Effect of ozonolysis pretreatment on enzymatic digestibility of sugarcane bagasse

Bagasse was pretreated with ozone to increase the enzymatic hydrolysis extent of potentially fermentable sugars.  Through a 3×4 factorial design, this research studies the influence of operating parameters (moisture content and retention time) on ozonization pretreatment of bagasse in a fixed bed reactor under room conditions.  Enzymatic hydrolysis yields of up to 67% were obtained compared to 20% in non-ozonized bagasse.  Moisture content and retention time showed significant effect on ozonolysis.  And the most efficient conversion was obtained in 50% w/w humidity and 3.5 h retention time.  The study also revealed that smaller particle size in the raw bagasse improved the performance of ozonolysis as well.   Keywords: Sugarcane bagasse, ozonolysis, bioethanol, enzymatic hydrolysis ity �T92�Ϲ0��pan lang=EN-US style='font-size:9.0pt;line-height:140%;mso-fareast-language:ZH-CN'> mm2/s was obtained for raw groundnut oil than 7.60 mm2/s obtained for groundnut oil ethyl ester.  At 15ºC, specific gravity of raw groundnut oil and its ethyl ester were 0.9 (1.047 times that of AGO) and 0.85 (1.012 times that of AGO) respectively and are within limit specified by international standards.  The biofuels contained lower amounts of sulphur (9.73% for groundnut oil ethyl ester and 12.8% for raw groundnut oil) than the reference AGO which was 61.8%.  Higher pour (4ºC and 3ºC) points, cloud (7ºC and 8ºC) points and flash (200ºC and >280ºC) points were obtained for groundnut oil ethyl ester and the raw groundnut oil respectively compared to -16ºC, -12ºC and 74ºC respectively obtained for AGO.  The fatty acid profile of the groundnut oil reveals 75.03% unsaturated fatty acids in the oil composition.  Groundnut oil ethyl ester was found to have better fuel quality than raw groundnut oil and it has potentials to fuel a diesel engine.  Keywords: groundnut oil, automotive gas oil, transesterification, ethyl esters, biodiesel, fuel, diesel engine, Nigeria&nbsp

Application of Neural Networks and multiple regression models in greenhouse climate estimation

Artificial Neural Networks (ANNs) are biologically inspired computer programs designed to simulate the way in which the human brain processes information. After a comprehensive literature survey on the application of ANNs in greenhouses, this work describes the results of using ANNs to predict the roof temperature, inside air humidity, soil temperature and inside soil humidity (Tri, RHia, Tis, RHis), in a semi-solar greenhouse according to use some inside and outside parameters in the institute of renewable energy in East Azerbaijan province, Iran. For this purpose, a semi-solar greenhouse was designed and constructed for the first time in Iran. The model database selected beside on the main and important factors influence the four above variables inside the greenhouse. Neural estimation models were constructed with (Vo, Tia, Toa, Ir, Tis, RHia, Tri) as the inputs and (Tri, RHis, Tis, RHia) as the outputs. Optimal parameters for the network were selected via a trial and error procedure on the available data. Results showed that MLP (Multilayer Perceptron) algorithm with 4-6-1(4 inputs in first layer, 6 neurons in hidden layer and an output) and 4-9-1(4 inputs in first layer, 9 neurons in hidden layer and an output) topologies can predict inside soil and air humidity and inside roof and soil temperature with a low error (RMSE=0.25°C, 0.30%, 1.06°C and 0.25% for Tri, RHis, Tis and RHia), respectively. Also the results showed that regression model has a low error to predict Tri (RMSE=0.71°C) and high error to estimate Tis (2.71°C), respectively. In overall, the error for regression model to predict all 4 parameters (Tri, RHis, Tis, RHia) was about 2 times higher than MLP method. It is concluded that ANN represents a promising tool for predicting inside climate in a greenhouse and will be useful in automatic greenhouses. For practical application, however, the farmers should use metrological and experimental data for 12 months of the year to decrease the prediction error

Analysis of Energy Efficiency of Mechanized Cultivation in Potato Production Using a Data Envelopment Analysis Approach

Vo l u m e 1 0 -N u m b e r 1 7 -M a y 2 0 1 4 ( 6 1 -6 8

Theoretical and experimental research on effect of fins attachment on operating parameters and thermal efficiency of solar air collector

Flat plate air collector is a type of heat exchanger which absorbs radiated solar energy and exchanges it to heat. According to low efficiency of this type of collectors, a suitable approach is investigated in this paper so as to increase thermal performance of the system. Thermal efficiency of solar collector for two models C1 (without fins) and C2 (with fins) both of 1 m2 surface area with forced convection flow is studied theoretically and experimentally. Rectangular fins are attached over back board in air channel to create turbulence in air flow. In order to measure air temperature, 17 thermal sensors (LM35) are exploited, among which 11 were mounted on absorber plate and the remaining 6 on the back board. Physical design of experimental model are performed in Solidwork and programming of theoretical work in Matlab software. In this research, a fan with constant mass flow rate of 0.033 kg/s is utilized for producing air flow. Results indicate that applying fins in air channel not only reduces Nusselt number from 19.67 to 16.23, but also due to decreasing hydraulic diameter and creating air flow turbulence, causes increase of heat transfer coefficient from absorber plate to air flow and consequently reduction of total heat loss and higher outlet air temperatures. Average difference of outlet air temperature between experimental and theoretical results for both collectors (C1 and C2) was recorded respectively as 7.6% and 9.4%. Thermal efficiency was respectively calculated 30% and 51% for experimental types with and without fins and 33% and 55% for those of theoretical work which generally seem reasonable. Keywords: Solar air collector, Fins, Thermal efficiency, Energy efficiency, Renewable energ

Application of dynamic model to predict some inside environment variables in a semi-solar greenhouse

Greenhouses are one of the most effective cultivation methods with a yield per cultivated area up to 10 times more than free land cultivation but the use of fossil fuels in this production field is very high. The greenhouse environment is an uncertain nonlinear system which classical modeling methods have some problems to solve it. There are many control methods, such as adaptive, feedback and intelligent control and they require a precise model. Therefore, many modeling methods have been proposed for this purpose; including physical, transfer function and black-box modeling. The objective of this paper is to modeling and experimental validation of some inside environment variables in an innovative greenhouse structure (semi-solar greenhouse). For this propose, a semi-solar greenhouse was designed and constructed at the North-West of Iran in Azerbaijan Province (38°10′N and 46°18′E with elevation of 1364 m above the sea level). The main inside environment factors include inside air temperature (Ta) and inside soil temperature (Ts) were collected as the experimental data samples. The dynamic heat transfer model used to estimate the temperature in two different points of semi-solar greenhouse with initial values. The results showed that dynamic model can predict the inside temperatures in two different points (Ta and Ts) with RMSE, MAPE and EF about 5.3 °C, 10.2% and 0.78% and 3.45 °C, 7.7% and 0.86%, respectively. Keywords: Semi-solar greenhouse, Dynamic model, Commercial greenhous

Modeling and experimental validation of heat transfer and energy consumption in an innovative greenhouse structure

The commercial greenhouse is one of the most effective cultivation methods with a yield per cultivated area up to 10 times more than free land cultivation but the use of fossil fuels in this production field is very high. The objectives of this paper are to modeling and experimental evaluation of heat and mass transfer functions in an innovative solar greenhouse with thermal screen. For this propose, a semi-solar greenhouse was designed and constructed at the North-West of Iran in Azerbaijan Province (38°10′N and 46°18′E with elevation of 1364 m above the sea level). The inside environment factors include inside air temperature below screen (Ta), inside air temperature above screen (Tas), crop temperature (Tc), inside soil temperature (Ts), cover temperature (Tri) and thermal screen temperature (Tsc) were collected as the experimental data samples. The dynamic heat and mass transfer model used to estimate the temperature in six different points of the semi-solar greenhouse with initial values and consider the crop evapotranspiration. The results showed that dynamic model can predict the inside temperatures in four different points (Ta, Tc, Tri, Ts) with MAPE, RMSE and EF about 5–7%, 1–2 °C and 80–91% for greenhouse without thermal screen and about 3–7%, 0.6–1.8 °C and 89–96% for six different points of greenhouse with thermal screen (Ta, Tc, Tri, Ts, Tas, Tsc), respectively. The results of using thermal screen at night (12 h) in autumn showed that this method can decrease the use of fossil fuels up to 58% and so decrease the final cost and air pollution. This movable insulation caused about 15 °C difference between outside and inside air temperature and also made about 6 °C difference between Ta and Tas. The experimental results showed that inside thermal screen can decrease the crop temperature fluctuation at night

Energy Consumption, Input-Output Relationship and Cost Analysis for Greenhouse Productions in Esfahan Province of Iran

The objectives of this study were to determine the energy consumption and evaluation of inputs sensitivity for greenhouse vegetable production in the Esfahan province of Iran. Data were collected from 60 farmers using a face–to–face questionnaire method. The majority of farmers in the surveyed region were growing cucumber and tomato. The results revealed that cucumber production was the most energy intensive rather than tomato production. Cucumber production consumed a total of 124.44 G J ha–1 followed by tomato with 116.76 G J ha–1. The energy ratio (energy use efficiency) for greenhouse tomato and cucumber were estimated to be 0.92 and 0.56 respectively. This indicated an intensive use of inputs in greenhouse vegetable production not accompanied by increase in the final product. Econometric model evaluation showed the impact of human power for both tomato and cucumber production was significant at 1% levels and had the highest impact among the other inputs in greenhouse tomato and cucumber production. Economic analysis indicated that the total costs of production for one hectare of tomato and cucumber production were around 34939 and 31956$, respectively. Accordingly, the benefit–cost ratio for these productions was 2.74 and 1.79, respectively. The total amounts of CO2 for tomato and cucumber production were calculated as 4.622 and 4.930 tons ha–1 respectively, which indicated the high CO2 output in both cultivations. The use of diesel fuel and pesticide is in excess for tomato and cucumber production, causing an environmental risk problem in the region