Prediction of Mechanical Properties of Thermally Modified Wood Based on TSSA-BP Model

Drying kinetics prediction and quality effect of ultrasonic synergy vacuum far-infrared drying of Codonopsis pilosula

By using ultrasonic synergy vacuum far-infrared drying (US-VFID), the effects of different conditions on the drying kinetics, functional properties, and microstructure of Codonopsis pilosula slices were studied. The sparrow search algorithm (SSA) was used to optimize the back-propagation (BP) neural network to predict the moisture ratio during drying. With the increase of ultrasonic frequency, power and radiation temperature, the drying time of C. pilosula was shortened. The drying time of US-VFID was 25% shorter than VFID, when radiation temperature was 50°C, ultrasonic power was 48 W, and frequency was 28 kHz. The SSA-BP neural network, the average absolute error prediction was 0.0067. Compared with hot air drying (HAD), the total phenolic content and antioxidant activity of C. pilosula by US-VFID were increased by 29.47% and 8.67%, respectively, and a reduction in color contrast of 16.19%. The dilation and generation of microcapillary of C. pilosula were more obvious. The study revealed US-VFID could be used for the selection and process control of agro-processing methods for C. pilosula products.

Prediction of Uranium Adsorption Capacity in Radioactive Wastewater Treatment with Biochar

Recently, Japan's discharge of wastewater from the Fukushima nuclear disaster into the ocean has attracted widespread attention. To effectively address the challenge of separating uranium, the focus is on finding a healthy and environmentally friendly way to adsorb uranium using biochar. In this paper, a BP neural network is combined with each of the four meta-heuristic algorithms, namely Particle Swarm Optimization (PSO), Differential Evolution (DE), Cheetah Optimization (CO) and Fick's Law Algorithm (FLA), to construct four prediction models for the uranium adsorption capacity in the treatment of radioactive wastewater with biochar: PSO-BP, DE-BP, CO-BP, FLA-BP. The coefficient of certainty (R2), error rate and CEC test set are used to judge the accuracy of the model based on the BP neural network. The results show that the Fick's Law Algorithm (FLA) has a better search ability and convergence speed than the other algorithms. The importance of the input parameters is quantitatively assessed and ranked using XGBoost in order to analyze which parameters have a greater impact on the predictions of the model, which indicates that the parameters with the greatest impact are the initial concentration of uranium (C0, mg/L) and the mass percentage of total carbon (C, %). To sum up, four prediction models can be applied to study the adsorption of uranium by biochar materials during actual experiments, and the advantage of Fick's Law Algorithm (FLA) is more obvious. The method of model prediction can significantly reduce the radiation risk caused by uranium to human health during the actual experiment and provide some reference for the efficient treatment of uranium wastewater by biochar.

Effects of UV radiation on natural and synthetic materials

The deleterious effects of solar ultraviolet (UV) radiation on construction materials, especially wood and plastics, and the consequent impacts on their useful lifetimes, are well documented in scientific literature. Any future increase in solar UV radiation and ambient temperature due to climate change will therefore shorten service lifetimes of materials, which will require higher levels of stabilisation or other interventions to maintain their lifetimes at the present levels. The implementation of the Montreal Protocol and its amendments on substances that deplete the ozone layer, controls the solar UV-B radiation received on Earth. This current quadrennial assessment provides a comprehensive update on the deleterious effects of solar UV radiation on the durability of natural and synthetic materials, as well as recent innovations in better stabilising of materials against solar UV radiation-induced damage. Pertinent emerging technologies for wood and plastics used in construction, composite materials used in construction, textile fibres, comfort fabric, and photovoltaic materials, are addressed in detail. Also addressed are the trends in technology designed to increase sustainability via replacing toxic, unsustainable, legacy additives with 'greener' benign substitutes that may indirectly affect the UV stability of the redesigned materials. An emerging class of efficient photostabilisers are the nanoscale particles that include oxide fillers and nanocarbons used in high-performance composites, which provide good UV stability to materials. They also allow the design of UV-shielding fabric materials with impressive UV protection factors. An emerging environmental issue related to the photodegradation of plastics is the generation of ubiquitous micro-scale particles from plastic litter exposed to solar UV radiation.