Hybrid Adaptive Neuro-fuzzy Inference Research Articles

Deep learning with improved hybrid neuro-turning for predictive control of flux based on experimental DCMD module design of water desalination system

This study explores two scenarios for optimizing the predictive control of flux in water desalination systems: experimental design of direct contact membrane distillation (DCMD) and artificial intelligence (AI) models. Deep learning networks, specifically Long Short-Term Memory (LSTM), and standalone AI models, including hybrid Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN), were evaluated. Evaluation criteria, along with 2D graphical visualization, were used to assess model accuracy. ANFIS-C4 emerged as the standout model, achieving perfect predictive skills with 100 % accuracy and low RMSE of 0.0522 in the training phase, and maintaining high performance in testing with 99.73 % accuracy and RMSE of 0.7121. Similarly, ANN-C4 demonstrated near-perfect accuracy with 100 % accuracy and RMSE of 0.0643 in the training phase and also maintained excellent performance in the testing phase. Despite requiring multiple inputs, ANFIS and ANN show remarkably high capability in predictive accuracy. Among LSTM models, LSTM-C3 achieved the highest training DC of 91.35 %, indicating robust performance in capturing training data variability. LSTM-C2 showed superior generalization with a testing DC of 0.8539, despite using only two input parameters. The narrow distribution of predicted values around the median in violin plots for ANFIS-C4 and ANN-C4, along with their low RMSE, validates their superior performance. The exceptional results of ANFIS-C4 and ANN-C4 highlight their potential in water desalination applications, supporting Sustainable Development Goal 6 (SDGs-6) and Environmental Protection Agency (EPA) objectives for sustainable water management. This research underscores the importance of advanced computational models in enhancing the efficiency and reliability of water desalination systems, contributing to global efforts in sustainable water resource management.

Hybrid ANFIS-ant colony optimization model for prediction of carbamazepine degradation using electro-Fenton process catalyzed by Fe@Fe2O3 nanowire from aqueous solution

Pharmaceutical compounds, such as carbamazepine, have become a concern in aquatic environments due to their wide usage, resistance to degradation, and inefficiency of wastewater treatment processes. Therefore, it is important to find methods to remove these compounds from aqueous solutions. This study focuses on using Fe@Fe2O3 nanowires combined with electro Fenton process in an electrochemical reactor to decompose carbamazepine (CBZ). Various parameters were considered in 81 experiments, such as pH, current density, concentrations of FeSO4·7H2O, carbamazepine, Fe@Fe2O3, and reaction time. The removal efficiency ranged from 30.4 % to 88.6 %. This study utilized a hybrid adaptive neuro fuzzy inference system (ANFIS) combined with the ant colony optimization algorithm (ACO). The ACO is integrated with the ANFIS models to determine the optimum values for the studied parameters. The implementation of optimization through ACO enhances the ANFIS models' ability. A total of 65 data points were used for training the model, and 16 data points were used to test it. The model's performance was assessed using the coefficient of determination (R2 = 0.9988), the root-mean-squared error (RMSE = 4.54E-03), and the average absolute relative deviation (AARD% = 0.7153). The optimal input parameters for CBZ removal were determined as follows: concentrations of CBZ, FeSO4·7H2O and Fe@Fe2O3 nanowire dose of 9.0 mg/L, 4.5 mg/L, and 1050.0 mg/L; contact time of 45.0 min; pH of 4.0; and current of 0.18 A, resulting in a removal efficiency of 91.2 %. Additionally, the extended Fourier amplitude sensitivity test (EFAST) sensitivity analysis was applied to assess the influence of individual input parameters or their interactions on the model's output.

A Proposed Hybrid Machine Learning Model Based on Feature Selection Technique for Tidal Power Forecasting and Its Integration

Renewable energy resources are playing a crucial role in minimizing fossil fuel emissions. Integrating machine learning techniques with tidal power forecasting could greatly enhance the accuracy and reliability of predictions, which is crucial for efficient energy production and management. A hybrid approach combining different methods often yields better results than relying on individual techniques. The accuracy of tidal current power is very important, especially for smart grid applications. This work proposes hybrid adaptive neuro-fuzzy inference system (ANFIS) with the Kalman filter (KF) and a neuro-wavelet (WNN) for tidal current speed, direction, and power forecasting. The turbine used in this study is driven by a direct drive permanent magnet synchronous generator (DDPMSG). The predictions of individual and hybrid models including the ANFIS, the Kalman filter, and the WNN for tidal current speed and the power it generates are compared with another dataset as a way of validation which is the tidal currents direction. Also, other published work results in the literature are compared to the proposed work. Different hybrid models are proposed for smart grid integration. The results of this work indicate that the hybrid model of the WNN and the ANFIS for tidal current power or speed forecasting has the highest performance compared to all other models.

Comparative assessment of deep belief network and hybrid adaptive neuro-fuzzy inference system model based on a meta-heuristic optimization algorithm for precise predictions of the potential evapotranspiration

Accurately predicting potential evapotranspiration (PET) is crucial in water resource management, agricultural planning, and climate change studies. This research aims to investigate the performance of two machine learning methods, the adaptive network-based fuzzy inference system (ANFIS) and the deep belief network (DBN), in forecasting PET, as well as to explore the potential of hybridizing the ANFIS approach with the Snake Optimizer (ANFIS-SO) algorithm. The study utilized a comprehensive dataset spanning the period from 1983 to 2020. The ANFIS, ANFIS-SO, and DBN models were developed, and their performances were evaluated using statistical metrics, including R2, Radj2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${R}_{adj}^2$$\\end{document}, NSE, WI, STD, and RMSE. The results showcase the exceptional performance of the DBN model, which achieved R2 and Radj2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${R}_{adj}^2$$\\end{document} values of 0.99 and NSE and WI scores of 0.99 across the nine stations analyzed. In contrast, the standard ANFIS method exhibited relatively weaker performance, with R2 and Radj2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${R}_{adj}^2$$\\end{document} values ranging from 0.52 to 0.88. However, the ANFIS-SO approach demonstrated a substantial improvement, with R2 and Radj2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${R}_{adj}^2$$\\end{document} values ranging from 0.94 to 0.99, suggesting the value of optimization techniques in enhancing the model’s capabilities. The Taylor diagram and violin plots with box plots further corroborated the comparative analysis, highlighting the DBN model’s superior ability to closely match the observed standard deviation and correlation and its consistent and low-variance predictions. The ANFIS-SO method also exhibited enhanced performance in these visual representations, with an RMSE of 0.86, compared to 0.95 for the standard ANFIS. The insights gained from this study can inform the selection of the most appropriate modeling technique, whether it be the high-precision DBN, the flexible ANFIS, or the optimized ANFIS-SO approach, based on the specific requirements and constraints of the application.

Proposed Fault Detection Algorithm with Optimized Hybrid Speed Control

The Brushless DC (BLDC) motor is a common choice for industrial applications, particularly in the automotive sector, owing to its high efficiency and robust capabilities. To detect the position of the motor rotor, hall-effect sensors can be used, but these sensors may prevent the system from operating if they fail. Consequently, fault-tolerant control (FTC) has been proposed in several studies to ensure continuity of operation in the event of sensor failure. This paper proposes an innovative method of fault detection in the hall effect sensor for a BLDC motor using combinatorial functions. This paper proposes an innovative method of hall-effect sensor fault detection for a BLDC motor using combinatorial functions. For the speed control of the BLDC under study, a hybrid adaptive neuro-fuzzy inference control (ANFIS) is implemented. In addition, the FTC signal reconstruction technique adopted has been improved to achieve motor start-up despite a fault in one of the sensors, thanks to well-defined fault detection algorithms. Simulation results are presented for each sensor failure case to test the effectiveness of the method used.

Estimation of CO2 solubility in aqueous solutions of commonly used blended amines: Application to optimised greenhouse gas capture

One of the key concerns in the 21st century, alongside the growing population, is the increase in energy consumption and the resulting global warming. The impact of CO2, a prominent greenhouse gas, has garnered significant attention in the realm of CO2 capture and gas purification. CO2 absorption can be enhanced by introducing some additives into the aqueous solution. In this study, the accuracies of some of the most up-to-date computational approaches are investigated. The employed machine learning methods are hybrid-adaptive neuro-fuzzy inference system (Hybrid-ANFIS), particle swarm optimization-adaptive neuro-fuzzy inference system (PSO-ANFIS), least-squares support vector machines (LSSVM) and genetic algorithm-radial basis function (GA-RBF). The developed models were used in estimating the solubility of CO2 in binary and ternary amines aqueous solutions. i.e. blends of monoethanolamine (MEA), triethanolamine (TEA), aminomethyl propanol (AMP), and methyldiethanolamine (MDEA). This modeling study was undertaken over relatively significant ranges of CO2 loading (mole of CO2/mole of solution) as a function of input parameters, which are 0.4–2908 kPa for pressure, 303–393.15 K for temperature, 36.22–68.89 g/mol for apparent molecular weight, and 30–55 wt % for total concentration. In this work, the validity of approaches based on different statistical graphs was investigated, and it was observed that the developed methods, especially the GA-RBF model, are highly accurate in estimating the data of interest. The obtained AARD% values for the developed models are 18.63, 8.25, 12.22, and 7.54 for Hybrid-ANFIS, PSO-ANFIS, LSSVM, and GA-RBF, respectively.

Interleaved boost converter voltage regulation using hybrid ANFIS-PID controller for off-grid microgrid

The utilization of a microgrid with a photovoltaic (PV) and wind generation system presents a challenge due to their voltage and power output variations. This problem is majorly addressed within the converter section of the microgrid using maximum power point tracking (MPPT) algorithms and voltage regulation strategies. This paper presents an interleaved boost converter (IBC) modeling and voltage control using a hybrid adaptive neuro-fuzzy inference system-proportional plus integral plus derivative (ANFIS-PID) controller for an off-grid microgrid. The modeling used the interleaving technique to obtain the microgrid’s transfer function (TF) and case study simulation models within MATLAB and Simulink environments. The performance of the ANFIS-PID controller, which regulates voltage in the microgrid, was compared to that of the traditional proportional integral (PI) controller. Results indicated that the hybrid ANFIS-PID controller performed better than the PI controller in terms of reduced settling time, overshoot, rise time, and the ability to address the nonlinear dynamics of the microgrid.

Interleaved boost converter voltage regulation using hybrid ANFIS-PID controller for off-grid microgrid

The utilization of a microgrid with a photovoltaic (PV) and wind generation system presents a challenge due to their voltage and power output variations. This problem is majorly addressed within the converter section of the microgrid using maximum power point tracking (MPPT) algorithms and voltage regulation strategies. This paper presents an interleaved boost converter (IBC) modeling and voltage control using a hybrid adaptive neuro-fuzzy inference system-proportional plus integral plus derivative (ANFIS-PID) controller for an off-grid microgrid. The modeling used the interleaving technique to obtain the microgrid’s transfer function (TF) and case study simulation models within MATLAB and Simulink environments. The performance of the ANFIS-PID controller, which regulates voltage in the microgrid, was compared to that of the traditional proportional integral (PI) controller. Results indicated that the hybrid ANFIS-PID controller performed better than the PI controller in terms of reduced settling time, overshoot, rise time, and the ability to address the nonlinear dynamics of the microgrid.

Fractionation of dyes/salts using loose nanofiltration membranes: Insight from machine learning prediction

Wastewater (WW) served as the crucial indicator for sustainable development, human health, and the ecosystem. Nanofiltration (NF) membranes are efficient in contaminants, dye, organics, ionic strength, hardness, and salt treatment from WW. Membranes such as loose NF play crucial roles in dye and salt removal fractionation. This study proposed an insight into machine learning (ML) techniques based on an established experimental laboratory using a loose NF membrane and loose layer surface functionalization of ultrafiltration (UF) membranes with nano-silver-immobilized polydopamine. For this purpose, the obtained data from experimental work were pre-processed and fed into four different computational models viz: hybrid adaptive neuro-fuzzy inference system (ANFIS), robust-linear regression (RLR), support vector regression (SVR), and multi-linear regression (MLR) for the prediction of fractionation of dyes/salts rejection variables (flux (LMH), rejection (%), rejection of dye/salt (%). Based on feature selection, two different model combinations were established, and four statistical evaluation criteria were used to assess the prediction performance. The results justified that SVR-M2 outperformed other models for predicting flux (LMH) and rejection (%) with 95% and 98% accuracy, respectively. Similarly, hybrid ANFIS-M2 proved merit for modelling the rejection of dye/salt (%) with 72% accuracy. The prediction using all the models was found reliable and satisfactory except with the rejection of dye/salt (%), with ranged between marginal and good. The experimentally designed membrane and ML feasibility are excellent examples of fractionating divalent salts and dye.

Enhancement and prediction of a stepped solar still productivity integrated with paraffin wax enriched with nano-additives

The present study deals with the emhancement of thermophysical properties of paraffin wax using Silver nanoparticles and to study the feasibility of its application in a stepped solar still through an experimental approach. Along with the experimentation, the yield, temperature of water are predicted using a machine learning approach. The thermophysical properties such as melting temperature, latent heat, thermal conductivity and thermal stability of the paraffin wax using different concentrations (1 and 2%) are investigated and compared to that of paraffin wax without nanoadditives. The thermal conductivity of paraffin wax was enhanced by about 35.71% and 78.57% using nano-additives concentrations of 1% and 2%, respectively. Three different stepped SS namely, SS with paraffin wax, SS with paraffin wax doped with Ag nanoparticles, and SS without paraffin wax are fabricated and tested for the climatic conditions of Coimbatore, India. From the experimental results of fresh water generation, it is identified that the SS using nanocomposite PCM and PCM without nanoadditives are enhanced by about 75.65% and 114.81% respectively, while compared to the SS without any thermal energy storage. In order to estimate the amount of water that can be produced by each of the three solar stills, a single adaptive neuro-fuzzy inference system (ANFIS) and a hybrid adaptive neuro-fuzzy inference system-particle swarm optimizer (PSO) were used as machine learning models. According to the statistical assessment, the ANFIS-PSO model had a greater level of accuracy than that of the standalone ANFIS. ANFIS-PSO had very high determination coefficient ranged between 0.981 and 0.995 which indicates its capability to predict water yield of the tested solar stills.

Hybrid Controlled Multi-Input DC/DC Converter for Electric Vehicle Application

The use of energy sources for electric vehicle (EV) applications relies heavily on the power electronic interlinking and its successful control mechanism. A hybrid adaptive neuro-fuzzy inference system (ANFIS) proportional-integral (PI) based control strategy for a multi-input DC-DC converter is investigated in-depth for this purpose. At steady state, the proposed hybrid ANFIS PI controller uses the standard PI controller, and during the transient state, the ANFIS PI control strategy is used. Furthermore, the proposed control scheme aids in the tracing of a predetermined speed pattern in order to achieve total EV. A detailed simulation analysis of the proposed control strategy is carried out and its performace was compared with traditional controllers. The result indicated that the designed control strategy is reliable since it offers bidirectional power management, high gain with quick reaction, and low steady-state error. It enables rapid tracking and achieves improved dynamic response through increased flexibility and energy efficiency. The proposed scheme reduces component stress, as demonstrated by a reliability assessment using the MIL-HDBK-217F military handbook. At nominal power, the converter achieves 96.954% efficiency. An 80 W converter prototype was created and tested in the laboratory to monitor the real-time system’s response time.

Forecasting of University Students' Performance Using A Hybrid Model of Neural Networks and Fuzzy Logic

Abstract: Artificial intelligence techniques can be applied in forecasting the academic performance of university students, with aim of detecting the factors that influence their learning process which allows instructors and university administration to take more effective actions to increase the university student's performance. Identifying the students' performance will improve the quality of education which will be through analyzing and forecasting the students' performance at the course level and degree level. This research focuses on first-year students' performance in two university-requirement courses, depending on features such as attendance, assessment marks, exams, assignments, and projects. Forecasting the students' performance in the whole degree will depend on these features; high school average, Grade Point Average (GPA) for each semester, drop courses, selected core courses in the degree, period of study, and final GPA. A hybrid Adaptive Neuro-Fuzzy Inference System (ANFIS) model was used toperform the forecasting process. In this way, based on the datasets collected from the selected courses, or the whole degree, the future results can be forecasted and suggestions can be made to carry out corrective steps to improve the final results. The experiments result of the applied models performed that ANFIS-Grid outperforms the ANFIS-Cluster, wherein each model produces the lowest error of 0.7%, where it just fails in one sample from thirteen samples, while the ANFISCluster after modification produces an error equal to 0.15%. Keywords:University Student Performance, Forecasting, Fuzzy logic, Neural Network, Adaptive Neuro-Fuzzy Inference System.

Hybrid ANFIS models were used to calculate the capillary water absorption values of construction stones

The most significant parameter in groundwater movement in stones is capillary water absorption. Specifying the capillary water absorption (CWP) of rocks needs hard and laborious experimental work, while prediction models can reduce the cost and required time. To this aim, different rock specimens were gathered from various rocks. For the prediction processes, the hybrid adaptive neuro-fuzzy inference system (ANFIS) models also were proposed to determine the optimal value of two constituent parameters of the ANFIS, which the particle swarm optimization (PSO) and whale optimization algorithm (WOA) algorithm applied to the ANFIS for this aim. Results present that ANFIS processes have passable accomplishment in forecasting the CWA with R2 larger than 0.832 and 0.917 for the training and testing data, respectively, a good connection among actual and anticipated values. Considering developed models, the ANFIS model optimized with WOA performs better than another model in training and testing datasets. In the training dataset, the value of R2 and RRSE is 0.917 and 29.29% for the WOA-ANFIS model, while the PSO-ANFIS model is 0.911 and 30.50%, respectively. Overall, it is clear that WOA-ANFIS can be recognized as the proposed model, which shows its capability to find the optimal value of two constituent parameters of the ANFIS.

Optimizing Network Intrusion Detection with a Hybrid Adaptive Neuro Fuzzy Inference System and AVO-based Predictive Analysis

Optimizing Network Intrusion Detection with a Hybrid Adaptive Neuro Fuzzy Inference System and AVO-based Predictive Analysis

Development of a Hybrid Adaptive Neuro-fuzzy Inference System with Coulomb-Counting State-of-Charge Estimator for Lithium–Sulphur Battery

This study presents the development of an improved state of charge (SOC) estimation technique for lithium–sulphur (Li–S) batteries. This is a promising technology with advantages in comparison with the existing lithium-ion (Li-ion) batteries such as lower production cost and higher energy density. In this study, a state-of-the-art Li–S prototype cell is subjected to experimental tests, which are carried out to replicate real-life duty cycles. A system identification technique is then used on the experimental test results to parameterize an equivalent circuit model for the Li–S cell. The identification results demonstrate unique features of the cell’s voltage-SOC and ohmic resistance-SOC curves, in which a large flat region is observed in the middle SOC range. Due to this, voltage and resistance parameters are not sufficient to accurately estimate SOC under various initial conditions. To solve this problem, a forgetting factor recursive least squares (FFRLS) identification technique is used, yielding four parameters which are then used to train an adaptive neuro-fuzzy inference system (ANFIS). The Sugeno-type fuzzy system features four inputs and one output (SOC), totalling 375 rules. Each of the inputs features Gaussian-type membership functions while the output is of a linear type. This network is then combined with the coulomb-counting method to obtain a hybrid estimator that can accurately estimate SOC for a Li–S cell under various conditions with a maximum error of 1.64%, which outperforms the existing methods of Li–S battery SOC estimation.

Crude rubber seed oil esterification using a solid catalyst: Optimization by hybrid adaptive neuro-fuzzy inference system and response surface methodology

Esterifying high free fatty acid (FFA) oil with acid is necessary to avoid soap formation during biodiesel production. Thus, this study evaluated the efficacies of response surface methodology (RSM) and adaptive neuro-fuzzy inference system (ANFIS) in modeling the esterification process for crude rubber seed oil (CRSO) with a high FFA catalyzed by dehydrated Fe2(SO4)3. A central composite design (CCD) with three factors and five levels was applied to examine the influence of methanol:CRSO molar ratio (25:1–75:1), Fe2(SO4)3 loading (8–16 wt %), and time (3–4 h) on reduction of the high FFA (22.2%) of CRSO. The performance of the particle swarm optimization (PSO), genetic algorithm (GA), and RSM were assessed in optimizing the process variables. Statistics for the ANFIS and RSM models showed that both could reliably describe the esterification process with low mean relative percent deviation (MRPD) of 1.77 and 4.98; and high coefficients of determination (R2) of 0.9838 and 0.9730, respectively. The optimization results are in this order: ANFIS-PSO, ANFIS-GA, RSM-PSO, RSM, and RSM-GA. The ANFIS-PSO hybrid predicted the best optimal condition as Fe2(SO4)3 loading of 16.97 wt%, methanol:CRSO molar ratio of 44.21:1, and time of 3.39 h with the lowest FFA of 0.56%.

Robust mobile robot navigation in cluttered environments based on hybrid adaptive neuro-fuzzy inference and sensor fusion

Collision-free navigation of mobile robots is a challenging task, especially in unknown environments, and various studies have been carried out in this regard. However, the previous studies have shortcomings, such as low performance in cluttered and unknown environments, high computational costs, and multiple controller models for navigation. This paper proposes an adaptive neuro-fuzzy inference system (ANFIS) and global positioning system (GPS) for control and navigation to overcome these problems. The proposed method automates the navigation of a mobile robot while averting obstacles in unknown and densely cluttered environments. Furthermore, the mobile robots’ global path planning and steering are controlled using GPS and heading sensor data fusion to achieve the target coordinates. A fuzzy inference system (FIS) is adopted to model obstacle avoidance where distance sensors data is converted into fuzzy linguistics. Moreover, a type-1 Takagi–Sugeno FIS is used to train a five-layered neural network for the local planning of the robot, and ANFIS parameters are tuned using a hybrid learning method. In addition, an algorithm is designed to generate a dataset for testing and training the ANFIS controller. All the testing and training are conducted in MATLAB, while simulations are carried out using CoppeliaSim. Comprehensive experiments are performed to validate the robustness of the proposed method. The results of the experiments show that the proposed approach outperforms various state-of-the-art neuro-fuzzy, CS-ANFIS, multi-ANFIS, and hybrid ANFIS navigation and obstacle avoidance methods in finding a near-optimal path in unknown environments.

Insights into modelling and evaluation of thermodynamic and transport properties of refrigerants using machine-learning methods

The thermophysical properties of refrigerating systems should be accurately understood for designing low-temperature refrigeration cycles of economic acceptance. The present work has tried to simplify this complicated procedure by proposing reliable and new correlative methods for determining thermodynamic and transport properties of four refrigerating substance classes, namely halocarbon, inorganic, hydrocarbon, and cryogenic fluids. New machine learning methods e.g., particle swarm optimisation adaptive neuro-fuzzy inference system (PSO-ANFIS), genetic programming (GP), and hybrid adaptive neuro-fuzzy inference system (Hybrid ANFIS) algorithms were utilised. The development of a new, simple and comprehensive correlation was for the first time introduced to estimate saturated vapour enthalpy, entropy, velocity of sound, and viscosity of refrigerants without having in-depth knowledge of complicated parameters. The accuracy and validity of the proposed models were assessed using a variety of statistical and graphical demonstrations. The findings were compared, and it was found that Hybrid ANFIS models are more accurate because Absolute Average Relative Errors (%AARD) for enthalpy, entropy, the velocity of sound, and viscosity were estimated as 0.5558, 1.3105, 0.5215, and 1.5727 in respective order. In addition, the proposed models' results were compared to the results of recently previously published models, and it confirms the reliability of our results. The innovation of this research is the design of reliable correlative methods having elevated precisions for thermodynamic and transport specifications of refrigerating substances.

A new combination approach for optimal design of sedimentation tanks based on hydrodynamic simulation model and machine learning algorithms

The current study aims to propose a novel approach to design rectangular primary sedimentation tanks using computational fluid dynamics (CFD) and artificial intelligence. Hybrid adaptive neuro-fuzzy inference system and particle swarm optimization algorithm (ANFIS-PSO) is trained using CFD model's results. ANFIS-PSO is coupled with two multi-objective algorithms to optimize two objectives: Mean velocity and baffles' configurations. For the single-baffle tank, the optimal baffle position regarding flow velocity was approximately at 0.13 of the basin length, with the height ratio (ratio of baffle height to weir height) of 0.21, but the most cost-effective design was obtained when the single baffle was placed at about 0.06 of total length, with the height ratio of 0.17. Using two baffles could enhance the tank's hydraulic performance, especially when the first and second baffles were at 0.11 and 0.25 of the tank's length, respectively, with the height ratio of 0.33 and 0.22, respectively.

An ANFIS-RSM based modeling and multi-objective optimization of syngas powered dual-fuel engine

The present study investigates the possibility of increasing efficiency and lowering pollutant emissions from a syngas-powered engine by modulating operational parameters such as engine load and syngas composition. Although artificial intelligence-based technologies are becoming more common for modeling single-fuel mode engines, they are rarely employed to simulate a dual-fuel syngas/diesel engine. In the first of its kind Endeavor in the area of syngas fueled engines, a hybrid adaptive neuro-fuzzy inference system (ANFIS)-response surface methodology (RSM) is investigated. The performance of syngas (H2+CO), a novel synthetic gaseous fuel, was studied in four different combinations. ANFIS and RSM-based prediction models were developed using engine performance and emissions data collected over the whole load range. While ANFIS surpassed RSM in model prediction, RSM was useful in establishing mathematical links between engine input and output. The ANFIS model developed had a good correlation between R (0.996–0.9998) and R2 (0.992–0.9972), as well as low model errors as determined by the root means square error range (0.0086–5.936) and mean absolute percentage error range (0.0028–0.0194). Theil's U2 was used to calculate the model's uncertainty, which was estimated to be 0.0065–0.0439. The superior forecasting abilities of ANFIS models were proved by low errors and uncertainty. The performance of the syngas-powered engine was optimized using the desirability approach to achieve optimum efficiency while emitting the least amount of pollution. The ideal engine load and syngas compositions for maximum production were 67.99% engine load and 72.4:27.6 as H2:CO syngas mix.