Inlet Air Rates Research Articles

Prediction compost criteria of organic wastes with Biochar additive in in-vessel composting machine using ANFIS and ANN methods

In-vessel composting machine with the agitating system, circulating aeration system, and heating system on vegetable and food waste with coco peat additives and biochar obtained from coco peat was investigated. The composting process was tested at 55 °C, at three fresh inlet air rates of 20%, 30%, and 50%, three initial carbon-to-nitrogen (C/N) ratios of 18, 22, 26, and the addition of coco peat biochar of 5%, 10% w.b. (wet basis). To predict compost evaluation indicators of Electrical conductivity (EC), pH, C/N & GI, artificial neural network (ANN), and neural-fuzzy inference systems were used. The evaluation of the output parameters of compost showed high efficiency of the process. The amount of EC, acidity, and GI increased for all treatments, and the C/N ratio decreased. Also, the initial C/N ratio of 22 and fresh inlet air (FIA) of 30% were considered as the optimal setting conditions of the device. Treatment containing 5% biochar in the C/N of 22 resulted in the highest germination index of 93.55%. The best values of the coefficient of determination for the output parameters of the compost production process (EC, pH, C/N & GI) in the artificial neural network were 0.9252, 0.9863, 0.9691, and 0.9909 respectively. Moreover, the best values of the coefficient of determination in the fuzzy neural inference system for the output parameters of the compost include EC, pH, C/N and GI were 0.999, 0.999, 0.994, and 0.992, respectively. Also, the lowest values of MAE and RMSE in the fuzzy neural inference system for the output parameters of the compost include EC, pH, C/N, and GI were 0.0308, 0.0001, 0.2420, and 0.003 for MAE; and 0.0021, 3.66E−05, 0.1908 and 0.0041 for RMSE, respectively.

Performance evaluation of a solar air heating system integrated with a phase change materials energy storage tank for efficient thermal energy storage and management

This research aims to manage thermal energy in a solar system to make it more functional due to solar energy variability. A parabolic trough collector (PTC) was integrated with a tank of phase change materials (PCMs) to produce hot air with a temperature close to the PCM melting point over a lengthy period. A comprehensive computational fluid dynamic (CFD) model was developed for the system. After model validation, parametric sensitivity analysis was performed and the proper ranges of the effective parameters were attained. Obtained results reveal that the PTC dimensions must be selected based on inlet temperature to the PCM bed as follows that could melt the entire or a part of PCMs. The PTC's length to its aperture ratio, Rl in the range of 0.85–2.25, bed length and diameter greater than 0.9 and 0.35 m, respectively, bed porosity in the range of 0.1–0.5, and inlet air rate in the range of 0.01–0.06 kg/s can satisfy the objectives of the present work. The system can produce up to 1025 m³ of hot air with a temperature of 60 °C and ±2 °C fluctuations during 12.5 h of system operation from the early hours of the day until late at night.

Exergoeconomic analysis of a solar photovoltaic-based direct evaporative air-cooling system

The target of this work is to perform the exergoeconomic analysis of a solar photovoltaic-based direct evaporative air-cooling system. The system was investigated experimentally with different thicknesses of cellulose and straw pads. For inlet air rates up to 1000 m3/h, the maximum changes in the humidity and temperature of outlet air are achieved by a pad with a thickness of 30 cm. A comprehensive mathematical model was developed for the system and the exergoeconomic analysis of the system was carried out. The influence of the effective parameter on the performance of the system was investigated. For an inlet air with a temperature of 30 °C and relative humidity of 30%, the maximum system exergy efficiency was obtained about 20%. With changes in the water temperature from 15 to 27 °C, inlet air rate from 300 to 1500 m3/h, and inlet air temperature from 26 to 34 °C, the system exergoeconomic factor changes up to 60%. The current system was compared with a conventional system and results showed that for four early years of systems lifetime, the exergoeconomic factor of the conventional system is greater than the solar system due to its lower initial investment, and for later years is lower due to its larger operating cost.

Treatment of overhaul wastewater containing N-methyldiethanolamine (MDEA) through modified Fe-C microelectrolysis-configured ozonation: Investigation on process optimization and degradation mechanisms.

In this study, the microelectrolysis system was applied to generate strong reductants, such as free hydrogen [H] and O∙, and thus removing N-Methyldiethanolamine (MDEA) in overhaul wastewater. Effects of initial influent pH, mass ratio of filings to wastewater, air aeration rate, and reaction temperature on the removal of MDEA were investigated intensively. Experimental results indicate that optimum removal rate of MDEA can be obtained at pH = 2, inlet air rate = 1 L min-1, mass ratio of filings to wastewater = 1:1 and temperature = 25 °C. About 96.0% Total Organic Carbon (TOC) in overhaul wastewater can be mineralized by ozonation-microelectrolysis-ozonation (OMIO) treatment process. By analyzing the effluent at various stages, it was established that microelectrolysis played a leading role in the destruction of MDEA. The degradation mechanism of MDEA has been clarified through detecting the degradation products with Gas Chromatography-Mass Spectrometer (GC-MS). Subsequently, ozone reacts with intermediate products generated by MDEA degration through OH· pathway under alkaline condition. In short, these results suggest that OMIO system should be proposed as a promising treatment process for the MDEA wastewater.