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A prognostic approach based on a MISO (multiple inputs and single output) fuzzy logic model was introduced to estimate the pressure difference across a gas turbine (GT) filter house in a heavy-duty power generation system. For modelling and simulation of clogging of the GT filter house, nine real-time process variables (ambient temperature, humidity, ambient pressure, GT produced load, inlet guide vane position, airflow rate, wind speed, wind direction and PM10 dust concentration) were fuzzified using a graphical user interface within the framework of an artificial intelligence-based methodology. The results revealed that the proposed fuzzy logic model produced very small deviations and showed a superior predictive performance than the conventional multiple regression methodology, with a very high determination coefficient of 0.974. A complicated dynamic process, such as clogging phenomenonin heavy-duty GT system, was successfully modelled due to high capability of the fuzzy logic-based prognostic approach in capturing the nonlinear interactions. [ABSTRACT FROM AUTHOR]

Commercial turfgrass cultivation is one of the main ornamental industries world-wide; however, successive turfgrass sod cutting from the same site removes surface soil, leading to a decline in soil organic matter, impairment of soil fertility and degradation of environment. The present study was aimed to investigate the applicability of poultry abattoir sludge compost (PASC) and biochar (BC) on the establishment of turfgrass by evaluating plant growth performance and mitigation of soil loss by organic waste amendments. The experimental study was designed on the soil which had originally low-organic matter content and previously used as a turfgrass sod harvested site in a sandy loam soil. Incorporation of PASC to soil improved the physicochemical properties in terms of bulk density (BD), water holding capacity (WHC), cation exchange capacity (CEC), pH, total nitrogen, total organic carbon (TOC), and organic matter (OM) by 37 (±2)%, 45 (±3)%, 55 (±3)%, 21 (±2)%, 48 (±2)%, 90 (±10)%, and 96 (±4)%, respectively. PASC-amended treatments enhanced the turfgrass growth rate more than the BC due to its increased nutrient availability. Incorporation of 100 Mg ha–1 (mega gram per hectare) PASC in surface soil with or without BC decreased the mineral soil removal rate by half of the respective soil (control) treatments. The results of the present study confirmed the utilization of PASC and BC as promising agro-industrial-based fertilizers in turfgrass sod production for sustainable soil and nutrient management. [ABSTRACT FROM AUTHOR]

Sustainable uses of the struvite (magnesium ammonium phosphate hexahydrate, MgNH4PO4·6H2O, MAP) recovered from the synthetic wastewater, as a high-quality slow-release fertilizer for the growth of nine medicinal plants and a fire-retardant barrier on the flammability of cotton fabric and wooden plate, were explored in this study. The previous experimental results demonstrated that under the optimal conditions, about 98.7% of NH 4 + − N (initial NH 4 + − N = 1000 mg/L) could be effectively and successfully recovered from simulated wastewater in the form of MAP precipitate. Rates of increase in total fresh weights, total dry weights, and fresh heights of plants grown in soil fertilized with the struvite were determined as 67%, 52%, and 12% for valerian; 121%, 75%, and 18% for cucumber; 421%, 260%, and 47% for dill; 314%, 318%, and 27% for coriander; 432%, 566%, and 30% for tomato; 285%, 683%, and 26% for parsley; 200%, 225%, and 9% for basil; 857%, 656%, and 92% for rocket; and 146%, 115%, and 28% for cress, respectively, compared to the control pots. The microstructure, elemental composition, surface area, thermal behaviour, and functional groups of the grown crystals were characterized using SEM, EDS, BET, TGA-DTG-DSC, and FTIR analyses, respectively. Flammability tests and thermal analyses concluded that the dried and crumbled/implanted form of struvite used as a fire-retardant barrier demonstrated a remarkable flame-resistant behaviour for both cotton fabric and wooden plate. Findings of this experimental study clearly corroborated the versatility of struvite as non-polluting and environmentally friendly clean product for the sustainable usage in different fields [ABSTRACT FROM AUTHOR]

This study was conducted to determine the concentration of airborne respirable crystalline silica (ARCS) particles and to evaluate its risk level among workers employed in eight different workplaces in Iran. They included sandblasting operations, ceramic production, sand and gravel work, brick production, asphalt manufacturing, foundry operation, glass making, and stone cutting. Personal exposure to ARCS was measured in randomly selected samples from each workplace. The maximum and minimum average of ARCS concentration in the eight workplaces were observed in foundry operation (0.338 ± 0.110 mg/m3) and glass manufacturing (0.125 ± 0.093 mg/m3), respectively. The average concentration of ARCS in all assessed working conditions were higher than the permitted limit of occupational contact recommended by Technical Committee of the Iranian Ministry of Health, treatment and medical education (0.025 mg/m3). Overall, the results showed a serious lack of control in Iranian industries with respect to workers' exposures to ARCS. [ABSTRACT FROM PUBLISHER]

This study was conducted to determine the concentrations of cadmium, copper, lead, and vanadium in seawater samples taken from the vicinity of Single Buoy Mooring 3 (SBM3) at Mina Al Fahal in the Sultanate of Oman. SBM3 is one of three locations at Mina Al Fahal (SBM1 and SBM2) associated with importing and exporting refined petroleum products. Using graphite furnace atomic absorption spectrophotometry, concentrations of these four metals in seawater samples were measured in six samples. None of the metals was present in concentrations greater than the European Commission/United Kingdom Environmental Quality Standards, but a comparison to previous data collected at SBM2 indicates that concentrations have increased approximately 40% over the past two decades. To better identify sources of the metals, it is imperative to monitor effluents and collect more targeted samples at and/or near discharge points. [ABSTRACT FROM AUTHOR]

Two three-layer artificial neural network (ANN) models were respectively developed to predict biogas and methane production rates in a pilot-scale mesophilic up-flow anaerobic sludge blanket (UASB) reactor treating molasses wastewater. Eight process-related variables such as volumetric organic loading rate (OLR), influent and effluent pH, operating temperature, influent and effluent alkalinity, effluent chemical oxygen demand (COD), and volatile fatty acid (VFA) concentrations were selected for the implementation of an artificial intelligence-based approach. A tangent sigmoid transfer function (tansig) at the hidden layer and a linear transfer function (purelin) at the output layer were conducted for the proposed ANN models. Several benchmark comparisons were conducted to obtain an optimal algorithm for training the network. After backpropagation training combined with principal component analysis (PCA), the scaled conjugate gradient algorithm (trainscg) was found as the best of the 11 training algorithms. The number of neurons in the hidden layer was optimized as nine and 12 with the minimum mean squared errors (MSE) of 0.06238 and 0.06488, respectively, for the estimation of biogas and methane production rates. ANN-predicted results were also compared to the outputs of two non-linear regression models by means of several statistical indicators, such as determination coefficient (R2), unsystematic root mean-square error (RMSEU), index of agreement (IA), and fractional variance (FV). Computational results clearly demonstrated that, compared to the conventional multiple regression-based methodology, the proposed ANN-based models produced smaller deviations and exhibited superior predictive accuracy with satisfactory determination coefficients of about 0.935 and 0.924, respectively, for the forecasts of biogas and methane production rates. [ABSTRACT FROM AUTHOR]