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A rapid and efficient method for the simultaneous monitoring and recovery of Auramine O (AO) and Methylene Blue (MB) dyes from water samples is presented. This method, named ultrasound-assisted dispersive-magnetic nanocomposites-solid-phase microextraction (UA-DMN-μSPE), utilizes NiCoMn/Fe 3 O 4 @C composite sorbents. Response surface methodology (RSM) combined with artificial neural networks (ANN) and generalized regression artificial neural network (GRNN) under central composite design (CCD) was employed to optimize various parameters for efficient extraction, followed by further refinement using desirability function analysis (DFA) and genetic algorithms (GA). Under optimized conditions, the method achieved exceptional recovery rates (99.5 ± 1.2% for AO and 99.8 ± 1.1% for MB) with acetone as the eluent. Additionally, a high preconcentration factor of 45.50 and 47.30 for AO and MB, respectively, was obtained. Low detection limits of 0.45 ng mL⁻1 (AO) and 1.80 ng mL⁻1 (MB) were achieved with wide linear response ranges (5–1000 and 5–2000 ng mL⁻1 for AO and MB, respectively). The method exhibited good stability with RSDs below 3% for five recycling runs, and minimal interference from various ions was observed. This UA-DMN-μSPE-UV/Vis method offers significant advantages in terms of efficiency, preconcentration, and detection limits, making it a valuable tool for the analysis of AO and MB in water samples. [Display omitted] • UA-DMN-μSPE method optimized using RSM, ANN, and GRNN ensures high-performance MB and AO extraction. • Optimized UA-DMN-μSPE-UV/Vis method, developed with DFA and GA, enhances recovery, preconcentration, and selectivity for MB and AO. • Concurrent monitoring & recovery offers sustainable solution for dye removal. • Potential to address environmental & health concerns from non-degradable dyes. [ABSTRACT FROM AUTHOR]

The large consumption of natural gas, one of the most important energy sources in the world, necessitates reliable, precise, and accurate calculation of gas flow rate and amount in order to use this resource in an efficient and sustainable way. The present computational study investigates the possibilities of several soft-computing strategies in estimating the mass flow rate of natural gas flow stream (kg/h) (output variable) based on four input variables of orifice plate diameter ratio, differential pressure of orifice plate (kPa), operating pressure of the natural gas (bar), and operating temperature of the natural gas (°C). A genotype/phenotype genetic algorithm (gene expression programming (GEP) technique), two decision tree-based methods (random forest (RF), random tree (RT) models), and two kernel-based approaches (Gaussian process regression (GPR) and support vector machines (SVM) methods) were applied for the first time to predict gas mass flow rate. Coefficient of correlation (CC), mean absolute error (MAE), root mean square error (RMSE), Scattering index (SI), Nash–Sutcliffe efficiency (NSE), and mean absolute relative error (MARE) were computed as the statistical performance evaluators to determine of the best-performing soft-computing approach. The performance assessment indices corroborated the superiority of the Pearson VII universal kernel function-based GPR model (GPR-PUKF) model (CC = 0.9997, MAE = 64.8091 kg/h, RMSE = 248.7584 kg/h, SI = 0.0237, and NSE = 0.9993 for the testing dataset) over other data-intelligent models in predicting the gas mass flow rate. In addition, statistical results revealed that the predictions of the RF method were better than those of the GEP- and RT-based models, but the GEP approach showed the lowest performance among all applied models. Although the CC values of all models were satisfactory (>0.993), the percentile deviation of GPR model (1.7325%) from the actual values showed competitive lower values, indicating its superior performance than other models (GEP = 15.1436%, RF = 6.5403%, RT = 9.5576%, and SVM = 3.2107%). This study highlighted the significance of employing advanced soft-computing approaches in determining the mass flow rate of natural gas, a vital source of energy, as well as its value to the gas sector. [Display omitted] • Soft-computing implementation for prediction of natural gas mass flow rate. • Benchmarking of genetic/decision tree/kernel-based data-intelligent models. • Lower deviations (1.73% and 0.36%) of GPR-PUKF over other methods. • Superiority of RF (6.54%) over GEP (15.14%) and RT (9.56%) in terms of errors. • Flexibility of soft computation in highly nonlinear real-world gas measurement. [ABSTRACT FROM AUTHOR]

Utilization of the struvite recovered from sheep slaughterhouse wastewater was explored for the first time in sustainable cement production and fire-resistant wooden structure design. Sheep abattoir-originated struvite precipitation process was optimized using a chemical combination of MgCl 2.6H 2 O + NaH 2 PO 4.2H 2 O, a molar ratio of Mg2+:NH 4 +-N:PO 4 3--P = 1.2:1:1, a reaction pH of 9.0, an initial ammonium concentration of 240 mg NH 4 +-N/L, and a reaction time of 15 min. Based on both American (ASTM C305-14) and Turkish (TS EN 196–1) standard methods, struvite was used in proportions of 10–30% by weight for struvite-substituted cement production. The best compressive strength values were achieved with 85.5% cement clinker (C), 4.5% gypsum (G), and 10% struvite (S) for the struvite-replaced cement (C85.5G4.5S10). According to the US EPA's greenhouse gas protocol, it was estimated that producing 10% struvite-substituted cement would result in 9.97% lower absolute CO 2 emissions than producing 100% Portland cement. It was also found that slaughterhouse-derived struvite could compete with commercial water-based fire retardant solution and exhibit acceptable flame resistance behavior for wooden structures. The versatility of sheep abattoir-oriented struvite was confirmed as an environmentally sustainable and clean by-product for different structural uses. [Display omitted] • First time use of SSW-derived struvite in ecologically friendly cement production. • Novel application of SSW-sourced struvite in fire resistant wood structure design. • More than 70% of NH 4 +-N removal/recovery from SSW via struvite precipitation. • Attractiveness and versatility of struvite in sustainable abattoir waste management. • Noticeable impact of partial cement substitution on reduction of CO 2 emissions. [ABSTRACT FROM AUTHOR]

[Display omitted] • CFRP-wrapped R/S RC columns were simulated using soft-computing methodology. • GPR, SVM, and M5P were inter-compared in prediction of K s for the first time. • GPR/SVM-based kernels and (un)pruned M5P were used for the first time for K s. • GPR-PUKF model outperformed than SVM and M5P models with lower deviations. • The total thickness of CFRP was the most effective parameter for predicting the K s. In this study, various soft-computing models (Gaussian process regression (GPR) and support vector machines (SVM) based on the polynomial kernel function (PKF), Pearson VII universal kernel function (PUKF), and radial basis kernel function (RBKF), as well as pruned/unpruned M5P tree models) were simultaneously applied for the first time in prediction of the lateral confinement coefficient (K s) of CFRP-wrapped rectangular/square (R/S) RC columns, and their corresponding predictive successes were appraised statistically. For this aim, short side of the column section (b), long side of the column section (h), total thickness of CFRP (t), compressive strength of the unconfined concrete (f' c 0), and the elastic modulus of CFRP (E CFRP) were used as independent input variables whereas the K s was the output variable. Results indicated that the performance of the Pearson VII kernel function-based Gaussian process regression (GPR-PUKF) model was superior to other models for the training and testing stages. A sensitivity investigation showed that the total thickness of CFRP (t) was the most effective parameter for predicting the K s using GPR-PUKF-based model. Findings of the present computational assessment obviously revealed that the employed soft-computing strategy had the capability of accurately estimating the K s of R/S RC columns wrapped with CFRP. [ABSTRACT FROM AUTHOR]

[Display omitted] • Electricity consumption is mapped using a combined cartographic methodology. • State of the sky impact is explored for the first time on energy management strategies. • LF strategy provides the best load monitoring by minimizing the generator uptime. • Energy management strategies play a pivotal role as hybrid solar system control tools. A novel optimized sizing and management strategy of a grid-connected hybrid photovoltaic (PV)-solar-battery-group system were proposed for the electrification of residential consumers in Northwest Africa (a case of Mauritania), and the influence of the state of the sky (clear, moderately overcast, and overcast) was analyzed according to the load flowing (LF) and the cycle charging (CC) strategies. In order to mitigate the pressure on the national grid, consolidate the consumer autonomy, minimize the cost of medium- and long-term consumption bills, and CO 2 -related emissions, a cartographic approach was conducted as the first attempt to map the electricity consumption potential for buildings in the city of Nouakchott (Mauritania) using a geo-referenced database. ArcGIS®, HOMER Pro®, and MATLAB® softwares were used for the establishment of the load profile, optimized sizing of the PV-batteries-group-grid system, and calculation of the lightness index, respectively. The LF strategy provided the best monitoring of the load throughout the day by minimizing the generator uptime. The techno-economic analysis revealed the values of cost of energy (COE) and net present cost (NPC) as follows: COE = $0.0549/kWh and NPC = $24,796 for the LF PV-batteries-grid strategy, COE = 0.0646 $/kWh and NPC = $23,380 for the CC PV-batteries-grid strategy, and COE = $0.17/kWh and NPC = $23,262 for the case of the grid on its own. [ABSTRACT FROM AUTHOR]

This study focuses on predicting the output power of wind turbines (WTs) using the wind speed and WT operational characteristics. The main contribution of this work is a model identification method based on an adaptive neuro-fuzzy inference system (ANFIS) through multi-source data fusion on a moving window (MoW). The proposed ANFIS-MoW-based approach was applied to data in different time series windows, namely the very short-term, short-term, medium-term and long-term time horizons. Data collected from a 30-MW wind farm on the west coast of Nouakchott (Mauritania) were used in the computational analysis. In comparison to nonparametric models from the literature and models employing artificial intelligence machine learning techniques, the proposed ANFIS-MoW model demonstrated superior predictions for the output power of the WT with the fusion of very few data collected from different WTs. Moreover, for various time series windows (TSW) and meteorological conditions, additional benchmarking demonstrated that the ANFIS-MoW-based method outperformed five existing ANFIS-based models, including grid partition (ANFIS-GP), subtractive clustering (ANFIS-SC), fuzzy c-means clustering (ANFIS-FCM), genetic algorithm (ANFIS-GA), and particle swarm optimization (ANFIS-PSO). The results indicated that the suggested methodology is a promising soft-computing tool for accurately estimating the WT output power for WTs' sustainability through better control of their operation. [Display omitted] • A novel ANFIS-based moving window approach for wind power prediction is proposed. • The proposed model is capable of wind power forecasting for different time series horizon. • Real dataset from 30-MW wind farm in Africa is employed for the modeling study. • Performance of the applied neuro-fuzzy tool is validated on various climatic conditions. • Superiority of the ANFIS-MoW model is approved compared to existing models. [ABSTRACT FROM AUTHOR]

Owing to its importance in extraction of natural gas from underground gas storage as well as its crucial role in determination of final gas mixture in the production facilities of gas/oil industry, the dry content of wet gas mixture needs to be calculated precisely. The present study explores the potential of different soft-computing techniques in estimation of the dry gas flow rate (kg/h) (output variable) of wet gas mixture based on two input variables of wet gas flow rate (kg/h) and absolute gas humidity (g/m3). Decision tree-based methods (M5P tree, random forest (RF), random tree (RT), and reduced error pruning tree (REPT) models), kernel function-based approaches (Gaussian process regression (GPR) and support vector machines (SVM)), and non-parametric regression-based technique (multivariate adaptive regression splines (MARS)) were implemented for the first time to estimate the dry gas flow rate, and their respective prediction performances were analyzed statistically. Coefficient of correlation (CC), Nash–Sutcliffe efficiency (NSE), root mean square error (RMSE), mean absolute error (MAE), Legates and McCabe's index (LMI), and Willmott's Index (WI) were used as the statistical indicators for validating the performance of each soft-computing model. While M5P model (MAE = 122.2382 kg/h, RMSE = 580.5626 kg/h, CC = 0.9875 for the testing data set) was better than other tree-based models (MAE = 363.2802–542.6119 kg/h, RMSE = 871.9363–1025.3444 kg/h, CC = 0.9587–0.9706 for the testing data set) and MARS model (MAE = 128.0083 kg/h, RMSE = 622.9515 kg/h, CC = 0.9852 for the testing data set), the statistical indicators approved the superiority of the radial basis kernel function-based GPR model (GPR-RBKF) model (MAE = 163.3266 kg/h, RMSE = 483.1359 kg/h, CC = 0.9915 for the testing data set) over other implemented models in predicting the dry gas flow rate. The findings highlighted the potential of soft-computing methodologies in precise estimation of dry gas flow rate in wet gas mixture, particularly, in situations where the measurement of such parameters with traditional deterministic models is practically not possible. [Display omitted] • Soft-computing methods for estimation of dry content of wet gas mixture. • Performance evaluation of M5P, RF, RT, REPT, GPR, SVM, and MARS. • Superiority of GPR-RBKF over other models in terms of accuracy. • Superiority of M5P over other tree decision tree models in terms of error. • Higher precision of Gaussian processing with kernel-based regression vector. [ABSTRACT FROM AUTHOR]

This study proposes an original adaptive neuro-fuzzy inference system modeling approach to predict the output power of a wind turbine. The model's input includes the wind speed, turbine rotational speed, and mechanical-to-electrical power converter's temperature. The structure of the adaptive neuro-fuzzy inference system-based model was first identified using the processed data gathered from wind turbine number 1 of a 30-MW wind farm in Nouakchott (Mauritania). Then, the proposed data-driven model was trained and validated according to two new scenarios based on the data set from four identical wind turbines operated in the same climatic conditions and the data set from the same wind turbines operated under different climatic conditions. Benchmarking involved the proposed model, existing approaches in the literature, and five adaptive neuro-fuzzy inference system-based models, including grid partition, subtractive clustering, fuzzy C-means clustering, genetic algorithm, and particle swarm optimization, on the same data set to validate their prediction performance. Compared with existing adaptive neuro-fuzzy inference system-based models, the proposed approach was proven to be a promising methodology with higher accuracy for estimating the output power of wind turbines operating in different climatic conditions. According to the results from two different scenarios, the lowest value of the fitting rate and the highest values of the normalized mean square error, normalized mean absolute error, and root mean square error for the validating period were 0.9977, 0.0047, 0.0473, and 46.5831 kW, respectively. Moreover, the proposed model showed superior forecasting performance and thus better accuracy in estimating wind power output compared to other adaptive neuro-fuzzy inference system-based models. [Display omitted] • Original ANFIS approach was developed for wind turbine (WT) output power prediction. • Scenarios-based validation with real data from wind farm enhanced model building. • Machine learning embedded fuzzy logic boosted the model identification strategy. • Benchmarking of existing models corroborated the superiority of preprocessed ANFIS. • Neuro-fuzzy tool accurately estimated WT output power in various climatic conditions. [ABSTRACT FROM AUTHOR]