Utilising standard samples instead of randomly collected food waste in composting: Implementation strategy and feasibility evaluation.

Randomly collected food waste results in inaccurate experimental data with poor reproducibility for composting. This study investigated standard food waste samples as replacements for randomly collected food waste. A response surface methodology was utilised to analyse data from a 28-day compost process optimisation experiment using collected food waste, and the optimal combination of composting parameters was derived. Experiments using different standard food waste samples (high oil and salt, high oil and sugar, balanced diet, and vegetarian) were conducted for 28 days under optimal conditions. The ranking of differences between the standard samples and collected food waste was vegetarian > balanced diet > high oil and sugar > high oil and salt. Statistical analysis indicated t-tests for increased oil and salt samples and collected food waste were not significant, and Cohen's d effect values were minimal. High oil and salt samples can be used as replacements for collected food waste in composting experiments.

Prediction of cooling effect of constant temperature community bin based on BP neural network.

In order to explore the influence of outdoor microclimate on the cooling effect of constant temperature community bin, the temperature prediction model was predicted. The temperature and microclimate data sets of the community bin were collected in summer from May 2021 to September 2021. The climatic characteristics included cloudy and sunny conditions, and the environmental factors included outdoor temperature, air speed, air relative humidity, and solar radiation intensity. Stepwise regression method was used to test the significance of environmental factors, and the corresponding regression equation was obtained. BP neural network was used to establish temperature prediction models under cloudy and sunny conditions, respectively. The results showed that the coefficient of determination (R2) of the two models was above 0.8, and the environmental factors with significant influence were screened out. The root mean square error (RMSE) between the training value and the actual value established by BP neural network was 0.83 °C, and the determination coefficient (R2) was 0.968. Under sunny conditions, the root mean square error (RMSE) of predicted value and measured value was 0.65 °C, and the determination coefficient (R2) was 0.982. According to the analysis of the sample data, it showed that the BP neural network was more accurate than stepwise regression, and could be used to predict the temperature of community bin, which provided model basis for the practical application of intelligent temperature control community bin in summer.

Design and parameter optimization of community bins based on evaporative cooling.

Aiming at the challenge of bad odors emanating from rotting perishable kitchen waste in summer within the garbage collection chain, the fan-pad system, which is widely used in agricultural facility systems, was applied to the design of community bins. In this regard, a community bin based on a fan-pad operating mechanism was developed. Computational fluid dynamics (CFD) was then used to numerically analyze the cooling effect of the community bin. Under conditions comprising of an outdoor temperature of 35 °C, an installation height of the wet curtain of the cooling system h1, an installation height of the fan h2, and varying distance d between the trash can and the wet curtain, and the average temperature T of 16 temperature measurement points was analyzed. Design-Expert software was used to design the Box-Behnken Design (BBD) experiment, and regression analysis was carried out. Under the optimized parameter combination, the average temperature T of the temperature measurement point was 24.82 °C by simulation. Through field test verification, it was established that the maximum relative error between the temperature of each measuring point and the simulation result was 1.54%, and the average temperature T fluctuated within the range of 0.5 °C of the simulation value.Implications Statement: By creatively integrating the agricultural apparatus with our daily community bin, our research presents great interdisciplinary originality and potential in wide application. Meanwhile, our research also gives a new solution to saving the energy wasted by community dust room based on air conditioner cooling. We hold the believe that our research can arrest great attention of scholars, managers, and people interested in sanitation apparatus. Additionally, our research also provides more theoretical and technical support for the design and application of sanitation collecting apparatus.

A Review on the Wear, Corrosion and High-Temperature Resistant Properties of Wire Arc-Sprayed Fe-Based Coatings.

Among different thermal spraying methods, arc-spraying has been widely used due to its low operating costs and high deposition efficiency. The rapid progress of cored wire technology in arc-spraying has increased possibilities for the preparation of new Fe-based coating materials with enhanced properties by adding reinforcement particles and alloying elements to suit the different applications. Fe-based coatings have been extensively used because of their high strength, toughness, lower production costs, and availability of raw materials. This makes them suitable replacements for Ni-based coatings in ambient and high-temperature applications. This review discusses the research status and developments of the arc-sprayed Fe-based coatings. The study specifically reviews the wear behavior, corrosion analysis, and high-temperature resistant properties of arc-sprayed Fe-based coatings, aiming to develop an understanding of the protection mechanisms for Fe-based coatings. The performance of the Fe-based coatings depends on the integrity of the coating structure. Optimizing arc-spraying parameters minimizes defects (pores, grain boundaries, unmelted particles, oxides, and microcracks) that deteriorate the coating properties. High amorphous phase content, ceramic reinforcement particles and alloying elements enhance the corrosion, tribological, and high-temperature resistant properties of Fe-based coatings. In high-temperature applications, Fe-based coatings form oxide scales that protect the coating from further oxidation; thus, it is important to select the optimum composition for the alloying elements.