermore, the proposed model results can be applied in atomic models phenomena. Numerical results showed that the optimizers provided a high level of accuracy, as well as the efficiency with marginal computational requirements infinite domain. Statistical analysis is presented, particularly fitting of normal distribution based on absolute errors (AEs) of proposed solvers and Chi-square distribution data curve fitting. It provides evidence that the present solution is highly accurate in the context of confidence bands. [ABSTRACT FROM AUTHOR]

The crude distillation unit (CDU) is one of the most energy-intensive processes of a petroleum refinery. The composition of crude is subject to change on regular basis. The uncertainty in crude oil composition causes wastage of a substantial amount of energy in the CDU operation. In this study, a novel approach based on a multi-output artificial neural networks (ANN) model was devised to cope with variations (uncertainty) in crude composition. The proposed method is an extended version of another method of cut-point optimization based on hybridization of Taguchi and genetic algorithm. A data comprised of several hundred variations of crude compositions and their optimized cut point temperatures, derived from the hybrid approach, was used to train the ANN model. The proposed method was validated on a simulated CDU flowsheet for a Pakistani crude, i.e., Zamzama. The proposed method is faster and computationally less expensive than the hybrid method. In addition, it can efficiently predict optimum cut point temperatures for any variant of the crude composition. [ABSTRACT FROM AUTHOR]

This study addresses the challenge of mitigating global warming by focusing on DC microgrids integrating renewable energy sources. The research specifically explores the modeling and nonlinear control design of DC microgrids featuring a novel renewable source called hybrid photoelectrochemical and voltaic cells (HPEV), alongside fuel cells and an energy storage system. The HPEV and fuel cells serve as primary sources, while the energy storage system includes a battery bank and ultracapacitor as secondary power sources. The primary objective is to derive a mathematical model for the considered DC microgrid, ensuring each power source maximizes output despite disturbances and varying climatic conditions. To optimize power extraction from HPEV, an artificial neural network is implemented. Subsequently, a nonlinear sliding mode control is applied to manage and stabilize the DC bus voltage, with global asymptotic stability confirmed through Lyapunov stability criteria. Additionally, the study introduces an energy management algorithm for effective power management within the microgrid. The system's efficiency is validated through MATLAB Simulink simulations under variable load demands, comparing the results with those obtained from a Lyapunov redesign controller. The study concludes with real-time hardware-in-loop experiments, further validating the system's performance and comparing experimental results with simulated outcomes. • Mathematical modeling and simulation of HESS are done for DC microgrid. • SMC and Lyapunov Redesign controller are designed for regulation of the DC bus. • AI based ANN technique is applied for MPPT of HPEV under varying temperature and irradiance. • Global stability of the proposed HESS is authenticated using Lyapunov stability criteria. • The proposed model is verified by MATLAB simulations with HIL experimentation. [ABSTRACT FROM AUTHOR]

The problem of extracting maximum power from a photovoltaic (PV) system with negligible power loss is concerned with the power generating capability of the PV array and nature of the output load. Changing weather conditions and nonlinear behavior of PV systems pose a challenge in tracking of varying maximum power point. A robust nonlinear controller is required to ensure maximum power point tracking (MPPT) by handling nonlinearities of a system and making it robust against changing environmental conditions. Sliding mode controller is robust against disturbances, model uncertainties and parametric variations. It depicts undesirable phenomenon like chattering, inherent in it causing power and heat losses. In this paper, a supertwisting sliding mode algorithm based nonlinear robust controller has been designed for MPPT of a PV system which not only removes the chattering but also enhances the overall system's dynamic response. Moreover, supertwisting sliding mode controller is robust against changing environmental conditions like change in temperature and irradiance. Noninverting DC-DC Buck-Boost converter has been used as an interface between source and the load. The efficiency of MPPT of a PV system depends upon the accuracy of reference for peak power voltage, therefore an efficient mechanism for reference generation has also been proposed in this work. The reference for peak power voltage has been generated by using a trained artificial neural network, which is to be tracked by proposed nonlinear controllers. Sliding mode controller (SMC) and synergetic controllers have also been designed for MPPT of a PV system in order to compare them with supertwisting sliding mode controller (ST-SMC). Global asymptotic stability of the system has been ensured by using Lyapunov stability criterion. The performance of the proposed nonlinear controllers has been validated in MATLAB/Simulink ODE 45 environment. ST-SMC has also been compared with recently proposed integral backstepping controller and other conventional MPPT controllers given in the literature. The simulation results show the better performance of ST-SMC in terms of best dynamic response and robustness. [ABSTRACT FROM AUTHOR]

In this study, a computational intelligence technique based on three different designs of artificial neural networks (ANNs) is presented to solve the nonlinear Troesch’s boundary value problem arising in plasma physics. The structure of three ANN models is formulated by incorporating log-sigmoid (ANN-LS), radial-base (ANN-RB) and tan-sigmoid (ANN-TS) kernel functions in the hidden layers. Mathematical modeling of the problem is constructed for all three feed-forward ANN models by defining an error function in an unsupervised manner. Sequential quadratic programming method is employed for the learning of unknown parameters for all three ANN-LS, ANN-RB and ANN-TS schemes. The proposed models are applied to solve variants of Troesch’s problems by taking the small and large values of critical parameter in the system. A comparison of the proposed solution obtained from these models has been made with the standard numerical results of Adams method. The accuracy and convergence of the proposed models are investigated through results of statistical analysis in terms of performance indices based on the mean absolute deviation, root-mean-square error and variance account for. [ABSTRACT FROM AUTHOR]