RESUMEN
Urban biowaste, which refers to the organic fraction of municipal solid waste (MSW), is a predominant type of waste in low- and middle-income countries. Therefore, the sustainable management of urban biowaste is a key problem in solid waste management (SWM). However, treatment technologies usually come with secondary pollution and by-products that need further treatment, which would limit the economic and environmental benefits of the waste management system. The concept of industrial ecology has the potential to be introduced into the waste management field to solve this problem. Based on a practical case study of a city in eastern China, this paper conducted a complete urban biowaste treatment system planning exercise using concepts in industrial ecology. The establishment of the new system has significant economic, environmental and social benefits. The total output value of the project would reach 342 million yuan in 2025, corresponding to 945,000 tons of solid wastes consumed every year, which would significantly reduce the environmental pollution caused by solid waste. More than 700 jobs could also be created. In addition, the establishment of an urban biowaste sustainable management system requires the joint effort of enterprise, government and the public, which in turn requires innovation in business models and management policies. Therefore, a special management committee should be established to promote collaboration among stakeholders, and an online platform that enables end-to-end process supervision covering the entire system, including emergency management mechanisms, needs to be established to ensure the long-term stable operation of various treatment facilities. The results of this paper show that synergies among technologies should be promoted to provide systemic benefit, and that the infrastructure for pollution control should be shared to ensure high utilization.
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Eliminación de Residuos , Administración de Residuos , China , Ciudades , Planificación de Ciudades , Residuos SólidosRESUMEN
The objective of this study is to apply machine learning models to accurately predict daily biomethane production in an industrial-scale co-digestion facility. The methodology involved applying elasticnet, random forest, and extreme gradient boosting to input-output data from an industrial-scale anaerobic co-digestion (ACoD) facility. The models were used to predict biomethane for 1-day, 3-day, 5-day, 10-day, 20-day, 30-day, and 40-day time horizons. These models were fit on four years of operational data. The results showed that elastic net (a model with assumptions of linearity) was clearly outperformed by random forest and extreme gradient boosting (XGBoost), which had out-of-sample R2values ranging between 0.80 and 0.88, depending on the time horizon. In addition, feature importance and partial dependence analysis demonstrated the marginal and interaction effects on biomethane of selected biowaste inputs. For instance, food waste co-digested with percolate were shown to have strong positive interaction effects. One implication of this study is that XGBoost and random forest algorithms applied to industrial-scale ACoD data provide dependable prediction results and may be a useful complement for experimental and mechanistic/theoretical models of anaerobic digestion, especially where detailed substrate characterization is difficult. However, these models have limitations, and suggestions for deriving additional value from these methods are proposed.
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Aprendizaje Automático , Anaerobiosis , Alimentos , Metano , Eliminación de ResiduosRESUMEN
A precise energy conservation and emission reduction (ECER) path in industrial sector contains two aspects: applying effective ECER measures and focusing on processes with significant ECER potential. However, most studies have investigated the ECER effects of an individual measure or only evaluated industrial-level ECER potential. Therefore, the objective of this study is to find a precise ECER path in China's iron and steel industry through quantitative analysis methods. First, this article adopts scenario analysis to simulate situations where different ECER measures are adopted and designs calculation methods to quantitatively evaluate the ECER effects in each scenario in 2020 and 2025. Second, through analysis of the application of ECER measures to certain processes, we calculate the ECER potential of different individual processes in the iron and steel industry. In addition, the conservation supply curve method and the quadrant method are used to measure the level of advanced technology application. The results show that: (1) for four types of ECER measures, the limitation of production output measure is most effective, contributing to 6.98% and 12.50% decreases in total industrial energy consumption and pollutant emissions in 2020 and 2025; moreover, the contribution of the adjustment of scale structure measure is comparatively low. (2) The sintering and ironmaking processes have strong ECER potential in 2020, while the steel making process also has high ECER potential in 2025. (3) 21 technologies are divided into 4 quadrants based on energy, popularity, and economic performance. In addition, we provide some suggestions for future ECER policies. In sum, this article provides an in-depth example of determining a precise ECER path in an important industry.
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Hierro , Acero , China , Industrias , TecnologíaRESUMEN
The objective of this study is to apply natural language processing to identifying innovative technology trends related to food waste treatment, biogas, and anaerobic digestion. The methodology used involved analyzing large volumes of text data mined from 3186 patents related to these three fields. Latent Dirichlet Allocation and the perplexity method were used to identify the main topics which the patent corpora were comprised of and which technological concepts were most associated with each topic. In addition, term frequency-inverse document frequency (TF-IDF) was used to gauge the "emergingness" of certain technical concepts across the patent corpora in various years. The key results were as follows: (1) perplexity computations showed that a 20 topic models were feasible for these patent corpora; (2) topics were identified, providing an accurate picture of the patenting landscape in the analyzed fields; (3) TF-IDF analysis on unigrams, bigrams, and trigrams, supplemented with network graph analysis, revealed emerging technology trends in each year. This study has important implications for governments who need to decide where to invest resources in anaerobic food waste treatment.
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Alimentos , Procesamiento de Lenguaje Natural , Eliminación de Residuos/métodos , Anaerobiosis , Biocombustibles , Reactores Biológicos , MetanoRESUMEN
Urbanization, one of the predominant trends of the 21st century, places great stress on urban water supply networks. This paper aimed to identify the most important variables driving urban water supply patterns in China, a region which has seen rapid urban growth in the last few decades. In addition, a principal component analysis-informed urban water sustainability index was developed in order to benchmark cities. The research involved applying statistical learning and other analytical methods to 12 years of urban water supply data for 627 cities across China. The findings were as follows: (1) PCA showed that approximately 46.8% of variability in the data could be explained by two principal components. Component 1 (37.26%) was more closely associated with variables related to water supply and sale, supply pipelines, and water supply finance. C2 (9.51%) was clearly related to urban water prices and average per capita water use. (2) Random forest and XGBoost algorithms were effective in classifying cities according to their region, with model testing accuracies of 87.69% and 88.32% respectively. (3) Chinese cities have consistently suffered water loss/leakage rates above 20% since 2001, and water prices are closely associated with leakage. (4) China's urban water sustainability has increased by just 3.56% between 2001 and 2013; Southwest China saw the highest growth rate in urban water supply sustainability. The implications of our research effort will be useful for decision makers in water-stressed urban areas around the world who are seeking novel insights in how to leverage statistical learning techniques to gain insights into urban drinking water supply patterns.
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Agua Potable , Urbanización , China , Ciudades , Población Urbana , Abastecimiento de AguaRESUMEN
Catering companies around the world generate tremendous amounts of waste; those in China are no exception. The paper discusses the design, implementation, and evaluation of a sensor-based Internet of Things (IoT) network technology for improving the management of restaurant food waste (RFW) in the city of Suzhou, China. This IoT-based system encompasses the generation, collection, transportation and final disposal of RFW. The Suzhou case study comprised four steps: (1) examination of the required functionality of an IoT-enabled system in the specific context of Suzhou; (2) configuration of the system architecture, both software and hardware components, according to the identified functionality; (3) installation of the components of the IoT system at the facilities of the stakeholders across the RFW generation-collection-transportation-disposal value chain; and (4) evaluation of the performance of the entire system, based on data from three years of operation. The results show that the system had a strong impact. Positive results include: (1) better management of RFW generation, as evidenced by a 20.5% increase in RFW collected via official channels and a 207% increase in the number of RFW generators under official contract; (2) better law enforcement in response to RFW malpractice, enabled by the monitoring capabilities of the IoT system; and (3) an overall reduction in illicit RFW activities and better process optimization across the RFW value chain. Negative results include: (1) Radio-frequency identification (RFID) tags need to be renewed often due to the frequent handling of waste bins, thus increasing operating costs; (2) dynamic/automatic weight sensors had a higher degree of error than the more time-consuming static/manual weighing method; and (3) there were disagreements between the city's government agencies about how to interpret data from the IoT system, which led to some inefficiencies in management. In sum, the Suzhou IoT system enabled data-driven management of RFW and had a net positive impact for the stakeholders involved.
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Internet , Restaurantes , Administración de Residuos , China , CiudadesRESUMEN
Initial bulk density (IBD) is an important variable in anaerobic digestion since it defines and optimizes the treatment capacity of a system. This study reveals the mechanism on how IBD might affect anaerobic digestion of waste. Four different IBD values: D1 (500-700kgm-3), D2 (900-1000kgm-3), D3 (1100-1200kgm-3) and D4 (1200-1400kgm-3) were set and tested over a period of 90days in simulated landfill reactors. The main variables affected by the IBD are the methane generation, saturation degree, extraction of organic matter, and the total population of methanogens. The study identified that IBD >1000kgm-3 may have significant effect on methane generation, either prolonging the lag time or completely inhibiting the process. This study provides a new understanding of the anaerobic digestion process in saturated high-solids systems.
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Euryarchaeota , Anaerobiosis , Reactores Biológicos , Fibras de la Dieta , Digestión , Metano , Eliminación de ResiduosRESUMEN
The objective of this research was to conduct a performance evaluation of three food waste/biowaste-to-biogas pilot projects across 7 scenarios in China based on multi-criteria decision analysis (MCDA) methodology. The projects ranked included a food waste-biogas project in Beijing, a food waste-biogas project in Suzhou and a co-digestion project producing biomethane in Hainan. The projects were ranked from best to worst based on technical, economic and environmental criteria under the MCDA framework. The results demonstrated that some projects are encountering operational problems. Based on these findings, six national policy recommendations were provided: (1) shift away from capital investment subsidies to performance-based subsidies; (2) re-design feed in tariffs; (3) promote bio-methane and project clustering; (4) improve collection efficiency by incentivizing FW producers to direct waste to biogas projects; (5) incentivize biogas projects to produce multiple outputs; (6) incentivize food waste-based projects to co-digest food waste with other substrates for higher gas output.
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Biocombustibles , Alimentos , Restaurantes , Administración de Residuos/métodos , Beijing , China , Toma de Decisiones Asistida por Computador , Política Ambiental , Metano/biosíntesis , Proyectos PilotoRESUMEN
This paper analyses the food waste collection and transportation (C&T) system in a pilot project in Suzhou by using a novel performance evaluation method. The method employed to conduct this analysis involves a unified performance evaluation index containing qualitative and quantitative indicators applied to data from Suzhou City. Two major inefficiencies were identified: a) low system efficiency due to insufficient processing capacity of commercial food waste facilities; and b) low waste resource utilization due to low efficiency of manual sorting. The performance evaluation indicated that the pilot project collection system's strong points included strong economics, low environmental impact and low social impact. This study also shows that Suzhou's integrated system has developed a comprehensive body of laws and clarified regulatory responsibilities for each of the various government departments to solve the problems of commercial food waste management. Based on Suzhou's experience, perspectives and lessons can be drawn for other cities and areas where food waste management systems are in the planning stage, or are encountering operational problems.