Steam - oxygen gasification of refuse derived fuel in fluidized beds: Modelling and pilot plant testing

A one-dimensional kinetic model for steam‑oxygen gasification of refuse derived fuel in a bubbling fluidized bed reactor has been developed. The model incorporates the reaction network of steam‑oxygen gasification within the fluid dynamics of a fluidized bed to predict waste and tars conversion, gas composition and overall gasification performance. The model was validated by comparing outlet products composition and temperature profile with experimental data from a pilot-scale fluidized bed gasifier, operated at different conditions. The model showed accurate predictive capability and ease of computation. The effects of the operating conditions on gas yield and process efficiency were evaluated and the most appropriate fuel feeding height, equivalent ratio and the relative amount of steam to inject were identified

Axial segregation behaviour of a reacting biomass particle in fluidized bed reactors: experimental results and model validation

Axial segregation behaviour of a single biomass particle in a lab-scale bubbling fluidized bed has been investigated from both experimental and modelling perspectives. Experiments were conducted using beech wood particles of different sizes, ranging from 8 to 12 mm under either oxidizing or inert conditions. The fluidized bed reactor was operated at temperatures and fluidization velocity ratios, U/Umf, in the range of 500–650 °C and 1–2, respectively. A one-dimensional model has been developed to predict the axial location of the particle over time, taking into account both dynamic and thermal conversion mechanisms. X-ray imaging techniques allowed to identify endogenous bubbles released during devolatilization and carry out direct measurements of their size. This information was used to propose an expression for the lift force acting on the fuel particle. The model showed very accurate predictions and the segregation behaviour of the fuel particle appeared to be independent of the nature of the fluidizing medium

Novel nanocomposite clay brick for strain sensing in structural masonry

The monitoring of civil structures is critical in ensuring users\u27 safety. Structural health monitoring (SHM) is the automation of this monitoring task. It is typically used to identify incipient damages through a spatio-temporal comparison in structural behaviors. Traditional sensors exhibit mechanical characteristics that are usually very different from those of the structures they monitor, which is a factor limiting their durability. Ideally, the material of a sensor would share the same mechanical characteristics as the material onto or into which it is installed. A solution is to fabricate multifunctional materials, capable of serving both structural and sensing functions, also known as smart materials. Recent developments in nanotechnologies have given us various engineered nanoparticles with enhanced mechanical and electrical capabilities. Among them, conductive piezoresistive nanopowders, such as carbon-based ones, show promise at developing smart materials. The nanofillers, spread into a structural material matrix, can provide the material with self-sensing capabilities. Such materials can then be used to detect variations in their external stresses or strains by detecting variations in their electrical characteristics, such as electrical resistivity and conductivity. This paper presents a new smart clay brick for strain sensing in masonry structures. The optimal fabrication process in terms of stability of the nanoparticles at high temperature and the electromechanical properties of the smart brick are investigated. Results show a clear strain sensitivity of the brick sensors subjected to external loads and show their promise for SHM applications

Detection of grapevine yellows symptoms in Vitis vinifera L. with artificial intelligence

Grapevine yellows (GY) are a significant threat to grapes due to the severe symptoms and lack of treatments. Conventional diagnosis of the phytoplasmas associated to GYs relies on symptom identification, due to sensitivity limits of diagnostic tools (e.g. real time PCR) in asymptomatic vines, where the low concentration of the pathogen or its erratic distribution can lead to a high rate of false-negatives. GY's primary symptoms are leaf discoloration and irregular wood ripening, which can be easily confused for symptoms of other diseases making recognition a difficult task. Herein, we present a novel system, utilizing convolutional neural networks, for end-to-end detection of GY in red grape vine (cv. Sangiovese), using color images of leaf clippings. The diagnostic test detailed in this work does not require the user to be an expert at identifying GY. Data augmentation strategies make the system robust to alignment errors during data capture. When applied to the task of recognizing GY from digital images of leaf clippings—amongst many other diseases and a healthy control—the system has a sensitivity of 98.96% and a specificity of 99.40%. Deep learning has 35.97% and 9.88% better predictive value (PPV) when recognizing GY from sight, than a baseline system without deep learning and trained humans respectively. We evaluate six neural network architectures: AlexNet, GoogLeNet, Inception v3, ResNet-50, ResNet-101 and SqueezeNet. We find ResNet-50 to be the best compromise of accuracy and training cost. The trained neural networks, code to reproduce the experiments, and data of leaf clipping images are available on the internet. This work will advance the frontier of GY detection by improving detection speed, enabling a more effective response to the disease

Biohydrogen: A life cycle assessment and comparison with alternative low-carbon production routes in UK

This study focuses on the production of hydrogen from municipal solid waste (MSW) for applications in transportation. A life cycle assessment (LCA) was conducted on a semi-commercial advanced gasification process for Biohydrogen (Bio-H2) production from MSW to evaluate its environmental impact on five impact categories: Climate Change, Acidification, Eutrophication Fresh Water, Ecotoxicity Freshwater and Photochemical Ozone Formation (human health). The biogenic composition of waste and the effect of carbon sequestration were analysed for Bio-H2, uncovering a net-negative carbon process. The counterfactual case of MSW incineration further bolsters the carbon savings associated to Bio-H2. The production of Bio-H2 from waste is proven to be competitive against alternative hydrogen productions routes, namely blue hydrogen (Blue-H2) produced via steam methane reforming/autothermal reforming coupled with carbon capture and storage (CCS), and green hydrogen (Green-H2) from solar and offshore wind, with respect to climate change. These climate change advantages are shown to carry forward in the context of decarbonisation of electricity grid mix, as analysed by scenarios taken for 2030 and ‘net-zero’ 2050

Advanced Thermochemical Conversion of Various Waste Feedstocks with CCS for Clean Hydrogen Production ‐ a Life Cycle Assessment

This research work focuses on a complex product system that c) utilises different waste feedstock, thereby diverting its fate from landfill or incineration b) produces hydrogen as the main product (for heating and transport applications) c) captures and permanently sequesters carbon dioxide, a by-product. The multi-functionality of such a system lends itself to complexities arising from the choice of system boundaries, functional unit, and assumptions in order to integrate mature, commercial scale elements of the process with other sections at a lower technology readiness level. This research focuses on novel waste technology and their integration into connected systems, specifically transport of waste from source and its pre-treatment, hydrogen for heating and captured carbon dioxide for permanent sequestration. Recently, hydrogen from low-carbon routes has garnered attention as a high-density energy vector with low greenhouse-gas production emissions and no emissions at its point of use. The UK Hydrogen strategy sets forth a target of 5GW of low carbon hydrogen production capacity by 2030 [1]. A proposed low-carbon route to produce hydrogen is the gasification of waste feedstock coupled with pre-combustion capture and long-term geological storage of carbon dioxide. This research also analyses the effect of waste feedstock composition on the environmental impact of the process. The three feedstock analysed are waste wood, municipal solid waste (MSW) and mixed plastic waste (MPW). The facility is designed and modelled to convert approximately 110,000 tonnes per annum of chosen waste to approximately 50 MWh of grid-quality hydrogen. Carbon dioxide is captured using a Benfield CO2 stripper technology. Following the guidelines of the ISO 14040 and ISO 14044 standards the LCA methodology was applied. The goal of this study was to investigate the environmental and carbon performance of converting waste wood, MSW and MPW to hydrogen, while also capturing CO2 process emissions. The comprehensive LCA also includes the integration into connected systems, namely the transport of waste, pipeline transport and sequestration of CO2. All impact categories were considered according to the EF 3.0 method. A hotspot analysis of the process reveals largest climate change burdens during the gasification (syngas generation) and the carbon capture stages due to high thermal and electricity consumption. Although pretreatment of waste is overall a minimal climate change contributor of the process, variations in feedstock composition and flowrates result in large relative differences as MPW benefits from a high calorific content and requires significantly lower feedstock for equivalent hydrogen production. The sequestration of biogenic CO2 from the natural carbon cycle uniquely results in negative carbon dioxide emissions. Thus, distinguishing between sequestration of biogenic and fossil carbon is the most significant differentiator in how these technologies fare environmentally. Using waste wood and MSW feedstock result in negative emission processes, while MPW does not advantage from biogenic carbon sequestration. Despite this, results when considering counterfactual scenarios, namely incineration and landfill, reveal the avoided burdens of MPW treatment. Nonetheless, gasification technology applied to the treatment of waste to produce hydrogen with CCS is proposed as a suitable technology for treatment of varied waste feedstock. [1] Business, Energy & Industrial Strategy. 2021. UK Hydrogen Strategy. ISBN 978-1-5286-2670-

Molecular typing of bois noir phytoplasma strains in the chianti classico area (Tuscany, Central Italy) and their association with symptom severity in vitis vinifera 'sangiovese'

Bois noir (BN) is the most widespread disease of the grapevine yellows complex in the Euro-Mediterranean area. BN is caused by 'Candidatus Phytoplasma solani' (BNP), transmitted from herbaceous plants to grapevine by polyphagous insect vectors. In this study, genetic diversity among BNP strains and their prevalence and possible association with grapevine symptom severity were investigated in a Sangiovese clone organic vineyard in the Chianti Classico area (Tuscany). Field surveys over 2 years revealed a range of symptom severity on grapevine and an increase of BN incidence. ATaqMan allelic discrimination assay detected only tufB type b among BNP strains, suggesting the prevalence of the bindweed-related ecology. Nucleotide sequence analyses of vmp1 and stamp genes identified 12 vmp1 and 16 stamp sequence variants, showing an overall positive selection for such genes. The prevalent genotype was Vm43/St10, reported for the first time in this study and closely related to strains identified only in the French Eastern Pyrenees. BNP strains identified in the examined vineyard and mostly grouped in separate bindweed-related phylogenetic clusters showed statistically significant differences in their distribution in grapevines exhibiting distinct symptom severity. These results suggest the possible occurrence of a range of virulence within BNP strain populations in the Chianti Classico area

Proposal of A New Bois Noir Epidemiological Pattern Related to ‘Candidatus Phytoplasma Solani’ Strains Characterized by A Possible Moderate Virulence in Tuscany

Bois noir (BN), associated with 'Candidatus Phytoplasma solani' (CaPsol), is the most widespread disease of the grapevine yellows complex worldwide. In this work, BN epidemiology was investigated in a case study vineyard where an unusual CaPsol strain, previously detected only in other host plants, was found to be prevalent in grapevine. Experimental activities included: symptom observation; sampling of symptomatic vines, Auchenorrhyncha specimens, and weeds; molecular detection and typing of CaPsol strains; statistical analyses for determining possible relationships between CaPsol relative concentration, strain type, and symptom severity. Among insects, Reptalus quinquecostatus was the most abundant and was found to be highly infected by CaPsol, while Hyalesthes obsoletus, the main CaPsol vector, was not caught. Moreover, R. quinquecostatus harbored CaPsol strains carrying uniquely the stamp sequence variant St10, also identified as prevalent in vines and in the majority of weeds, and all the secY variants identified in the vineyard. Statistical analyses revealed that CaPsol strains carrying the St10 variant are not associated with severe symptoms, suggesting their possible moderate virulence. Based on such evidence, a new BN epidemiological pattern related to these CaPsol strains and involving grapevine, R. quinquecostatus, and/or weeds is proposed. Furthermore, the possible presence of other players (vectors and weeds) involved in CaPsol transmission to grapevines was highlighted

The monitoring program of grapevine phytoplasmas in Tuscany (Italy): Results of a four year survey

Quantitative PCR protocols for phytoplasma detection were used to monitor grapevine yellows (GY) in 373 vineyards located in nine Tuscan districts. Among more than 70,000 plants visually monitored, 1.867 plants were sampled and “flavescence dorée” phytoplasmas (FD) were detected in 122 plants and mainly identified as trains belonging to 16SrV-C subgroup. The “bois noir” (BN) phytoplasma was found in 734 samples, with prevalence of tufB type-b strains. The 2013–2015 monitoring program was strongly influenced by the first survey (2012) in which FD was found consistently in the North West (15 samples), whereas only a few cases were observed in the East territory (2 samples). Both areas were thoroughly monitored in the following years: few foci were found in the East (2 in 2014, 1 in 2015), while several infected areas were found in the North West (6, 10 and 22 foci in 2013, 2014 and 2015, respectively). Definitely, the novel FD foci detected in the survey (17, 6, 12 and 23 in each year of survey) and the widespread of BN, suggest a dangerous distribution of GY in Tuscan