Biosystems Engineering最新文献

Upflow anaerobic sludge blanket (UASB) reactors are widely used for wastewater treatment in tropical regions, yet the resulting effluent requires further post-treatment to meet the quality standards for a safe discharge or reuse. This study proposes a novel technology for the post-treatment of effluents from UASB reactors, high rate algal ponds (HRAP). Firstly, experimental data from two pilot HRAP treating the primary effluent from an UASB reactor was used to calibrate a mathematical biokinetic model (BIO_ALGAE). Then, different operational strategies were simulated by varying the HRAP hydraulic retention time (HRT) (4, 6, and 8 days) and footprint, water quality, and bioenergy production, considering a wastewater treatment plant of 10,000 population equivalent. Experimental results showed a removal efficiency of 70% for the chemical oxygen demand, 42% for total suspended-solids, 57% for ammonium nitrogen, and 30% for orthophosphate in the pilot plant. According to the calibrated model output, the quality of the effluent is similar with HRT of 6 and 8 days, but with a HRT of 4 days N removal would be compromised. Bearing in mind that the lower the HRT, the lower the footprint of the wastewater treatment plant, 6 days seems a important trade-off. The findings of this study are promising, presenting a new conception for treating effluents from UASB reactors. This could enhance the quality of wastewater treatment in tropical countries and suggest potential uses for the generated by-products.

3D reconstruction of seedling can provide comprehensive and quantitative spatial structure information, offering an effective digital tool for breeding research. However, accurate and efficient reconstruction of seedling is still a challenging work due to limited performance of depth sensor for seedling with small-size stem and unavoidable error for multi-view point cloud registration. Therefore, in this paper, we propose an accurate multi-view 3D reconstruction method for seedling using 2D image contour to constrain 3D point cloud. The rotation axis is calibrated and optimised by minimising point-to-contour distance between 2D image contour and projected exterior points from 3D point cloud. Then, to remove outliers and noise, we introduce the seedling mask of 2D image to constrained and delete projected outlier points of 3D model from corresponding view. Furthermore, we propose a residual-guided method to recognise missing region for 3D model and complete 3D model of small-size stem. Finally, we can obtain an accurate 3D model of seedling. The reconstruction accuracy is evaluated by average distance between projected contour of 3D model and 2D image contour of all views (0.3185 mm). Then, the phenotypic parameters were calculated from 3D model and the results are close to manual measurements (Plant height: R² = 0.98, RMSE = 2.3 mm, rRMSE = 1.52%; Petioles inclination angle: R² = 0.99, RMSE = 0.73°, rRMSE = 1.41%; Leaf area: R² = 0.66, RMSE = 1.05 cm², rRMSE = 7.63%; Leaf inclination angle: R² = 0.99, RMSE = 1.01°, rRMSE = 1.72%; Stem diameter: R² = 0.95, RMSE = 0.12 mm, rRMSE = 5.43%). Breeders can improve the selection of more resilient varieties and cultivars to different growing conditions starting from the dynamic analysis of their phenotype.

Computational fluid dynamics (CFD) simulations have been extensively used in designing air distribution systems for controlled environment agriculture (CEA). In recent years, more application studies using CFD simulations can be found for vertical farms due to the increasing interest in indoor vertical farming systems. However, it is well-known that CFD simulations are sensitive to many computational parameters and settings. The requirement of a crop response model in the CFD simulation for a vertical farm makes it even more complicated. Despite increased interest, guidelines for CFD simulations in vertical farms are scarce based on a literature study. Therefore, a systematic sensitivity analysis is conducted for a small generic multi-layer vertical farm with sole source lighting, which was the object of study in the literature before. The impact of a wide range of computational and physical parameters is investigated, including grid resolution, turbulence model, turbulence intensity, discretisation scheme, drag coefficient of the crops and computational time. The analysis shows that in this case (inlet Re = 46,923, Ar = 0.078, cultivated with lettuce), the RNG k-ε turbulence model outperforms other commonly used two-equation turbulence models. Compared to the experimental results from the literature, the simulation results from the first-order upwind scheme show large discrepancies, especially on the coarse grid. Although the influence of drag coefficient on the airflow inside the crop canopy is pronounced, little difference is observed in the air distributions in the vertical farm away from the crops.

It is challenging to control pathogens in contaminated seeds without damaging seed vigour by using heat treatments. In this study, a treatment process for pasteurising watermelon seeds was developed using the emerging technology of radio frequency (RF) energy. To reduce the temperature difference between the layers, the polypropylene frame (covering 40% of the sample surface area) with the inside medium of air was added to the centre of the first layer. The length, width, height, and thickness of the polypropylene frame were 134, 94, 17, and 2 mm, respectively. The optimised process involved that seeds with stacked four layers were heated by RF energy for 6.5 min followed by holding in the hot air oven at 64.5 ± 0.5 °C for 50 min. After that, samples with separated four layers were dried in the hot air oven for 30 min followed by cooling for 6 min in forced ambient air (6.0 ± 0.1 m s⁻¹). This treatment produced in excess of a 4-log reductions of Acidovorax citrulli while differences in germination rate, germination energy, vigour index, germination index, and leachate electrical conductivity were insignificant (P > 0.05) between control and the optimised process treated seeds. Meanwhile, the average moisture content of seeds was reduced to 8.05% wet basis after pasteurising. These findings can further be expanded to develop potential industrial applications of RF pasteurisation for maintaining quality in agriculture products.

To investigate the hydraulic effects and performance of the micro-pressure filtration and cleaning tank under conditions with sandy and brackish water, physical model tests were conducted with five groups of flow rates (6–14 m³ h⁻¹), four groups of sediment contents (0.5–2.0 g l⁻¹), five groups of mineralisation degrees (0–5.0 g l⁻¹), and three groups of screen apertures (0.125, 0.150, and 0.180 mm). Dimensional analysis, multiple linear regression analysis, and the non-dominated sorting genetic algorithm II (NSGA-II) were used to analyse the test results. The results showed that the optimal operating conditions of the micro-pressure filtration and cleaning tank under the scope of this test were a screen aperture of 0.175 mm, a flow rate of 13 m³ h⁻¹, a sediment content of 1.8 g l⁻¹, and a mineralisation degree of 4.7 g l⁻¹. The micro-pressure filtration and cleaning tank was intermittently discharged and rinsed, the discharge time was 30–40 s, and the flow rate of discharge and rinsing was 5.54 m³ h⁻¹. Prediction models of the head loss and the filtration efficiency of the filter were established. The coefficients of determination (R²) were greater than 0.9, the average relative errors of the predicted and measured values were 2.98% and 2.17%, respectively, and the corresponding root mean square errors were 0.0549 m and 0.642. The research results can be used as a reference for in-depth investigations on the performance of the micro-pressure filtration equipment in front of pumps.

Sugarcane tip cutting is essential to reducing the rate of impurities in the harvest. To achieve adaptive regulation of the tip-cutting position by a sugarcane harvester, we propose a method for estimating the height of the tip-cutting position of sugarcane. The RGB and Binocular depth cameras are aligned to process the sugarcane tip region image. This involves threshold segmentation, morphological operations, and contour detection to identify the tip-cutting position and upper boundary contours. The depth image is segmented using contour pixel information and merged to form a colour depth image of the sugarcane's tip. This image is then transformed using depth data and triangular parallax principles to determine the height of the sugarcane tip-cutting position. The proposed method was evaluated in various sugarcane plantation environments. Comparative analysis between the proposed method and manual measurements of actual cutting position heights revealed that the RMSE ranged from 1.22 cm to 1.78 cm, and R² varied between 0.79 and 0.86. These results demonstrate the effectiveness of the proposed method in accurately extracting the height information of sugarcane tip-cutting positions, which has a specific application value for the adaptive adjustment of the tip-cutting device of the sugarcane harvester.

Wind-disturbance is a potential eco-friendly technique for tackling leggy seedlings. This study uses orthogonal experimental design and seedlings vigour assessment by strong seedling index (SSI) to investigate wind-disturbance on regulating tomato seedlings growth. Changes in endogenous hormone levels and biomechanical properties of tomato seedlings were investigated using enzyme-linked immunosorbent assay and uniaxial tension tests. Results showed that factors influencing significantly SSI, in descending order, are wind disturbance time (T), seedling age at the onset of wind disturbance (SA), wind velocity (V), and interval time (I). The wind-disturbance effect was found to be optimal with SSI = 0.126 for a condition where V, T, I and SA values are 3 m s⁻¹, 1 min, 30 min, and 15 days, respectively. V and T were positively correlated with the ethylene and abscisic acid content in seedling leaves, abscisic acid and auxin content in stems, cytokinin and ethylene content in roots, and the elastic moduli of stems and roots but negatively associated with the cytokinin content in stems and leaves, auxin and abscisic acid content in roots, and leaves’ elastic modulus. Wind disturbance mechanism for controlling seedlings growth involved eliciting the accumulation of abscisic acid in stems and leaves and reduction of the auxin content in roots to about the optimal threshold for roots growth thereby reducing seedling stems and leaves development and promoting a better roots growth and a high SSI. This work offers theoretical insights and technical guidance for utilising wind-disturbance as a sustainable seedling cultivation and personalised seedling management approach.

Measuring and modelling manure temperatures are crucial for estimating greenhouse gas emissions from liquid manure storage. The manure temperature was recorded at various depths in two swine slurry storage tanks situated in Vallentuna (VA) and Örsundsbro (OR) in Sweden. These data were used to assess the effectiveness of a revised mechanistic model for estimating manure temperatures, which incorporates the effects of wall shading, snow cover, and manure input mixing. The average manure temperatures were higher than air temperatures in the summer and fall. This indicated that using air temperature would result in an underestimation of methane emissions when applying the 2019 IPCC Refinement methodology. The revised model estimated manure temperatures for spring, summer, fall, and winter as 4.8, 16.1, 7.8, and 2.6 °C at the VA tank and 11.6, 17.1, 9.5, and 3.6 °C at the OR tank. The root mean square errors between daily simulated and observed temperatures in the summer decreased in both tanks due to incorporating shadow effect into the revised model. Fall estimates did not improve, possibly because of uncertainties from slurry removal and higher precipitation inputs. Sensitivity analysis indicated that solar radiative heat input was reduced with higher tank walls and smaller tank diameters when applying the revised model. Wall shading may influence manure temperatures in tanks with small diameters at high-latitude locations. This study offers insights into understanding the relationship between manure temperatures and its thermal balance influenced by latitude, storage design, snow cover and mixing, and its implications for accurately estimating methane emissions.

In micro-irrigation systems, distinct media filters and filtering materials are employed to remove suspended solids from irrigation water and thereby avoid emitter obstruction. Turbidity is related to suspended solids and dissolved oxygen depends on organic matter load. At this time, no models exist that are trustworthy enough to forecast the dissolved oxygen and turbidity at the outlet when utilising various media configurations and filter types. The objective of this investigation was to construct a model that can identify turbidity and dissolved oxygen at the filter outlet in advance. This study presents an algorithm for meta-heuristic optimisation inspired by populations termed Differential Evolution (DE) in conjunction with Support Vector Regression (SVR) (DE/SVR-relied model). This is an effective machine learning method, with seven kernel types for calculating the output turbidity (Turb_o) and the output dissolved oxygen (DO_o) from a dataset comprising 1,016 samples of various reclaimed water-using filter types. The type of media and filter, the height of the filter bed, the cycle duration, and the filtration velocity, as well as the electrical conductivity at the filter inlet, pH, inlet dissolved oxygen, water temperature, and the input turbidity are all tracked and analysed in order to achieve this. The best-fitted DE/SVR-relied model was constructed to predict the Turb_o and DO_o as well as the input variables' relative importance. Determination coefficients for the best-fitted DE/SVR-relied model for the testing dataset were 0.89 and 0.92 for outlet turbidity (Turb_o) and outlet dissolved oxygen (DO_o), respectively, showing a good predictive performance which are of great importance for the management of drip irrigation systems.

Greenhouse production of fruits and vegetables in developed countries is challenged by labour scarcity and high labour costs. Robots offer a good solution for sustainable and cost-effective production. Acquiring accurate spatial information about relevant plant parts is vital for successful robot operation. Robot perception in greenhouses is challenging due to variations in plant appearance, viewpoints, and illumination. This paper proposes a keypoint-detection-based method using data from an RGB-D camera to estimate the 3D pose of peduncle nodes, which provides essential information to harvest the tomato bunches. Specifically, this paper proposes a method that detects four anatomical landmarks in the colour image and then integrates 3D point-cloud information to determine the 3D pose. A comprehensive evaluation was conducted in a commercial greenhouse to gain insight into the performance of different parts of the method. The results showed: (1) high accuracy in object detection, achieving an Average Precision (AP) of [email protected]=0.96; (2) an average Percentage of Detected Joints (PDJ) of the keypoints of [email protected] = 94.31%; and (3) 3D pose estimation accuracy with mean absolute errors (MAE) of 11^o and 10^o for the relative upper and lower angles between the peduncle and main stem, respectively. Furthermore, the capability to handle variations in viewpoint was investigated, demonstrating the method was robust to view changes. However, canonical and higher views resulted in slightly higher performance compared to other views. Although tomato was selected as a use case, the proposed method has the potential to be applied to other greenhouse crops, such as pepper, after fine-tuning.