• Energy Research

This perspective article aims to identify key research priorities to make the waste-to-energy sector compatible with the societal goals of circularity and carbon neutrality. These priorities range from fundamental research to process engineering innovations and socio-economic challenges. Three focus areas are highlighted: (i) the optimization of flue gas cleaning processes to minimize gaseous emissions and cross-media, (ii) the expansion of process control intelligence to meet targets for both material recovery and energy recovery, and (iii) climate neutrality, with the potential for negative emissions via the removal of atmospheric carbon dioxide across the full cycle of the waste resource. For each area, recent research trends and key aspects that are yet to be addressed are discussed.

Lignocellulosic solid fuels derived from biomass are emerging as a promising alternative to fossil fuels. However, due to a high moisture content, low energy density, and bulky volume, biomass is less favorable in terms of transport, storage, handling, and conversion. Pretreatment of biomass has shown to address these limitations, improving the efficiency of converting biomass into bioenergy. Among various methods, torrefaction is particularly promising, as it produces solid biogenic fuels that show a relatively higher conversion rate into bioenergy (on an equal mass basis) compared to the original biomass from which they are derived. This paper provides a comprehensive review of the state-of-the-art in torrefaction and its role in thermochemical conversion processes. Particularly, the characteristics of spent coffee grounds (SCGs) are examined, together with the potential of torrefaction for solid biofuel production from SCGs. Reactor technologies that may be used for this purpose are explored and the impact of process parameters and operating conditions is discussed. To gain deeper understanding of the reactions and mass & heat transfer phenomena involved in torrefaction, commonly applied reaction kinetics and previously established Computational Fluid Dynamics (CFD) models are reviewed. Challenges associated with scaling up methods for industrial applications and future perspectives of torrefaction in bioenergy production are also discussed. Finally, conclusions are drawn and research needs for further improvement of the torrefaction process are highlighted.

The data set is a collection of 257 unique data points collected from different sources literature around the world. Each Data point is composed of proximate, ultimate, structural properties and the High Heating Value(HHV) of biomass (agricultural and municipal solid organic waste). Proximate properties include the Ash content(Ash), Volatile Matter(VM), and Fixed Carbon(FC) of the biomass. Ultimate properties include the elemental Carbon(C), Hydrogen(H), Oxygen(O) and Sulphur(S). Structural/lignocellulosic composition including Cellulose(Cel), Hemicellulose(Hemi) and Lignin(Lig) is also included together with the HHV(MJ/kg). All the compositional columns are presented as percentage (wt%) on dry basis while HHV is presented in MJ/kg. The column “Biomass” names the material as described in the source (for example, rice husk, sugarcane bagasse, paper sludge, mixed food waste). The column “Source” provides the bibliographic reference or identifier from which the observation was extracted. Random missing values were imputed with k-nearest-neighbours to yield a complete, analysis-ready table; no generated (cGAN-produced) samples are stored here. The accompanying manuscript tests the hypothesis that lignocellulosic composition and heating value can be predicted from standard fuel analyses even with a small, heterogeneous data set and that accuracy improves when conditional generative adversarial networks are used to increase the training distribution and when multi-output ensemble regressors using chained targets are used to capitalize on cross-property correlations. Users need to read totals accordingly: ash + volatile matter + fixed carbon is approximately 100 wt% (dry), elemental totals are slightly less than 100 wt% due to occasional by-difference oxygen, and cellulose + hemicellulose + lignin do not necessarily sum to 100 wt% because extractives or minor constituents are sometimes reported separately by some sources. The descriptive statistics cited in our article refer to a winsorised copy used for robustness checks; the file deposited here is the harmonized, literature-derived dataset.

The data set is a collection of 257 unique data points collected from different sources literature around the world. Each Data point is composed of proximate, ultimate, structural properties and the High Heating Value(HHV) of biomass (agricultural and municipal solid organic waste). Proximate properties include the Ash content(Ash), Volatile Matter(VM), and Fixed Carbon(FC) of the biomass. Ultimate properties include the elemental Carbon(C), Hydrogen(H), Oxygen(O) and Sulphur(S). Structural/lignocellulosic composition including Cellulose(Cel), Hemicellulose(Hemi) and Lignin(Lig) is also included together with the HHV(MJ/kg). All the compositional columns are presented as percentage (wt%) on dry basis while HHV is presented in MJ/kg. The column “Biomass” names the material as described in the source (for example, rice husk, sugarcane bagasse, paper sludge, mixed food waste). The column “Source” provides the bibliographic reference or identifier from which the observation was extracted. Random missing values were imputed with k-nearest-neighbours to yield a complete, analysis-ready table; no generated (cGAN-produced) samples are stored here. The accompanying manuscript tests the hypothesis that lignocellulosic composition and heating value can be predicted from standard fuel analyses even with a small, heterogeneous data set and that accuracy improves when conditional generative adversarial networks are used to increase the training distribution and when multi-output ensemble regressors using chained targets are used to capitalize on cross-property correlations. Users need to read totals accordingly: ash + volatile matter + fixed carbon is approximately 100 wt% (dry), elemental totals are slightly less than 100 wt% due to occasional by-difference oxygen, and cellulose + hemicellulose + lignin do not necessarily sum to 100 wt% because extractives or minor constituents are sometimes reported separately by some sources. The descriptive statistics cited in our article refer to a winsorised copy used for robustness checks; the file deposited here is the harmonized, literature-derived dataset.

This review discusses how Machine Learning has been applied to predict the quality of biomass briquettes produced from agricultural and municipal solid organic waste, which are crucial for advancing green and low-carbon energy solutions. Traditional methods of assessment of briquette quality involve destructive laboratory experiments, do not favor sample reuse, are time-consuming, and labor-intensive, posing barriers to efficient production. This paper reviews literature on various Machine Learning models applied for predicting and optimizing briquette quality parameters, including combustion, physical, and emission properties. Several Machine Learning models have shown promising results in predicting and optimizing these key parameters for example, a Random Forest model with R2 of 0.9936 in deformation energy prediction and Artificial Neural Networks with R2 of 0.8936 in the prediction of impact resistance. By enhancing the accuracy and efficiency of briquette quality predictions, Machine Learning algorithms contribute to the development of high-quality biomass briquettes, thereby creating sustainable and low-carbon energy systems. This review points to critical literature gaps regarding model generalizability across diverse biomass feedstocks and integration of broader quality parameters. Addressing these gaps will advance AI-based solutions, promote greener energy practices, and support sustainable development. The findings are intended to aid researchers, industry professionals, and policymakers in advancing the production of high-quality biomass briquettes for cleaner energy and sustainable development.

Due to ongoing developments in the EU waste policy, Waste-to-Energy (WtE) plants are to be optimized beyond current acceptance levels. In this paper, a non-exhaustive overview of advanced technical improvements is presented and illustrated with facts and figures from state-of-the-art combustion plants for municipal solid waste (MSW). Some of the data included originate from regular WtE plant operation - before and after optimisation - as well as from defined plant-scale research. Aspects of energy efficiency and (re-)use of chemicals, resources and materials are discussed and support, in light of best available techniques (BAT), the idea that WtE plant performance still can be improved significantly, without direct need for expensive techniques, tools or re-design. In first instance, diagnostic skills and a thorough understanding of processes and operations allow for reclaiming the silent optimisation potential.

This paper reviews the role of conventional waste-to-energy, i.e. incineration of (mainly) municipal solid waste with energy recovery, in the circular economy. It shows that, although waste-to-energy figures on a lower level in the European waste hierarchy than recycling, it plays, from an overall sustainability point of view, an essential, complementary and facilitating role within the circular economy. First of all, waste-to-energy combusts (or should combust) only waste that is non-recyclable for economic, technical or environmental reasons. This way waste-to-energy is compatible with recycling and only competes with landfill, which is lower in the waste hierarchy. Furthermore, waste-to-energy keeps material cycles, and ultimately the environment and humans largely free from toxic substances. Finally, waste-to-energy allows recovery of both energy and materials from non-recyclable waste and hence contributes to keeping materials in circulation. These arguments are elaborated and illustrated with many examples. This paper also points out the pitfalls of a circular economy if it merely focuses on material cycles, disregarding economic, environmental, social and health aspects of sustainability.

SO2 and HCl emission data from a large-scale Waste-to-Energy (WtE) plant are analysed as a function of combustion process parameters. Basic principles of flow, mass transfer and reactor engineering are applied to the waste layers on the grates in order to explain the obtained results. Strong similarity with fixed bed reactor systems is observed. The findings from this study contribute to the understanding of mechanisms of S and Cl release from waste in WtE processes, and support developments in areas of emissions reduction and corrosion abatement in WtE plants. To the authors’ knowledge, such extensive analysis has never been performed before on the industrial scale of WtE.

Abstract Municipal Solid Waste Incineration (MSWI) has become the most widespread Best Available Technology (BAT) to treat residual waste streams in a reliable and safe way. As such, MSWI has contributed to achieve the landfill diversion targets in many EU member states. Modern waste incinerators, also referred to as Waste-to-Energy (WtE) plants, have furthermore evolved to producers of electricity, heat and steam for energy-consuming industries, agriculture and residences. However, due to the specific composition and properties of MSW and similar waste, and due to the historical development of MSWI, the exploitation of WtE plants as combined heat and power (CHP) plants is not straightforward. The aims of this paper are to develop a better understanding of these limitations, to point out possibilities for increasing the level of energy recovery and utilization in WtE plants, and to document this approach with data and experiences from selected WtE plants currently integrated in CHP schemes. Finally, some design and operational challenges for waste-fired CHP plants are further elaborated from a WtE plant supplier’s perspective.