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The use of various advanced oxidation methods in the treatment of wastewater has become the subject of many studies published in recent years. In particular, it is exceedingly significant to compare these treatment methods for industrial wastewater to reduce environmental effects and optimize plant operations and economics. The present study is the first to deal with the treatability of real epoxy paint wastewater (EPW) using MW- and UV-assisted Fenton processes within an optimization framework. A three-factor, three-level Box–Behnken experimental design combined with response surface methodology (RSM) was conducted for maximizing the chemical oxygen demand (COD) and color removal efficiencies of ultraviolet (UV)/Fenton and microwave (MW)/Fenton processes in the treatment of the real epoxy paint wastewater (EPW, initial COD = 4600 ± 90 mg/L, initial color = 114 ± 4 Pt-Co), based on 15 different experimental runs. Three independent variables (reaction time ranging from 20 to 60 min (UV) and from 5 to 15 min (MW), power ranging from 20 to 40 W (UV) and from 300 to 600 W (MW), and H2O2/Fe2+ ratio ranging from 0.2 to 0.6 (for both UV and MW)) were consecutively coded as A, B, and C at three levels (−1, 0, and 1), and four second-order polynomial regression equations were then derived to estimate the responses (COD and color removals) of two distinct systems. The significance of the independent model components and their interrelations were appraised by means of a variance analysis with 99% confidence limits (α = 0.01). The standardized differences of the independent variables and the consistency between the actual and predicted values were also investigated by preparing normal probability residual plots and experiment-model plots for all processes. The optimal operating conditions were attained by solving the quadratic regression models and analyzing the surface and contour plots. UV/Fenton and MW/Fenton processes, which constitute combined Fenton processes, were performed using advanced oxidation methods, while Fenton processes were utilized as the standard method for wastewater treatment. When UV/Fenton and MW/Fenton processes were applied separately, the COD removal efficiencies were determined to be 96.4% and 95.3%, respectively. For the color parameter, the removal efficiencies after the application of both processes were found to exceed 97.5%. While these efficiencies were achieved in 1 h with a 38 W UV unit, they were achieved in 15 min with a MW power of 570 W. According to the RSM-based regression analysis results, the R2 values for both processes were greater than 0.97 and p values were less than 0.003. [ABSTRACT FROM AUTHOR]

This computational study explores the potential of several soft-computing techniques for wind turbine (WT) output power (kW) estimation based on seven input variables of wind speed (m/s), wind direction (°), air temperature (°C), pitch angle (°), generator temperature (°C), rotating speed of the generator (rpm), and voltage of the network (V). In the present analysis, a nonlinear regression-based model (NRM), three decision tree-based methods (random forest (RF), random tree (RT), and reduced error pruning tree (REPT) models), and multilayer perceptron-based soft-computing approach (artificial neural network (ANN) model) were simultaneously implemented for the first time in the prediction of WT output power (WTOP). To identify the top-performing soft computing technique, the applied models' predictive success was compared using over 30 distinct statistical goodness-of-fit parameters. The performance assessment indices corroborated the superiority of the RF-based model over other data-intelligent models in predicting WTOP. It was seen from the results that the proposed RF-based model obtained the narrowest uncertainty bands and the lowest quantities of increased uncertainty values across all sets. Although the determination coefficient values of all competitive decision tree-based models were satisfactory, the lower percentile deviations and higher overall accuracy score of the RF-based model indicated its superior performance and higher accuracy over other competitive approaches. The generator's rotational speed was shown to be the most useful parameter for RF-based model prediction of WTOP, according to a sensitivity study. This study highlighted the significance and capability of the implemented soft-computing strategy for better management and reliable operation of wind farms in wind energy forecasting. [ABSTRACT FROM AUTHOR]

A high pH, low solubility of bound plant nutrients, and negative impacts on microbial communities are common drawbacks of biomass ash (BA) vermicomposting. In this study, nutrient-rich BA mixed with cow manure was tested at three different application rates to obtain final nitrogen (N), phosphorus (P), and potassium (K) contents of 3.5%, 7.0%, and 10.0% for bio-based fertilizers via vermicomposting. The results showed that all BA blends made with cow manure increased fermentation temperatures and allowed successful worm activity during the subsequent vermicomposting phase. The order of indicator enzyme activities in all vermicomposting samples was urease (220 μg NH4 g−1 h−1) > β-glucosidase (95 μg PNP g−1 h−1) > alkaline phosphatase (91 μg PNP g−1 h−1) > arylsulfatase (83 μg PNP g−1 h−1) > acid phosphatase (60 μg PNP g−1 h−1). As an indicator of nutrient bioavailability, high correlations were observed between enzyme activities and microbial diversity in vermicompost samples. Determination coefficients (R2) obtained from multiple linear regressions between enzyme activities and bacterial population for T0, T1, T2, and T3 were determined as 0.90, 0.65, 0.73, and 0.90, respectively. According to a novel metagenome-based approach proposed within the scope of the present study, the stimulatory effects of Flavobacteriales, Burkholderiales, Saccharimonadales, and Pseudomonadales on enzyme activities for the nutrient solubility were found to be significant and positive. The findings of this study demonstrated that worm composting could be a sustainable bio-based technology for the production of slow-release fertilizer from nutrient-rich waste material. [ABSTRACT FROM AUTHOR]

The integration of smart city technologies into waste management is a challenging field for decision makers due to its multivariate, multi-limiting, and multi-stakeholder structure, despite its contribution to the ecological and economic sustainability understanding of cities. The success of smart sustainable waste management strategies depends on many environmental, technical, economic, and social variables, and many stakeholders are involved in these processes. Using fuzzy multi-criteria decision-making (MCDM) methods helps decision makers determine effective, affordable, and acceptable smart waste management strategies. Although MCDM methods are widely used in various environmental engineering applications, the determination of smart sustainable waste management strategies using these methods has not yet received enough attention in the literature. This study aims to contribute to this gap in the literature by evaluating four different smart waste management strategies using a hybrid fuzzy MCDM method. The performance of the proposed strategy alternatives according to fifteen sub-criteria (under four main criteria selected from the literature) was evaluated using a combined application of fuzzy analytic hierarchy process (fuzzy AHP) and fuzzy technique for order preference by similarity to obtain the ideal solution (fuzzy TOPSIS). For this evaluation, the subjective opinions of ten different experts working in academia, in the private sector, or in the public sector were obtained using prepared questionnaires. As a result, the sub-criteria of fewer atmospheric emissions (0.42), operational feasibility (0.64), initial investment costs (0.56), and increased awareness of sustainable cities (0.53) had the highest weight values in their main criteria groups. The performance ranking of the alternatives according to the closeness coefficient (CCi) values was obtained as A2 (0.458) > A3 (0.453) > A4 (0.452) > A1 (0.440), with A3 being slightly ahead of A4 due only to a 0.001 higher CCi value. To test the reliability and stability of the obtained performance ranking results, a sensitivity analysis was also performed using eighteen different scenarios, in which the weights of the different sub-criteria were increased by 25% or decreased by 50%, or they were assumed to be 1 and 0, or all sub-criteria in the same group had equal weight values. Since the performance ranking of the alternatives did not change, the ranking obtained at the beginning was found to be robust against the sub-criterion weight changes. [ABSTRACT FROM AUTHOR]

The suitability of recycled poly(ethylene terephthalate) (R-PET) for 3D-printing applications was evaluated by studying the melt flow characteristics of the polymer. R-PET is known to experience significant deterioration in its mechanical properties when recycled due to molecular weight loss that results from reprocessing. Lower molecular weight hinders R-PET from being 3D-printable due to low viscosity and melt strength. The hypothesis was that R-PET can be modified with reasonable effort and resources to a 3D-printable thermoplastic if the low viscosity problem is tackled. Higher viscosity will enhance both the melt strength and the melt flow characteristic of the polymer, making it more suitable for processing and 3D printing. Reactive extrusion was selected as the method for modifying the polymer to achieve the objective via a coupling reaction with chain extender PMDA (pyromellitic dianhydride). A decrease in the melt flow index (MFI) from 90 to 1.2 (g/10 min) was recorded when PMDA was added at 0.75 wt% which lowered the MFI of modified R-PET to a comparable value to commercial 3D-printing filaments. Furthermore, FT-IR analysis was performed to investigate the chemical composition of the product. Finally, a 3D-printing filament was made from the modified R-PET by mimicking the main processing stations that exist in the filament-making process, which are the extrusion stage, water bath cooling stage and spooling stage. With 0.75 wt% PMDA, the melt strength was satisfactory for pulling the filament and, therefore, a filament with on-spec dimension was produced. Finally, a small object was successfully 3D-printed using the filament product at a minimum recommended temperature of 275 °C. [ABSTRACT FROM AUTHOR]

The separation of solid particles from gas-solid process streams is an important unit operation in many chemical processes. Out of the many different types of separation equipment that are used for gas-solid separation, cyclone separators are widely used for their operational flexibility, efficiency and capital cost. This study focused on the design of an optimal configuration for several cyclones used in a fertilizer plant. The granulation step in the fertilizer plant leads to using different size cyclones and a different number of cyclones in series or parallel or a mix of both arrangements. A Mixed Integer Nonlinear Programming (MINLP) model is formulated to find the best cyclone arrangement with the optimal number of cyclones and dimensions from several combinations of 1D3D and 2D2D cyclones arranged in parallel-series for a high volume and heavy loading of solid particles. The objective function was to minimize the total cost, including the operating cost and the capital cost. The results indicated that a maximum of 90% efficiency is achieved with a parallel-series arrangement of 1D3D and 2D2D cyclones to be an optimal configuration for the maximum reduction in pollution level. [ABSTRACT FROM AUTHOR]

The dye removal efficiency of the green algae Ulva lactuca from textile wastewater was assessed in a batch experiment series by evaluating the initial dye concentration, contact time, pH, biosorbent quantity, and temperature. In addition, the structural characterization of Ulva lactuca for adsorption was determined using an FT-IR spectrum, SEM image, and BET analysis. After this process, methylene blue was used as a model compound for the adsorption of organic pollutants from aqueous solutions. The compatibility of isotherm models, as well as the isotherm constants and correlation values for the adsorption balance system, were determined using the experimental data. The optimum adsorption was found at around pH 7, in 10 minutes of contact time through biosorbent dose of 0.5 g/L and initial dye concentration of 5 mg/L. Within this context, biosorption value at optimum values was 0.322 mg/g. At optimum conditions, the estimated maximum removal percentage value was 89%. Langmuir, Freundlich, Temkin, and Dubinin-Radushkevich isotherm models respec-tively had determination coefficients of 0.999, 0.983, 0.988, and 0.930. The Langmuir isotherm model was rather found to be more compatible with experimental protocols. As a result of the studies for adsorption kinetics, the determination coefficient of the pseudo-second-order kinetic models was calculated as 1.000. According to thermodynamic experiments which were interpreted as an exothermic event, the adsorption event occurred non-spontaneously. [ABSTRACT FROM AUTHOR]

Accurate perception of the key stratum instability can improve the safety of coal mining and also provide a basis for alleviating overlying rock strata destruction and environmental disturbance. To efficiently evaluate the instability of the key stratum and its threat to safe mining and environmental protection, the fracture characteristics and weakening mechanisms were studied through physical simulation, theoretical analysis, and field measurement. A scheme and the parameters of confined blasting in water-filled deep hole presplit technology (CBWDHPT) for thick and hard roof (THR) weakening were proposed. Research studies showed that, after the THR fractured into large blocks, the subsequent sliding instability induced serious support-crushing accidents; however, increasing the support strength could only provide limited control. Confined water and infiltrated modified rock mass functioned as the transfer load medium of the explosives, and the CBWDHPT fully utilized high explosion energy to break rocks. Consequently, the collapse and filling of the immediate roof and low-positioned THR, as well as the timely cutting off the middle-positioned THR, could be realized, which alleviated the migration space of THR blocks, overlying strata destruction, and earth-surface step subsidence. Finally, the environmentally friendly strategy (including the CBWDHPT and hydraulic support optimization) for overlying rock strata protection was proposed. In the industrial test, the THR was broken into blocks of different sizes after utilizing the CBWDHPT, and the support working resistance was significantly decreased. It was concluded that the environmentally friendly strategy could effectively reduce the risk of overlying rock strata destruction. [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]

The discussers mentioned that "the developed explicit solution of Eq. (7) of the original paper is applicable only to the transitional region and cannot predict the pipe diameter for either the rough ( HT ht ) or the smooth ( HT ht ) flow regime because for these values, Eq. (7) yields HT ht ." These ranges correspond an absolute roughness of the pipe wall ( HT ht ) range of 0.000396-8.999052 mm. [Extracted from the article]

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]