Iranian Journal of Biosystems Engineering
Year:2020 | Volume:51 | Issue:1 |Papers Count:19

Sugar cane is one of the most important industrial plants which is the first source of sugar production in Iran (about 40-50%). The sugar industry plays a key role among the various industries of the country with the daily supply of energy to the citizens. In addition to household consumption, sugar is of particular importance in the food industry because of its sweetening and volume properties. Sugarcane content is often in the range of 10-15% and in some cases up to 17%. Various factors such as variety and date of planting or harvesting of last year are important to start harvesting. On the other hand, Sugarcane cannot be stored in the factory and its sugars factors are decomposed quickly by storage and sugar cane weight decreases due to loss of moisture. It would be better if the factory consumes more fresh sugarcane. Therefore, an electronic nose instrument was used to test the variety of sugarcane syrup and its association with the odors emitted from it to identify the variety of sugarcane for harvesting time. Four sugarcane varieties (CP57, CP69, IRC99-02, and CP48) were selected from the sugarcane sample fields. Linear discriminant analysis (LDA), principal component analysis (PCA) and neural networks (ANN) were used to detect the different sugarcane varieties. The results showed that all three methods had high accuracy in variety classification. But the LDA and PCA methods performed better than the ANN method. So that, the classification accuracy of sugarcane varieties was 98. 33%, 97% and 96. 7%, respectively. The results showed the high ability of the olfactory machine to diagnose between the sugarcane varieties, which can be used as a rapid and low cost instrument in the sugarcane industry.

The objective of the current study was to investigate the energy consumption in Karun Sugar Factory and to assess, economically and technically, the potential of substituting natural gas by bagasse as a source of energy. The necessary data were collected throughout the factory documents and the factory expert’ s interview during years 1395-1396. The results showed that the annual consumption of electricity and heat in the Karun sugar factory was 24 GWh and 890. 83 tonnes steam respectively, which resulted in the emission of 195 tonnes of air pollutants with a social cost of 24. 79 billion Rials. The net heat rate of the factory’ s power plant calculated to be 27. 15 MJ/KWh. Using 270, 000 tonnes of surplus bagasse, there is the potential to generate 15. 14% of total sugar plant energy and reduce 29, 000 tonnes of pollutants per year. The rate of return and return time of the capital needed for modification the current boilers was 88. 95% and 2 years respectively. Using bagasse instead of natural gas leads to 30. 6 Billion Rials reduction in variable costs of the factory based on financial data of the year 1395.

Aflatoxin B1 (AFB1) is one of the most toxic Aflatoxins that contaminates agricultural products and causes deathlike effects on human health. Determination of AFB1 in food by biosensors is fast, low cost and accurate. In this paper, modeling and simulation of chemical reactions in the AFB1 potentiometric biosensor is performed to determine the optimal reaction rate constants. Enzymatic reactions are simulated using COMSOL software and reaction rates are optimized by Artificial Neural Network (ANN) and Genetic Algorithm (GA). The fitness function of GA is defined by deploying ANN. The data generated during the simulation step were used to train and evaluate the performance of the neural network. Compared with experimental data, COMSOL model simulated biosensor response with MAPE equal to 0. 1023 %. In addition trained ANN with 5-48-1 structure predicted biosensor response with MAPEs equal to 0. 7074 %, 0. 9458 %, 0. 7473 % and 0. 7492 % for train, validation, test and total data sets respectively. Reaction rates were optimized by Artificial Neural Network (ANN) and Genetic Algorithm. Modeling results showed that trained Neural Network using Genetic Algorithm optimized reaction rates has the lowest MAPE equal to 0. 0026 % compared with other models in prediction of AChE enzyme inhibition by AFB1.

In this study, the effect of effective variables of moisture content, particle size, type of adhesive and tablet shape on the qualitative properties (difference of color indices Δ L*, Δ a* and Δ b*), physical (unit density and shrinkage) and mechanical (diffusion force) indices of compressed tablets made from tomato powder were studied. Independent variables were including three levels of particle size of tomato powder (particles smaller than 0. 3 mm, particles ranging from 0. 3 to 0. 75 mm and particles larger than 0. 75 mm), three levels of moisture content (18, 36 and d. b. 54%), three forms of compressed pills (spherical, cylindrical and cubic), three types of adhesive (55% fructose syrup, water and sugar). For optimization, the surface response and Di-optimal method were used. The results showed that optimum spot was obtained for compressed pills consisting of tomato powder with a moisture content of %33. 4 db, particle size of fructose 0. 3 mm, and cylindrical tablet form, dried in oven at 60 ° C. Under these conditions, the desirability index was 0. 826, and the optimal value of the independent variables Δ L*, Δ a* and Δ b* (the difference of color indices with fresh tomatoes), penetration force, unit density and shrinkage were 35. 89, 15. 6, 23. 23, 267. 2 N, 2299 kg/m3 and %12. 2, respectively.

In this paper the energy and exergy analysis of flat plate solar collector simulator equipped with Inclined Broken Rib roughness was investigated based on experimental data in open circuit as well as optimizing system operating conditions. The experiments were carried out with nine levels of mass flow rates (0. 03, 0. 04, 0. 05, 0. 06, 0. 07, 0. 08, 0. 09, 0. 10 and 0. 11 kg/s), five levels of heat flux (1000, 1100, 1200, 1300 and 1400 W/m^2) and three levels of ambient air temperature (20, 25 and 30 ° C). The results showed that the highest and lowest values of energy efficiency were found 49. 8 and 0. 3%, in treatments with ambient temperature of 20 and 25 ° C, mass flow rate of 0. 11 kg/s and heat flux of 1000 and 1400 W/m^2, respectively. Also, the highest and lowest exergy efficiency were calculated 5. 75 and 0. 607% in treatments with ambient temperature of 20 and 30 ° C, mass flow rate of 0. 11 kg/s and 0. 03 and heat flux of 1000 W/m^2, respectively. The response surface methodology was employed to optimize solar collector operating conditions. Optimum operating conditions were found to be an ambient temperature of 20 ° C, mass flow rate of 0. 11 kg/s and heat flux of 1000 W/m^2. At this optimum condition, the energy and exergy efficiency were found to be 42. 08 and 5. 76%, respectively at a desirability level of 0. 92.

Anaerobic digestion in order to produce biogas is a proven method for producing renewable energy from municipal solid waste. In this research, organic fractions of municipal solid waste compounds were determined in Karaj metropolitan area. The organic waste components were monitored in five categories of fruits, fat and protein, starches, vegetables, and cellulose wastes in the winter and summer seasons. Then, a sample representing the average amount of waste components was synthesized and biomethane yield, digestibility indicators and kinetic modeling parameters of biogas production were investigated in batch tests at mesophilic temperature at two concentrations of 8 and 15 TS%. The most part in the organic fraction was fruit and vegetable waste with a total of 62. 9% and 70. 6% in winter and summer, respectively. The biomethane yield and methane content at 8% and 15% TS had significant difference with 385. 2 and 289. 2 L/kg VS and of 66. 8 and 58. 8%, respectively, but there was no significant difference for VS removal with 87. 99% and 84. 72%. As a result, for source separated MSW, anaerobic digestion at the lower TSs has better results than dry. Continuous anaerobic digestion at 30 day hydraulic retention time is more effective for specific biomethane production and high volumetric biogas production under stable conditions.

Municipal solid waste, known as reject material, is increasingly being added to the world, causing more problems, such as air pollution and greenhouse gas emissions in the environment. As a result, the need for proper and sustainable waste management is felt more by managers. Accordingly, compost production is one of the methods used in agriculture in addition to reducing pollution. In Rasht, this method is used to manage 400 tonns of waste per day. In the process of composting, pollutants are created that affect the environment. In this study, the CML-IA baseline V3. 04 / World 2000 method using SimaPro software was applied to evaluate the life cycle and 11 impact categories have been investigated and finally the results were normalized and weighed. The functional unit in this study was 400 tonnes of compost produced per day. The results showed that the global warming potential was calculated with 4. 28×103 kgCO2 and the largest share in this section was due to direct emissions and transportation. Also, normalization results showed that compost production from waste had the most effect on marine aquatic ecotoxicity and human toxicity potential, respectively.

In this study pressure drop of bin containing corn as granular porous material with two common inlet duct arrangements (H, F) for a laboratory silage was studied by using experimental method. Higher pressure was obtained for the F arrangement compare to the H arrangement. The Ergun equation was fitted on the pressure drop of empirical data and the correction coefficients of modified Ergun equation for both H and F arrangements were determined. The coefficient of determination, mean root square error and mean relative error were used as indices to investigate the goodness of fitness between the empirical data and Ergun equation, and the equation had good fitness on the empirical data. The numerical simulation was carried out by finite element simulation of COMSOL Multiphysics v5. 3 software using the modified Ergun equation. Moreover, distribution of velocity and flow lines within the bin was also presented.

The purpose of this study was to identify, prioritize and control the factors affecting water productivity in sugar cane production. In this study, to calculate water productivity, the indices such as BPD and CPD, NBPD is used. Given that the research goal was to prioritize the factors affecting the productivity of water input during the sugar cane production process, the identified factors were evaluated using Analytical Hierarchy Process Analysis (AHP). Then, CART and CHAID decision trees were used in modeling the water input efficiency and the factors influencing it. According to the results, the average CPD, BPD and NBPD indices for sugar cane were 2. 37 kg m-3, 1082. 71 toman m-3, and 528. 03 toman m-3, respectively. The variables of economic productivity of water, the value of sales of the product (income), the amount of sugar produced, production costs, water consumption per irrigation interval, irrigation intervals, electrical conductivity of the soil after harvest, river electrical conductivity, mean time of each irrigation interval, Plant height, plant age, number of irrigation cycles, drainage water drainage PH and width of the cultivating line before discovery are the most important and influential variables in the decision tree models of CHAR and CHAID. The accuracy of the CART model in training and testing was 96% and 92%, respectively, and the accuracy of the CHAID model in education and testing is 97% and 90%, respectively. Also, based on the prioritization of the factors affecting productivity, using the hierarchical analysis method, the quantity and quality of irrigation water, climate conditions, plant conditions, managerial and human factors, and soil conditions were ranked with a coefficient of 0. 459, 0. 231, 0, 0. 091 and 0. 069, respectively.

In this present study, the effect of ultraviolet radiation in the UV-C range with the wavelength of 254 nm on the increasing shelf-life of pomegranate arils was evaluated. For this purpose, at the first, a continuous ultraviolet irradiation system was constructed and pomegranate arils were irradiated with two doses of 6. 3 and 8. 4 kJ/m2. The results showed that the simple and interactive effects of UV irradiation, container type and storage time on the weight loss and color indices were significant and on average caused 27% decrease in weight loss, 7% increase in the L* index, 6. 7% decrease in the a* value and 10% increase in the b* value of the control samples. With increasing irradiation dose, the total bacterial and fungal count were significantly reduced by 1. 65 Log cfu g-1. Furthermore, the irradiation had a significant effect on the studied sensory properties (aroma, color, texture, and overall acceptance). Generally, based on the results of sensory evaluation, color indices and microbial growth, irradiation dose of 6. 3 kJ/m2 is recommended to increase pomegranate arils shelf-life in non-porous packaging.

Internal temperatures of greenhouse and its control is one of the important parameters in greenhouses and plays a key role in the economics of production. Although the greenhouse is a closed environment, it is not completely isolated from the outside. Therefore, the conditions inside the greenhouse are constantly changing under the influence of outside climate change. The purpose of this study was to estimate the internal air temperature of polyethylene greenhouse with respect to the external parameters of the greenhouse including air temperature (Tout), air relative humidity (Hout), solar radiation (S) and wind speed (V). For this purpose, different method of artificial neural networks including Multilayer Perceptron (MLP), Radial Basis Function (RBF) and Adaptive Nero Fuzzy Inference System (ANFIS) were used. Comparison between different neural network models showed that RBF method had better prediction performance than MLP and ANFIS with higher coefficient of determination (R2=0. 93) and lower error (RMSE=2. 25). The results of the RBF model estimation for the prediction future temperature indicated an acceptable error in the prediction by the model for the next two hours and thus, the farmers had enough time to provide the necessary measures to prevent the greenhouse temperature rise in the future and save in energy consumption.

In the process of crop production, selecting the appropriate spraying method is very important. On the other hand, selecting the most appropriate criterion for evaluating and selecting pesticides is essential. In the first stage of this research, according to the important criteria of evaluation of spraying operations; the sprayers of Knapsack micronair, Tractor boom, Tractor lance, Knapsack atomizer and Turboliner were compared for sparying wheat fields in Alborz, West azarbaijan, Khoozestan and Razavi khorasan proviences with a completely randomized design. In the second step, the results of the first experiment were analyzed using AHP method. According to the results, the scoring methods were evaluated based on the criteria of spraying, spraying volume, drift, field capacity, spray uniformity, crop blunder, spraying nominal power, effectiveness and cost, after normalizing and analyzing the data, weight of Knapsack micronir, Tractor boom, Turboliner, Knapsack atomizer, and Tractor Lance sprayers were 0. 337, 0. 223, 0. 175, 0. 0 170 and 0. 078, respectively. The Knapsack micronair and Tractor lance sprayers was the best and worst sprayer according to the weights obtained. The inconsistency coefficient of weights was 0. 08. The highest and lowest criterion weight was 0. 253 and 0. 038 respectively and related to the effectiveness of controlling weeds and pests and crop blunder. In this situation, the inconsistency coefficient was 0. 09.

The aim of this study is to develop imaging algorithms to improve the grade of nuts with shell defects such as oily stains, dark stains, adhering hull, damage seed defects, and fungal decay. All these defects indicate the risk of Aflatoxin contamination. Convolutional Neural Networks (CNNs) have become prominent in various fields of machine vision and image classification. In this study, a laboratory hardware setup based on a convolutional neural network is designed for sorting pistachios. The total number of collected data is 958 images, which includes 276 images of defective pistachios and 682 images of healthy pistachios. The classification of healthy and defective images has been accomplished by 3 types of deep convolutional neural networks including Google net, resnet18 and vgg16. The accuracy and specificity of the results obtained using the pre-trained deep neural network models of Google net, resnet18 and vgg16 are 95. 8%-97. 1%, 97. 2%-96. 7%, and 95. 83%-97. 08%, respectively.

Presence of foreign materials with the product is one of the important problems of wheat production. The economic value of the produced wheat and the degree of purity of the produced seeds increases with the separation of wheat mass impurities. Hence, in this research, a gravity separator table was used to remove impurities from wheat bulk. The machine has adjusting five parameters of air velocity, frequency of oscillation, amplitude of oscillation, longitudinal slope and latitudinal slope of the table. The effect of these parameters was studied to achieve maximum impurity separation from wheat bulk. Statistical analysis was performed in two factorial experiments based on completely randomized design. In the first experiment, the effects of three parameters of longitudinal slope, latitudinal slope and frequency of oscillation of the table were investigated and in the second experiment the effect of two other parameters was investigated. Also, using dimensional analysis, a dimensionless number parameter v/aw was obtained which was effective in evaluating the effect and reducing the number of parameters. The results showed that the maximum separation of impurities from wheat bulk was 87. 03% at longitudinal slope of 2. 5 ° , latitudinal slope of 1. 5 ° , frequency of oscillation of 395 cycles per minute, amplitude of oscillation of 5 mm and air velocity of 6. 75 m/s, . Also, with increasing longitudinal slope from 2. 5 ° to 4. 5 ° , latitudinal slope from 0. 75 ° to 2. 5 ° (in most cases) and the amplitude of oscillation of the table from 5 to 7 mm, the separation of impurities was reduced and with increasing the air velocity from 5. 25 to 6. 75 m/s the separation of impurities was increased.

Developing a biosensor faces the different challenges for parameter optimization and calibration. In this study, a machine learning based approach is used to model and optimize the effective parameters of an electrochemical nanobiosensor based on thiolated probe-functionalized gold nanorods (GNRs) decorated on the graphene oxide (GO) sheet on the surface of a glassy carbon electrode (GCE). The response of the biosensor was considered as the output and eight effective factors including GO concentration, GNR concentration, probe concentration, probe time, MCH time, hybridization time, Oracet Blue (OB) concentration, and OB incubation time were used as inputs to train and model an artificial neural network. The experimental results demonstrate that the output of the developed model has an acceptable compatibility with the results obtained in the laboratory. The developed model is able to predict the output of the nanobiosensor with accuracy of 96. 91% and the mean absolute percentage error (MAPE) value of 5. 5090 %. Finally, genetic algorithm is used to find the optimum values of these parameters which yield the maximum value of the nanobiosensor output. The optimization results indicated that this method has better performance compared to the laboratory results and this method can be used for nanobiosensor design.