The role of activated carbon size in the catalytic cracking of naphthalene

Activated carbons are efficient catalysts for tar cracking, suitable for hot cleaning of the syngas produced during biomass- and waste-to-energy gasification processes. This study investigates the conversion of naphthalene, utilised as reference for tar compounds, when catalysed by a coal-derived activated carbon. The attention focuses on the influence of the operating temperature, in the range 750-900°C, and the size of selected activated carbon, which has been used under form of pellets, granules and powders. The conversion efficiency improves when the temperature raised from 750°C to 900°C (from 79% to 99%, for the pellets), and when the catalyst size reduced from pellets to powders (from 79% to 97%, at 750°C). The diffusional resistance in the catalyst particles has been then quantified in terms of Thiele modulus and internal effectiveness factor. A gradual reduction of catalyst surface area has been also observed for longer tests, due to the progressive deposition of soot from naphthalene decomposition over and inside the porous structure of the activated carbon. The carbon content of these deposits has been quantified, showing larger percentages on the surface of granules and powders.Activated carbons are efficient catalysts for tar cracking, suitable for hot cleaning of the syngas produced during biomass- and waste-to-energy gasification processes. This study investigates the conversion of naphthalene, utilised as reference for tar compounds, when catalysed by a coal-derived activated carbon. The attention focuses on the influence of the operating temperature, in the range 750–900 °C, and the size of selected activated carbon, which has been used under form of pellets, granules and powders. The conversion efficiency improves when the temperature raised from 750 °C to 900 °C (from 79% to 99%, for the pellets), and when the catalyst size reduced from pellets to powders (from 79% to 97%, at 750 °C). The diffusional resistance in the catalyst particles has been then quantified in terms of Thiele modulus and internal effectiveness factor. A gradual reduction of catalyst surface area has been also observed for longer tests, due to the progressive deposition of soot from naphthalene decomposition over and inside the porous structure of the activated carbon. The carbon content of these deposits has been quantified, showing larger percentages on the surface of granules and powders

Differential diagnosis of neurodegenerative dementias with the explainable MRI based machine learning algorithm MUQUBIA

Biomarker-based differential diagnosis of the most common forms of dementia is becoming increasingly important. Machine learning (ML) may be able to address this challenge. The aim of this study was to develop and interpret a ML algorithm capable of differentiating Alzheimer's dementia, frontotemporal dementia, dementia with Lewy bodies and cognitively normal control subjects based on sociodemographic, clinical, and magnetic resonance imaging (MRI) variables. 506 subjects from 5 databases were included. MRI images were processed with FreeSurfer, LPA, and TRACULA to obtain brain volumes and thicknesses, white matter lesions and diffusion metrics. MRI metrics were used in conjunction with clinical and demographic data to perform differential diagnosis based on a Support Vector Machine model called MUQUBIA (Multimodal Quantification of Brain whIte matter biomArkers). Age, gender, Clinical Dementia Rating (CDR) Dementia Staging Instrument, and 19 imaging features formed the best set of discriminative features. The predictive model performed with an overall Area Under the Curve of 98%, high overall precision (88%), recall (88%), and F1 scores (88%) in the test group, and good Label Ranking Average Precision score (0.95) in a subset of neuropathologically assessed patients. The results of MUQUBIA were explained by the SHapley Additive exPlanations (SHAP) method. The MUQUBIA algorithm successfully classified various dementias with good performance using cost-effective clinical and MRI information, and with independent validation, has the potential to assist physicians in their clinical diagnosis

Differential diagnosis of neurodegenerative dementias with the explainable MRI based machine learning algorithm MUQUBIA

Biomarker-based differential diagnosis of the most common forms of dementia is becoming increasingly important. Machine learning (ML) may be able to address this challenge. The aim of this study was to develop and interpret a ML algorithm capable of differentiating Alzheimer’s dementia, frontotemporal dementia, dementia with Lewy bodies and cognitively normal control subjects based on sociodemographic, clinical, and magnetic resonance imaging (MRI) variables. 506 subjects from 5 databases were included. MRI images were processed with FreeSurfer, LPA, and TRACULA to obtain brain volumes and thicknesses, white matter lesions and diffusion metrics. MRI metrics were used in conjunction with clinical and demographic data to perform differential diagnosis based on a Support Vector Machine model called MUQUBIA (Multimodal Quantification of Brain whIte matter biomArkers). Age, gender, Clinical Dementia Rating (CDR) Dementia Staging Instrument, and 19 imaging features formed the best set of discriminative features. The predictive model performed with an overall Area Under the Curve of 98%, high overall precision (88%), recall (88%), and F1 scores (88%) in the test group, and good Label Ranking Average Precision score (0.95) in a subset of neuropathologically assessed patients. The results of MUQUBIA were explained by the SHapley Additive exPlanations (SHAP) method. The MUQUBIA algorithm successfully classified various dementias with good performance using cost-effective clinical and MRI information, and with independent validation, has the potential to assist physicians in their clinical diagnosis

Technical and Environmental Performances of Alternative Thermochemical Treatments for Mixed Plastics Waste

There is a general and important effort to increase circularity of plastics, by proposing new behaviours and styles of life (“how to use and reuse plastics”), new eco-design criteria (“design for recycling” and “design from recycling”), innovative recycling processes able to increase the quantity and quality of recovered resources (“Plastics-to-Plastics” but also “to-Chemicals”, and “to-Fuels”). The attention in mainly focused on mixed plastics waste (MPW) whose treatment shows the major technical difficulties, due to the co-presence of various and generally non-compatible, polymers, with different kinds of additives, pigments and fillers. Please click Additional Files below to see the full abstract

Combined Use of an Information System and LCA Approach to Assess the Performances of a Solid Waste Management System

A municipal solid waste information system, named W-MySir, is utilised to acquire high-quality data to implement an attributional life-cycle assessment (LCA) focused on the evolution of the environmental performances of municipal solid waste management in a specific area. The main aim was to investigate how this combined approach can be used for monitoring progress of the management scheme toward important targets, such as being CO2-neutral, increasing the circularity of the service, and planning a transparent approach to cost evaluation. The analysis was applied to the municipality of Procida, one of the three islands of Naples Bay (Italy), and focused on the last ten years of activity of the local solid waste service. The results of the life cycle impact assessment are reported in terms of the main impact categories. They indicate a positive evolution of the environmental performances, with improvements of up to 140% for global warming potential. The positive results are mainly due to the large increase in household source separation and separate collection in Procida during the period under analysis, together with the availability of a more integrated and sustainable regional system of solid waste management. Further improvements may be achieved through better performance at the sorting and remanufacturing stages of dry recyclable fractions and the availability of anaerobic digestion units to produce biomethane from organic fractions of municipal solid waste. The combined approach indicates potential further benefits for both the tools: LCAs could provide reliable results in shorter times; information systems could offer a wider spectrum of services for monitoring and planning waste management systems in a sustainable way

The effect of steam concentration on hot syngas cleaning by activated carbons

Activated carbons are recognised as inexpensive, easily available, and efficient catalysts for tar cracking reactions at high temperatures. Their use in hot syngas cleaning is limited by the rapid deactivation resulting from the coke deposition, and the consequent masking phenomenon over the activated carbon surface. This study contributes to a better understanding of the problem and sheds light on possible solutions, by investigating how the temperature (750 ◦C–850 ◦C) and the steam concentration (0%–10%) can affect the extent of the water-gas and reforming reactions occurring between steam and the coke covering the catalyst surface. The activated carbons have been fully characterised before and after the tests utilizing naphthalene as a tar model compound, to study the evolution of their structure after long duration tests. Steam positively affects the naphthalene conversion efficiency, preserving the characteristics of the material. For example, at a temperature of 800 ◦C and a steam concentration of 7.5%, a naphthalene conversion of over 90% is achieved even after 7 h of testing. XRD and Raman spectroscopy analyses suggest that naphthalene cracking produces a coke layer that is more amorphous and, above all, more reactive towards the water gas reaction than the original activated carbon structure