Data Driven Modelling and Optimization of MEA Absorption Process for CO2 Capture

Global warming is a rising issue and there are many research studies aiming to reduce greenhouse gas emissions. Carbon capture and storage technologies improved throughout the years to contribute as a solution to this problem. In this work the post-combustion carbon capture unit is used to develop surrogated models for operation optimization. Previous work included mechanistic and detailed modeling of steady-state and dynamic systems. Furthermore, control structures and optimization approaches have been studied. Moreover, various solutions such as MEA, DEA, and MDEA have been tested and simulated to determine the efficiency and the behavior of the system. In this work a dynamic model with MEA solution developed by (Nittaya, 2014) and (Harun, 2012) is used to generate operational data. The system is simulated using gProms v.5.1 with six PI controllers. The model illustrated that the regeneration of the solvent is the most energy-consuming part of the process. Due to the changes in electricity supply and demand, also, the importance of achieving a specific %CC and purity of carbon dioxide as outputs of this process, surrogated models are developed and used to predict the outputs and to optimize the operating conditions of the process. Multiple machine learning and data-driven models has been developed using simulation data generated after a proper choice of the operating variables and the important outputs. Steady-state and transient state models have been developed and evaluated. The models were used to predict the outputs of the process and used later to optimize the operating conditions of the process. The flue gas flow rate, temperature, pressure, reboiler pressure, reboiler, and condenser duties were selected as the operating variables of the system (inputs). The system energy requirements, %CC, and the purity of carbon dioxide were selected to be the outputs of the process. For steady-state modeling, artificial neural network (ANN) model with backpropagation and momentum was developed to predict the process outputs. The ANN model efficiency was compared to other machine learning models such as Gaussian Process Regression (GPR), rational quadratic GPR, squared exponential GPR, tree regression and matern GPR. The ANN excelled all other models in terms of prediction and accuracy, however, the other model’s regression coefficient (R2) was never below 0.95. For dynamic modelling, recurrent neural networks (RNN) have been used to predict the outputs of the system. Two training algorithms have been used to create the neural network: Levenberg-Marquardt (LM) and Broyden-Fletcher-Goldfrab-Shanno (BFGS). The RNN was able to predict the outputs of the system accurately. Sequential quadratic programming (SQP) and genetic algorithm (GA) were used to optimize the surrogated models and determine the optimum operating conditions following an objective of maximizing the purity of CO2 and %CC and minimizing the system energy requirements

Predicting Thermoelectric Power Plants Diesel/Heavy Fuel Oil Engine Fuel Consumption Using Univariate Forecasting and XGBoost Machine Learning Models

Monitoring and controlling thermoelectric power plants (TPPs) operational parameters have become essential to ensure system reliability, especially in emergencies. Due to system complexity, operating parameters control is often performed based on technical know-how and simplified analytical models that can result in limited observations. An alternative to this task is using time series forecasting methods that seek to generalize system characteristics based on past information. However, the analysis of these techniques on large diesel/HFO engines used in Brazilian power plants under the dispatch regime has not yet been well-explored. Therefore, given the complex characteristics of engine fuel consumption during power generation, this work aimed to investigate patterns generalization abilities when linear and nonlinear univariate forecasting models are used on a representative database related to an engine-driven generator used in a TPP located in Pernambuco, Brazil. Fuel consumption predictions based on artificial neural networks were directly compared to XGBoost regressor adaptation to perform this task as an alternative with lower computational cost. AR and ARIMA linear models were applied as a benchmark, and the PSO optimizer was used as an alternative during model adjustment. In summary, it was possible to observe that AR and ARIMA-PSO had similar performances in operations and lower error distributions during full-load power output with normal error frequency distribution of −0.03 ± 3.55 and 0.03 ± 3.78 kg/h, respectively. Despite their similarities, ARIMA-PSO achieved better adherence in capturing load adjustment periods. On the other hand, the nonlinear approaches NAR and XGBoost showed significantly better performance, achieving mean absolute error reductions of 42.37% and 30.30%, respectively, when compared with the best linear model. XGBoost modeling was 8.7 times computationally faster than NAR during training. The nonlinear models were better at capturing disturbances related to fuel consumption ramp, shut-down, and sudden fluctuations steps, despite being inferior in forecasting at full-load, especially XGBoost due to its high sensitivity with slight fuel consumption variations

Adaptive Robust Control of Biomass Fuel Co-Combustion Process

The share of biomass in energy production is constantly growing. This is caused by environmental and industry standards and EU guidelines. Biomass is used in the process of co-firing in large power plants and industrial installations. In the existing power stations, biomass is milled and burned simultaneously with coal. However, low-emission combustion techniques, including biomass co-combustion, have some negative side effects that can be split into two categories. The direct effects influence the process control stability, whereas the indirect ones on combustion installations via increased corrosion or boiler slagging. The effects can be minimised using additional information about the process. The proper combustion diagnosis as well as an appropriate, robust control system ought to be applied. The chapter is devoted to the analysis of modern, robust control techniques for complex power engineering applications

Biofuels and noise in tractor engines

Transport is included among the most important noise sources due to continuous increasing number of vehicles. In order to comply with the European regulations related to both the presence of renewable origin basedfuels and pollution (air and noise) reduction, biodiesel emerges as an excellent alternative. Provided that biodiesel properties are closely correlated with the chemical composition of the raw material used to produce it, this PhD thesis aims to study the effect of the chemical composition on air and noise emissions to find out the “ideal” raw material to produce biodiesel. Moreover, to study the effect of biodiesel on noise emission different models of sound prediction were developed. Finally, the influence of biodiesel chemical composition on sound quality has been assessed. The thesis comprises five chapters. First chapter presents an introduction to the PhD thesis, where the study purpose and objectives are stated and justified. Second chapter focuses on the effect of biodiesel chemical properties on combustion and air and noise emissions. In chapter 3, to predict noise emissions and their relation with the percentage of biodiesel blended with diesel fuel, two ANN-based models considering saturated and monounsaturated fatty acid methyl esters are presented. In addition, several response surface models have been developed to show the relationship between biodiesel chemical properties and noise emission by means of simple models, as well as the trend of the exhaust emissions and noise radiated for different engine operating conditions. Chapter 4 is composed of three evaluations of substitution monopole models for engine noise sound synthesis: the first work is based on Airborne Source Quantification (ASQ) technique, improved by means of regularization strategies. In the second evaluation, a novel model based on Product Unit Neural Networks (PUNN) is proposed and compared to ASQ technique. In the third evaluation, to improve the results achieved with the PUNN-based model, an ensemble of evolutionary Product Unit (PU) and Radial Basis Function (RBF) Neural Networks is suggested. In the fifth chapter, the effect of biodiesel properties on the tractor cabin mock up comfort from the driver’s point of view has been studied. Moreover, several response surface models have been developed to correlate different sound quality metrics with biodiesel chemical properties. Finally, a conclusions section, the proposal of future research lines and the compendium of references used in this PhD thesis are provided.Una de las principales fuentes de ruido la proporciona el transporte, debido al constante crecimiento del número de vehículos. Para cumplir con los objetivos establecidos por la UE relativos tanto al incremento del uso de energías renovables como a la reducción de emisiones contaminantes (gaseosa y acústica), el biodiésel surge como una excelente alternativa. Puesto que las propiedades del biodiésel están correlacionadas con la composición química de los aceites vegetales empleados, en esta tesis doctoral se ha estudiado el efecto de aquélla sobre las emisiones, con el objeto de encontrar la composición ideal para producir biodiésel. Además, se han desarrollado distintos modelos de predicción de ruido para comprobar el efecto del incremento del porcentaje de biodiésel en mezclas con gasóleo sobre el ruido emitido. La influencia de la composición química sobre la calidad del sonido también se ha analizado. De este modo, la tesis se compone de cinco capítulos. El primer capítulo presenta una introducción de la tesis doctoral, donde se muestran, justificadamente, los objetivos a alcanzar. El segundo capítulo estudia el efecto de la composición química del biodiésel sobre las emisiones contaminantes y el ruido emitido. En el capítulo tres, se desarrollan dos modelos de predicción de ruido basados en redes neuronales considerando biodiésel o ésteres metílicos de ácidos grasos de dos tipos, con alto grado de saturación y monoinsaturados. Además, se proponen varios modelos de predicción de ruido basados en propiedades y emisiones contaminantes del biodiésel. El capítulo cuatro se compone de la evaluación de modelos de fuentes de ruido en vehículos mediante distintas técnicas: primero por el método de cuantificación de fuentes aéreas (Airborne Source Quantification (ASQ)) con estrategias de regularización. La segunda técnica propuesta se basa en el empleo de redes neuronales para altas frecuencias y ASQ para bajas y medias frecuencias, siguiendo el comportamiento del sistema. Finalmente, en la tercera evaluación, se propone una mejora del método previo mediante la fusión de dos métodos de redes neuronales artificiales basados en Unidades Producto Evolutivas y Funciones de Base Radial. En el capítulo cinco, se estudia el efecto de las propiedades del biodiésel en el confort de la cabina de un tractor, desde el punto de vista del conductor. Este estudio se acompaña del desarrollo de modelos de predicción de parámetros de calidad del sonido, sonoridad (loudness) y aspereza (roughness), basados en propiedades del biodiésel. Finalmente, se ha incluido una sección de conclusiones generales, futuras líneas de investigación y un compendio de las referencias empleadas en esta tesis doctoral

Artificial neural network modeling and sensitivity analysis of performance and emissions in a compression ignition engine using biodiesel fuel

In the present research work, a neural network model has been developed to predict the exhaust emissions and performance of a compression ignition engine. The significance and novelty of the work, with respect to existing literature, is the application of sensitivity analysis and an artificial neural network (ANN) simultaneously in order to predict the engine parameters. The inputs of the model were engine load (0, 25, 50, 75 and 100%), engine speed (1700, 2100, 2500 and 2900 rpm) and the percent of biodiesel fuel derived from waste cooking oil in diesel fuel (B0, B5, B10, B15 and B20). The relationship between the input parameters and engine cylinder performance and emissions can be determined by the network. The global sensitivity analysis results show that all the investigated factors are effective on the created model and cannot be ignored. In addition, it is found that the most emissions decreased while using biodiesel fuel in the compression ignition engine

Economic, Environmental, and Health Impact Analysis of Developing Hydrogen Economy in Canada

The greatest challenge to the development of a cleaner energy system is economic issues. However, if the environmental and health externalities of the current energy system is considered, other energy alternatives become economically competitive. Therefore, hydrogen can become an option in different energy sectors. As an energy vector, hydrogen can be represented as the missing link between clean energy sources and energy consumers. The real cost of an energy system includes environmental and health-related hidden costs. The current energy system imposes lots of critical damages to the environment and human lives. All these damages are avoidable if governments follow the prevention policy instead of the cure policy. In other words, governments can support developing clean energy solutions by incentivizing them. In this regard, the government should be aware of the hidden costs of energy for both fossil-fuel-based and hydrogen-based energy systems. Therefore, in this work, a comprehensive cost calculation is conducted for using hydrogen in different energy sectors in this work. The result from this work shows that the idea of Hydrogen Economy is economically competitive with the current energy system, if the hidden costs of environmental and health effects are taken into account. The first study is focused on developing a five-year mathematical model for finding the optimal sizing of renewable energy technologies for achieving specific CO2 emission reduction targets. An industrial manufacturing facility that uses CHP for electricity generation and natural gas for heating is considered the base case in this work. The CHP capacity is 4500 kW and the furnace is operated 8 AM to 4 PM with a natural gas consumption of 4000 m3/h. Different renewable energy technologies are assumed to be developed each year to achieve a 4.53% annual CO2 emission reduction target. The results of this study show that wind power is the most cost-effective technology for reducing emissions in the first and second years, with a cost of 44 and 69 CAD per tonne of CO2, respectively. On the other hand, hydrogen is more cost-effective than wind power in reducing CO2 emissions from the third year onward. The cost of CO2 emission reduction with hydrogen doesn't change drastically from the first year to the fifth year (107 and 130 CAD per tonne of CO2). Solar power is a more expensive technology than wind power for reducing CO2 emissions in all years due to lower capacity factor (in Ontario), more intermittency (requiring mores storage capacity), and higher investment cost. A hybrid wind/battery/hydrogen energy system has the lowest emission reduction cost over five years. The emission reduction cost of such a hybrid system increases from 44 CAD per tonne of CO2 in the first year to 156 CAD per tonne of CO2 in the fifth year. The developed model can be used for long-term planning of energy systems to achieve GHG emission targets in regions/countries with fossil fuel-based electricity and heat generation infrastructure. The second study develops a multi-objective model to determine the optimal sizes and locations of the hydrogen infrastructure needed to generate and distribute hydrogen for the critical Highway Corridor (HWY 401) in Ontario. The model is used to aid the early-stage transition plan for converting conventional vehicles to FCEVs in Ontario by proposing a feasible solution to the infrastructure dilemma posed by the initial adoption of hydrogen as fuel in the general market. The health benefit from the pollution reduction is also determined to show the potential social and economic incentives of using FCEVs. The results show that hydrogen production and delivery cost can reduce from $22.7/kg H2 in a 0.1$14.7/kg H2 in a 1% market share scenario. The environmental and health benefit of developing hydrogen refueling infrastructure for heavy-duty vehicles is 1.63 million dollar per year and 1.45 million dollars per year, respectively. Also, every kilogram of H2 can avoid 11.09 kg CO2 from entering the atmosphere. In a 1% market share scenario, the proposed hydrogen network avoids more than 37,000 tonnes of CO2 per year. The third study aims to determine the economic burden of environmental and health impacts caused by Highway 401 traffic. Due to the high volume of vehicles driving on the Toronto Highway 401 corridor, there is an annual release of 3771 tonnes of carbon dioxide equivalent (CO2e). These emissions are mainly emitted onsite through the combustion of gasoline and diesel fuel. The integration of electric and hydrogen vehicles shows maximum reductions of 405–476 g CO2e per vehicle kilometer. Besides these carbon dioxide emissions, there is also a large number of hazardous air pollutants. The mass and concentrations of criteria pollutants of PM2.5 and NOx emitted by passenger vehicles and commercial trucks on Highway 401 were determined using the MOVES2014b software to examine the impact of air pollution on human health. Then, an air dispersion model (AERMOD) was used to find the concentration of different pollutants at the receptor’s location. The increased risk of health issues was calculated using hazard ratios from literature. Finally, the health cost of air pollution from Highway 401 traffic was estimated to be CAD 416 million per year using the value of statistical life, which is significantly higher than the climate change costs of CAD 55 million per year due to air pollution. The fourth study discovers the health benefit of reducing fossil-fuel vehicle market share and utilizing more Zero-Emission Vehicles (ZEVs). A historical dataset from 2015-2017 is used to learn a Long Short-Term Memory (LSTM) model that can predict future NOx concentration based on traffic volume, weather condition, time, and past NOx concentration. The developed model is used in a modified manner to predict NOx concentration in the long term. Then, the developed model is utilized to predict annual average NOx reduction in four different scenarios. Interpolation methods are used to predict pollution reduction in all Dissemination Areas (DA) of Toronto. Finally, a health cost assessment is conducted to estimate the health benefit from different scenarios. The results show that the western areas of Toronto experience more NOx concentration reduction in all scenarios, which is the result of a stronger correlation between traffic volume and pollution in those areas. Also, by 10% reduction in fossil-fuel traffic volume, 70 deaths can be prevented annually, equivalent to CAD 560 million health benefit per year. There are plenty of opportunities for future work in this area to make more robust energy models which can take all aspects of implementing the idea of Hydrogen Economy. First, the impact of using different types of hydrogen storage can be investigated in terms of cost. Also, a comprehensive hydrogen-based energy model can be optimized if the cost-benefit analysis is conducted in all energy sectors. Finally, different objective functions such as energy, environmental, health, and social costs can be optimized to reach an optimal sustainable energy system for Ontario