Highlights An integrated GIS-based tool was developed for optimally locating bioenergy facilities. Waste and lignocellulosic biomass potential and distribution were assessed for Alberta. A case study for Alberta’s Industrial Heartland identified facility locations for two scenarios. Ten optimal locations were identified across Alberta for bioconversion of waste and biomass feeds. Abstract . Quantifying the availability of feedstock and determining an optimal location are key to ensuring the sustainability of a waste to value-added (W2VA) facility. This study aims to identify lignocellulosic biomass (agricultural and forest residues) and municipal solid waste (MSW) potential, find geographical point-source locations for the distributed biomass, and identify optimal locations for W2VA facilities across the province of Alberta, Canada, using an integrated geographic information system (GIS) based approach. MSW potential is estimated using population and average annual waste generation per capita, while agriculture and forest residue are estimated using production data and harvesting residue factor. A GIS-based framework is developed to locate biomass collection points by latitude and longitude for distributed biomass and to estimate their associated biomass potential. An integrated framework is subsequently developed to optimally locate W2VA facilities that have minimal environmental, economic, and social impacts. An array of geographical constraints is then considered in a suitability analysis and network analysis framework. An estimate of the annual availability of feedstock using the most recent data shows MSW, agricultural residue, and forest residue potentials of 4,330,000 wet megagrams (Mg), 4,060,000 dry Mg, and 2,070,000 dry Mg, respectively, in Alberta. Optimal W2VA facility locations are identified for Alberta’s Industrial Heartland (AIH) considering waste heat from the areas as an additional energy source. Ten other locations where facilities can be operated sustainably are identified across the province. This study can be used as a framework by municipalities and communities in any jurisdiction in the world to geographically locate biomass source and collection points, along with their annual capacity, and the corresponding optimal site for a W2VA facility. Keywords: Biomass, Biorefinery, GIS, suitability analysis, Integrated methodology, Municipal solid waste, Sustainability, Waste management, Waste-to-energy.
HighlightsTwo different configurations of 3D printed flumes of two different materials were tested for accuracy and variability.Discharge equations were developed for 3D printed 0.122 m HS and 0.102 m Palmer-Bowlus flumes.3D flumes are accurate and show no statistical variability between prints, providing a low-cost flow measurement tool.Abstract. Flumes are specially shaped, engineered structures that have been used widely for measuring flow. Flumes are typically fabricated from aluminum or fiberglass; however, these types of flumes can be costly if purchased commercially and may lack machine precision if custom fabricated. This limits availability for widespread monitoring by smaller municipalities, engineering firms, or researchers with limited budgets. Using 3D printing technology (additive manufacturing) to produce flumes is very cost-effective, but variability between flumes and materials has not been tested, and discharge equations have not been developed for 3D printed flumes. In this study, a laboratory-scale setup was used to develop discharge equations for two types of 3D printed flumes (0.122 m HS flume and 0.102 m Palmer-Bowlus flume) made from two 3D printing materials: polylactic acid (PLA) and polyethylene terephthalate glycol modified (PETG). Variability between the same type of flume and between different materials for the same type of flume was analyzed to evaluate the consistency of the discharge equation with flumes of the same type. Eight models were developed to fit each dataset (PLA, PETG, and combined PLA and PETG) for both flume types and evaluated for goodness-of-fit and information criteria (AIC and BIC for model parsimony) to select the discharge equation for each flume type. Discharge equations were consistent for the same type of flume across each print and across different print materials. The discharge equations of 3D printed 0.122 m HS flumes and 0.102 m Palmer-Bowlus flumes are Q = 0.45624 × H2.351 and Q = 0.0001176 + 1.309 × (H - 0.0174625)2.235, respectively. The discharge equations of both flume types had R2adj values greater than 97% for the measured data of each individual flume. Both 3D printed flumes were consistent in measuring flow and are suitable for hydrologic monitoring. Keywords: 3D printing, Additive manufacturing, Discharge equation, Flume, Hydrologic monitoring.
HighlightsA non-destructive, in situ, and low-cost root phenotyping system was developed.The system can collect color images and 3D cloud points of corn roots in soil.When tested in a greenhouse, the scanning process did not cause significant disturbance of corn plants.The results showed significant differences in root growth for different watering treatments and growth stages.Abstract. Plant root phenotyping technologies play an important role in breeding, plant protection, and other plant science research projects. Root phenotyping researchers urgently need technologies that are low-cost, in situ, non-destructive to roots, and suitable for the natural soil environment. Many recently developed root phenotyping methods, such as minirhizotron, X-CT, and MRI scanners, have unique advantages in observing plant roots, but they also have disadvantages and cannot meet all the critical requirements simultaneously. This study focused on the development of a new plant root phenotyping robot, called MISIRoot, that is minimally invasive and works in situ in natural soil. The MISIRoot system mainly consists of an industrial-level robotic arm, a miniature camera with lighting, a plant pot holding platform, and image processing software for root recognition and feature extraction. MISIRoot can acquire high-resolution color images of roots in soil with minimal disturbance to the roots and measure the roots’ three-dimensional (3D) structure with an accuracy of 0.1 mm. In tests, well-watered and drought-stressed groups of corn plants were measured with MISIRoot at the V3, V4, and V5 growth stages. The system successfully acquired RGB color images of the roots and 3D point cloud data containing the locations of the detected roots. The plants measured with MISIRoot and the plants not measured (control) were carefully compared with the results from a hyperspectral imaging facility (reference). No significant differences were found between the two groups of plants at different growth stages. Keywords: 3D point cloud, Low-cost phenotyping, Minimally invasive root measurement, Plant root phenotyping, Robotic arm application, Root imaging.
Highlights The TS-PEF diminished the color change compared to individual treatments. The concentration of Fe, Cr, and Ni increased after the treatments of PEF and TS-PEF. The volatile flavor compounds decreased after the treatment of TS and TS-PEF. Abstract . Chinese rice wine (CRW) is a traditional wine prevalent in East Asia. In current industrial production of bottled CRW, an additional thermal pasteurization is required at the bottling stage to extend the shelf life of the product, and this thermal treatment results in quality degradation and even carcinogen formation. In our previous studies, non-thermal technologies such as pulsed electric fields (PEF), thermosonication (TS), and their combination (TS-PEF) have been shown to significantly inactivate Saccharomyces cerevisiae in CRW as an alternative to thermal pasteurization of bottled wine. However, studies of their effects on the quality of CRW are very limited. In this study, we compared the physicochemical properties of CRW treated by pasteurization, PEF, TS, and TS-PEF. The results showed that the basic physicochemical indicators of CRW (total sugar, non-sugar solids, total acidity, amino acid nitrogen, and pH) did not change significantly after all these treatments. The TS-PEF treatment reduced the total color difference caused by the TS or PEF treatments. However, corrosion of the electrodes during PEF and TS-PEF treatment caused a significant increase in iron, chromium, and nickel concentrations. In addition, volatile flavor compounds, such as alcohols, esters, acids, and aldehydes, were reduced much more after TS and TS-PEF treatment than after pasteurization and PEF. Further research is needed on minimizing the negative impacts of these new technologies on the flavor of CRW in the context of effective sterilization. Keywords: Chinese rice wine, Metal ion, Physicochemical properties, Pulsed electric fields, Ultrasound, Volatile flavor compounds.
HighlightsA custom-built camera module named PhenoStereo was developed for high-throughput field-based plant phenotyping.Novel integration of strobe lights facilitated application of PhenoStereo in various environmental conditions.Image-derived stem diameters were found to have high correlations with ground truth, which outperformed any previously reported sensing approach.PhenoStereo showed promising potential to characterize a broad spectrum of plant phenotypes.Abstract. The stem diameter of sorghum plants is an important trait for evaluation of stalk strength and biomass potential, but it is a challenging sensing task to automate in the field due to the complexity of the imaging object and the environment. In recent years, stereo vision has offered a viable three-dimensional (3D) solution due to its high spatial resolution and wide selection of camera modules. However, the performance of in-field stereo imaging for plant phenotyping is adversely affected by textureless regions, occlusion of plants, variable outdoor lighting, and wind conditions. In this study, a portable stereo imaging module named PhenoStereo was developed for high-throughput field-based plant phenotyping. PhenoStereo features a self-contained embedded design, which makes it capable of capturing images at 14 stereoscopic frames per second. In addition, a set of customized strobe lights is integrated to overcome lighting variations and enable the use of high shutter speed to overcome motion blur. PhenoStereo was used to acquire a set of sorghum plant images, and an automated point cloud data processing pipeline was developed to automatically extract the stems and then quantify their diameters via an optimized 3D modeling process. The pipeline employed a mask region convolutional neural network (Mask R-CNN) for detecting stalk contours and a semi-global block matching (SGBM) stereo matching algorithm for generating disparity maps. The correlation coefficient (r) between the image-derived stem diameters and the ground truth was 0.97 with a mean absolute error (MAE) of 1.44 mm, which outperformed any previously reported sensing approach. These results demonstrate that, with proper customization, stereo vision can be an effective sensing method for field-based plant phenotyping using high-fidelity 3D models reconstructed from stereoscopic images. Based on the results from sorghum plant stem diameter sensing, this proposed stereo sensing approach can likely be extended to characterize a broad range of plant phenotypes, such as the leaf angle and tassel shape of maize plants and the seed pods and stem nodes of soybean plants. Keywords: Field-based high-throughput phenotyping, Point cloud, Stem diameter, Stereo vision.
Accurately estimating the peak cutting force for cutting citrus fruit stems is helpful for the design of the end-effector of a harvesting robot and improves the harvesting success rate of the harvesting robot. In this study, five factors that influence the cutting of citrus fruit stems with a harvesting robot in a natural environment were analyzed, including cutting speed (v), the gap between the two cutting blades of the end-effector (s), the diameter of the stem (d), and the deflection (theta(def)) and inclination (theta(inc)) angles of the stem. An experimental platform was built for cutting citrus fruit stems with the blade gap (s) as a fixed value. A mechanical model for calculating the maximum cutting force was established based on the partial least squares method, and d, v, theta(def), and theta(inc) were the independent variables of the model and were verified by repeated tests. According to the test results, the mechanical model was modified when the citrus fruit stem diameter was greater than or equal to 3.2 mm, and the modified model was also verified by tests. The verification results indicated that, compared with the actual peak force for cutting citrus fruit stems, the percentage error of the peak force calculated with the model was 11.51% when the stem diameter was less than 3.2 mm and 4.94% when the stem diameter was greater than or equal to 3.2 mm, which can provide a significant reference for evaluating the peak force for cutting citrus fruit stems and designing the end-effector of citrus harvesting robots.
Highlights E-nose and computer vision combined with data fusion strategies were applied to trace tea origins. Pearson correlation analysis, IG, and F-scores were applied to modify the fusion strategies. The classification performances of different fusion strategies were compared. The strategies of IG_SVM_FL and IG_SVM_DS achieved the best results. Abstract . The traceability of tea origins is of great significance. In this study, an electronic nose (E-nose) and computer vision system (CVS) were jointly applied to acquire aroma and image signals of tea samples, aiming at identifying Longjing teas from different geographic origins including Jinyun (120° 7' E, 28° 65' N), Xihu (120° 13' E, 30° 27' N), Xinchang (120° 9' E, 29° 50' N), and Qian Daohu (119° 3' E, 29° 60' N). Data fusion was used to integrate the E-nose and CVS signals for comprehensively characterizing the tea samples. Four traditional fusion strategies including k-nearest neighbors (KNN) and support vector machine (SVM) based feature-level fusion (KNN_FL and SVM_FL) and Dempster-Shafer (D-S) evidence theory based decision-level strategies (KNN_DS and SVM_DS) were applied for classification modeling. Pearson analysis, information gain (IG), and F-scores were employed to modify the traditional fusion strategies to reduce inconsistent and redundant information in the fusion process. The results indicated that the original fusion strategies had no superiority over independent E-nose and CVS decision-making. With the feature selection methods, the modified fusion strategies generally exhibited better performance than the independent decision-making and original fusion strategies. Moreover, the IG-based fusion strategies, encompassing IG_SVM_FL and IG_SVM_DS, achieved the highest classification accuracy of 100%. Keywords: Computer vision, Electronic nose, Feature selection, Fusion strategies, Tea origins.
HighlightsModeling provides the relationships between path, kinematics, geometry, and towed implements.Linear interpolation allows trajectories to be compared if data recording is random.Correction coefficients can be a solution to compensate for soil resistance.Abstract. Automatic guidance systems and autonomous vehicles require tested methods of path generation to ensure successful maneuvers (such as automatic trajectory correction and headland turn management). In this study, an evolution of Zakin’s kinematic modeling, as applied in the automobile industry, is proposed for an agricultural poly-articulated vehicle (representing a tractor or other type of towing vehicle with one or more towed implements attached with an articulated hitch). Geometry, vehicle ground speed, and angular steering velocity are considered in the generation of maneuvering paths. Based on the specifics of real field conditions (slope, plant residue, resistance due to soil compaction, etc.), the initial model was improved by introducing correction coefficients. An experimental setup is proposed using a tractor with two towed implements and a testing method involving point-to-point path comparison. The modeling method has potential for integrating more complex procedures (such as path generation, geolocation, and following) into the design of a maneuvering management system for agricultural machines, which can contribute to the efficiency of field operations. Keywords: Agricultural vehicle, Headland turn automation, Maneuverability, Modeling, Path generation, Path planning, Poly-articulated vehicle.
Highlights Juice extraction resulted in a decrease in the nutritive value of the bagasse as compared with the initial biomass. Silages made from the second pressing bagasse were well conserved. Sweet sorghum silage has a better nutritive value than sweet pearl millet. Abstract . Pressing the biomass of sweet sorghum and sweet pearl millet in-field is one of the suggested options for bioethanol production. The extracted juice can be delivered to an ethanol plant, and the bagasse (pressing residue) can be used for ruminant feeding. Efficient carbohydrate extraction is highly important for good ethanol yield. However, enough carbohydrates must remain in the bagasse for its adequate conservation as silage. In this study, the ensilability and the chemical composition of the second pressing bagasse of sweet sorghum and sweet pearl millet were investigated. The bagasse was obtained following a second pressing of the first pressing bagasse after its impregnation with water based on three water:bagasse ratios (0.5, 1, and 1.5). Results indicated that water:bagasse ratio did not affect water-soluble carbohydrate (WSC) extraction for both crops. The second pressing bagasse of sweet sorghum and sweet pearl millet contained 80.5 ±4.6 and 60 ±4.6 g of WSC kg-1 dry matter (DM), respectively. The second pressing bagasse of both crops had reduced nutritive value compared to the initial biomass, i.e., higher neutral detergent fiber (NDF) and acid detergent fiber (ADF) concentrations along with lower non-structural carbohydrate (NSC) concentration, in vitro true digestibility of DM (IVTD), and in vitro NDF digestibility (NDFd). The second pressing bagasses of both crops also showed good ensilability, but sweet sorghum bagasse silages were of better nutritive value than sweet pearl millet bagasse silages (ADF = 446.2 ±3.7 vs. 463.2 ±3.7 g kg-1 DM, IVTD = 813.8 ±3.4 vs. 708.8 ±6.8 g kg-1 DM, and NDFd = 741.8 ±4.8 vs. 596.2 ±8.5 g kg-1 NDF, respectively). The water:bagasse ratio used for bagasse impregnation before the second pressing only affected the NDF concentration of silages, as a higher NDF concentration was obtained with a water:bagasse ratio of 1.5. Sweet sorghum and sweet pearl millet can be considered dual-purpose crops; the extracted juice can be fermented into ethanol, and the second pressing bagasse can be used to make good-quality silage. Keywords: Bagasse impregnation, Nutritive value, Silage, Sweet pearl millet, Sweet sorghum, Water-soluble carbohydrates.
HighlightsHigh ecohydrological activity drives macropore prevalence in riparian buffers.An abundance of macropore flow (MF) was confirmed in a field riparian buffer in Kenya.Source-response (SR) and multilayer kinematic diffusive wave (MKDW) MF models are compared.A novel MKDW modeling framework efficiently identifies and predicts preferential flow in riparian buffers.Abstract. The significant ecohydrological activity typical of riparian buffers makes them potential hotspots of macropores, i.e., structured preferential flow pathways, through the soil vadose zone. The prevalence of these preferential pathways can allow transported contaminants to bypass the soil matrix and quickly reach a seasonal shallow water table and the adjacent surface waterbody. This quick transport can ultimately limit the role of riparian buffers for runoff pollution control. Currently, there are no management tools that incorporate macropore flow (MF) when assessing riparian buffer performance. The objective of this study was to experimentally quantify and mathematically simulate macropore flow and arrival time in a riparian buffer under field conditions. Three infiltration experiments were conducted with a grid of 20 time-domain transmission (TDT) dielectric soil moisture sensors along a field riparian buffer transect in Kenya to quantify the presence of macropore flow and to test two non-Darcian soil MF models, including the source-responsive (SR) model and the modified kinematic-dispersive wave (MKDW) model developed in this study, by adding a user-defined multilayer convection scheme and a new hysteresis function between water flux and content. The abundance of MF in the riparian buffer was corroborated experimentally. Modeling results showed that the MKDW model was an efficient (average NSE of 0.937 and 0.721 for calibration and testing, respectively), flexible, and robust method to identify and represent non-linear and non-sequential MF signals at any soil depth and antecedent conditions. The SR model was computationally inexpensive and provided good calibration results (NSE = 0.867) but required piecemeal recalibration of the travel time and maximum water content at each layer and yielded lower performance in testing. The Akaike (AIC) and Bayesian (BIC) information criteria showed that MKDW outperformed SR when accounting for the trade-off between model complexity and efficiency. The results support further research focused on independent characterization of model parameters at the field scale, and the inclusion of MKDW in holistic riparian buffer management and decision-support tools such as VFSmod. Keywords: Kinematic-dispersive wave, Macropore flow, Numerical modeling, Preferential flow, Riparian vadose zone.
HighlightsLater planting and greater site elevation or latitude decreased seasonal growing degree days and cotton yield in Kansas.Higher irrigation capacity (rate) usually increased lint yield, which was probably due to increased early boll load.Strategies for splitting land allocations between high irrigation rates and dryland did not increase production.Cotton may reduce irrigation withdrawals from the Ogallala aquifer, but the Kansas growing season limits production.Abstract. Precipitation in the western Great Plains averages about 450 mm, varying little with latitude and providing 40% to 80% of potential crop evapotranspiration (ETc). Supplemental irrigation is required to fully meet crop water demand, but the Ogallala or High Plains aquifer is essentially non-recharging south of Nebraska. Pumping water from this aquifer draws down water tables, leading to reduced water availability and deficit irrigation to produce an alternate crop such as cotton (Gossypium hirsutum L.) with a lower peak water demand than corn (Zea mays L.). Our objective was to compare simulated cotton yield response to emergence date, irrigation capacity, and application period at three western Kansas locations (Colby, Tribune, and Garden City) with varying seasonal energy or cumulative growing degree days (CGDD) and compare split center pivot deficit irrigation strategies with a fixed water supply (i.e., where portions of the center pivot land area are managed with different irrigation strategies). We used actual 1961-2000 location weather records with the GOSSYM simulation model to estimate yields of cotton planted into soil at 50% plant-available water for three emergence dates (DOY 145, 152, and 159) and all combinations of irrigation period (0, 4, 6, 8, and 10 weeks beginning at first square) and capacity (2.5, 3.75, and 5.0 mm d-1). Simulated lint yield and its ratio to ETc, or water use efficiency (WUE), consistently decreased with delayed planting (emergence) as location elevation or latitude increased due to effects on growing season CGDD. Depending on location, simulated cotton lint consistently increased (p = 0.05) for scenarios with increasing irrigation capacity, which promoted greater early season boll load, but not for durations exceeding 4 to 6 weeks, probably because later irrigation and fruiting did not complete maturation during the short growing season. Cotton WUE generally increased, with greater yields resulting from earlier emergence and early high-capacity irrigation. We calculated lower WUE where irrigation promoted vigorous growth with added fruiting forms that delayed maturation and reduced the fraction of open bolls. The irrigation strategy of focusing water at higher capacities on a portion of the center pivot in combination with the dryland balance did not increase net yields significantly at any location because the available seasonal energy limited potential crop growth and yield response to irrigation. However, the overall net lint yield was numerically larger for focused irrigation strategies at the southwest Kansas location (Garden City). Based on lint yields simulated under uniform or split center pivot deficit irrigation, we conclude that cotton is poorly suited as an alternative crop for central western and northwestern Kansas because of limited growing season CGDD. Keywords: Cotton, Crop simulation, Deficit irrigation, Evapotranspiration, Irrigation capacity, Split center pivot irrigation, Water use efficiency, Yield limiting factors.
Highlights A small, automatically guided electric robot boat was developed to control the growth of aquatic plants in lakes. The robot boat was able to mow without becoming entangled with aquatic plants due to the paddle propulsion mechanism. The average speed was 0.41 m s -1 , and the lateral deviation from the set path was 9.4 cm. The growth restraint effect was confirmed using aerial images collected by a drone. Abstract . At present, aquatic plants are becoming a problem in various lakes. Aquatic weeds that grow to high densities cause decreases in the oxygen concentration in the water, accumulation of sludge, and adverse effects on water use facilities and fisheries. Although there are large machines for cutting and harvesting aquatic plants, their use is limited due to their high cost, large size, and heavy weight. On the other hand, manual cutting is very labor intensive and inefficient. Therefore, in this research, we developed a labor-saving and low-cost robot boat to mow aquatic plants. This robot boat is automatically controlled by a real-time kinematic global navigational satellite system (RTK-GNSS) and is characterized by its small size, light weight, excellent portability, and low energy consumption. In addition, a paddle mechanism that replaces a conventional propeller was introduced as a propulsion mechanism to prevent entanglement of water plants and enable long-term automatic navigation. Mowing lotus (Nelumbo nucifera) in Lake Izunuma, Miyagi prefecture, Japan, confirmed that mowing could be properly conducted with a lateral deviation of 9.4 cm, average speed of 0.41 m s-1, and average power consumption of 518 W even when lotus was at its densest growth. If the cutting width of the robot boat was 1.2 m and the sidelap width was 0.3 m, the theoretical field work rate would be 0.133 ha h-1. Images captured by a drone confirmed that cutting with the robot boat was effective in suppressing the growth of lotus. It was found that the effect of suppressing growth was maximized by mowing at the time when the stopping leaves appeared in the late growth stage of lotus. Keywords: Autonomous navigation, Lotus, Moving baseline, Robot operation system, RTK-GNSS, Virtual private network.
Highlights Wheat flour cohesion was modeled using the Johnson-Kendall-Roberts (JKR) contact model. The size-based separation was highly influenced by particle size, particle roughness, cohesion, and sieve opening size. Sieve blinding happened at 15.25 and 10.32 s of sieving for hard red winter (HRW) and soft red winter (SRW) wheat flour particles, respectively. Abstract . Sifting or size-based separation of flour particles is an important operation in the wheat milling process. During the separation process, the flour particles often behave as imperfect solids with discontinuous flow and tend to form agglomerates due to interparticle cohesion. Interparticle cohesion in flours is highly dependent on the particle physical and chemical parameters and influences the sieving process. This study presents the development of a discrete element method (DEM) model to predict the size-based separation of wheat flours at 10% and 14% moisture contents (wet basis). DEM models of the size-based separation process were developed using the Hertz-Mindlin contact model. To account for the interparticle cohesion, the Johnson-Kendall-Roberts (JKR) model was coupled with the contact model. The size-based separation of hard red winter (HRW) and soft red winter (SRW) wheat flours was simulated and then validated using lab-scale experiments. Both the modeling and experimental approaches indicated that the percent particle separation was higher in the sieves with larger openings. Particle size, roughness, and cohesion affected the size-based separation in sieves with smaller openings. The model simulation results for the percent mass retained on the screens and the sieve blinding time were comparable with the experimental results. The standard error of prediction (SEP) ranged from 0.13 to 8.27, which indicates that this approach will be useful to predict the size-based separation of cohesive fine particles. The developed model will also be useful to estimate the sieve blinding time during sifting processes. Keywords: Cohesion, Johnson-Kendall-Roberts model, Sifting, Wheat milling.
HighlightsFluidized bed drying of rough riceat 40°C with or without ambient air dehumidification worked best based on the energy and exergy utilization.The dryer lost exergy in the exit air, which was the primary cause of thermal inefficiency; recirculation of the exit air could improve the exergy efficiency.Ambient air dehumidification did not reduce the dryer’s energy utilization and exergy efficiency for rough rice.Abstract. Fluidized bed drying of rough rice in the U.S. has not been used to its full potential due to a lack of research to address rice quality impacts and energy consumption. Little research has been done to analyze the energy and exergy of fluidized bed drying of rough rice. Thermal analysis allows using the drying air’s energy better and improving the drying system’s thermal efficiency. In this study, energy utilization and energy utilization ratio were calculated using the first law of thermodynamics, while exergy loss and exergy efficiency were determined using the second law. Drying air temperature (40°C, 45°C, or 50°C), drying bed condition (fluidized or fixed), drying duration (30, 45, or 60 min), and ambient air dehumidification (yes or no) were the tested factors. A lab-scale drying system designed in a previous study was used. Three replicates were performed to minimize any bias or human errors. All factors significantly affected the energy and exergy of the drying process, except dehumidification and replication. The minimum and maximum energy utilization values were 0.01 and 0.55 kJ s-1 for fixed bed drying at 40°C for 30 min with dehumidification and fluidized bed drying at 50°C for 60 min without dehumidification, respectively. The minimum and maximum exergy efficiency values were 13.46% and 49.14% for fixed bed drying at 45°C for 45 min with dehumidification and fluidized bed drying at 40°C for 60 min with dehumidification, respectively. The primary cause of thermal inefficiency was attributed to the energy and exergy losses in the exit air, while the secondary source was the exergy and energy losses from the drying chamber and inlet air pipes. Costly solutions could be recirculation of the exit air and better insulation of the drying chamber and inlet pipes. However, using the optimal drying conditions for the energy and exergy utilization of the drying air is suggested. This study found that fluidized bed drying was better than fixed bed drying overall. At the primary drying stage, fluidized bed drying had a higher exergy efficiency, energy utilization, and energy utilization ratio than fixed bed drying. At 40°C, fluidized bed drying with or without ambient air dehumidification worked best based on the energy and exergy utilization of the drying system. Keywords: Dehumidification, Energy, Exergy, Fixed bed, Fluidized bed, Rice drying.
Abstract. This study focuses on the modeling of sorption characteristics of three varieties of soybeans (Akras R2, Lono R2, and Podaga R2). Three pretreatments related to post-harvest conditions were tested on the soybean varieties: (1) freshly harvested soybeans, (2) soybeans subjected to three drying and wetting cycles, and (3) soybeans subjected to three freezing and thawing cycles. The adsorption and desorption experiments were conducted at 5°C, 10°C, 15°C, 20°C, 25°C, and 30°C using a dynamic equilibrium relative humidity (ERH) apparatus. Equilibrium moisture content (EMC) and the corresponding ERH were measured. The parameters calculated for the modified Halsey equation are applicable for storage temperatures above 10°C in the relative humidity (RH) ranges of 10% to 80% for desorption and 30% to 80% for adsorption. No significant differences were found in sorption isotherms among the soybean varieties. However, the soybean varieties responded differently to the different pretreatments (i.e., drying/wetting and freezing/thawing cycles). The adsorption isotherms of Akras and Lono soybeans showed significant differences at 10°C to 30°C when subjected to drying and wetting cycles, while Akras and Podaga soybeans showed significant differences in the same temperature range when subjected to freezing and thawing cycles. The effect of drying and wetting cycles on the desorption isotherms was found only for Akras soybeans at 10°C and 15°C below 63% and 71% RH, respectively, and for Lono soybeans at 25°C and 30°C above 69% RH for both temperatures. In general, the effect of both pretreatments on the sorption isotherms of soybeans was a reduction in EMC of up to 20%, when compared to fresh samples at selected storage temperatures. The findings of this study serve as a primary tool for developing a lookup table for safe storage guidelines for soybeans. Keywords: Equilibrium moisture content, Equilibrium relative humidity, Halsey equation, Oswin equation, Soybeans.
HighlightsAnnual average NH3 concentrations in two retrofitted manure-belt layer houses were 4.0 ±3.3 and 5.2 ±3.0 ppmv.Seasonal and diurnal variations were observed for NH3 concentrations (higher in colder seasons and early morning).Annual average NH3 emission rates from the two layer houses were 0.081 ±0.004 and 0.099 ±0.004 g d-1 hen-1.No consistent pattern was found for either seasonal or diurnal variations in NH3 emission rates.Abstract. Ammonia emission measurements at poultry houses are necessary to assess air quality and emission factors associated with poultry operations, but no data have been reported for manure-belt layer houses retrofitted from high-rise layer houses. Two commercial retrofitted manure-belt layer houses (both 121.9 m long, 19.5 m wide, and 7.7 m high; 170,000 bird nominal capacity each) in Ohio with mixed usage of tunnel and cross ventilation systems were continuously monitored for one year. The daily averages of the exhaust NH3 concentrations varied from 0.03 to 17.7 ppmv in house 1 and 0.37 to 14.4 ppmv in house 2 with annual means (±SD) of 4.0 ±3.3 and 5.2 ±3.0 ppmv, respectively. The NH3 emission factors based on the full year of data for houses 1 and 2 were 0.081 ±0.004 and 0.099 ±0.004 g d-1 hen-1 (12.5 ±10.1 and 15.2 ±10.6 kg d-1 house-1 or 24.9 ±20.0 and 31.1 ±23.4 g d-1 AU-1), respectively. Seasonal variations were observed for NH3 concentrations, with higher concentrations in winter and lower concentrations in summer. Within a day, NH3 concentrations were highest from 4:00 to 8:00 and lowest from 16:00 to 20:00. No consistent pattern was observed for seasonal or diurnal variations of NH3 emission rates. Higher NH3 concentrations and emissions were observed at the east and west exhaust air streams of the houses compared to the north and south exhaust air streams due to the unique configuration of the ventilation systems. NH3 emission was correlated with exhaust absolute humidity, hen caloric intake, feed consumption, and protein percentage of feed. Keywords: Ammonia emission factor, Diurnal variation, High-rise, Retrofitted poultry house, Seasonal variation, Spatial variation.
HighlightsAn integrated GPS/INS/VNS navigation system was developed to improve navigation accuracy.An adaptive federal Kalman filter with information distribution factors was used to fuse navigation information.Detection of seedling row lines was achieved based on subregional feature points clustering.A modified rice transplanter was developed as an experimental platform for automatic navigation.Abstract. In this article, an integrated global positioning system (GPS), inertial navigation system (INS), and visual navigation system (VNS) navigation method based on an adaptive federal Kalman filter (KF) is presented to improve positioning accuracy for a rice transplanter operating in a paddy field. The proposed method used GPS/VNS to aid the INS and reduce the influence of the accumulated error of the INS on navigation accuracy. An adaptive federal KF algorithm was designed to fuse navigation information from different sensors. The information distribution factor of each local filter was obtained adaptively on the basis of its own error covariance matrix. Computer simulation and transplanter tests were conducted to verify the proposed method. Results showed that the proposed method provided accurate and reliable navigation information outputs and achieved better navigation performance compared with single GPS navigation and an integrated method based on a conventional federal KF. Keywords: Federal Kalman filter, GPS/INS/VNS, Information distribution factor, Information fusion, Integrated navigation.
Highlights Cottonseed oil (CSO), a cotton byproduct, has advantages for climate change compared to other seed oils. Results show that the cultivation phase is the main impact driver for all vegetable oils analyzed in this study. Refined CSO (U.S.) can reduce climate change impacts by up to 83% as compared to the other oils analyzed. Abstract . Edible vegetable oils are a major source of climate change impacts and an environmental concern in the processed food industry. This study consists of a cradle-to-grave life cycle assessment (LCA) of refined U.S. cottonseed oil (CSO), global soybean oil, U.S. canola oil, and palm oil sourced from Indonesia and Malaysia. Considering the oils equivalent for deep frying, they are compared on a 1 kg of oil basis. Analysis includes sensitivity analyses for modeling allocation choices and oil mixes as well as uncertainty analysis. Results show that the cultivation phase is the main impact driver for all vegetable oils analyzed, which favors CSO (U.S.) because it is a co-product. Refined CSO (U.S.) can reduce climate change impacts by up to 83%. Overall, refined CSO (U.S.) was a top performer in six of the eight impact categories evaluated. When ranking the oils, refined CSO (U.S.) was the preferred choice. Despite being the preferred choice, there are tradeoffs with CSO, such as water scarcity. In the context of global-scale commercial frying applications, e.g., McDonald’s daily French fry production of 9 million tons per day, switching the frying oil to refined CSO (U.S.) represents potential savings of 1,130 to 2,188 tons of CO2-eq d-1. For fast-food chains seeking to reduce their climate change impacts, refined CSO (U.S.) may be useful in frying applications. However, opportunities may exist for improvement in water use efficiency in the cultivation phase, which reinforces the need for continuous improvements in agriculture. Keywords: Comparative life cycle assessment, Canola oil, Cottonseed oil, Cotton sustainability, Fast-food industry, LCA, Palm oil, Soybean oil, Vegetable frying oils.
HighlightsDesign, description, and comparison of static (S) and dynamic (D) grain respiration measurement systems (GRMS).No differences were detected between dry matter loss rates (vDML) from S-GRMS and D-GRMS for soybeans at 18% moisture content and 30°C stored for 20 d.Literature reports variable vDML estimates for soybeans stored in S-GRMS and D-GRMS; more studies should be conducted with a wider range of storage conditions before developing maximum allowable safe storage time guidelines.Abstract. Time to reach 0.5% dry matter loss (DML) is the estimated maximum allowable storage time (MAST) for shelled corn and has been suggested for use with other grains. Respiration studies have reported various estimates of this threshold depending on the type of grain respiration measurement system (GRMS) and storage conditions tested. The objectives of this study were (1) to design and evaluate two GRMS in which oxygen needed for respiration was limited in a static system (S-GRMS) or continuously supplied in a dynamic system (D-GRMS) during storage and (2) to compare the effects of GRMS on DML rates (vDML) for 18% moisture content soybeans stored at 30°C for 20 d. In this study, S-GRMS and D-GRMS units were designed to conduct respiration tests. Respired CO2 (mg CO2) was measured over time and used to calculate the specific mass of respired CO2 (mg CO2 kg-1 d.b. beans) and subsequent DML (%) using stoichiometric ratios from the respiration chemical reaction. DML rates, vDML (% d-1), were estimated by least squares linear regression of DML and time data. Four replications of respiration tests were conducted in each GRMS. Average estimates of vDML were 0.0157% d-1 and 0.0189% d-1 for S-GRMS and D-GRMS, respectively. Mean vDML from D-GRMS tests was 1.2 times greater than mean vDML from S-GRMS but not statistically different (p = 0.09). However, the coefficient of variation was 8 times greater for D-GRMS than for S-GRMS. More studies with a wider range of storage conditions should be conducted for development of a safety factor between both systems prior to using data from respiration of soybeans in the literature to estimate MAST. Keywords: Dry matter loss, Grain storage, Respiration, Soybeans.
HighlightsRespiration rate increased with the percentage of split soy beans.The splits multiplier for soybeans appears to be more sensitive than the corn damage multiplier.The relationship between the split percentage and splits multiplier for soybeans is approximately linear.A value for the splits multiplier is provided for 18% moisture content soybeans stored at 35°C.Abstract. The objectives of this study were to compare the effects of 0% to 16% (w/w) splits content (xs) of soybeans stored at 35°C and 18% moisture content (w.b.) on dry matter loss rates (vDML) and to determine a splits multiplier (MS) for soybeans stored at these conditions, similar to the damage multiplier (MD) used in ASABE Standard D535 for shelled corn. Effects of percentage by weight splits (xs) on MS and safe storage time are expected to be greater than MD for corn because soybeans are prone to cracking and splitting, lipid oxidation, and protein degradation, all of which lead to grain dry matter and quality losses. Results indicated that vDML increased with increasing xs, and the mean vDML was 1.5 times greater for samples with 16% splits than for samples with 4% splits. The MS for soybeans was linearly correlated with xs, decreasing from 1.0 to 0.60 for 0% to 16% splits, respectively. Soybeans appeared to be more sensitive to percent by weight splits than corn was to the presence of damaged kernels. In Standard D535, the MD for corn only decreased from 1.0 to 0.8 when percent (w/w) damaged kernel content increased from 30% to 40%. In comparison, it should be noted that damage to soybeans was defined differently from damage to corn. This research is useful in defining MS for 18% moisture content soybeans stored at 35°C under hermetic conditions. The procedures outlined in this article may be used in the future to more formally define a soybean MS that covers a wider range of moisture contents, storage conditions, and possibly an MD based on other factors included in the USDA definitions of damaged soybean kernels. Keywords: Allowable storage time, Grain quality, Splits multiplier.
