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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 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]

A hybrid programming methodology was proposed to derive a simple empirical formulation for the estimation of the required pipe diameter in the sizing problems (Type 3) of pipe distribution systems. The model was derived based on multiple regression-based analysis using the Richardson's extrapolation approach and the Levenberg–Marquardt algorithm with double precision. The proposed formulation was developed using a total of 300,000 different data points within the framework of MATLAB and DataFit scientific software. The application of the model was explored for a wide range of five fundamental pipeline design variables [absolute roughness of the pipe wall (ε=0–9 mm), water temperature (T=5°C–100°C), pipe length (L=5–500 m), flow rate (Q=0.01–1 m3/s), and head loss (Δh=1–30 m)] and tested against a total of 10,000 additional computational scenarios and the available models reported in the literature. The uncertainty prediction of the proposed formula was quantified and compared with those of existing prediction models. For the new empirical equation, the mean prediction errors between the estimated and the theoretical diameter values (calculated from the numerical solution of the Colebrook–White equation) were significantly smaller than those of existing models. Moreover, the narrowest uncertainty bands, the lowest 95% confidence prediction error intervals, and the lowest amounts of expanded uncertainty (U95) were achieved for the proposed model. Other statistics (e.g., mean absolute relative error, absolute relative error, coefficient of variation of root mean squared error, determination coefficient) also corroborated that the proposed empirical model produced realistic estimations that were superior to those obtained from other well-known explicit models in the literature. The findings of this study concluded that the computational analysis yielded a simple mathematical structure to be easily and accurately used for educational and practical purposes. [ABSTRACT FROM AUTHOR]

The waste collection process is an issue where numerous studies have already been conducted in the existing literature especially based on finding the optimal routes for the garbage trucks assigned in this service process. In this manner, A Mixed-Integer Linear Programming (MILP) based route optimization model has been conducted as the first attempt for waste collection-oriented electric garbage trucks routing process in this study. Even different studies exist in the literature for the route optimization of conventional fuels-based garbage trucks as mentioned above, no studies devote to considering the electric garbage trucks to the best of our knowledge. Besides, it is not easy to reach the detailed garbage collection area information in the literature. In this manner, data have been obtained by real field measurements in a region within the service area of Bakirkoy Municipality, Istanbul, Turkey. A unit energy consumption value that can be considered as a reference in the future has also been obtained using real data. Besides, real road information data have been integrated to the data used as input while assessing the optimization approach and the system analyses have been conducted in a more realistic concept. The proposed concept has led to an increased reality of nearly 38% for the analysis of the results under conditions closer to real-time, and a decrement of nearly 32% has been obtained. It is expected that this study may lead a conceptual input to an enhanced and greener waste collection process. • Mixed-Integer Linear Programming was introduced for electric garbage trucks routing process. • A unit energy consumption value was obtained as a new reference via real field measurements. • Average electric energy consumption was obtained as 0.86 kWh/km as a more realistic case. • Proposed approach decreased energy consumption for the collection area by nearly 32%. • Proposed approach provided techno-economic contribution for sustainable energy management. [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]

Struvite precipitation is widely used as an established and promising physicochemical treatment method due to its higher effectiveness for removing and recovering excess nitrogen and phosphorus from wastewater, and production of a beneficial byproduct at the end of the process. However, the majority of the literature focus not on the economic aspects of the process, but rather on the effect of different chemical combinations and changing operating conditions. In order to fulfill this gap, this study aims at a comprehensive feasibility analysis of struvite recovery process for a full-scale fertilizer production industry with a 500 m 3 /day capacity. For quantitative assessment, the experimental conditions and chemical combinations that will allow the receiving environment discharge standard for ammonium nitrogen are optimized by taking into account a large number of economic and operating parameters. The effect of change in the struvite sale price on the profit share is examined for the optimum conditions, and the investment and operating costs are calculated by getting the latest up-to-date values from the market. It is determined that when the struvite sale price is raised to 560 €/ton, the facility will obtain a net profit of €445.62/day, and be able to pay for itself in approximately six years. The findings of this study corroborate the economic feasibility of struvite recovery process as a clean and eco-friendly technology at the plant level for a sustainable nutrient management. Clearly, it appears likely that the struvite precipitation method will become more widespread in the future as an effective and non-polluting process. [ABSTRACT FROM AUTHOR]

The operation conditions for the implementation of the Fenton process are of utmost importance because there are problems related to the proportion of H2O2 dosage to COD and Fe2+ dosage to the specified H2O2 amount. The relevant literature shows that COD/H2O2 ratios range between 0.0084 and 113.9. Similarly, the COD/Fe2+ ratio varies between 0.079 and 292.6, while the H2O2/Fe2+ ratio varies between 0.09 and 287. Moreover, the ratio of the maximum value to the minimum value used in the operations on the basis of COD is 13560 for COD/H2O2 (0.0084-113.9), 2210 for COD/Fe2+ (0.079-174.7), and finally 3190 for H2O2/Fe2+ (0,09-287). The aim of this study was to re-evaluate these values that significantly differ from each other with specific emphasis on textile wastewater and considering stoichiometric ratios. Results showed that values ranging between 0.43 and 4.0 for COD/H2O2 and those ranging between 0.75 and 3.0 for H2O2/Fe2+ are more suitable. The results showed that in a Fenton process conducted by dosing H2O2 at different times, the TOC reduction efficiencies increased from 80.8 up to 88.9%. Similarly, the color reduction efficiency also rose from 96.5 to 98.7%. [ABSTRACT FROM AUTHOR]

Ameliorative effects of sheep slaughterhouse waste-derived soil amendments (struvite, blood meal, bone meal) were explored and quantified by a series of comparative greenhouse trials. A scoring matrix system was developed for 25 different test plants using 300 agricultural measurements obtained for three basic growth parameters (fresh-dry plant weights and plant heights) and four different fertilizer sources including solid vermicompost. More than 70% of NH 4 +-N recovery from sheep slaughterhouse wastewater was achieved 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 NH 4 +-N concentration of 240 mg/L, and a reaction time of 15 min. According to SEM micrographs, surface morphology of struvite exhibited a highly porous structure composed of irregularly shaped crystals of various sizes (11.34–79.38 μm). FTIR spectroscopy verified the active functional groups on the proximity of all fertilizer sources within the spectral range of 500–3900 cm−1. TGA-DTG-DSC thermograms of struvite revealed that the mass loss occurred in two temperature regions and reached a maximum mass loss rate of 1.63%/min at 317 °C. The average percentages of increase (57.55–100.62%) and performance points (69–79) corroborated that the fertility value of struvite ranked first on average in cultivation of the analyzed plant species. Findings of this agro-valorization study confirmed that sheep slaughterhouse waste-derived fertilizers could be a beneficial way to promote bio-waste management and environmentally friendly agriculture. [Display omitted] • Sheep slaughterhouse waste-derived soil amendments have agro-potential and show noticeable plant growth effects. • More than 70% of NH 4 +-N is recovered from SSW as strutive, ranking first as a high-quality fertilizer for growing 25 plant species with performance scores of 69–79. • SSW-recovered struvite shows a two-stage mass loss (with consecutive losses of 4% and 25%) reaching a maximum rate of 1.63%/min at 317 °C. • Common onion, carrot, and black bean benefit most from the fertilization with ≥70% increases (115–323%, 76–185%, and 73–200%, respectively) in all plant growth measures. • Conversion of sheep slaughterhouse waste stream into beneficial agro-materials could be a viable way of promoting bio-waste management and environmentally friendly agriculture. [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]

In the present paper, the removal of ethidium bromide (EtBr) from aqueous solutions in a batch system using natural (NP) and aluminium-coated pumice (ACP) as alternative low-cost adsorbents was investigated. The maximum adsorption capacity, q m (mg/g) was 58.82 and 76.92 mg/g for NP and ACP, respectively, operating at an initial pH of 8, an adsorbent dose of 8 g/L, a contact time of 210 min and an initial EtBr concentration of 30 mg/L. The equilibrium data of both adsorbents fitted the Freundlich isotherm model, indicating the heterogeneity of the adsorbent surface. In addition, the adsorption rate of both adsorbents was well described by the pseudo-second-order kinetics model. This indicated chemisorption was the rate-controlling step of the adsorption process which occurred by ion exchange. Within the performed study, a three-layer artificial neural network (ANN) model was also developed to predict the efficiency of EtBr removal. Computational results clearly demonstrated that the ANN model was able to predict the combined effect of initial pH, adsorbent dose, contact time and initial EtBr concentration on the adsorption efficiency with a very high determination coefficient ( R 2 = 0.998) and a low relative error (RE = 0.037). [ABSTRACT FROM AUTHOR]

Worldwide ageing infrastructures which are vulnerable to seismic lateral loads and located in high seismicity regions have arrested the interest of many researchers to find alternative materials and techniques to strengthen in bending and shear, for example reinforced concrete (RC) beams, slabs, columns, etc. There are several strengthening/repair techniques and materials in literature. Although the method of strengthening concrete structures with fiber reinforced polymers (FRP) is a relatively new technique, it has existed for more than two decades. In this context, several confinement models have been developed for FRP-confined concrete for the prediction of stress–strain response and several researchers have developed various constitutive models to measure the increase in the axial strength of concrete due to the confinement effect of FRP laminates. In this study, RC columns wrapped with carbon FRP (CFRP) considering some existing confinement models in the literature have been investigated. Moreover, based on the experimental data set in the literature, a new artificial intelligence-based algorithm (a Mamdani-type fuzzy inference system) was implemented to model the strength enhancement of CFRP confined RC columns using fuzzy logic methodology. Fuzzy logic predicted results were compared with the outputs of a non-linear regression analysis-based exponential model derived in the scope of the present work. The best predictive performances of the models were assessed by means of various descriptive statistical indicators. The comparison of the proposed prognostic approach with existing empirical and experimental data exhibits a very good precision of the developed artificial intelligence-based model in predicting the lateral confinement coefficient in CFRP wrapped RC columns. [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]

The present analysis was conducted as the first research to assess the techno-economic viability of the value-added by-products (struvite, blood meal, bone meal, and raw sheepskin) from a medium-scale sheep slaughterhouse facility with a slaughtering capacity of 300 sheep per day. For this aim, a comparative technical and economic feasibility analysis was performed to assess the synergistic use of slaughterhouse-oriented rendering wastes and struvite recovery from real sheep abattoir effluent within the framework of detailed cost breakdown, break-even point, and payback period analyses. The experimental findings clearly showed that under the optimal conditions (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), struvite precipitation could effectively remove about 73%, 64%, 59%, and 82% of NH 4 +-N, TCOD, SCOD, and color, respectively, from the real sheep slaughterhouse waste stream. Based on various up-to-date techno-economic items considered within the break-even point analysis, the sheep slaughterhouse facility was estimated to achieve the targeted net income (€100/day) for any selling prices of €1041.30/ton, €640.05/ton, €263.72/ton, and €1.012/hide, respectively, for struvite, blood meal, bone meal, and raw sheepskin. Steel construction and chemicals were determined as the most costly components for CAPEX (capital expenditures) and OPEX (operating expenditures), respectively, and selling prices of bone meal and raw sheepskin were found to be the most critical income items on the profitability of the slaughterhouse facility. Co-monetary assessment of the struvite process and valorized compounds corroborated the economic viability of the proposed project with the payback periods of about 6.3 and 5.5 years, respectively, for the current market and the profit-oriented conditions without subsidy. The findings of this feasibility analysis, as the first of its own, could be used as guideline for simplifying the decision-making with regards to the feasibility of similar facilities and commercialization of profitable by-products. [Display omitted] • Income-generating real sheep slaughterhouse concept is described for the first time. • High profit corridor is reached when the sales price of struvite exceeds €1041.30/ton. • Daily net profit is predicted as €12.22/kg NH 4 +-N removed at optimum MAP conditions. • Facility can pay for itself less than 6 years for unsubsidized profit-oriented case. • Valorized by-products offer an eco-sustainable way for meat processing facilities. [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]

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]

Abstract: Oil composition and properties including density, viscosity, asphaltene, saturate, aromatics and resin contents are responsible factors for the formation of water-in-crude-oil emulsions. These factors can be used to develop an stability index which determines states of water-in-oil emulsion in terms of either an unstable, entrained, mesostable or stable conditions. It is important to note that most of the regression models cannot capture the non-linear relationships involved in the formation of these emulsions. This study deals with the prediction of water-in-oil emulsions stability by an adaptive neuro-fuzzy inference system (ANFIS) with basic compositional factors such as density, viscosity and percentages of SARA (saturates, aromatics, resins, and asphaltenes) components. In the computational method, grid partition and subtractive clustering fuzzy inference systems were tried to generate the optimum fuzzy rule base sets. The stability estimation was conducted by applying hybrid learning algorithm and the model performance was tested by the means of distinct test data set randomly selected from the experimental domain. The ANFIS-based predictions were also compared to the conventional regression approach by means of various descriptive statistical indicators, such as root mean-square error (RMSE), index of agreement (IA), the factor of two (FA2), fractional variance (FV), proportion of systematic error (PSE), etc. With trying various types of fuzzy inference system (FIS) structures and several numbers of training epochs ranging from 1 to 100, the lowest root mean square error (RMSE=2.0907) and the highest determination coefficient (R 2 =0.967) were obtained with subtractive clustering method of a first-order Sugeno type FIS. For the optimum ANFIS structure, input variables were fuzzified with four Gaussian membership functions, and the number of training epochs was computed as 21. In the computational analysis, the predictive performance of the ANFIS model was examined for the following ranges of the clustering parameters: range of influence (ROI)=0.45–0.60, squash factor (SF)=1.20–1.35, accept ratio (AR)=0.40–0.55, and reject ratio (RR)=0.10–0.20. Results indicated that ROI, SF, AR and RR were obtained to be 0.54, 1.25, 0.50 and 0.15, respectively, for the best FIS structure. It was clearly concluded that the proposed ANFIS model demonstrated a superior predictive performance on forecasting of water-in-oil emulsions stability. Findings of this study clearly indicated that the neuro-fuzzy modeling could be successfully used for predicting the stability of a specific water-in-oil mixture to provide a good discrimination between several visual stability conditions. [Copyright &y& Elsevier]

• Available literature on LCA and structural characteristics of buildings is reviewed. • Low-rise buildings have received more attention than high-rise buildings. • Energy use and greenhouse gas emissions are the main focus of the existing literature. • The use phase of buildings accounts for the majority of life cycle impacts. • The construction phase may play a large role, particularly in energy efficient buildings. An overview of current status of the available literature on Life Cycle Energy, Life Cycle Greenhouse gasses, and Conventional Life Cycle Assessment of commercial and residential buildings was presented with respect to their height. A narrative literature review was carried out to provide a comprehensive overview as well as to highlight the recent contributions related to the environmental evaluation of high-rise and low-rise buildings. The study was carried out by searching the databases of the Scopus and Elsevier in conjunction with ScienceDirect and Google Scholar databases. The reason for this was to cover the published papers in this field up to the highest degree of accuracy. By means of the search of publications quoting the use of LCA in construction sector for the period from 1997 to 2018, more than 230 peer-reviewed publications referencing the use of life cycle assessment in buildings have been identified. The review shows that low-rise buildings (1~5 floors) compared to high-rise ones (≥ 5 floors) received significant attention as the studies focusing on the life cycle assessment of low-rise buildings were about twice in number more than the studies related to the life cycle assessment of high-rise buildings. In case of high-rise buildings, commercial buildings gained more attention by over 60% of the reviewed studies, while for low-rise buildings, residential buildings took the leverage by accounting to over 70% of the reviewed studies. The more frequently studied life cycle stages were those related to the manufacturing and use phases. Similarly, the most considered impact categories were the global warming potential and embodied energy. The reported values for embodied energy of high-rise buildings had a great variation ranging from 0.533 MJ/m2 to 883.1 GJ/m2, while the same values for low-rise buildings ranged from 0.21 to 374.4 GJ/m2. In terms of global warming potential, high-rise buildings emitted 10 to 10,010 kg CO 2 -eq/m2 per year, however, some studies revealed the potential of timber structure in emission reduction by values ranging from 234.8 to 1338 kg CO 2 -eq/m2. The emissions associated by low-rise buildings ranged from 0.07 to 35,765 kg CO 2 -eq/m2, and the respective values for emission reduction by timber structures were between 12.9 and 361 kg CO 2 -eq/m2. The results also indicate that a wide range of building's lifespan varying from 20 to over 100 years were utilized in life cycle assessment of different types of buildings. Functional unit was also another parameter that showed a broad variation both in terms of unit and definition. While the majority of researchers considered "m2" as the functional unit (61%), "whole building" was also considered as the functional unit in almost 20% of the reviewed studies, indicating the lack of standardized definition for functional unit for more practical outcomes. Ecoinvent was the most referred inventory database (65%) for life cycle assessment of buildings followed by University of Bath ICE (11%), U.S. database (9%), and Australian material inventory database (7%). SimaPro dominated computer-aided softwares with 40% of citations among the reviewed studies. ATHENA Impact Estimator and GaBi software gathered the attention of the reviewed studies by 7.5% and 4%, respectively. The review finally highlights that the variations in building design (structure and materials), lifetime, functional unit, and scope restrict to compare the findings and results of studies with each other. [ABSTRACT FROM AUTHOR]

The efficiency of new and nano-scale adsorbents including multi-walled carbon nanotubes (MWCNTs) and γ-alumina in the removal of cobalt(II) from aqueous solutions was experimentally evaluated in a batch-system reactor. To the best of our knowledge, no previous study has specifically attempted to introduce a hybrid strategy based on artificial neural network and genetic algorithm techniques for modelling and optimizing adsorptive removal of cobalt(II) from aqueous solutions via the proposed nanoparticles. The analyses of SEM, TEM, and FTIR were used to characterize both adsorbents. The response surface methodology (RSM) approach suggested a second-order polynomial model with a p -value < 0.0001 and R 2 of 0.9980 for MWCNTs adsorbent and a p -value < 0.0001 and R 2 of 0.9992 for γ-alumina adsorbent. The artificial neural network (ANN) approach suggested a three-layered feed-forward backpropagation model with R 2 of 0.9794 for MWCNTs adsorbent and R 2 of 0.9823 for γ-alumina adsorbent. The results linked to optimization by RSM showed that the maximum cobalt(II) removal efficiency of about 90% was achieved in the case of the MWCNTs adsorbent under the conditions of pH = 10, contact time = 38.6 min, MWCNTs dosage = 1.57 mg/L, and initial cobalt(II) concentration = 56.57 mg/L. About 93% of cobalt(II) removal could be obtained in the case of γ-alumina adsorbent under the conditions of pH = 10, contact time = 35.5 min, γ-alumina dosage = 1.63 g/L, and initial cobalt(II) concentration = 52.15 mg/L. The optimization values using the genetic algorithm (GA) technique were almost the same as those obtained from the RSM method. The kinetic model of Ho and McKay's pseudo-second order (PSO) and the isotherm model of Dubinin–Radushkevich were found to be the best-fitted to the experimental for both MWCNTs and γ-alumina. In addition, the maximum monolayer adsorption capacity of MWCNTs and γ-alumina adsorbents for the adsorption of cobalt(II) was 78.94 mg/g and 75.78 mg/g, respectively. Also, a thermodynamic study exhibited a favorable and spontaneous adsorption process for both materials. The present study clearly concluded that the proposed adsorbents could be effectively used for the removal of cobalt(II) from aqueous solutions at lower adsorbent dose and shorter contact times than various adsorbents reported in literature. Unlabelled Image • Adsorptive removal of cobalt (II) was explored via MWCNTs and nano-alumina. • ANN and genetic algorithm were proposed for this first time in cobalt (II) adsorption. • Isotherms and kinetic data followed Dubinin–Radushkevich and PSO models, respectively. • A cobalt (II) removal efficiency above 90% was possible at pH = 10 and 1 g adsorbent/L. • Favorable and spontaneous removal was corroborated by thermodynamic study. [ABSTRACT FROM AUTHOR]

The present analysis was conducted as the first study to investigate the biochemical methane potential of four different agro-industrial wastewaters originating from chocolate, slaughterhouse, gum, and beet sugar industries under the same anaerobic fermentation conditions. To the best of our knowledge, no previous study has specifically attempted to pinpoint a hybrid programming strategy for making a quantitative description of the anaerobic biodegradability of these waste streams. Thus, considering the scarcity of the literature in this field, a comprehensive study was conducted to evaluate the amount of bio-methane obtainable from the investigated organic wastes and to predict their kinetics using three different sigmoidal microbial growth curve models (modified Gompertz equation, transference function (reaction curve-type model), and logistic function) within the framework an original MATLAB®-based coding scheme. The results showed that methane productions started immediately after 4 h of incubation for all substrates and reached their maximum rates of 118, 116, 108, 34 mL CH 4 /g VS/day, respectively, for wastewaters from chocolate, slaughterhouse, gum, and beet sugar industries. The corrected mean steady state methane contents were 61.7%, 73.4%, 62.8%, and 62.1% in the respective order. The highest methane yield (943 mL CH 4 /g VS) was obtained from the slaughterhouse wastewater, and this value was 1.32, 1.58, and 4.56 times higher than those obtained in the anaerobic digestion of chocolate, gum, and beet sugar wastewaters, respectively. Among the three kinetic models tested, the logistic function best explained the behavior of the observed data of all substrates using a Quasi-Newton cubic line search procedure (R 2 = 0.987–0.996) with minimum number of non-linear iterations and function counts. Deviations between the measured and the outputs of the best-fit kinetic model were less than 4.3% in prediction of methane production potentials, suggesting that the proposed computational methodology could be used as a well-suited and robust approach for modeling and optimization of a highly non-linear biosystem. Image 1038 • Biochemical methane potential of four agro-industrial substrates was explored. • A new hybrid computational strategy was proposed to estimate the kinetic parameters. • Logistic model with Quasi-Newton algorithm showed the best fit of the observed data. • Highest methane production potential was obtained from slaughterhouse wastewater. • Studied agro-industrial feedstocks showed potential energy/economic value added. [ABSTRACT FROM AUTHOR]

Based on the occurrence conditions of a thick and hard main roof and wedge-structure immediate roof in the Zhuxianzhuang Coal Mine, the fracture characteristics and instability migration law of a thick and hard roof (THR) were examined via physical simulations. Mining zones were divided with respect to the strata behaviors and roof control difficulty levels, and the principles and methods of zonal control under THR were put forward. This study proposed a coordinated control strategy of using confined blasting in water-filled deep holes, and reasonable support optimization, which could effectively reduce the roof fracture size, increases the supporting intensity and eliminate roof-control disasters. The length of confined blasting blocks and supporting intensity were calculated using a mechanical model for roof control in the strong strata behavior zone and less-strong strata behavior zone. These key parameters were determined as 20–25 m and 1.15–1.28 MPa, respectively, and the mining strategy was successfully applied in working face 880, performing high security and reasonable economical efficiency. [ABSTRACT FROM AUTHOR]

This paper focuses on investigating students’ reasons for their reluctance to attend faculty members’ office hours. Study participants included 500 male and female students from the Colleges of Engineering (n = 248) and Arts and Social Sciences (CASS) (n = 252) at Sultan Qaboos University (SQU). The study followed a descriptive-analytical approach, and a questionnaire was utilized to collect people’s views. The results of the study indicate that the rates of SQU students’ attendance to office hours were low, and some students (11.2%) do not attend at all as they consider these office hours a waste of time. The main reasons behind the students’ lack of interest in office hours were busy student timetables, conflicts between faculty office hours and students’ timetables, and easier and faster ways of getting information than visiting faculty members. Additional reasons were related to faculty members’ personalities and their discouraging attitudes toward attending office hours. The researchers recommend that SQU adopt a new strategy for encouraging faculty members to hold office hours, familiarizing students with the importance of office hours and assigning part of a course’s grades to meeting with faculty members’ office hours. [ABSTRACT FROM AUTHOR]

The study investigated the pretreatment of real paracetamol (PCT) wastewater of a pharmaceutical industry by Fenton process. At the best experimental conditions (COD/H2O2 = 1/1, Fe+2/H2O2 = 1/70, settling method:centrifuging, pH 6 at settling step), 92.7, 92.7, 95.5, 99.1, 99.9 and 99.4% of chemical oxygen demand (COD), total organic carbon (TOC), 5-day biological oxygen demand (BOD5), PCT, para-amino phenol (PAP) and aniline were removed, respectively. Changes in the concentrations of these parameters were also investigated for both oxidation and settling steps of Fenton process. It was found that COD and TOC were removed at the settling step (precipitation) whereas PCT, PAP and aniline were removed at the oxidation step. Mass balance calculations were also studied to show the mass distributions of COD in different phases (gas + foam, effluent and sludge). Fenton process was found as an effective method for the pretreatment of real PCT wastewater for discharging in a determined collective treatment plant. [ABSTRACT FROM AUTHOR]

In the present study, defluoridation study of liquid phase with the help of multi-walled carbon nanotubes (MWCNTs) and single-walled carbon nanotubes (SWCNTs) were well investigated and elucidated. The impact of different experimental conditions such as solution pH, initial fluoride concentration, adsorbent dose, and contact time was well studied and optimized for the maximum fluoride removal from water. The experimental data were fitted by the Freundlich, Langmuir and Dubinin–Radushkevich (D–R) isotherm models and the related equilibrium constants were calculated. The results of the isotherm studies showed that fluoride removal by both adsorbents followed the Freundlich isotherm model. Kinetic studies were conducted and the results demonstrated that the experimental data were fitted well with the pseudo-second order kinetic model. Furthermore, two multiple regression-based equations were also derived to model the removal of fluoride from aqueous solutions by the carbon nanotubes. This study demonstrated that the polynomial equations satisfactorily described the behavior of the present defluoridation process for both MWCNTs ( R 2 = 0.913) and SWCNTs ( R 2 = 0.941). [ABSTRACT FROM AUTHOR]

Contribution of marine aerosols to marine boundary layer was investigated in Istanbul, Turkey. Average fluxes of Na+ and Cl− were determined to be 49.0 and 64.9 mg (m2 day)−1, with the highest values being 510.8 and 825.2 mg (m2 day)−1, respectively. Monthly fluxes of other parameters (flouride, chloride, bromide, phosphate, lithium, sodium, potassium, ammonium, nitrite, nitrate, sulfate, magnesium, calcium, zinc, lead, cobalt, cadmium, nickel, manganese, chromium, vanadium, copper, iron, aluminum and boron) were also studied. Contribution of sea salt aerosols from Black Sea was of great importance along the Bosphorous line, while other sources are also effective along the coasts of Marmara Sea. [ABSTRACT FROM AUTHOR]

Due to its importance in flow measurement and instrumentation, as well as its frequent application in differential pressure flowmeters, orifice discharge coefficient (C d) needs to be estimated precisely. In this study, different soft computing models (including multiple linear regression (MLR), group method of data handling (GMDH), multivariate adaptive regression splines (MARS), M5P tree model, and random forest (RF)) were employed for the first time in estimation of the C d value, and their respective prediction performances were analyzed statistically. Coefficient of correlation (CC), mean absolute error (MAE), root mean square error (RMSE), scattering index (SI), and Nash–Sutcliffe model efficiency coefficient (NSE) were used as the statistical indicators for validating the performance of each soft computing model. The statistical indicators approved the superiority of the RF model over the other models, while the MARS model also showed a competitive prediction potential over M5P, GMDH, and MLR models. The findings of this computational study clearly demonstrated that the implemented soft computing strategy had the capability to be used in precise estimation of the C d of the orifice meter, specifically, in situations where the measurement of the parameters in deterministic equation is not practically feasible. [Display omitted] • Differential pressure flowmeters were simulated using soft computing methodology. • MLR, GMDH, MARS, M5P, and RF were used to estimate C d for the first time. • Effectiveness of models was tested by CC, MAE, RMSE, SI, and NSE metrics. • RF outperformed other models, and MARS showed competitive prediction accuracy. • RF-based soft computation was of potential to be used in precise estimation of C d. [ABSTRACT FROM AUTHOR]

Background: Azo dyes represent the most commonly used group of dyes in the textile industry. These organic dyes are mainly resistant to biodegradation and may exhibit toxic and carcinogenic properties. The purpose of this study was to investigate the effects of doping zinc oxide (ZnO) nanoparticles (NPs) with transition metals (silver, manganese, and copper) on the photocatalytic efficiency of ZnO NPs in the removal of Direct Blue 15 dye from aqueous environments under ultraviolet (UV) radiation and visible light irradiation. Methods: One or two metals were used for doping the NPs. In total, seven types of undoped and transition metal-doped NPs were synthesized using the thermal solvent method with ZnO precursors and transition metal salts. The characteristics of the synthesized NPs were determined based on the scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), X-ray diffraction (XRD), Fourier transform infrared (FTIR) spectroscopy, dynamic light scattering (DLS), atomic force microscopy (AFM), and zeta potential measurements. Results: The produced ZnO NPs did not exhibit any particular photocatalytic activities under UV radiation and visible light irradiation. The highest removal efficiency under UV radiation was about 74% in the presence of silver-doped ZnO NPs, while the maximum efficiency under visible light was 70% in the presence of copper-doped ZnO NPs. The lowest removal efficiency was related to pure ZnO, which was 18.4% and 14.6% under UV and visible light irradiation, respectively. Although the efficiency of dye removal under visible light was not high compared to UV radiation, this efficiency was noteworthy in terms of both practical and economic aspects since it was achieved without the presence of ultraviolet radiation. Conclusions: The synthesis of transition metal-doped ZnO nanophotocatalysts (with one or two metals) under UV radiation or visible light irradiation could be used as an efficient and promising technology for the photocatalytic removal of Direct Blue 15 dye from aqueous environments. [ABSTRACT FROM AUTHOR]

The aim of this study was to investigate the applicability of the adsorption process for the removal of organophosphorus pesticide malathion 57% from water by using multi-walled carbon nanotubes (MWCNTs). The impact of various experimental conditions such as pH, quantity of adsorbent, concentration of pesticides, contact time and temperature was studied and optimized for the maximum removal of malathion. Unlike conventional optimization, a limited number of experiments (26 steps) were performed in a cost-effective manner for different independent variables such as MWCNTs concentration (0.1–0.5 g/L), the malathion (57%) concentration (6 mg/L and 10 mg/L), contact time (2–30 min) and pH (neutral range). Based on the experimental data obtained in a lab-scale batch study, a three-factor response surface modeling (RSM) approach was implemented in order to optimize the conditions for maximum removal of malathion, and compare experimental results with standardized malathion samples. The optimized conditions to achieve the maximum removal of malathion (100%) were determined to be a malathion concentration of 6 mg/L, an initial MWCNTs concentration of 0.5 g/L, and a contact time of 30 min. Findings of this study clearly indicated that 100% of the malathion could be cost-effectively removed by MWCNTs in conditions predicted by the proposed optimization methodology. [ABSTRACT FROM AUTHOR]

Azinphos-methyl and chlorpyrifos are the organophosphorous pesticides which pose serious threats to the environment including their detrimental effect on humans and, therefore, their removal from the environment is a must. Hence, in the present paper the ultrasound technique was applied to remove the above-mentioned hazardous compounds. For this, the effect of influential parameters such as pH, initial pesticide concentration, frequency, electric power and treatment time on the ultrasound degradation of azinphos-methyl and chlorpyrifos was well investigated and elucidated. The results obtained showed that azinphos-methyl and chlorpyrifos were effectively and rapidly degraded by the ultrasound technique. Thus, the operating optimal conditions (initial pH 9, initial pesticide concentration 1 mg/L, frequency 130 kHz, electric power 500 W and treatment time 20 min) lead to a degradation of 78.50% for azinphos-methyl and of 98.96% for chlorpyrifos with in 20 min of contact time. Two multiple regression-based equations were derived to describe the degradation process of the pesticides by the ultrasound treatment. The result of this study showed that the polynomial equations satisfactorily described the behavior of the present process for various operating conditions. [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]

[Display omitted] • AgFeO 2 /GO interface synthesized by ultrasonic hydrothermal and Hummer's techniques. • High LMF removal efficiency achieved with low-energy consumption in limited duration. • Bifunctional properties toward photodegradation and disinfection in an aqueous system. • Heterojunction system that facilitates the photo-induced electron charge carriers. The bi-functional nanoscale catalysts are of growing attention to address environmental problems. The present work successfully fabricated the hexagonal silver delafossite oxide bridged graphene oxide ultrathin nanosheets interfaces (AgFeO 2 /GO) to target photodegradation of an antibiotic and inactivation of model waterborne pathogens for the first time. The AgFeO 2 /GO composite was prepared via facile hydrothermal treatment followed by ultrasonic agitation by varying the GO loading. The AgFeO 2 /GO composite and their parent constituents were verified by advanced analytical, microscopic, and spectroscopic techniques like FESEM, EDX, HR-TEM, DLS, XRD, FTIR, XPS, UV–Vis, and PL. The AgFeO 2 /GO3 composite showed excellent photocatalytic activity towards mineralization of a broad-spectrum antibiotic, lomefloxacin (LMF) under the irradiation of visible light (30 W, LED). A maximum of ~88 % of LMF (20 mg/L) was degraded within 75 min of catalysis. The superoxide and hydroxyl radicals were predominant in LMF breakdown. In addition, AgFeO 2 /GO3 was proved effective up to three consecutive trials retaining 78.85 % efficiency. On the other hand, AgFeO 2 /GO3 heterojunctions demonstrated fair bactericidal activity against pathogens, Escherichia coli and Staphylococcus aureus. Disc diffusion method of antimicrobial assay confirmed the activity of AgFeO 2 /GO3 on par with standard drugs. Therefore, in both the functionalities, the GO incorporation along with AgFeO 2 standpoints enhanced photoactivity and catalytic inactivation making it a sought-after, bi-functional, and efficient environmental catalyst. [ABSTRACT FROM AUTHOR]

Despite the remarkable potential for biogas production from livestock and organic wastes, the number of constructed biogas digesters for the continent is in the order of thousands and in case of Mauritania is lower than the number of the provinces. While majority of the existing digesters left far behind the expected efficiency, lack of a comprehensive economic assessment along with an efficient design questions their practical application. The present study introduced the first assessment to evaluate the biogas potential from livestock manures and waste from slaughterhouses in Northwest Africa (a case of Mauritania). Using ArcGIS® software, a database was conducted to build maps that show the amount of waste products, the potential of biogas, and equivalent amounts of energy. These were used to assess the potential of biogas and the corresponding potential energy for each geographical department in Mauritania. The results indicated that the southern provinces had the highest biogas potential with the maximum and the average values of 520 and 258.7 (±125.8) × 106 m3/year. On the other hand, the lowest biogas production potential (27.7 × 106 m3/year) was recorded for northern provinces with the maximum and the average values of 135 and 76.4 (±39.7) × 106 m3/year. The results showed that 63,579 × 106 kg of waste associated with livestock (cattle) and slaughterhouse applications were annually produced in the country. It was determined that this quantity could generate 2451 × 106 m3 of biogas per year, corresponding to an energy potential of 52,704 × 106 MJ/year. Considering the rapid depletion of conventional energy sources and the significance of biogas as a renewable fuel, a detailed feasibility analysis (for the biogas production in each province of Mauritania by means of community type fixed-dome digesters) was also performed in the scope of this study. The results of the comprehensive cost breakdown analysis revealed that the revenues obtained from sale of biogas-generated electricity and digested slurry (as fertilizer) could able to pay the initial investment within approximately 6.5 years without subsidy. The findings of this study, as the first of its own, could be used to comprehend how utilization of information such as slaughterhouse and livestock population, nutrition habitats, land-usage maps and geographic information system (GIS) can be employed to germinate a model for more comprehensive assessment of biogas production potential from livestock manure and slaughterhouse wastes, specifically in case of northern African countries. Moreover, in case of biogas plant, this model could be employed as a decision-making tool to identify the highly qualified location for construction of the biogas plant. • Mapping of biogas production potential is conducted in Mauritania for the first time. • 63,579 × 106 kg of waste can generate 2451 × 106 m3 of biogas per year in the country. • The southern provinces are found to have approximately 4 times more biogas potential. • Energy production in Mauritania heavily depends on oil, natural gas, and wood fuels. • A nationwide biogas digester project can pay for itself in 6.5 years without subsidy. [ABSTRACT FROM AUTHOR]