Controlo de temperatura de um gasificador de biomassa

In recent history, the growing environmental crisis and the unsustainable overuse of fossil fuels have become a catalyst for the development of environmentally friendly or carbon neutron energy sources. Such fact lead to the reemergence of gasification in the research and development community. This technology was prominent during World War II due to the unavailability of oil existent at that time, mostly using coal as fuel. With the end of the war, so came the end of its development. Initially, the literature will be reviewed in order to assess the instrumentation technologies needed to measure the gasification process’ operational parameters, and thus, allow its monitoring and control. In order to facilitate the analysis of the data from the developed instrumentation system, a visualization tool was developed. The literature was then reviewed again in order to find the most suitable model topology for the gasification process. This revealed neural networks as the most reliable model architecture for such endeavor. A gasification model was then devised using experimental data present in the literature. The devised model was then used to establish a simulation and controller design environment. This enabled the development of Model Predictive Controller to control the temperature inside the gasifier. The devised model showed great potential as a prediction model, in spite of the deterioration presented when used as a simulator. The developed controller was able to stabilize the model generated output for all tested set-points. The develop work constitutes a solid ground for future work.O desenfreado crescimento da crise ambiental e uso insustentável de combustíveis fósseis vivido nas últimas décadas tem vindo a tornar-se num catalisador na busca de soluções carbonicamente neutras de produção de energia. Este facto levou ao ressurgimento dos processos de gasificação, principalmente de biomassa, como um tema na comunidade de pesquisa e desenvolvimento. Esta tecnologia foi predominante durante a segunda guerra mundial, período no qual a dificuldade de obtenção de petróleo levou acréscimo da sua necessidade, sendo carvão o combustível utilizado. Com o fim da guerra, veio também o fim do seu desenvolvimento. Inicialmente, será realizada uma revisão de literatura que culminará na escolha dos instrumentos de medição e atuação necessários para proceder à monitorização e controlo dos parâmetros operacionais do processo de gasificação. De modo a facilitar a analise dos dados presentes nestes sensores foi desenvolvida uma aplicação de visualização de informação. Findada esta etapa procedeu-se a uma nova revisão da literatura focada na procura de um modelo para o processo de gasificação. Esta revisão revelou as redes neuronais como sendo a melhor topologia para descrever o processo. Utilizando dados disponíveis na literatura procedeu-se à identificação do sistema em causa. O modelo desenvolvido foi utilizado para estabelecer um ambiente de simulação e desenho de controladores e assim, desenvolver um controlador preditivo baseado em modelo para controlar a temperatura dentro do gasificador. O modelo desenvolvido apresenta um grande potencial como modelo de predição, apesar da deterioração do seu desempenho quando usado como simulador. O controlador desenvolvido foi capaz de estabilizar a saída gerada pelo modelo de simulação para todos os set-points testados. O trabalho desenvolvido constitui uma base de trabalho bastante completa que deverá facilitar desenvolvimentos futuros.Mestrado em Engenharia Eletrónica e Telecomunicaçõe

Application of Machine Learning Models with Numerical Simulations of an Experimental Microwave Induced Plasma Gasification Reactor

This thesis aims to contribute to the future development of this technology by providing an in-depth literature review of how this technology physically operates and can be numerically modeled. Additionally, this thesis reviews literature of machine learning models that have been applied to gasification to make accurate predictions regarding the system. Finally, this thesis provides a framework of how to numerically model an experimental plasma gasification reactor in order to inform a variety of machine learning models

Biomass Gasification and Applied Intelligent Retrieval in Modeling

Gasification technology often requires the use of modeling approaches to incorporate several intermediate reactions in a complex nature. These traditional models are occasionally impractical and often challenging to bring reliable relations between performing parameters. Hence, this study outlined the solutions to overcome the challenges in modeling approaches. The use of machine learning (ML) methods is essential and a promising integration to add intelligent retrieval to traditional modeling approaches of gasification technology. Regarding this, this study charted applied ML-based artificial intelligence in the field of gasification research. This study includes a summary of applied ML algorithms, including neural network, support vector, decision tree, random forest, and gradient boosting, and their performance evaluations for gasification technologies

Performance modelling and validation of biomass gasifiers for trigeneration plants

Esta tesis desarrolla un modelo sencillo pero riguroso de plantas de trigeneración con gasificación de biomasa para su simulación, diseño y evaluación preliminar. Incluye una revisión y estudio de diferentes modelos propuestos para el proceso de gasificación de biomasa.Desarrolla un modelo modificado de equilibrio termodinámico para su aplicación a procesos reales que no alcanzan el equilibrio así comodos modelos de redes neuronales basados en datos experimentales publicados: uno para gasificadores BFB y otro para gasificadores CFB. Ambos modelos, ofrecen la oportunidad de evaluar la influencia de las variaciones de la biomasa y las condiciones de operación en la calidad del gas producido. Estos modelos se integran en el modelo de la planta de trigeneración con gasificación de biomasa de pequeña-mediana escala y se proponen tres configuraciones para la generación de electricidad, frío y calor. Estas configuraciones se aplican a la planta de poligeneración ST-2 prevista en Cerdanyola del Vallés.This thesis develops a simple but rigorous model for simulation, design and preliminary evaluation of trigeneration plants based on biomass gasification. It includes a review and study of various models proposed for the biomass gasification process and different plant configurations. A modified thermodynamic equilibrium model is developed for application to real processes that do not reach equilibrium. In addition, two artificial neural network models, based on experimental published data, are also developed: one for BFB gasifiers and one for CFB gasifiers. Both models offer the opportunity to evaluate the influence of variations of biomass and operating conditions on the quality of gas produced. The different models are integrated into the global model of a small-medium scale biomass gasification trigeneration plant proposing three different configurations for the generation of electricity, heat and cold. These configurations are applied to a case study of the ST-2 polygeneration plant foreseen inCerdanyola del Valles

Modelling of Temperature and Syngas Composition in a Fixed Bed Biomass Gasifier using Nonlinear Autoregressive Networks

To improve biomass gasification efficiency through process control, a lot of attention had been given to development of models that can predict process parameters in real time and changing operating conditions. The paper analyses the potential of a nonlinear autoregressive exogenous model to predict syngas temperature and composition during plant operation with variable operating conditions. The model has been designed and trained based on measurement data containing fuel and air flow rates, from a 75 kWth fixed bed gasification plant at Technical University Dresden. Process performance changes were observed between two sets of measurements conducted in 2006 and 2013. The effect of process performance changes on the syngas temperature was predicted with prediction error under 10% without changing the model structure. It was concluded that the model could be used for short term predictions (up to 5 minutes) of syngas temperature and composition as it strongly depends on current process measurements for future predictions. For long term predictions other types of dynamic neural networks are more applicable

An improved kinetic modelling of woody biomass gasification in a downdraft reactor based on the pyrolysis gas evolution

Biomass gasification technology is evolving and more research through modelling alongside the experimental work needs to be performed. In the past, all the attention has been concentrated on the combustion and reduction stages to be the controlling reactions while the pyrolysis is modelled as an instantaneous process. In this study, a new enhanced model for the gasification process in the downdraft reactor is proposed with a more realistic representation of the pyrolysis stage as a temperature-dependent sequential release of gases. The evolution of the pyrolysis gas, followed by the combustion and reduction reactions, are kinetically controlled in the proposed model which is developed within the Aspen Plus software package. The simulation of the reactor temperature profile and the evolution of the pyrolysis gas is carried out in an integrated MATLAB and Aspen Plus model. The proposed model has been validated against experimental data obtained from the gasification of different woody biomass types and considering a range of scale reactor and power loads. The predicted results are in very good agreement with the experimental data, and therefore the model can be used with confidence to perform a sensitivity analysis to predict the performance of a gasifier at different load levels corresponding to the air flow rate range of 3–10 L/s. As the supplied air flow rate increases, the LHV decreases but the gas yield behaves conversely, and in turn the cold gas efficiency is maintained at a good level of energy conversion at ≥ 70%. Furthermore, the variation in the biomass moisture content, which is commonly in the range of 5–25 % has a significant effect on the gasification efficiency. Such that biomass that has a high moisture content substantially reduces the CO content and consequently the LHV of the produced gas. Hence, it is important to maintain the moisture content at the lowest level

Hydrogen production from plastic waste: A comprehensive simulation and machine learning study

Gasification, a highly efficient method, is under extensive investigation due to its potential to convert biomass and plastic waste into eco-friendly energy sources and valuable fuels. Nevertheless, there exists a gap in comprehension regarding the integrated thermochemical process of polystyrene (PS) and polypropylene (PP) and its capability to produce hydrogen (H2) fuel. In this study a comprehensive process simulation using a quasi-equilibrium approach based on minimizing Gibbs free energy has been introduced. To enhance H2 content, a water-gas shift (WGS) reactor and a pressure swing adsorption (PSA) unit were integrated for effective H2 separation, increasing H2 production to 27.81 kg/h. To investigate the operating conditions on the process the effects of three key variables in a gasification reactor namely gasification temperature, feedstock flow rate and gasification pressure have been explored using sensitivity analysis. Furthermore, several machine learning models have been utilized to discover and optimize maximum capacity of the process for H2 production. The sensitivity analysis reveals that elevating the gasification temperature from 500 °C to 1200 °C results in higher production of H2 up to 23 % and carbon monoxide (CO). However, generating H2 above 900 °C does not lead to a significant upturn in process capacity. Conversely, an increase in pressure within the gasification reactor is shown to decrease the system capacity for generating both H2 and CO. Moreover, increasing the mass flow rate of the gasifying agent to 250 kg/h in the gasification reactor has shown to be merely productive in process capacity for H2 generation, almost a 5 % increase. Regarding pressure, the hydrogen yield decreases from 22.64 % to 17.4 % with an increase in pressure from 1 to 10 bar. It has been also revealed that gasification temperature has more predominant effect on Cold gas efficiency (CGE) compared to gasification pressure and Highest CGE Has been shown by PP at 1200 °C. Among the various machine learning models, Random Forest (RF) model demonstrates robust performance, achieving R2 values exceeding 0.99

Mathematical modelling of a small biomass gasifier for synthesis gas production

The depletion of fossil fuels coupled with the growing demands of the world energy has ignited the interest for renewable energies including biomass for energy production. A reliable affordable and clean energy supply is of major importance to the environment and economy of the society. In this context, modern use of biomass is considered a promising clean energy alternative for the reduction of greenhouse gas emissions and energy dependency. The use of biomass as a renewable energy source for industrial application has increased over the last decade and is now considered as one of the most promising renewable sources. The direct combustion of biomass in small scales often results in incomplete and inconsistent burning process which could produce carbon monoxide, particulates and other pollutants. Therefore, biomass is required to be transformed into more easily handled fuel such as gases, liquids and charcoal using technologies such as pyrolysis, gasification, fermentation, digestion etc. Biomass gasification upon which this thesis focuses is one of the promising routes amongst the renewable energy options for future deployment. Gasification is a process of conversion of solid biomass into combustible gas, known as producer gas by partial oxidation. This research work is carried out to investigate various methods employed for modelling biomass gasifiers, it also studies the chemistry of gasification and reviews various gasification models. In this work, a mathematical model is developed to simulate the behaviour of downdraft gasifiers operating under steady state and determine the synthesis gas composition. The model distinctly analyses the processes in each of the three zones of the gasifier; pyrolysis, oxidation and reduction zones. Air is used as the gasifying agent and is introduced into the pyrolysis and oxidation zones of the gasifier for both single and double air operations. These zones have been modelled based on thermodynamic equilibrium and kinetic modelling; the model equations are solved in MATLAB. Given the biomass properties, consumption, air input, moisture content and gasifier specifications, the MATLAB model is able to accurately predict the temperature and distribution of the molar concentrations of the synthesis gas constituents. The downdraft gasifier is also represented in Aspen HYSYS based on the same models to study the effect of both single and double air gasification operation. For known biomass properties, consumption, air input, moisture content and gasifier operating conditions, the Aspen HYSYS model can accurately predict the distributions of the molar concentrations of the syngas constituents (CO, CO2, H2, CH4, and N2). The models were validated by comparing obtained theoretical results with experimental data published in the open literature. Parametric studies were carried out to study the effects of equivalence ratio, moisture content, temperature on the gas compositions and its energy content. The proposed equilibrium model displayed a variable ability for the prediction of various product yields with this being a function of the feedstock studied. It also demonstrated the ability to predict product gases from various biomasses using both single and double stage air input. In the case of gasification with double air stage supply, higher amounts of methane are obtained with specific tendencies of the gases reaching a peak at certain conditions. The kinetic model was partially successful in predicting results and comparable with experimentally published results for a range of conditions. There were discrepancies particularly with CH4 formation and the operating temperatures predictions which were usually consistently lower than those actually measured experimentally. The use of the PFR, however, did show a greater potential for the use in further modelling