Optimization of rendering process of poultry by-products with batch cooker model monitored by electronic nose

The best available controlled technology for transforming the disposal of animal by-products and mortalities is rendering. Two aspects of rendering process are mentioned in this research; product quality and emissions. A model of batch cooker with temperature, pressure and agitator speed controllers was designed and developed in order to optimize the process and to investigate the effect of changes in rendering conditions on quality of poultry by-product meal and also on pollutant emissions. An electronic nose system was designed and built based on metal oxide semiconductor sensors to monitor the gases emitted from batch cooker model. Also, GC-MS was used to identify the emitted components. In order to optimize the rendering process, response surface methodology was performed on temperature, cooking time and agitator speed variables. Results showed that the temperature of 140 °C (internal pressure equivalent to about 3.2 bar), the cooking time of 45 min and the agitator speed of 20 rpm optimized the process of batch cooking to maximize the percentage of protein and minimize the percentage of fat, moisture content, energy consumption and emission of pollutants. By GC-MS analysis, about 100 compounds include hydrocarbons, volatile fatty acids, sulfur-containing compounds, alcohols, ketones, aldehydes, and furans were observed in the emission of a batch cooker model. The major groups were organic acids and amides. Principle component analysis showed the most suitable sensors for detecting unpleasant odors from rendering plants.

Integration of artificial intelligence methods and life cycle assessment to predict energy output and environmental impacts of paddy production

Prediction of agricultural energy output and environmental impacts play important role in energy management and conservation of environment as it can help us to evaluate agricultural energy efficiency, conduct crops production system commissioning, and detect and diagnose faults of crop production system. Agricultural energy output and environmental impacts can be readily predicted by artificial intelligence (AI), owing to the ease of use and adaptability to seek optimal solutions in a rapid manner as well as the use of historical data to predict future agricultural energy use pattern under constraints. This paper conducts energy output and environmental impact prediction of paddy production in Guilan province, Iran based on two AI methods, artificial neural networks (ANNs), and adaptive neuro fuzzy inference system (ANFIS). The amounts of energy input and output are 51,585.61MJkg^-1 and 66,112.94MJkg^-1, respectively, in paddy production. Life Cycle Assessment (LCA) is used to evaluate environmental impacts of paddy production. Results show that, in paddy production, in-farm emission is a hotspot in global warming, acidification and eutrophication impact categories. ANN model with 12-6-8-1 structure is selected as the best one for predicting energy output. The correlation coefficient (R) varies from 0.524 to 0.999 in training for energy input and environmental impacts in ANN models. ANFIS model is developed based on a hybrid learning algorithm, with R for predicting output energy being 0.860 and, for environmental impacts, varying from 0.944 to 0.997. Results indicate that the multi-level ANFIS is a useful tool to managers for large-scale planning in forecasting energy output and environmental indices of agricultural production systems owing to its higher speed of computation processes compared to ANN model, despite ANN's higher accuracy.

Differentiation of cumin seeds using a metal-oxide based gas sensor array in tandem with chemometric tools

Cumin is a plant of the Apiaceae family (umbelliferae) which has been used since ancient times as a medicinal plant and as a spice. The difference in the percentage of aromatic compounds in cumin obtained from different locations has led to differentiation of some species of cumin from other species. The quality and price of cumin vary according to the specie and may be an incentive for the adulteration of high value samples with low quality cultivars. An electronic nose simulates the human olfactory sense by using an array of sensors to distinguish complex smells. This makes it an alternative for the identification and classification of cumin species. The data, however, may have a complex structure, difficult to interpret. Given this, chemometric tools can be used to manipulate data with two-dimensional structure (sensor responses in time) obtained by using electronic nose sensors. In this study, an electronic nose based on eight metal oxide semiconductor sensors (MOS) and 2D-LDA (two-dimensional linear discriminant analysis), U-PLS-DA (Partial least square discriminant analysis applied to the unfolded data) and PARAFAC-LDA (Parallel factor analysis with linear discriminant analysis) algorithms were used in order to identify and classify different varieties of both cultivated and wild black caraway and cumin. The proposed methodology presented a correct classification rate of 87.1% for PARAFAC-LDA and 100% for 2D-LDA and U-PLS-DA, indicating a promising strategy for the classification different varieties of cumin, caraway and other seeds.

Real-time color change monitoring of apple slices using image processing during intermittent microwave convective drying

An intermittent microwave convective drying method combined with a real-time computer vision technique was employed to detect the effect of drying parameters on color properties of apple slices. The experiments were performed at air temperature of 40 to 80℃, air velocities of 1–2 m/s, microwave powers of 200–600 W, and pulse ratios (PRs) of 2–6. Drying rate and drying time varied from 0.014 to 0.000001 min−1 and 27 to 244 min, respectively. The normalized lightness values had ascending and descending parabolic trends with decrease in product moisture content. With descending dimensionless moisture content, redness, yellowness, color change, hue angle, and chroma were enlarged. The normalized redness values changed from −4 to 3. Models relating drying parameters with drying time, drying rate, and lightness were obtained and found to be significant ( P < 0.01). Results indicated that microwave power and PRs had more influence on lightness and color change than other parameters.

Optimization of intermittent microwave-convective drying using response surface methodology

In this study, response surface methodology was used for optimization of intermittent microwave–convective air drying (IMWC) parameters with employing desirability function. Optimization factors were air temperature (40–80°C), air velocity (1–2 m/sec), pulse ratio) PR ((2–6), and microwave power (200–600 W) while responses were rehydration ratio, bulk density, total phenol content (TPC), color change, and energy consumption. Minimum color change, bulk density, energy consumption, maximum rehydration ratio, and TPC were assumed as criteria for optimizing drying conditions of apple slices in IMWC. The optimum values of process variables were 1.78 m/sec air velocity, 40°C air temperature, PR 4.48, and 600 W microwave power that characterized by maximum desirability function (0.792) using Design expert 8.0. The air temperature and microwave power had significant effect on total responses, but the role of air velocity can be ignored. Generally, the results indicated that it was possible to obtain a higher desirability value if the microwave power and temperature, respectively, increase and decrease.

Optimization on drying conditions of a solar electrohydrodynamic drying system based on desirability concept

The purpose of this article was to present a new drying method for agricultural products. Electrohydrodynamic (EHD) has been applied for drying of agricultural materials due to several advantages such as energy saving, low cost equipment, low drying temperatures, and superior material quality. To evaluate this method, an EHD dryer based on solar (photovoltaic) energy was designed and fabricated. Moreover, the optimum condition for the EHD drying of kiwi fruit was studied by applying the Box–Behnken design of response surface methodology. The desirability function was applied for optimization in case of single objective and multiobjective functions. By using the multiobjective optimization method, maximum desirability value of 0.865 was obtained based on the following: applied voltage of 15 kV, field strength of 5.2 kV cm⁻¹, without forced air stream, and finally a combination of 17 discharge electrodes (needles). The results indicated that increasing the applied voltage from 6 to 15 kV, moisture ratio (MR) decreased, though energy efficiency and energy consumption were increasing. On the other hand, field strength of 5.2 kV cm⁻¹ was the optimal point in terms of MR.

Modeling of electrohydrodynamic drying process using response surface methodology

Energy consumption index is one of the most important criteria for judging about new, and emerging drying technologies. One of such novel and promising alternative of drying process is called electrohydrodynamic (EHD) drying. In this work, a solar energy was used to maintain required energy of EHD drying process. Moreover, response surface methodology (RSM) was used to build a predictive model in order to investigate the combined effects of independent variables such as applied voltage, field strength, number of discharge electrode (needle), and air velocity on moisture ratio, energy efficiency, and energy consumption as responses of EHD drying process. Three-levels and four-factor Box-Behnken design was employed to evaluate the effects of independent variables on system responses. A stepwise approach was followed to build up a model that can map the entire response surface. The interior relationships between parameters were well defined by RSM.

Image analysis and green tea color change kinetics during thin-layer drying

This study was conducted to investigate the effect of air temperature and air flow velocity on kinetics of color parameter changes during hot-air drying of green tea, to obtain the best model for hot-air drying of green tea, to apply a computer vision system and to study the color changes during drying. In the proposed computer vision system system, at first RGB values of the images were converted into XYZ values and then to Commission International d'Eclairage L*a*b* color coordinates. The obtained color parameters of L*, a* and b* were calibrated with Hunter-Lab colorimeter. These values were also used for calculation of the color difference, chroma, hue angle and browning index. The values of L* and b* decreased, while the values of a* and color difference (ΔE*ab ) increased during hot-air drying. Drying data were fitted to three kinetic models. Zero, first-order and fractional conversion models were utilized to describe the color changes of green tea. The suitability of fitness was determined using the coefficient of determination (R (2)) and root-mean-square error. Results showed that the fraction conversion model had more acceptable fitness than the other two models in most of color parameters.

A Multi-Scale Three-Dimensional CFD Model of a Full Loaded Cool Storage

A multi-scale three-dimensional computational fluid dynamics (CFD) model was developed to predict airflow, heat and mass transfer in a typical full loaded cool storage. In order to reduce the computational costs, the porous media parameters of the bed of the apples inside the vented containers were extracted using a series of wind tunnel CFD simulations and then applied in the cool storage model. The model was validated against experiments by means of velocity, product temperature, and product weight loss measurements in cool storage. The errors of about 23.2 and 9.1% were achieved for velocity magnitude prediction in the cool storage and the product weight loss after 54 days of cooling in the loaded cool storage, respectively. The model over predicted the cooling rate of the products temperature; however, it showed a good trend of cooling rate. About 11°C difference was observed between the hottest and the coldest product temperatures at half cooling time by experiments that were in good agreement with the simulation results with about 10°C. This difference changes versus time of cooling and reached to about 4°C at the end of the cooling time. The product’s temperature heterogeneity was predicted 1.9°C between the 7 and 9 hours of cooling and reduced to 0.6°C at the end of the cooling. The multi-scale model was capable of predicting air velocity, product temperature, and weight loss with reasonable accuracy and was reliable enough for numerical studies on larger domain with high reduction in computational costs.

Experimental evaluation of engine oil properties containing copper oxide nanoparticles as a nanoadditive

Background

The properties of lubricants are mainly the result of adding a material for improving or producing the required properties. Today, different materials with various nanostructures are used as new additives which, because of their unique properties, are used for improving the lubricant's properties. The purpose of the present research is to add copper oxide (CuO) nanoparticles to engine oil and evaluate the produced changes in some of its properties. Also, viscosity, pour point, and flash point of nanolubricants, which are made at different concentrations (0.1, 0.2, and 0.5 wt.%), and also their thermal conductivity coefficient as four quality parameters which are effective in the functionality of engine oil are evaluated.

Results

Upon the obtained results, thermal conductivity coefficient and flash point of nanolubricants with 0.1 wt.% concentration, respectively, had 3% and 7.9% improvement with respect to the base oil. Also, the oil's viscosity, as a very important factor in lubricating issue, at a lower concentration with respect to the engine oil without CuO had no appreciable change.

Conclusion

Among the different methods which have been used for dispersing nanoparticles inside the base oil, using planetary ball mill was determined as the most important method for stabilization of nanoparticles inside SAE 20W50 engine oil. Also, the physical properties of nanolubricants were measured based on the American Society for Testing and Materials standard methods.

Meat quality assessment by electronic nose (machine olfaction technology)

Over the last twenty years, newly developed chemical sensor systems (so called "electronic noses") have made odor analyses possible. These systems involve various types of electronic chemical gas sensors with partial specificity, as well as suitable statistical methods enabling the recognition of complex odors. As commercial instruments have become available, a substantial increase in research into the application of electronic noses in the evaluation of volatile compounds in food, cosmetic and other items of everyday life is observed. At present, the commercial gas sensor technologies comprise metal oxide semiconductors, metal oxide semiconductor field effect transistors, organic conducting polymers, and piezoelectric crystal sensors. Further sensors based on fibreoptic, electrochemical and bi-metal principles are still in the developmental stage. Statistical analysis techniques range from simple graphical evaluation to multivariate analysis such as artificial neural network and radial basis function. The introduction of electronic noses into the area of food is envisaged for quality control, process monitoring, freshness evaluation, shelf-life investigation and authenticity assessment. Considerable work has already been carried out on meat, grains, coffee, mushrooms, cheese, sugar, fish, beer and other beverages, as well as on the odor quality evaluation of food packaging material. This paper describes the applications of these systems for meat quality assessment, where fast detection methods are essential for appropriate product management. The results suggest the possibility of using this new technology in meat handling.