The treatment of excavated soil using the dry sieving method to produce recycled sand is an effective approach for resource utilization. Currently, the hot-air drying process used in this method exhibits high energy consumption. To address this issue, this study proposes a microwave drying technology to dry the excavated soil. Comparative experiments on microwave (1-6 kW) and hot-air (105-205 degrees C) drying of the excavated soil were conducted. The drying behavior and specific energy consumption of the excavated soil were investigated. The Weibull-Fick combined method was recommended for the segmental determination of the effective moisture diffusion coefficient, and the question of whether microwave drying adversely affects sand particles in the excavated soil was answered. The results revealed the following: Compared with hot-air drying, microwave drying demonstrated shorter drying time (3.5-38 min vs 75-1200 min), lower specific energy consumption (6.2-11.5 MJ/kg vs 22.3-55.4 MJ/kg), and a higher range of effective moisture diffusion coefficient (10-8-10-7 m2/s vs 10-9-10-8 m2/s). With increasing microwave power (3-6 kW), the time required for complete drying of the sample was reduced by up to 56 %. Under microwave drying, relaxing the termination moisture content criterion from 0 to 0.01 resulted in a 17 %-32 % reduction in specific energy consumption, accompanied by a 24 %-36 % decrease in drying time. Microwave drying did not damage sand particles within the excavated soil.

Protecting the environment is essential because a healthy ecosystem purifies air and water, maintains the soil, regulates the temperature, recycles nutrients, and provides food. However, when nations experience fast growth, they pay the utmost attention to their development and disregard the environmental and development-related consequences. The BRICS economies are examples of nations that have achieved high economic growth rates while polluting their environment via industrial expansion. Hence, this study aims to scrutinise the effects of forest rent, agricultural production, economic growth, and energy consumption on BRICS economies' carbon emissions and ecological footprint from 1995 to 2017. We adopted panel spatial correlation consistent least-squares dummy variables (PSCC-LSDV) estimation and panel quantile regression (PQR) techniques to perform the above-mentioned comparative analysis. The first-hand empirical consequences revealed that agricultural production, renewable energy consumption, and financial development condense the carbon discharge, and the rest of the variables trigger the carbon emission. In addition, GDPC, forest rents, non-renewable energy consumption, and domestic investment damage the environmental prominence by instigating an ecological footprint, whereas the remaining variables oblige to moderate the ecological footprint. Finally, this study recommends rigorous policies to mitigate pollution emissions to help reinstate environmental eminence.

Thorium extraction techniques, such as solvent extraction from monazite and electrosorption techniques from water leach purification (WLP) of radioactive waste residues, are important for thorium recovery, particularly in Malaysia. Despite their importance, previous studies have largely overlooked critical issues like radioactive hazards, human health risks, and environmental impacts associated with advanced thorium extraction methods. This study addresses these gaps by quantifying the environmental impact associated with solvent extraction and electrosorption techniques using a life cycle assessment (LCA) framework to compare environmental indicators for thorium recovery from monazite ore and WLP residues. The LCA was conducted from cradle to gate, incorporating inventory data from the Ecoinvent database 3 and SimaPro software version 9, with inputs of raw material extraction, transportation, energy consumption, and chemical uses. Emissions into air, water, and soil were quantified across all processing phases. The LCA midpoint findings reveal that thorium disulfate in monazite processing is the key contributor to global warming, producing 45 kg CO2-eq, whereas transportation and electricity consumption also considerably affect emissions, contributing 25.07 kg CO2-eq and 26.17 kg CO2-eq, respectively. Comparative analysis of midpoint indicators showed that solvent extraction had a more significant environmental impact than electrosorption in the context of human carcinogenic toxicity, freshwater ecotoxicity, and marine ecotoxicity. The damaged assessment highlighted endpoint indicators that monazite processing had a higher impact than WLP on human health (0.0364-0.0016 DALY), ecosystems (0.0016-0.0005 species & sdot;yr), and resources (0.0012-0.0005 USD, 2013), primarily due to the use of chemicals and emissions. Our study shows that electrosorption from WLP demonstrates superior environmental sustainability compared with solvent extraction from monazite, positioning it a more viable and efficient approach for radioactive waste treatment.

Drainage is a common practice in geotechnical engineering concerning dredged marine soils. Current drainage techniques, including surcharge preloading, vacuum preloading, and combined vacuum-surcharge preloading, have been proven to be effective in soft soil treatment, but are also criticized for their high energy consumption. This paper made a brief review on existing drainage techniques and proposed some prospects for the next-generation techniques in response to the public concern of sustainability. It is found that all conventional preloading techniques have been well studied from tests to modeling, and improved vacuum preloading tends to be used in combination with other techniques. Drainage techniques with lower energy consumption can be realized either by using renewable energy or designing biomimetic devices. The paper is expected to provide a comprehensive while concise report on recent advances in drainage techniques for dredged marine soils and in the meanwhile give an insight into the further development towards a more sustainable future.

The goal of the current study was to create and assess the effectiveness of a hand-pulled ergonomically designed flame weeder. The developed weeder was tested in the field at three operating pressures (20, 30 and 40 Psi) and forward speeds (1.00, 1.25 and 1.50 km/h) to study their effects on plant damage, survival rates, weight preservation rates, weed management effectiveness, soil temperatures, and gas and energy consumption. Thereafter, at optimized values of forward speed and operating pressure, a comparative assessment of flame weeding with traditional methods (mechanical and manual weeding) was done in terms of weed control effectiveness, operational time, energy consumption, and cost of operation. Results showed that the optimal performance of the designed flame weeder was achieved when operated at a speed of 1 km/h and an operating pressure of 40 psi. The survival rate, weight preservation rate, weed control efficiency, change in soil temperature, recovery rate, plant damage, gas consumption, and energy consumption were observed to be 27.3 %, 32.5 %, 91.1 %, 40.74 degrees C, 8.5 %, 2.2 %, 4.05 kg/h, and 2500.24 MJ/ha, respectively, at optimized values of forward speed (1.00 km/h) and operating pressure (40 Psi). The actual field capacity, field efficiency and operating cost of the flame weeder were 0.0755 ha/h, 94.94 %, and 3620.81 (sic)/ha, respectively. Hand weeding had the best level of weed control effectiveness, but it was a laborious, time-consuming process. When compared to manual weeding, flame weeding was 50.42 % cheaper and 94.82 % faster.

Particle crushing is a common phenomenon in granular soils, which can affect the physical and mechanical properties of soils. The index which used to quantify the amount of particle crushing is crucial for the relevant research. Based on Kick's comminution energy consumption theory, size potential regarding as a property of soil and representing the energy of particles in a certain state was defined in this study, and a crushing index was proposed based on it. This index was determined according to a density distribution of particle size that voids the problem of sieving error propagation when using a cumulative grain size distribution curve. The validation of the proposed crushing index showed that it could quantitatively describe the amount of particle breakage, regardless of material, gradation, test type and particle size scale. In addition, the proposed crushing index was considered as a soil property index and could be divided into two categories: the ultimate crushing index and the mobilized crushing index, where the mobilized crushing index is obtained from the ultimate crushing index by replacing the ultimate breakage state with the final breakage state. The test results showed that the mobilized crushing index was equivalent to the input energy ratio between current and final breakage states. This relationship contributes to the understanding of the evolution of the grading curve caused by particle breakage.

Mechanical tillage before cotton sowing is a crucial process in cotton production. Numerical simulations of soil cutting and energy consumption predictions, along with optimization methods, are very important for understanding the interaction between tillage tools and soil, as well as guiding energy-efficient cultivation practices. The focus of this study is on the problem of cutting sandy silt in Xinjiang cotton fields. Sandy silt can be characterized by its low cohesion and large, loose particles. Starting from the macroscopic physical and mechanical properties of the soil, a soil contact mechanics model considering soil plastic deformation and bonding forces between soil particles is established. By optimizing the cotton field soil discrete element model and parameter calibration methods, the accuracy of the soil cutting simulation is improved. The principles and modelling steps of discrete element method (DEM) simulations for cutting soil are explained in detail, enabling the analysis and evaluation of the complex dynamic behaviour of soil under large deformation conditions and the mechanical properties of the cutting tool. The average error between the energy consumption measured in field rotary tillage experiments and simulation results is 7.04%. By utilizing the simulation results as a dataset, an extreme learning machine (ELM) without a physical model is employed to replace traditional polynomial regression for rapid energy consumption prediction based on the cutting parameters. The average error between the prediction results and simulation results is 4.34%. By using response surface methodology based on the predicted energy consumption, optimal working parameters are determined, resulting in a 10.02% reduction in the power consumption compared to the initial parameter settings. This effectively achieves energy savings in rotary tillage and further validates the accuracy of the simulation method and prediction model.