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

Development of Real-time Optimal Control Strategy of Hybrid Transit Bus Based on Predicted Driving Pattern

The control strategy of a hybrid electric vehicle (HEV) has been an active research area in the past decades. The main goal of the optimal control strategy is to maximize the fuel economy and minimize exhaust emissions while also satisfying the expected vehicle performance. Dynamic programming (DP) is an algorithm capable of finding the global optimal solution of HEV operation. However, DP cannot be used as a real-time control approach as it requires pre-known driving information. The equivalent consumption minimization strategy (ECMS) is a real-time control approach, but it always results in local optima due to the non-convex cost function. In my research, a ECMS with DP combined model (ECMSwDP) was proposed, which was a compromise between optimality and real-time capability. In this approach, the optimal equivalent factor (lambda) for a real-time ECMS controller can be derived using ECMSwDP for a given driving condition. The optimal lambda obtained using ECMSwDP was further processed to derive the lambda map, which was a function of vehicle operation and driving information. Six lambda maps were generated corresponding to the developed representative driving patterns. At each distance segment of a drive cycle, the suitable lambda value is available from one of the six lambda maps based on the identified driving pattern and current vehicle operation.;An adaptive ECMS (A-ECMS) model with a driving pattern identification model is developed to achieve the real-time optimal control for a HEV. A-ECMS was capable of controlling the ratio of power provided by the ICE and battery of a hybrid vehicle by selecting the lambda based on the identified lambda map. The effect on fuel consumption of the control strategies developed using the rule-based controller, ECMSwDP, A-ECMS, and DP was simulated using the parallel hybrid bus model developed in this research. The control strategies developed using A-ECMS are able to significantly improve the fuel economy while maintaining the battery charge sustainability. The corrected fuel economy observed with A-ECMS with a penalty function and the average lambda of RDPs was 5.55%, 13.67%, and 19.19% gap to that observed with DP when operated over the Beijing cycle, WVU-CSI cycle, and the actual transit bus route, respectively. The corrected fuel economy observed with A-ECMS with lambda maps of the RDPs was 4.83%, 10.61%, and 14.33% gap to that observed with DP when operated on the Beijing cycle, WVU-CSI cycle, and actual transit bus route, respectively. The simulation results demonstrated that the proposed A-ECMS approaches have the capability to achieve real time suboptimal control of a HEV while maintaining the charge sustainability of the battery

Advanced Flame Monitoring and Emission Prediction through Digital Imaging and Spectrometry

This thesis describes the design, implementation and experimental evaluation of a prototype instrumentation system for burner condition monitoring and NOx emissions prediction on fossil-fuel-fired furnaces. A review of methodologies and technologies for burner condition monitoring and NOx emissions prediction is given, together with the discussions of existing problems and technical requirements in their applications. A technical strategy, incorporating digital imaging, UV-visible spectrum analysis and soft computing techniques, is proposed. Based on these techniques, a prototype flame imaging system is developed. The system consists mainly of an optical and fibre probe protected by water-air cooling jacket, a digital camera, a miniature spectrometer and a mini-motherboard with associated application software. Detailed system design, implementation, calibration and evaluation are reported. A number of flame characteristic parameters are extracted from flame images and spectral signals. Luminous and geometric parameters, temperature and oscillation frequency are collected through imaging, while flame radical information is collected by the spectrometer. These parameters are then used to construct a neural network model for the burner condition monitoring and NOx emission prediction. Extensive experimental work was conducted on a 120 MWth gas-fired heat recovery boiler to evaluate the performance of the prototype system and developed algorithms. Further tests were carried out on a 40 MWth coal-fired combustion test facility to investigate the production of NOx emissions and the burner performance. The results obtained demonstrate that an Artificial Neural Network using the above inputs has produced relative errors of around 3%, and maximum relative errors of 8% under real industrial conditions, even when predicting flame data from test conditions not disclosed to the network during the training procedure. This demonstrates that this off the shelf hardware with machine learning can be used as an online prediction method for NOx

Regulating the automobile

Division of Policy Research and Analysis. National Science Foundatio

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

Machine learning assisted optimization with applications to diesel engine optimization with the particle swarm optimization algorithm

A novel approach to incorporating Machine Learning into optimization routines is presented. An approach which combines the benefits of ML, optimization, and meta-model searching is developed and tested on a multi-modal test problem; a modified Rastragin\u27s function. An enhanced Particle Swarm Optimization method was derived from the initial testing. Optimization of a diesel engine was carried out using the modified algorithm demonstrating an improvement of 83% compared with the unmodified PSO algorithm. Additionally, an approach to enhancing the training of ML models by leveraging Virtual Sensing as an alternative to standard multi-layer neural networks is presented. Substantial gains were made in the prediction of Particulate matter, reducing the MMSE by 50% and improving the correlation R^2 from 0.84 to 0.98. Improvements were made in models of PM, NOx, HC, CO, and Fuel Consumption using the method, while training times and convergence reliability were simultaneously improved over the traditional approach

Model structure selection in powertrain calibration and control

This thesis develops and investigates the application of novel identification and structure identification techniques for I.C. engine systems. The legislated demand for reduced vehicle fuel consumption and emissions indicates that improved model-based dynamical engine calibration and control methods are required in place of the existing static set-point based mapping methods currently used in industry. The choice of structure of any dynamical engine model has significant consequences for the accuracy and the calibration/optimization time of engine systems. This thesis primarily addresses the issue of this structure selection. Linear models are well understood and relatively easy to implement however the modern I.C. engine is a highly nonlinear system which restricts the use of linear structures. Further the newer technologies required to achieve demanding fuel consumption and emission targets are increasingly more complex and nonlinear. The selection of appropriate nonlinear model regressor terms presents a combinatorial explosion problem which must be solved for accurate engine system modelling. In this thesis, two systematic nonlinear model structure selection techniques, namely stepwise regression with F-statistics and orthogonal least squares method with error reduction ratio, are accordingly investigated. SISO algebraic NARMAX engine models are then established in simulation studies with these methods and demonstrate the effectiveness of the approach. The thesis also investigates the development and application of multi-modelling techniques and the expansion of the model structure selection techniques to the identification of the local models terms within the multi-model structures for the engine. Based on the en- gine operating regions, novel multi-model networks can be established and several alternative multi-modelling techniques, such as LOLIMOT, Neural Network, Gaussian and log-sigmoid function weighted multi-models, for the multi-model engine system identification are explored and compared. An experimental validation of the methods is given by a black box identification of SISO engine models which are developed purely from the experimental engine test data sets. The results demonstrate that the multi-model structure selection techniques can be successfully applied on the engine systems, and that the multi-modelling techniques give good model accuracy and that good modelling efficiency can also be achieved. The outcome is a set of techniques for the efficient development of accurate nonlinear black-box models which can be acquired from experimental dynamometer test-bed data which should assist in the dynamic control of future advanced technology engine systems

Urban air quality monitoring, mapping and modelling to determine the main drivers of air pol-lution

Air pollution is a growing concern for human health, biodiversity and natural environment in large urban areas. It is, therefore, vital to monitor and model air quality (AQ) in urban areas to understand its spatiotemporal variabilities and its main drivers. Traditionally it was not possible to develop high-resolution AQ maps in urban areas due to sparse reference network. However, since the emergence of low-cost sensors (LCS), it has become possible to structure a dense network of sensors and develop high-resolution AQ maps. This is what this PhD project intends to achieve by: (a) analysing the suitability of LCS for urban AQ monitoring and how their measurements can be further improvement using advance calibration techniques, (b) deploying a dense network of AQ sensors based on multiple criteria and using sensors of different grades, (c) employing various AQ modelling and mapping techniques including geostatistical interpolations, land-use regression (LUR) and dispersion modelling, and (d) using data fusion approaches to fuse measured and estimated pollutant concentrations. A multi-criteria Air Quality Monitoring Network (AQMN) was structured based on economic, social and environmental indicators. The network was made of several layers of sensors including reference sensors, LCS (e.g., AQMesh pods and Envirowatch E-MOTEs) and IoT (internet of things) sensors. The data from the designed AQMN was used in AQ mapping, models validations, analysing spatiotemporal variability of pollutants and sensor calibration. Reference sensors were used as standard to calibrate measurements of the LCS employing multiple linear regression and generalised additive model. LUR models were developed for the first time in Sheffield using several land-use and emission related variables. In contrast to previous studies that mostly used linear techniques, here nonlinear regression approaches were also used for developing LUR models, which outperformed the linear counterparts. LUR models were trained and validated using annual average NO2 concentrations from diffusion tubes as well as from LCS. The models were cross-validated by comparing estimated and measured concentrations. LUR model demonstrated that among predictor variables altitude had negative significant effect, whereas major roads, minor roads and commercial areas had positive significant effect on NO2 concentrations. Furthermore, an Airviro dispersion model was developed and several emission scenarios were tested, which showed that NOx concentrations were mainly controlled by road traffic, whereas PM10 concentrations were controlled by point sources. To further improve the AQ maps, modelled and measured concentrations were fused (integrated) to produce high-resolution maps in Sheffield using data fusion technique known as Universal Kriging, which estimated realistic (based on priori expectations) NO2 concentration maps that inherited spatial patterns of the pollutant from the model estimations and adjusted the modelled values against the measured concentrations. The methodology was successful in demonstrating the spatial variability and highlighting the hotspots of NO2 concentrations in Sheffield. The main findings of the project are: (a) The project proposed a nonlinear generalised additive model for low-cost sensors calibrations in outdoor environment. Low-cost sensors are a cheaper source of AQ data, however, they require robust outfield calibrations. (b) A formal approach was proposed for structuring an AQMN in urban areas, which was based on multi-criteria. (c) It was shown that LUR model based on nonlinear machine learning approach outperformed the dispersion modelling approach. (d) Data fusion techniques (such as Universal krigging) were employed to integrate model estimations with measured concentrations. Such data fusion approaches are useful tools for improving data quality and producing high-resolution AQ maps. (e) Time series modelling ARIMA with exogenous variables (ARIMAX) outperformed other linear and nonlinear time series models, and is proposed as an early warning tool for predicting potential pollution episodes in order to be proactive in adopting precautionary measures. Limited data was available on particulate matter, especially on fine and ultrafine particulates, therefore, further work is required on particulate matter monitoring, modelling and management in urban areas