Experimental investigation and modelling of the heating value and elemental composition of biomass through artificial intelligence

Abstract: Knowledge advancement in artificial intelligence and blockchain technologies provides new potential predictive reliability for biomass energy value chain. However, for the prediction approach against experimental methodology, the prediction accuracy is expected to be high in order to develop a high fidelity and robust software which can serve as a tool in the decision making process. The global standards related to classification methods and energetic properties of biomass are still evolving given different observation and results which have been reported in the literature. Apart from these, there is a need for a holistic understanding of the effect of particle sizes and geospatial factors on the physicochemical properties of biomass to increase the uptake of bioenergy. Therefore, this research carried out an experimental investigation of some selected bioresources and also develops high-fidelity models built on artificial intelligence capability to accurately classify the biomass feedstocks, predict the main elemental composition (Carbon, Hydrogen, and Oxygen) on dry basis and the Heating value in (MJ/kg) of biomass...Ph.D. (Mechanical Engineering Science

Renewable Energy

This book discusses renewable energy resources and systems as well as energy efficiency. It contains twenty-three chapters over six sections that address a multitude of renewable energy types, including solar and photovoltaic, biomass, hydroelectric, and geothermal. The information presented herein is a scientific contribution to energy and environmental regulations, quality and efficiency of energy services, energy supply security, energy market-based approaches, government interventions, and the spread of technological innovation

Modeling and Optimizing a Vacuum Gas Oil Hydrocracking Plant using an Artificial Neural Network

In this research, based on actual data gathered from an industrial scale vacuum gas oil (VGO) hydrocracker and artificial neural network (ANN) method, a model is proposed to simulate yields of products including light gases, liquefied petroleum gas (LPG), light naphtha, heavy naphtha, kerosene, diesel and unconverted oil (off-test). The input layer of the ANN model consists of the catalyst, feed and recycle flow rates, and bed temperatures, while the output neurons are yields of those products. The results showed that the AAD% (average absolute deviation) of the developed ANN model for training, testing, and validating data are 0.445%, 1.131% and 0.755%, respectively. Then, by considering all operational constraints, the results confirmed that the decision variables (i.e., recycle rate and bed temperatures) generated by the optimization approach can enhance the gross profit of the hydrocracking process to more than $0.81 million annually, which is significant for the economy of the target refinery

Challenges and Prospects of Steelmaking Towards the Year 2050

The world steel industry is strongly based on coal/coke in ironmaking, resulting in huge carbon dioxide emissions corresponding to approximately 7% of the total anthropogenic CO2 emissions. As the world is experiencing a period of imminent threat owing to climate change, the steel industry is also facing a tremendous challenge in next decades. This themed issue makes a survey on the current situation of steel production, energy consumption, and CO2 emissions, as well as cross-sections of the potential methods to decrease CO2 emissions in current processes via improved energy and materials efficiency, increasing recycling, utilizing alternative energy sources, and adopting CO2 capture and storage. The current state, problems and plans in the two biggest steel producing countries, China and India are introduced. Generally contemplating, incremental improvements in current processes play a key role in rapid mitigation of specific emissions, but finally they are insufficient when striving for carbon neutral production in the long run. Then hydrogen and electrification are the apparent solutions also to iron and steel production. The book gives a holistic overview of the current situation and challenges, and an inclusive compilation of the potential technologies and solutions for the global CO2 emissions problem

Production and characterisation of pine wood powders from a multi-blade shaft mill

Wood is an important raw material for the manufacture of consumer products and in achieving societal goals for greater sustainability. Wood powders are feedstock for many biorefining and conversion techniques, including chemical, enzymatic and thermochemical processes and for composite manufacture, 3D printing and wood pellet production. Size reduction, therefore, is a key operation in wood utilisation and powder characteristics, such as shape, particle size distribution and micromorphology play a role in powder quality and end-use application. While in a green state, the native chemical composition and structure of wood are preserved. Powders are commonly produced from wood chips using impact mills, which require pre-sized, pre-screened and pre-dried chips. These steps necessitate repeated handling, intermediate storage and contribute to dry matter losses, operation-based emissions and the degradation of the wood chemistry.This thesis investigated a new size reduction technology, known as the multi-blade shaft mill (MBSM). The MBSM performance was studied through the milling of Scots pine (Pinus sylvestris L.) wood using a designed series of experiments and through modelling with multi-linear regression (MLR) analyses. Light microscopy combined with histochemical techniques were used to investigate particle micromorphology and distribution of native extractives in powders. The aim was to evaluate the technical performance of the MBSM with relation to operational parameters, to characterise the produced powders and to evaluate the technology through comparison with impact milling.The results showed that the MBSM could effectively mill both green and dry wood. Produced powders showed distinct differences compared to those obtained using a hammer mill (HM). The specific milling energy of the MBSM was lowest for green wood and within the range of other established size reduction technologies. However, much narrower particle size distributions were observed in MBSM powders and they had significantly greater amounts of finer particles. Particles with high aspect ratio and sphericity were a characteristic of MBSM powders and this Production and characterisation of pine wood powders from a multi-blade shaft mill was true for wood milled above and below its fibre saturation point. MBSM powders from green wood showed evidence of higher specific surface area, larger pore volume and greater micropore diameter than those from HM powder. Preliminary microscopic examination suggested that cell walls in MBSM powders showed evidence of retaining their original native wood structure. Consequently, their extractive content appeared intact. This was in contrast to HM powder and it may reflect the differences between the two size reduction mechanisms. According to the produced MLR models, the results suggest that MBSM milling is more akin to a sawing process and opposite to that of impact-based mills

Characterization of microplastics and natural gas by infrared spectrometry and multivariate modelling

Programa Oficial de Doutoramento en Ciencia e Tecnoloxía Ambiental. 5006V01[Abstract] The main objective of this work is to explore the use of infrared spectrometry combined with the application of multivariate chemometric models to the quantification of the major components of natural gas and to the identification of plastic samples, both artificially aged and collected from coastal ecosystems. The articles presented here deal with different themes related with these topics. In particular: the application of IR-inert gases to improve the spectra of other gases of interest; a report evidencing a general absence of important specifications of the instrumental setup of environmental studies on microplastics, suggesting a minimum of information to be offered; and a study of the effects of aging in both the surface morphology and the spectral characteristics of polyamide 6.6. Finally, chemometric models have been developed to identify the main constituent polymers of microplastics and to quantify the major components of natural gas samples, as well as their Wobbe index.[Resumen] El principal objetivo de este trabajo es explorar el uso de la espectrometría infrarroja combinada con la aplicación de modelos quimiométricos multivariables para cuantificar de la composición mayoritaria de muestras de gas natural y para identificar muestras de plásticos, tanto envejecidos de forma artificial como recolectados de ecosistemas costeros. Los artículos presentados tratan sobre diversos aspectos relacionados con estos temas. En concreto: el uso de gases inertes a la radiación infrarroja para mejorar los espectros de otros gases de interés; un informe que evidenciando una importante ausencia de especificaciones instrumentales básicas en los estudios medioambientales sobre microplásticos, donde se sugiriere un mínimo de información a aportar; y un estudio de los efectos del envejecimiento en la morfología y características espectrales de la poliamida 6.6. Finalmente, se han desarrollado modelos quimiométricos capaces de identificar los principales polímeros constituyentes de microplásticos y de cuantificar los componentes mayoritarios de muestras de gas natural, así como su índice Wobbe.[Resumo] O obxectivo principal deste traballo é explorar o uso da espectrometría infravermella combinada coa aplicación de modelos quimiométricos multivariabeis para a cuantificación da composición maioritaria de mostras de gas natural e a identificación de plástico, tanto envellecidos artificialmente como recollido de ecosistemas costeiros. Os diferentes artigos presentados tratan diversos aspectos relacionados con estes temas. Nomeadamente: a aplicación de gases inertes á radiación infravermella para mellorar os espectros doutros gases de interese; unha recompilación onde se constata a ausencia xeral especificacións instrumentais fundamentais nos estudos medioambientais de microplásticos, suxerindo un mínimo de información a aportar; e un estudo dos efectos do envellecemento na morfoloxía e características espectrais na poliamida 6.6. Finalmente, desenvolvéronse modelos quimiométricos capaces de identificar os polímeros constituíntes de microplásticos e de cuantificar os principais compoñentes de mostras de gas natural, ademais do seu índice de Wobbe

Surrogate Model Optimisation for PWR Fuel Management

Pressurised Water Reactor (PWR) fuel management is an operational problem for nuclear operators, requiring solutions on a regular basis throughout the life of the plant. A variety of conflicting factors and changing goals mean that fuel loading pattern design problems are multiobjective and, by design, have many input variables. This causes a combinatorial explosion, known as the ‘curse of dimensionality’, which makes these complex problems difficult to investigate. In this thesis, the method of surrogate model optimisation is adapted to PWR loading pattern generation. Surrogate models are developed based around three approaches: deep learning methods (convolutional neural networks and multi-layer perceptrons), the fission matrix and simulated quantum annealing. The models are used to predict core parameters of reactors in simplified optimisation scenarios for a microcore, a small modular reactor, and a ‘standard’ PWR. The experiments with deep learning models show that competitive results can be obtained for training sets using a much lower number of simulations than direct optimisation. Fission matrix experiments demonstrate the method to predict core parameters for the first time, with interesting preliminary results. Novel experiments using simulated quantum annealing demonstrate the technique is able to generate loading patterns by following heuristic rules and is suitable for application to custom optimisation hardware. The principal contribution of this work is to show that surrogate model optimisation can be used to augment fuel loading pattern optimisation, generating competitive results and providing enormous computational cost reduction and thus permitting more investigation within a given computational budget. These methods can also make use of new computational hardware such as neural chips and quantum annealers. The promising methods developed in this thesis thus provide candidate implementations that can bring the benefits of these innovations to the sphere of nuclear engineering