Kinetics of Dehydration of Green Alfalfa

ABSTRACT

Artificial drying is an important step in processing of green crops in order to preserve their freshness and nutrients for longer time at relatively lower costs. Forage crops, tea and tobacco are the major green crops where the commercial drying is a major operation in their processing. Fresh green alfalfa at about 75 to 80% moisture is subjected to drying in different types of dryers.ln the case of alfalfa, the raw material consists of leaves, stems, chops and fine stems, each varying distinctly in their physical and structural characteristics. The moisture content is reduced from initial level to about 10%. The drying air temperatures range from 40 b 800°C; the lower temperatures are used inconveyor dryers whereas high temperatures are used in rotary drum drycn.The results on drying behavior, and changes in physico-chemical propedes during drying for components of green alfalfa over the temperature range of 40 m 800°C are presented in this paper. The optimum temperature for drying from the stand point of color and protein solubility was found to be 175°C.     

Performance of a solar liquid desiccant air conditioner – An experimental and theoretical approach

In this paper the results of testing a solar liquid desiccant air conditioner (LDAC) in the tropical climate of Queensland, Australia have been presented. The system uses polymer plate heat exchanger (PPHE) for dehumidification/indirect evaporative cooling, and a cooling pad as the direct evaporative cooler for the dry air leaving the PPHE. Lithium chloride, which is an effective desiccant in air dehumidification, was used in the experiments and a scavenger air regenerator concentrates the dilute solution from the dehumidifier using hot water from flat plate solar collectors. The data obtained from performance monitoring of the solar LDAC operating on a commercial site in Brisbane was compared with a previously developed model for the PPHE. The comparison reveals that good agreement exists between the experiments and model predictions. The inaccuracies are well within the measuring errors of the temperature, humidity and the air and solution flow rates. The above tests further indicate a satisfactory performance of the unit by independently controlling the air temperature and humidity inside the conditioned space.

In order to prevent carryover of the solution particles into the environment, eliminators are used at outlet of the absorber unit and the regenerator. An alternative method in preventing the carryover is the use of indirect cooling, in which the supply air does not contact the solution. The method can be used to produce potable water from the atmospheric air in remote areas.

The liquid desiccant system can be used in the HVAC industry, either as a packaged roof-top air conditioner, or as an air handler unit for commercial applications. The system could also be used for space heating in winter due to the property of desiccants to provide heat when wetted.     

Multi-pressure absorption cycles in solar refrigeration:: A technical and economical study

A technical and economical study of regenerative absorption chillers with multi-pressure cycle has been undertaken as solar operated refrigeration systems. Referred to as advanced absorption chillers they represent one of the new technology options that are under development. Advanced absorption cooling technology offers the possibility of chillers with thermal COPs of 1.5 or greater at driving temperatures of 140°C, which reduces the collector area and the heat rejection requirements compared to current absorption cooling technology. Two different absorption systems have been considered. The first is an advanced, double-effect regenerative absorption cooling system, driven at 140°C, whose efficiency is about 55% of the Carnot efficiency. The second is an ideal, single-effect regenerative absorption system that achieves 70% of the Carnot efficiency driven at 140°C or 200°C. To evaluate the solar performance of a thermally driven chiller requires a separate analysis of the solar availability for a given location compared to the required monthly average solar input. In this analysis different systems, including the vapour compression chillers, have been compared in terms of the thermal and electrical energy input. An effective electrical COP may be computed assuming that the ratio of electrical energy cost to thermal energy cost is four, which is typical of today’s fossil fuel costs. The effective electrical COPs of different technical options can then be compared. Those systems with higher electrical COPs will have lower energy costs. If solar is to be competitive, then the cost of delivered solar thermal energy should be less than the cost of delivered fossil thermal energy.     

Activating solution gas drive as an extra oil production mechanism after carbonated water injection

Enhanced oil recovery (EOR) methods are mostly based on different phenomena taking place at the interfaces between fluid–fluid and rock–fluid phases. Over the last decade, carbonated water injection (CWI) has been considered as one of the multi-objective EOR techniques to store CO₂ in the hydrocarbon bearing formations as well as improving oil recovery efficiency. During CWI process, as the reservoir pressure declines, the dissolved CO₂ in the oil phase evolves and gas nucleation phenomenon would occur. As a result, it can lead to oil saturation restoration and subsequently, oil displacement due to the hysteresis effect. At this condition, CO₂ would act as in-situ dissolved gas into the oil phase, and play the role of an artificial solution gas drive (SGD).In this study, the effect of SGD as an extra oil recovery mechanism after secondary and tertiary CWI (SCWI-TCWI) modes has been experimentally investigated in carbonate rocks using coreflood tests. The depressurization tests resulted in more than 25% and 18% of original oil in place (OOIP) because of the SGD after SCWI and TCWI tests, respectively. From the ultimate enhanced oil recovery point of view, the efficiency of SGD was observed to be more than one-third of that of CWI itself. Furthermore, the pressure drop data revealed that the system pressure depends more on the oil production pattern than water production.     

An OWA‐Based Ranking Approach for University Books Recommendation

Generally the book recommendation approaches are personalized in nature, that is, they utilize the users’ purchasing behavior to recommend them the book similar to their preferences. The main problem with the personalized recommendation is its knowledge requirement about users’ past preferences. As a result, these techniques fail in producing appropriate recommendation for a new user whose preferences are not known. The personalized recommendation also needs extra space to store the users’ preferences. In this paper, a framework to recommend books to university students for their studies is presented. In order to answer which books are to be included in the syllabus, a specialized way of recommendation, where recommendations from experts of the subjects at different universities are considered, is presented. We have suggested a ranked recommendation approach for books, which employ Ordered Weighted Aggregation (OWA), a fuzzy‐based aggregation, to aggregate the several ranking of the top universities. On the one hand, it does not need user prior preferences, and on the other hand, it eases the complexities of personalized recommendation to huge number of users and replaces it with a single ranked recommendation. The experimental results are compared with the existing positional aggregation algorithm that demonstrates significant improvement in the results with respect to various performance metrics.     

A Comparative Assessment of Artificial Neural Network,Generalized Regression Neural Network,Least-Square Support Vector Regression,and K-Nearest Neighbor Regression for Monthly Streamflow Forecasting in Linear and Nonlinear Conditions

Monthly forecasting of streamflow is of particular importance in water resources management especially in the provision of rule curves for dams. In this paper, the performance of four data-driven models with different structures including Artificial Neural Network (ANN), Generalized Regression Neural Network (GRNN), Least Square-Support Vector Regression (LS-SVR), and K-Nearest Neighbor Regression (KNN) are evaluated in order to forecast monthly inflow to Karkheh dam, Iran, in linear and non-linear conditions while the optimized values of the model parameters are determined in the same condition via the Leave-One-Out Cross Validation (LOOCV) method. Results show that the performance of the models is different in linear and nonlinear conditions; the cumulative ranking of the models according to the three assessment criteria including NSE, RMSE and R² indicates that ANN performs best in linear conditions while LS-SVR, GRNN and KNN are in the next ranks, respectively. But in nonlinear conditions, the best performance belongs to LS-SVR, followed by KNN, ANN, and GRNN models.     

A regression modelling approach for optimizing segmentation scale parameters to extract buildings of different sizes

Multiresolution segmentation (MRS) is one of the most commonly used image segmentation algorithms in the remote-sensing community. This algorithm has three user-defined parameters: scale, shape, and compactness. The scale parameter (SP) is the most crucial one in determining the average size of the image segments generated. Since setting this parameter typically requires a trial-and-error process, automatically estimating it can expedite the segmentation process. However, most of automatic approaches are still iterative and can lead to a time-consuming process. In this article, we propose a new, non-iterative framework for estimating the SP with an emphasis on extracting individual urban buildings. The basis of the proposed method is to investigate the feasibility of associating the size of urban buildings with a corresponding ‘optimal’ (or at least reasonable) SP using an explicit mathematical equation. Using the proposed method, these two variables are related to each other by constructing a mathematical (regression) model. In this framework, the independent variables were chosen to be the typical size of buildings in a given urban area and the spatial resolution of the image under consideration; and the dependent variable was chosen to be the corresponding optimal SP. To assess the potential of the proposed approach, two regression models that yielded explicit equations (i.e. degree-2 polynomial (DP), and regression tree (RT)) were employed. In addition, as a sophisticated and versatile nonlinear model, support vector regression (SVR) was utilized to further measure the performances of DP and RT models compared with it. According to the comparisons, the DP model was selected as a representative of the proposed approach. In the end, to evaluate the proposed methodology, we also compared the results derived from the DP model with those derived from the Estimation of Scale Parameter (ESP) tool. Based on our experiments, not only did the DP model produce acceptable results, but also it outperformed ESP tool in this study for extracting individual urban buildings.     

Experimental and modeling investigation of dynamic interfacial tension of asphaltenic-acidic crude oil/aqueous phase containing different ions

In this way,after experimental measurement of interfacial tension,different models including mono-exponential decay,dynamic adsorption models and empirical equation are used to correlate this time-dependent behavior of interfacial tension(IFT).During the modeling approach,the induction,adsorption,equilibrium,and meso-equilibrium times as well as diffusivity of surface active components known as natural surfactant including asphaltene and resin from crude oil to the interface are obtained.In addition,the surface excess concentration of surface active components at the interface and Gibbs adsorption isotherm are utilized to analyze the measured dynamic IFTs.Finally,the mechanisms of crude oil/aqueous solution IFT including(a)the activity of surface-active components and(b)surface excess concentration of them at fluid/fluid interface are proposed and discussed in details.     

Magnetic nanocomposite of multi-walled carbon nanotube as effective adsorbent for methyl violet removal from aqueous solutions: Response surface modeling and kinetic study

Magnetic nanocomposite of multi-walled carbon nanotube (m-MWCNT) was synthesized for adsorptive removal of methyl violet (MV) from aqueous solutions. The experiments were conducted using a central composite design (CCD) with the variables of adsorbent dosage (0.4-1.2 g/L), solution pH (3-9), contact time (10-42 min) and ionic strength (0.02-0.1mol L^?1). Regression analysis showed good fit of the experimental data to a quadratic response surface model whose statistical significance was verified by analysis of variance. By applying the desirability functions, optimum conditions of the process were predicted as adsorbent dosage of 0.99g/L, pH=4.92, contact time of 40.98 minutes and ionic strength of 0.04 mol L^?1 to achieve MV removal percentage of 101.19. Experimental removal efficiency of 99.51% indicated that CCD along with the desirability functions can be effectively applied for optimizing MV removal by m-MWCNT. Based on the study, the adsorption process followed Langmuir isotherm model and pseudo-second-order kinetic model could realistically describe the dye adsorption onto m-MWCNT.