Anaerobic digestion (AD) serves as a promising alternative for waste treatment and a potential solution to improve the energy supply security. The feasibility of AD has been proven in some of the technologically and agriculturally advanced countries. However, development is still needed for worldwide implementation, especially for AD process dealing with municipal solid waste (MSW). This paper reviews various approaches and stages in the AD of MSW, which used to optimise the biogas production and quality. The assessed stages include pre-treatment, digestion process, post-treatment as well as the waste collection and transportation. The latest approaches and integrated system to improve the AD process are also presented. The stages were assessed in a relatively quantitative manner. The range of energy requirement, carbon emission footprint and the percentage of enhancement are summarised. Thermal hydrolysis pre-treatment is identified to be less suitable for MSW (-5% to +15.4% enhancement), unless conducted in the two-phase AD system. Microwave pre-treatment shows consistent performance in elevating the biogas production of MSW, but the energy consumption (114.24-8,040 kWeh t-1) and carbon emission footprint (59.93-4,217.78 kg CO2 t-1 waste) are relatively high. Chemical (∼0.43 kWeh m-3) and membrane-based (∼0.45 kWeh m-3) post-treatments are suggested to be a lower energy consumption approach for upgrading the biogas. The feasibility in terms of cost (scale up) and other environmental impacts (non-CO2 footprint) needs to be further assessed. This study provides an overview to facilitate further development and extended implementation of AD.
Lignocellulosic waste (LW) is abundant in availability and is one of the suitable substrates for anaerobic digestion (AD). However, it is a complex solid substrate matrix that hinders the hydrolysis stage of anaerobic digestion. This study assessed various pre-treatment and post-treatments of lignocellulosic waste for anaerobic digestion benefiting from advanced P-graph and GaBi software (Thinkstep, Germany) from the perspective of cost and environmental performances (global warming potential, human toxicity, ozone depletion potential, particulate matter, photochemical oxidant creation, acidification and eutrophication potential). CaO pre-treatment (P4), H2S removal with membrane separation post-treatment (HSR MS) and without the composting of digestate is identified as the cost-optimal pathway. The biological (P7- Enzyme, P8- Microbial Consortium) and physical (P1- Grinding, P2- Steam Explosion, P3- Water Vapour) pre-treatments alternatives have lower environmental impacts than chemical pre-treatments (P4- CaO, P5- NaOH, P6- H2SO4) however they are not part of the near cost optimal solutions. For post-treatment, the near cost optimal alternatives are H2S removal with organic physical scrubbing (HSR OPS) and H2S removal with amine scrubbing (HSR AS). HSR AS has a better performance in the overall environmental impacts followed by HSR MS and HSR OPS. In general, the suggested cost-optimal solution is still having relatively lower environmental impacts and feasible for implementation (cost effective). There is very complicated to find a universal AD solution. Different scenarios (the type of substrate, the scale, product demand, policies) have different constraints and consequently solutions. The trade-offs between cost and environment performances should be a future extension of this work.
This review covers the recent advancements in selected emerging energy sectors, emphasising carbon emission neutrality and energy sustainability in the post-COVID-19 era. It benefited from the latest development reported in the Virtual Special Issue of ENERGY dedicated to the 6th International Conference on Low Carbon Asia and Beyond (ICLCA'20) and the 4th Sustainable Process Integration Laboratory Scientific Conference (SPIL'20). As nations bind together to tackle global climate change, one of the urgent needs is the energy sector's transition from fossil-fuel reliant to a more sustainable carbon-free solution. Recent progress shows that advancement in energy efficiency modelling of components and energy systems has greatly facilitated the development of more complex and efficient energy systems. The scope of energy system modelling can be based on temporal, spatial and technical resolutions. The emergence of novel materials such as MXene, metal-organic framework and flexible phase change materials have shown promising energy conversion efficiency. The integration of the internet of things (IoT) with an energy storage system and renewable energy supplies has led to the development of a smart energy system that effectively connects the power producer and end-users, thereby allowing more efficient management of energy flow and consumption. The future smart energy system has been redefined to include all energy sectors via a cross-sectoral integration approach, paving the way for the greater utilization of renewable energy. This review highlights that energy system efficiency and sustainability can be improved via innovations in smart energy systems, novel energy materials and low carbon technologies. Their impacts on the environment, resource availability and social well-being need to be holistically considered and supported by diverse solutions, in alignment with the sustainable development goal of Affordable and Clean Energy (SDG 7) and other related SDGs (1, 8, 9, 11,13,15 and 17), as put forth by the United Nations.
Home composting can be an effective way to reduce the volume of municipal solid waste. The aim of this study is to evaluate the effect of Effective Microorganism™ (EM) for the home scale co-composting of food waste, rice bran and dried leaves. A general consensus is lacking regarding the efficiency of inoculation composting. Home scale composting was carried out with and without EM (control) to identify the roles of EM. The composting parameters for both trials showed a similar trend of changes during the decomposition. As assayed by Fourier Transform Infrared Spectroscopy (FTIR), the functional group of humic acid was initially dominated by aliphatic structure but was dominated by the aromatic in the final compost. The EM compost has a sharper peak of aromatic CC bond presenting a better degree of humification. Compost with EM achieved a slightly higher temperature at the early stage, with foul odour suppressed, enhanced humification process and a greater fat reduction (73%). No significant difference was found for the final composts inoculated with and without EM. The properties included pH (∼7), electric conductivity (∼2), carbon-to-nitrogen ratio (C: N 100%), humic acid content (4.5-4.8%) and pathogen content (no Salmonella, <1000 Most Probable Number/g E. coli). All samples were well matured within 2 months. The potassium and phosphate contents in both cases were similar however the EM compost has a higher nitrogen content (+1.5%). The overall results suggested the positive effect provided by EM notably in odour control and humification.
Plastic consumption and its end-of-life management pose a significant environmental footprint and are energy intensive. Waste-to-resources and prevention strategies have been promoted widely in Europe as countermeasures; however, their effectiveness remains uncertain. This study aims to uncover the environmental footprint patterns of the plastics value chain in the European Union Member States (EU-27) through exploratory data analysis with dimension reduction and grouping. Nine variables are assessed, ranging from socioeconomic and demographic to environmental impacts. Three clusters are formed according to the similarity of a range of characteristics (nine), with environmental impacts being identified as the primary influencing variable in determining the clusters. Most countries belong to Cluster 0, consisting of 17 countries in 2014 and 18 countries in 2019. They represent clusters with a relatively low global warming potential (GWP), with an average value of 2.64 t CO2eq/cap in 2014 and 4.01 t CO2eq/cap in 2019. Among all the assessed countries, Denmark showed a significant change when assessed within the traits of EU-27, categorised from Cluster 1 (high GWP) in 2014 to Cluster 0 (low GWP) in 2019. The analysis of plastic packaging waste statistics in 2019 (data released in 2022) shows that, despite an increase in the recovery rate within the EU-27, the GWP has not reduced, suggesting a rebound effect. The GWP tends to increase in correlation with the higher plastic waste amount. In contrast, other environmental impacts, like eutrophication, abiotic and acidification potential, are identified to be mitigated effectively via recovery, suppressing the adverse effects of an increase in plastic waste generation. The five-year interval data analysis identified distinct clusters within a set of patterns, categorising them based on their similarities. The categorisation and managerial insights serve as a foundation for devising a focused mitigation strategy.
Improper municipal solid waste (MSW) management contributes to greenhouse gas emissions, necessitating emissions reduction strategies such as waste reduction, recycling, and composting to move towards a more sustainable, low-carbon future. Machine learning models are applied for MSW-related trend prediction to provide insights on future waste generation or carbon emissions trends and assist the formulation of effective low-carbon policies. Yet, the existing machine learning models are diverse and scattered. This inconsistency poses challenges for researchers in the MSW domain who seek to identify and optimize the machine learning techniques and configurations for their applications. This systematic review focuses on MSW-related trend prediction using the most frequently applied machine learning model, artificial neural network (ANN), while addressing potential methodological improvements for reducing prediction uncertainty. Thirty-two papers published from 2013 to 2023 are included in this review, all applying ANN for MSW-related trend prediction. Observing a decrease in the size of data samples used in studies from daily to annual timescales, the summarized statistics suggest that well-performing ANN models can still be developed with approximately 33 annual data samples. This indicates promising opportunities for modeling macroscale greenhouse gas emissions in future works. Existing literature commonly used the grid search (manual) technique for hyperparameter (e.g., learning rate, number of neurons) optimization and should explore more time-efficient automated optimization techniques. Since there are no one-size-fits-all performance indicators, it is crucial to report the model's predictive performance based on more than one performance indicator and examine its uncertainty. The predictive performance of newly-developed integrated models should also be benchmarked to show performance improvement clearly and promote similar applications in future works. The review analyzed the shortcomings, best practices, and prospects of ANNs for MSW-related trend predictions, supporting the realization of practical applications of ANNs to enhance waste management practices and reduce carbon emissions.
Plastic waste and its environmental hazards have been attracting public attention as a global sustainability issue. This study builds a neural network model to forecast plastic waste generation of the EU-27 in 2030 and evaluates how the interventions could mitigate the adverse impact of plastic waste on the environment. The black-box model is interpreted using SHapley Additive exPlanations (SHAP) for managerial insights. The dependence on predictors (i.e., energy consumption, circular material use rate, economic complexity index, population, and real gross domestic product) and their interactions are discussed. The projected plastic waste generation of the EU-27 is estimated to reach 17 Mt/y in 2030. With an EU targeted recycling rate (55%) in 2030, the environmental impacts would still be higher than in 2018, especially global warming potential and plastic marine pollution. This result highlights the importance of plastic waste reduction, especially for the clustering algorithm-based grouped countries with a high amount of untreated plastic waste per capita. Compared to the other assessed scenarios, Scenario 4 with waste reduction (50% recycling, 47.6% energy recovery, 2.4% landfill) shows the lowest impact in acidification, eutrophication, marine aquatic toxicity, plastic marine pollution, and abiotic depletion. However, the global warming potential (8.78 Gt CO2eq) is higher than that in 2018, while Scenario 3 (55% recycling, 42.6% energy recovery, 2.4% landfill) is better in this aspect than Scenario 4. This comprehensive analysis provides pertinent insights into policy interventions towards environmental hazard mitigation.