In view of the low seeding efficiency and precision of seeders used for Brassica chinensis in China, a new double-row pneumatic precision metering device for Brassica chinensis was designed, fabricated, and evaluated. With the characteristics of small size and high sphericity of Brassica chinensis seeds in mind, the structure and key dimensions of the metering plate were determined, and a force analysis of the seed-filling process was carried out. The negative pressure (NP), angular velocity (AV) of the metering plate, and cone angle (CA) of the suction hole were selected as the main influencing factors of the experiment. In order to explore the influence of each single factor and the interaction between factors on the seeding performance, a single factor experiment and a central composite design (CCD) experiment were designed, respectively, and the experimental results were analyzed by analysis of variance (ANOVA). After optimizing the main influencing factors such that the target of the qualified index (QI) was greater than 94% and the miss index (MI) was less than 2.5%, it was found that when CA was 60°, NP was 1.55–1.72 kPa, and AV was 1.1–1.9 rad/s, the seeding performance was excellent. The bench verification results of seeding performance (94% ≤ Q ≤ 100%, 0 ≤ M ≤ 2.5%) and the coefficient of variation (CV) of seed mass (CV of seed mass in outer and inner circle: 5.15%; CV of total seed mass: 8.60%) under the condition of parameter optimization were analyzed; as a result, the accuracy of the parameter optimization was confirmed.

Recent advances in nickel-based catalysts for plastic pyrolysis, emphasizing reaction mechanisms, catalyst design, and emerging innovations that enhance product selectivity, yield efficiency, and sustainable polymer recycling.

Co-pyrolysis of food waste with coconut fiber to produce hydrogen is considered an effective strategy for clean waste disposal and high value-added product synthesis. This study investigated the co-pyrolysis mechanism and hydrogen yields of food waste and coconut fiber at different blending ratios by thermogravimetric analysis and regression optimization. A heating rate of 10 ℃/min ranging from 30 ℃ to 900 ℃ coupled with the Svante August Arrhenius kinetic equations were utilized to derive the activation energies of the samples. Thermogravimetric analysis presented positive synergistic effects between coconut fiber and food waste to accelerate weight loss rate, and the main pyrolysis peak (200 ~ 450 ℃) moved to low temperature areas as the coconut fiber proportion increased. Additionally, kinetics showed that the lowest activation energy was 29.28 kJ at 1:3 blending ratio of coconut fiber to food waste. Based on this, response surface methodology (RSM) by Design-Expert.V8.0.6 ((Box-Behnken Design) (BBD)) was applied to optimize the microwave co-pyrolysis parameters (1600 W microwave power, 5 mm feedstock size, and 74% food waste proportion), thus the predicted maximum hydrogen yield was 46.20 vol.%. This study contributes to ameliorating the potential for solid waste microwave co-pyrolysis for value-added products production, therefore providing a reference for the further development of clean disposal technology and waste resource utilization. GRAPHICAL ABSTRACT

An optimized tumbling process can significantly improve the oxygen dissolving capacity of composting and fertilizer quality: by increasing the fluffiness of the lower layer of the pile, localized anaerobic fermentation can be avoided, thereby enhancing compost quality. This paper presents a method for improving the oxygen dissolving capacity of rotary drum food waste composting through a combination of simulation optimization and experimental validation. First, the discrete element method was used to optimize the key parameters of the tumbling process. The response surface method was then employed to analyze the composting test results and determine the optimal conditions. To ensure the reliability of the equipment under this method, failure risk analysis was conducted using the finite element method. The simulation optimization results showed that with a rotary drum reactor speed of 3.5 r/min, a horizontal angle of inclination of 2.5°, a mixing blade angle of inclination of 43°, and a blade pitch of 580 mm, the fluffiness of the lower layer of the pile increased by 8.515%, achieving the best tumbling and indirectly enhancing oxygen dissolving capacity. The maximum deformation of the load-bearing components was only 0.0548 mm, and the minimum safety factor was 4.408 (≥1 is considered safe). A 14-day composting experiment was conducted to validate the optimized parameters. The results showed that the maximum temperature of the compost pile reached 68.34 °C (lasting 7 days), with the pH, moisture content, C/N ratio, humus substances, humic acid, and fulvic acid contents of the fertilizer all meeting or exceeding the levels recommended by Chinese national standards. These findings indicate that the optimized tumbling device effectively improved the stability and dissolved oxygen efficiency of food waste composting, providing valuable practical insights and a research foundation for enhancing oxygen efficiency in the composting of other organic wastes.

Current methods for detecting apple watercore are expensive and potentially damaging to the fruit. To determine whether different batches of apples are suitable for long-term storage or long-distance transportation, and to classify the apples according to quality level to enhance the economic benefits of the apple industry, it is essential to conduct non-destructive testing for watercore. This study proposes an innovative detection method based on optical parameter inversion and the MobileNetV3 model. Initially, a three-layer plate model of apples was constructed using the Monte Carlo method to simulate the movement of photons inside the apple, generating a simulated brightness map of photons on the apple’s surface. This map was then used to train the MobileNetV3 network with dilated convolution, resulting in a pre-trained model. Through transfer learning, this model was applied to measured spectral data to detect the presence of watercore. Comparative experiments were conducted to determine the optimal transfer strategy for the frozen layers, achieving model accuracy rates of 99.13%, 97.60%, and 95.32% for two, three, and four classifications, respectively. Furthermore, the model parameters were low at 7.52 M. Test results of this study confirmed the effectiveness and lightweight characteristics of the method that combines optical property parameter inversion, the DC-MobileNetV3 model, and transfer learning for detecting apple watercore. This model provides technical support to detect watercore and other internal diseases in apples.