نشریه مهندسی بیوسیستم ایران
سال پنجاه و چهارم شماره 1 (بهار 1402)

In line with the optimal management to water consumption, the usage of irrigation systems equipped with water cleanup systems increased the acceptance of these systems. In this research, a smart filter equipped with automatic self-cleaning technology was constructed. The experimental of this research in the form of a factorial test based on a completely randomized design with three factors working pressure, water quality and the diameter of filter openings with three repetitions in controlled laboratory conditions. The amount of water consumed will be higher for samples containing large amounts of impurities and filters with larger aperture diameters, and using filters with smaller aperture diameters and considering low operating pressure can reduce the amount of water consumed for washing. The results showed that the best quality of purified water (96.3%) and the lowest amount of water used for washing occurred (21.2 L) at a working pressure of 100 kPa, although this has increased the number of filter washing times. Setting the washing process at the beginning of the first point of pressure deviation causes a high amount of water to be purified in each cycle. Setting up the working pressure unit of the smart filter control unit within 100 kPa and using a net with an opening diameter of 0.1 mm reduces the amount of water used to wash (79%) and increases the quality of the treated water (90%), which will allow the use of waters containing large amounts of suspended soluble solid materials.

It is difficult to accurately measure the flow rate of seeds and fertilizers in grain drills, because the granules move in a continuous dense state. The purpose of this research was to monitor the mass flow of granular materials in the fall tube of the drill using a piezoelectric sensor. The laboratory system consisted of a repository box, a fluted roller metering device, a fall tube, and a piezoelectric sensor. To simulate the drill movement in the field, a vibrating stand was designed to oscillate the set-up in two perpendicular directions. The maximum amplitude stand was 8.99 cm according to the peaks and depressions available in a field after plowing. The sensor was evaluated in two static and dynamic modes for wheat seed, alfalfa seed, and triple superphosphate fertilizer with the usual drilling rate. The results showed the output signal of the sensor was proportional to all different mass flow rates in both static and dynamic states. The correlation coefficient was 0.95, 0.99, and 0.98 in static mode and 0.93, 0.86, and 0.98 in dynamic mode for wheat, alfalfa seed, and triple superphosphate fertilizer, respectively. In addition, the piezoelectric sensor could instantaneously monitor the sudden changes in the mass flow according to the reading of the digital scale. The results showed that the developed sensor can be used to monitor the mass flow rate of seeds and fertilizers in drills for calculating the drilling rate in real time.

In determining the mass flow rate of bulk materials such as grains that pass through closed channels such as pipes, the online measurement of the volume ratio of solid materials and their distribution in the pipe cross-section is of particular importance. Considering the features of conventional methods such as be creating obstacles in the passage of materials (permeability) and low accuracy, in this research the non-contact tomography method of electrical capacitance was investigated for monitoring the flow of solid materials. In this method, to measure the amount of material and its density, it uses the measurement of the dielectric properties of the material inside the pipe. The developed electro-capacitance tomography system has 8 main electrodes and 16 secondary electrodes, anti-noise guards and a transmitter and receiver circuit, which was installed on a non-conductive pipe with a diameter of 20 cm. The main problem in the performance of the electro-capacitance tomography is the noise and lack of optimal image reconstruction with the conventional LBP algorithm. In this study, the performance of Tikhonov algorithm was compared with the conventional LBP algorithm. In this research, by using different guards, the noise level of the system was reduced so that the signal-to-noise ratio reached 56.09 dB. The results of the comparison of two algorithms showed that the Tikhonov algorithm has a good behavior in reconstruction of a tomogram of the wheat mass next to the pipe walls compared to the LBP algorithm, and except for the condition that the pipe is completely full, in other filling patterns of the pipe, has a better performance.

Today, providing food security for the world's growing population by preserving the earth's resources & minimal environmental effects has become one of the basic & important challenges in sustainable agriculture, & the optimal use of resources is one of the main requirements of sustainable agriculture. In this study, the pattern of energy consumption during peach production, analysis & modeling of energy & performance of peach production in Nazarabad city was investigated. Data were collected through interviews & filling specialized questionnaires. The results showed that the total energy consumption & production were equal to 72716.83 & 5234.89 megajoules per hectare, respectively. Electricity was the most consumed input with a share of 59% of the total input energy. The indices of energy efficiency, energy efficiency, energy intensity and net energy were obtained as 0.07, (kg/MJ) 0.03, (MJ/Kg) 26.39 and (MJ) -67481 respectively. Modeling was done with three methods enhanced gradient regression, decision tree regression, and random forest regression, and RRMSE was -0.003, -0.0090, and -0.0091, and R2 was 0.98, 0.95, and 90, respectively was calculated. The results showed that the enhanced gradient method is able to accurately predict the values of the energy efficiency indices of peach production. The results showed that the energy efficiency and production can be predicted with high accuracy through the inputs of irrigation water, electricity, chemical and animal fertilizers, labor force, chemical poisons, diesel fuel and machines and machine learning method. Sensitivity analysis was performed with SHAP and the results showed that the most influential input in energy prediction was nitrogen fertilizer.

Fruit leathers are nutritious products that are made by dehydrating a thin layer of fruit puree or juice under specific conditions. The aim of this study was to investigate the effect of various plant components (pistachio, walnut, sesame, hemp and moringa leaves) on the physicochemical, sensory and antioxidant properties of plum leather. The amounts of 5% of these components were added to the plum leather formulation and the pH, total acidity, vitamin C content, color indices, mechanical property (rupture force), phenolic compounds, antioxidant activity, and sensorial properties of these treatments were compared with control. The results showed that the lowest pH (3.0) and the highest acidity (4.2% w/w) and vitamin C content (210 mg/100 g) were related to the control. Addition of different plant components increased the pH and decreased total acidity. Adding these components to plum leather increased the phenolic compounds and antioxidant activity of samples and the highest amount of phenolic compounds (1024 mg/100 g) and anti radical activity (52%) were observed in treatments with sesame powder. The highest amount of rupture force was related to the control (25.4 N). Other treatments did not show a significant difference in terms of rupture force (P> 0.05). In terms of sensorial characteristics, the highest overall acceptance was obtained by control and the samples containing pistachio and moringa leaves. Addition of these components decreased the brightness (L *) and yellowing (b *) indices and increased a * index compared to the control. In general, the results of this study showed that the addition of plant components, in addition to creating new leather formulations, increases their antioxidant properties and nutritional values.

Reducing the reserves of fossil energy sources serves as a warning sign for humanity. On the other hand, the increasing consumption of fossil fuels has led to significant environmental problems, such as global warming. These issues make the replacement of renewable energy sources with fossil fuels inevitable. Among various renewable energy sources, biomass is a reliable and sustainable resource. Thermochemical conversions of biomass are a promising method for converting raw biomass into liquid (bio-oil), solid (bio-char), and gas (biogas) fuels suitable for modern life. As one of the most important thermochemical conversions for efficient bio-oil production, pyrolysis has received significant attention. However, pyrolysis requires advanced equipment, precise product quantity, and quality measurement, which can be challenging and costly. Therefore, modeling has been extensively researched to enhance the performance and efficiency of pyrolysis. In recent years, machine learning has gained considerable attention in pyrolysis modeling, particularly for yield optimization, real-time monitoring, and process control. In addition to conventional techniques like artificial neural networks that capture nonlinear correlations between input and output values, combined machine learning models have been of particular interest for modeling and optimizing complex problems more effectively. This study provides a comprehensive overview of the research conducted on the application of machine learning in pyrolysis process modeling and assesses the prospects of this technology. These machine learning models have provided R2 between 0.26 in the weakest case and 0.99 in the best case for predicting bio-oil production. These values have been presented between 0.6 and 0.93 to predict the improvement of bio-oil quality modeling.