Yard waste prediction from estimated municipal solid waste using the grey theory to achieve a zero-waste strategy

Intelligent technologies powering clean incineration of municipal solid waste: A system review

Cleanliness has been paramount for municipal solid waste incineration (MSWI) systems. In recent years, the rapid advancement of intelligent technologies has fostered unprecedented opportunities for enhancing the cleanliness of MSWI systems. This paper offers a review and analysis of cutting-edge intelligent technologies in MSWI, which include process monitoring, intelligent algorithms, combustion control, flue gas treatment, and particulate control. The objective is to summarize current applications of these techniques and to forecast future directions. Regarding process monitoring, intelligent image analysis has facilitated real-time tracking of combustion conditions. For intelligent algorithms, machine learning models have shown advantages in accurately forecasting key process parameters and pollutant concentrations. In terms of combustion control, intelligent systems have achieved consistent prediction and regulation of temperature, oxygen content, and other parameters. Intelligent monitoring and forecasting of carbon monoxide and dioxins for flue gas treatment have exhibited satisfactory performance. Concerning particulate control, multi-objective optimization facilitates the sustainable utilization of fly ash. Despite remarkable progress, challenges remain in improving process stability and monitoring instrumentation of intelligent MSWI technologies. By systematically summarizing current applications, this timely review offers valuable insights into the future upgrade of intelligent MSWI systems.

Analysis and prediction of central nervous system tumor burden in China during 1990-2030

Central nervous system (CNS) tumors, due to their unique locations, pose a serious threat to human health and present challenges to modern medicine. These tumors exhibit notable epidemiological characteristics across various ethnicities, regions, and age groups. This study investigated the trend of disease burden of CNS tumors in China from 1990-2019 and predicted the incidence and death rate from 2020-2030. Employing data from the 2019 Global Burden of Disease (GBD) database, we utilized key indicators to scrutinize the disease burden associated with CNS tumors in China. The analysis employed the Joinpoint model to track the trend in disease burden, calculating both the annual percentage change (APC) and average annual percentage change (AAPC). Additionally, the Matlab software facilitated the creation of a gray model to forecast the incidence and death rate of CNS tumors in China spanning from 2020 to 2030." In 2019, the age-standardized incidence rate, prevalence rate, death rate, and disability-adjusted life years (DALYs) associated with CNS tumors in China were among the high level in the world. The standardized prevalence rate and DALYs of CNS tumors in China residents showed a stable fluctuation trend with age; however, age-standardized death and incidence rate demonstrated a generally upward trend with age. In China, the age-standardized prevalence and incidence rate of males were lower than those for female residents, while the age-standardized death rate and DALYs among males surpassed those of females. From 1990-2019, the age-standardized prevalence and incidence rate of CNS tumors in China exhibited an increasing trend. The age-standardized death rate and DALYs showed a contrasting trend. According to the gray model's prediction, incidence rate of CNS tumors would continue rising while the death rate is expected to decline in China from 2020-2023. The burden of CNS tumors in China has shown an upward trajectory, posing significant challenges to their treatment. It is necessary to pay attention to tertiary prevention, start from the perspective of high-risk groups and high-risk factors to reduce the burden of disease, and achieve "early detection, early diagnosis, and early treatment".

Using fresh vegetable waste from Chinese traditional wet markets as animal feed: Material feasibility and utilization potential

To develop new animal feed sources and establish a sustainable food upcycling system, the material feasibility and feeding potential of fresh vegetable waste (FVW) were clarified in this study. First, the FVW output of wet markets in Hangzhou, China was tracked and predicted. The results showed that the retail waste ratio of FVW in wet markets reached 9.3 %, predicting that China's FVW will reach 9034 kt in 2030. Second, the study revealed that the nutritive value of FVW was comparable to that of traditional alfalfa feed, suitable for use as animal feed. However, we found a high probability of microbial contamination. Therefore, FVW should have stricter classification and collection methods. Under this premise, the feeding utilization potential of FVW in wet markets is large. In 2030, the crude protein content may replace 2737 kt of alfalfa, saving 7.7 E + 08 m3 of water and 75,018 ha of land.

Hierarchy performance assessment of industrial solid waste utilization - tracking resource recycling and utilization centers in China

The massive production and accumulation of industrial solid waste (ISW) have led to environmental pollution and natural resource underutilization. China's efforts to build trial industrial waste resource utilization centers provide strong support for sustainable development. However, these centers and the factors driving ISW utilization have yet to be evaluated. This paper utilizes context-dependent data envelopment analysis models without explicit inputs (DEA-WEI) to evaluate the overall utilization performance of 48 industrial waste resource utilization centers in China from 2018 to 2020. It also builds a Tobit model to assess which indicators and waste types affect overall ISW utilization. The results show overall ISW utilization performance of centers in the sample has improved, with the average value falling from 1.7193 in 2018 to 1.5624 in 2020. However, there are clear regional performance gaps, with East China having the highest utilization performance (1.3113) while the Southwest had the lowest (2.2958). Finally, this paper proposes measures to improve the overall utilization of industrial waste resources based on an analysis of the factors driving solid waste utilization.

Ranking Canadian waste management system efficiencies using three waste performance indicators

Three waste management system (WMS) efficiency indicators are adopted to systematically assess WMS efficiency in Canada from 1998 to 2016. The study objectives are to examine the temporal changes in waste diversion activities and rank the performance of the jurisdictions using a qualitative analytical framework. Increasing Waste Management Output Index (WMOI) trends were identified in all jurisdictions, and more government subsidiaries and incentive packages are recommended. With the exception of Nova Scotia, statistically significant decreasing diversion gross domestic product (DGDP) ratio trends are observed. It appears that the increases in GDP from Sector 562 were not contributing to waste diversion. On average, Canada spent about $225/tonne of waste handled during the study period. Current spending per tonne handled (CuPT) trends are decreasing, with S ranging from + 5.15 to + 7.67. It appears that WMSs in Saskatchewan and Alberta are more efficient. The results suggest that the use of diversion rate alone to evaluate WMS may be misleading. The findings help the waste community to better understand the trade-offs between various waste management alternatives. The proposed qualitative framework utilizing comparative rankings is applicable elsewhere and can be a useful decision support tool for policy-makers.

An Improved Genetic Algorithm for Location Allocation Problem with Grey Theory in Public Health Emergencies

The demand for emergency medical facilities (EMFs) has witnessed an explosive growth recently due to the COVID-19 pandemic and the rapid spread of the virus. To expedite the location of EMFs and the allocation of patients to these facilities at times of disaster, a location-allocation problem (LAP) model that can help EMFs cope with major public health emergencies was proposed in this study. Given the influence of the number of COVID-19-infected persons on the demand for EMFs, a grey forecasting model was also utilized to predict the accumulative COVID-19 cases during the pandemic and to calculate the demand for EMFs. A serial-number-coded genetic algorithm (SNCGA) was proposed, and dynamic variation was used to accelerate the convergence. This algorithm was programmed using MATLAB, and the emergency medical facility LAP (EMFLAP) model was solved using the simple (standard) genetic algorithm (SGA) and SNCGA. Results show that the EMFLAP plan based on SNCGA consumes 8.34% less time than that based on SGA, and the calculation time of SNCGA is 20.25% shorter than that of SGA. Therefore, SNCGA is proven convenient for processing the model constraint conditions, for naturally describing the available solutions to a problem, for improving the complexity of algorithms, and for reducing the total time consumed by EMFLAP plans. The proposed method can guide emergency management personnel in designing an EMFLAP decision scheme.